From 521d9f1f6d67976c32c1ac63fb153efe37759393 Mon Sep 17 00:00:00 2001 From: dx-tan Date: Tue, 12 Dec 2023 18:48:09 +0700 Subject: [PATCH] Remove: Submodule --- cope2n-ai-fi/modules/_sdsvkvu/.gitignore | 24 - cope2n-ai-fi/modules/_sdsvkvu/.gitmodules | 4 - cope2n-ai-fi/modules/_sdsvkvu/LICENSE | 13 - cope2n-ai-fi/modules/_sdsvkvu/MANIFEST.in | 2 - cope2n-ai-fi/modules/_sdsvkvu/README.md | 122 --- cope2n-ai-fi/modules/_sdsvkvu/__init__.py | 8 - cope2n-ai-fi/modules/_sdsvkvu/draw_img.jpg | Bin 405763 -> 0 bytes cope2n-ai-fi/modules/_sdsvkvu/pyproject.toml | 6 - .../modules/_sdsvkvu/requirements.txt | 28 - cope2n-ai-fi/modules/_sdsvkvu/scripts/run.sh | 26 - .../_sdsvkvu/sdsvkvu.egg-info/PKG-INFO | 122 --- .../_sdsvkvu/sdsvkvu.egg-info/SOURCES.txt | 49 - .../sdsvkvu.egg-info/dependency_links.txt | 1 - .../_sdsvkvu/sdsvkvu.egg-info/not-zip-safe | 1 - .../_sdsvkvu/sdsvkvu.egg-info/requires.txt | 21 - .../_sdsvkvu/sdsvkvu.egg-info/top_level.txt | 1 - .../modules/_sdsvkvu/sdsvkvu/__init__.py | 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cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt_v2.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vat.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vtb.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/utils.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/word2line.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/setup.cfg delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/setup.py delete mode 100644 cope2n-ai-fi/modules/_sdsvkvu/test.py diff --git a/cope2n-ai-fi/modules/_sdsvkvu/.gitignore b/cope2n-ai-fi/modules/_sdsvkvu/.gitignore deleted file mode 100644 index 17d9c1a..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/.gitignore +++ /dev/null @@ -1,24 +0,0 @@ -# Model weights -weights/ -microsoft/ -nltk_data/ - -# Visualize -visualize - -# External -sdsvkvu/externals/ocr_engine_deskew/externals/ - -# -__pycache__ -*/__pycache__ -*/*/__pycache__ - -# -.git_temp/ - -# Packages -build/ -dist/ - - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/.gitmodules b/cope2n-ai-fi/modules/_sdsvkvu/.gitmodules deleted file mode 100644 index eb6d053..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/.gitmodules +++ /dev/null @@ -1,4 +0,0 @@ - -[submodule "sdsvkvu/externals/basic_ocr"] - path = sdsvkvu/externals/basic_ocr - url = https://code.sdsdev.co.kr/tuanlv/IDP-BasicOCR.git diff --git a/cope2n-ai-fi/modules/_sdsvkvu/LICENSE b/cope2n-ai-fi/modules/_sdsvkvu/LICENSE deleted file mode 100644 index 21ffbaa..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/LICENSE +++ /dev/null @@ -1,13 +0,0 @@ -Copyright 2023 tuanlv - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/MANIFEST.in b/cope2n-ai-fi/modules/_sdsvkvu/MANIFEST.in deleted file mode 100644 index e4bd723..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/MANIFEST.in +++ /dev/null @@ -1,2 +0,0 @@ -include sdsvkvu/weights/*/*.yaml -include sdsvkvu/weights/*/checkpoints/best_model.pth \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/README.md b/cope2n-ai-fi/modules/_sdsvkvu/README.md deleted file mode 100644 index d8b1dd1..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/README.md +++ /dev/null @@ -1,122 +0,0 @@ -

-

SDSVKVU

-

- - ***Feature*** - - Extract pairs of key-value in documents: Invoice/Receipt, Forms, Government documents (Id cards, driver license, birth's certificate) - - Language: VI + EN - - ***What's news*** - ### - Ver 0.0.1: - - Support inputs: image, PDF file (single or multi pages) - - Extract all pairs key-value return raw_outputs - + Weights: weights/key_value_understanding-20230716-085549_final - - For VAT invoices : Extract 14 specific fields - + Weights: weights/key_value_understanding-20230627-164536_fi - - For SBT invoices ("sbt" option): Extract table in SBT invoice - + Weights: weights/key_value_understanding-20230812-170826_sbt_2 - ### - Ver 0.0.2: Add more option: "vtb" - Vietin Bank - - For Vietin Bank document ("vtb" option): Extract 6 specific fileds - + Weights: weights/key_value_understanding-20230824-164236_vietin - ### - Ver 0.0.3: Add default option: - - Return all potential pairs of key-value, title, only key, triplet, and table with raw key - ### - Ver 0.0.4: Add more option: "manulife" - Manulife Issurance - - For Manulife Insurance document ("manulife" option): Extract all potential pairs of key-value, title, only key, triplet, and table with raw key + Type of medical documents - + Weights: weights/key_value_understanding-20231024-125646_manulife2 - ### Ver 0.1.0: Modify KVU model for SBT - ### - Ver 0.1.0: Add option: "sbt_v2" - SBT project - - For SBT imei/invoice ("sbt_v2" option): Extract 4 specific fields - + Weights: weights/key_value_understanding_for_sbt-20231108-143935 - - ## I. Setup - ***Dependencies*** - - Python: 3.10 - - Torch: 1.11.3 - - CUDA: 11.6 - - transformers: 4.30.0 - ``` - pip install -v -e . - ``` - - - ## II. Inference - run cmd: python test.py - ``` - import os - from sdsvkvu import load_engine, process_img - os.environ["CUDA_VISIBLE_DEVICES"]="1" - - if __name__ == "__main__": - kwargs = {"device": "cuda:0"} - img_dir = "/mnt/ssd1T/tuanlv/02-KVU/sdsvkvu/visualize/test_img/RedInvoice_WaterPurfier_Feb_PVI_829_0.jpg" - save_dir = "/mnt/ssd1T/tuanlv/02-KVU/sdsvkvu/visualize/test2/" - engine = load_engine(kwargs) - # option: "vat" for vat invoice outputs, "sbt": sbt invoice outputs, else for raw outputs - outputs = process_img(img_dir, save_dir, engine, export_all=False, option="vat") - ``` - - # Structure project - . - ├── sdsvkvu - │   ├── main.py - ├── externals - │   │   ├── __init__.py - │   │   ├── basic_ocr - │   │   │   ├── ... - │   │   ├── ocr_engine - │   │   │   ├── ... - │   │   ├── ocr_engine_deskew - │   │   │   ├── ... - │   ├── model - │   │   ├── combined_model.py - │   │   ├── document_kvu_model.py - │   │   ├── __init__.py - │   │   ├── kvu_model.py - │   │   └── relation_extractor.py - │   ├── modules - │   │   ├── __init__.py - │   │   ├── predictor.py - │   │   ├── preprocess.py - │   │   └── run_ocr.py - │   ├── requirements.txt - │   ├── settings.yml - │   ├── sources - │   │   ├── __init__.py - │   │   ├── kvu.py - │   │   └── utils.py - │   ├── utils - │   │   ├── dictionary - │   │   │   ├── __init__.py - │   │   │   ├── sbt.py - │   │   │   └── vat.py - │   │   │   └── vtb.py - │   │   │   ├── manulife.py - │   │   │   ├── sbt_v2.py - │   │   ├── __init__.py - │   │   ├── post_processing.py - │   │   ├── query - │   │   │   ├── __init__.py - │   │   │   ├── sbt.py - │   │   │   └── vat.py - │   │   │   └── vtb.py - │   │   │   ├── all.py - │   │   │   ├── manulife.py - │   │   │   ├── sbt_v2.py - │   │   └── utils.py - ├── weights - │   └── key_value_understanding-20230627-164536_fi - │   ├── key_value_understanding-20230812-170826_sbt_2 - │   └── key_value_understanding-20230716-085549_final - │   └── key_value_understanding-20230824-164236_vietin - │   └── key_value_understanding-20231024-125646_manulife2 - │   └── key_value_understanding_for_sbt-20231108-143935 - ├── LICENSE - ├── MANIFEST.in - ├── pyproject.toml - ├── README.md - ├── scripts - │   └── run.sh - ├── setup.cfg - ├── setup.py - ├── test.py - └── visualize \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/__init__.py deleted file mode 100644 index 1621835..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -import os -import sys -from pathlib import Path -cur_dir = str(Path(__file__).parents[0]) -sys.path.append(cur_dir) -sys.path.append(os.path.join(cur_dir, "sdsvkvu")) - -from sdsvkvu import load_engine, process_img, process_pdf, process_dir \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/draw_img.jpg b/cope2n-ai-fi/modules/_sdsvkvu/draw_img.jpg deleted file mode 100644 index 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-tldextract==5.1.1 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/scripts/run.sh b/cope2n-ai-fi/modules/_sdsvkvu/scripts/run.sh deleted file mode 100644 index 69c3092..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/scripts/run.sh +++ /dev/null @@ -1,26 +0,0 @@ -cd /mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu -export CUDA_VISIBLE_DEVICES=0 - -python sdsvkvu/main.py \ - --img_dir /mnt/hdd4T/OCR/tuanlv/00-Datasets/SBT_DATA/invoice_validation \ - --save_dir /mnt/hdd4T/OCR/tuanlv/02-KVU/02-KVU_test/visualize/sbt_invoice \ - --kvu_params "{\"device\":\"cuda:0\"}" \ - --doc_type "sbt_v2" \ - --export_img 1 - - -# python sdsvkvu/main.py \ -# --img_dir /mnt/hdd4T/OCR/tuanlv/00-Datasets/SBT_DATA/imei_validation \ -# --save_dir /mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/visualize/test_sbt_imei \ -# --kvu_params "{\"device\":\"cuda:0\"}" \ -# --doc_type "sbt_v2" \ -# --export_img 1 - - - -# python sdsvkvu/main.py \ -# --img_dir /mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/visualize/test_sbt2 \ -# --save_dir /mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/visualize/test_sbt2 \ -# --kvu_params "{\"device\":\"cuda:0\"}" \ -# --doc_type "sbt_v2" \ -# --export_img 1 \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/PKG-INFO b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/PKG-INFO deleted file mode 100644 index d9e424f..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/PKG-INFO +++ /dev/null @@ -1,122 +0,0 @@ -Metadata-Version: 2.1 -Name: sdsvkvu -Version: 0.0.1 -Summary: SDSV OCR Team: Key-value understanding -Home-page: https://github.com/open-mmlab/mmocr -Author: tuanlv -Author-email: lv.tuan3@samsung.com -License: Apache License 2.0 -Classifier: Development Status :: 4 - Beta -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 3.9 -Requires-Python: >=3.9 -Description-Content-Type: text/markdown -License-File: LICENSE - -

-

SDSVKVU

-

- - ***Feature*** - - Extract pairs of key-value in documents: Invoice/Receipt, Forms, Government documents (Id cards, driver license, birth's certificate) - - Language: VI + EN - - ***What's news*** - ### - Ver 0.0.1: - - Support inputs: image, PDF file (single or multi pages) - - Extract all pairs key-value return raw_outputs - + Weights: sdsvkvu/weights/key_value_understanding-20230716-085549_final - - For VAT invoices : Extract 14 specific fields - + Weights: sdsvkvu/weights/key_value_understanding-20230627-164536_fi - - For SBT invoices ("sbt" option): Extract table in SBT invoice - + Weights: sdsvkvu/weights/key_value_understanding-20230617-162324_sbt - ### - Ver 0.0.2: Add more option: "vtb" - Vietin Bank - - For Vietin Bank document ("vtb" option): Extract 6 specific fileds - + Weights: sdsvkvu/weights/key_value_understanding-20230824-164236_vietin - ### - Ver 0.0.3: Add default option: - - Return all potential pairs of key-value, title, only key, triplet, and table with raw key - - ## I. Setup - ***Dependencies*** - - Python: 3.10 - - Torch: 1.11.3 - - CUDA: 11.6 - - transformers: 4.30.0 - ``` - pip install -v -e . - ``` - - - ## II. Inference - run cmd: python test.py - ``` - import os - from sdsvkvu import load_engine, process_img - os.environ["CUDA_VISIBLE_DEVICES"]="1" - - if __name__ == "__main__": - kwargs = {"device": "cuda:0"} - img_dir = "/mnt/ssd1T/tuanlv/02-KVU/sdsvkvu/visualize/test_img/RedInvoice_WaterPurfier_Feb_PVI_829_0.jpg" - save_dir = "/mnt/ssd1T/tuanlv/02-KVU/sdsvkvu/visualize/test2/" - engine = load_engine(kwargs) - # option: "vat" for vat invoice outputs, "sbt": sbt invoice outputs, else for raw outputs - outputs = process_img(img_dir, save_dir, engine, export_all=False, option="vat") - ``` - - # Structure project - . - ├── sdsvkvu - │   ├── main.py - ├── externals - │   │   ├── __init__.py - │   │   ├── ocr_engine - │   │   │   ├── ... - │   │   ├── ocr_engine_deskew - │   │   │   ├── ... - │   ├── model - │   │   ├── combined_model.py - │   │   ├── document_kvu_model.py - │   │   ├── __init__.py - │   │   ├── kvu_model.py - │   │   └── relation_extractor.py - │   ├── modules - │   │   ├── __init__.py - │   │   ├── predictor.py - │   │   ├── preprocess.py - │   │   └── run_ocr.py - │   ├── requirements.txt - │   ├── settings.yml - │   ├── sources - │   │   ├── __init__.py - │   │   ├── kvu.py - │   │   └── utils.py - │   ├── utils - │   │   ├── dictionary - │   │   │   ├── __init__.py - │   │   │   ├── sbt.py - │   │   │   └── vat.py - │   │   │   └── vtb.py - │   │   ├── __init__.py - │   │   ├── post_processing.py - │   │   ├── query - │   │   │   ├── __init__.py - │   │   │   ├── sbt.py - │   │   │   └── vat.py - │   │   │   └── vtb.py - │   │   └── utils.py - │   └── weights - │   └── key_value_understanding-20230627-164536_fi - │   ├── key_value_understanding-20230617-162324_sbt - │   └── key_value_understanding-20230716-085549_final - │   └── key_value_understanding-20230824-164236_vietin - ├── LICENSE - ├── MANIFEST.in - ├── pyproject.toml - ├── README.md - ├── scripts - │   └── run.sh - ├── setup.cfg - ├── setup.py - ├── test.py - └── visualize diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/SOURCES.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/SOURCES.txt deleted file mode 100644 index fc2dccf..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/SOURCES.txt +++ /dev/null @@ -1,49 +0,0 @@ -LICENSE -MANIFEST.in -README.md -pyproject.toml -setup.cfg -setup.py -sdsvkvu/__init__.py -sdsvkvu/main.py -sdsvkvu.egg-info/PKG-INFO -sdsvkvu.egg-info/SOURCES.txt -sdsvkvu.egg-info/dependency_links.txt -sdsvkvu.egg-info/not-zip-safe -sdsvkvu.egg-info/requires.txt -sdsvkvu.egg-info/top_level.txt -sdsvkvu/externals/__init__.py -sdsvkvu/externals/basic_ocr/__init__.py -sdsvkvu/externals/basic_ocr/run.py -sdsvkvu/externals/ocr_engine/__init__.py -sdsvkvu/externals/ocr_engine/run.py -sdsvkvu/externals/ocr_engine_deskew/__init__.py -sdsvkvu/externals/ocr_engine_deskew/run.py -sdsvkvu/model/__init__.py -sdsvkvu/model/combined_model.py -sdsvkvu/model/document_kvu_model.py -sdsvkvu/model/kvu_model.py -sdsvkvu/model/relation_extractor.py -sdsvkvu/model/sbt_model.py -sdsvkvu/modules/__init__.py -sdsvkvu/modules/predictor.py -sdsvkvu/modules/preprocess.py -sdsvkvu/modules/run_ocr.py -sdsvkvu/sources/__init__.py -sdsvkvu/sources/kvu.py -sdsvkvu/sources/utils.py -sdsvkvu/utils/__init__.py -sdsvkvu/utils/post_processing.py -sdsvkvu/utils/utils.py -sdsvkvu/utils/word2line.py -sdsvkvu/utils/dictionary/__init__.py -sdsvkvu/utils/dictionary/sbt.py -sdsvkvu/utils/dictionary/sbt_v2.py -sdsvkvu/utils/dictionary/vat.py -sdsvkvu/utils/dictionary/vtb.py -sdsvkvu/utils/query/__init__.py -sdsvkvu/utils/query/all.py -sdsvkvu/utils/query/sbt.py -sdsvkvu/utils/query/sbt_v2.py -sdsvkvu/utils/query/vat.py -sdsvkvu/utils/query/vtb.py \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/dependency_links.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/not-zip-safe b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/not-zip-safe deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/requires.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/requires.txt deleted file mode 100644 index dac7835..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/requires.txt +++ /dev/null @@ -1,21 +0,0 @@ -nltk -six -deskew -jdeskew -pdf2image -omegaconf -imagesize -xmltodict -dicttoxml -terminaltables -Pillow==9.4.0 -nptyping==1.4.2 -opencv-python==4.5.4.60 -opencv-python-headless==4.5.4.60 -overrides==4.1.2 -sentencepiece==0.1.99 -seqeval==0.0.12 -tensorboard>=2.2.0 -scipy==1.9.1 -isort==5.9.3 -black==21.9b0 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/top_level.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/top_level.txt deleted file mode 100644 index f6f7634..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -sdsvkvu diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/__init__.py deleted file mode 100644 index b90f78f..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -from .main import load_engine -from .main import process_img -from .main import process_pdf -from .main import process_dir diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/.gitignore b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/.gitignore deleted file mode 100644 index 1250e95..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -__pycache__/ -visualize/ -results/ -*.jpeg -*.jpg -*.png diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/README.md b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/README.md deleted file mode 100644 index ca1b349..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/README.md +++ /dev/null @@ -1,47 +0,0 @@ -# OCR Engine - -OCR Engine is a Python package that combines text detection and recognition models from [mmdet](https://github.com/open-mmlab/mmdetection) and [mmocr](https://github.com/open-mmlab/mmocr) to perform Optical Character Recognition (OCR) on various inputs. The package currently supports three types of input: a single image, a recursive directory, or a csv file. - -## Installation - -To install OCR Engine, clone the repository and install the required packages: - -```bash -git clone git@github.com:mrlasdt/ocr-engine.git -cd ocr-engine -pip install -r requirements.txt - -``` - - -## Usage - -To use OCR Engine, simply run the `ocr_engine.py` script with the desired input type and input path. For example, to perform OCR on a single image: - -```css -python ocr_engine.py --input_type image --input_path /path/to/image.jpg -``` - -To perform OCR on a recursive directory: - -```css -python ocr_engine.py --input_type directory --input_path /path/to/directory/ - -``` - -To perform OCR on a csv file: - - -``` -python ocr_engine.py --input_type csv --input_path /path/to/file.csv -``` - -OCR Engine will automatically detect and recognize text in the input and output the results in a CSV file named `ocr_results.csv`. - -## Contributing - -If you would like to contribute to OCR Engine, please fork the repository and submit a pull request. We welcome contributions of all types, including bug fixes, new features, and documentation improvements. - -## License - -OCR Engine is released under the [MIT License](https://opensource.org/licenses/MIT). See the LICENSE file for more information. diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/TODO.todo b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/TODO.todo deleted file mode 100644 index 34df095..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/TODO.todo +++ /dev/null @@ -1,10 +0,0 @@ -☐ refactor argument parser of run.py -☐ add timer level, logging level and write_mode to argumments -☐ add paddleocr deskew to the code -☐ fix the deskew code to resize the image only for detecting the angle, we want to feed the original size image to the text detection pipeline so that the bounding boxes would be mapped back to the original size -☐ ocr engine import took too long -☐ add word level to write_mode -☐ add word group and line -change max_x_dist from pixel to percentage of box width -☐ visualization: adjust fontsize dynamically - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/__init__.py deleted file mode 100644 index aabc310..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# # Define package-level variables -# __version__ = '0.0' - -# Import modules -from .src.ocr import OcrEngine -# from .src.word_formation import words_to_lines -from .src.word_formation import words_to_lines_tesseract as words_to_lines -from .src.utils import ImageReader, read_ocr_result_from_txt -from .src.dto import Word, Line, Page, Document, Box -# Expose package contents -__all__ = ["OcrEngine", "Box", "Word", "Line", "Page", "Document", "words_to_lines", "ImageReader", "read_ocr_result_from_txt"] diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/.gitignore b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/.gitignore deleted file mode 100644 index e56dbb2..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/.gitignore +++ /dev/null @@ -1,9 +0,0 @@ -output* -*.pyc -*.jpg -check -weights/ -workdirs/ -__pycache__* -test_hungbnt.py -libs* \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/README.md b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/README.md deleted file mode 100644 index d1b9637..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/README.md +++ /dev/null @@ -1,29 +0,0 @@ -

-

Dewarp

-

- -***Feature*** -- Align document - - -## I. Setup -***Dependencies*** -- Python: 3.8 -- Torch: 1.10.2 -- CUDA: 11.6 -- transformers: 4.28.1 -### 1. Install PaddlePaddle -``` -python -m pip install paddlepaddle-gpu==2.4.2.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html -``` - -### 2. Install sdsv_dewarp -``` -pip install -v -e . -``` - - -## II. Test -``` -python test.py --input samples --out demo/outputs --device 'cuda' -``` diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/cls.yaml b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/cls.yaml deleted file mode 100644 index 4c4cfdc..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/cls.yaml +++ /dev/null @@ -1,3 +0,0 @@ -model_dir: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_ppocr_mobile_v2.0_cls_infer -gpu_mem: 3000 -max_batch_size: 32 \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/det.yaml b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/det.yaml deleted file mode 100644 index f218ef1..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/config/det.yaml +++ /dev/null @@ -1,8 +0,0 @@ -model_dir: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_PP-OCRv3_det_infer -gpu_mem: 3000 -det_limit_side_len: 1560 -det_limit_type: max -det_db_unclip_ratio: 1.85 -det_db_thresh: 0.3 -det_db_box_thresh: 0.5 -det_db_score_mode: fast \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/requirements.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/requirements.txt deleted file mode 100644 index 768fde9..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ - -paddleocr>=2.0.1 -opencv-contrib-python -opencv-python -numpy -gdown==3.13.0 -imgaug==0.4.0 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO deleted file mode 100644 index 634708d..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO +++ /dev/null @@ -1,45 +0,0 @@ -Metadata-Version: 2.1 -Name: sdsv-dewarp -Version: 1.0.0 -Summary: Dewarp document -Home-page: -License: Apache License 2.0 -Classifier: Development Status :: 4 - Beta -Classifier: License :: OSI Approved :: Apache Software License -Classifier: Operating System :: OS Independent -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.6 -Classifier: Programming Language :: Python :: 3.7 -Classifier: Programming Language :: Python :: 3.8 -Classifier: Programming Language :: Python :: 3.9 -Description-Content-Type: text/markdown - -

-

Dewarp

-

- -***Feature*** -- Align document - - -## I. Setup -***Dependencies*** -- Python: 3.8 -- Torch: 1.10.2 -- CUDA: 11.6 -- transformers: 4.28.1 -### 1. Install PaddlePaddle -``` -python -m pip install paddlepaddle-gpu==2.4.2.post116 -f https://www.paddlepaddle.org.cn/whl/linux/mkl/avx/stable.html -``` - -### 2. Install sdsv_dewarp -``` -pip install -v -e . -``` - - -## II. Test -``` -python test.py --input samples --out demo/outputs --device 'cuda' -``` diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/SOURCES.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/SOURCES.txt deleted file mode 100644 index 953a123..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/SOURCES.txt +++ /dev/null @@ -1,15 +0,0 @@ -README.md -setup.py -sdsv_dewarp/__init__.py -sdsv_dewarp/api.py -sdsv_dewarp/config.py -sdsv_dewarp/factory.py -sdsv_dewarp/models.py -sdsv_dewarp/utils.py -sdsv_dewarp/version.py -sdsv_dewarp.egg-info/PKG-INFO -sdsv_dewarp.egg-info/SOURCES.txt -sdsv_dewarp.egg-info/dependency_links.txt -sdsv_dewarp.egg-info/not-zip-safe -sdsv_dewarp.egg-info/requires.txt -sdsv_dewarp.egg-info/top_level.txt \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe deleted file mode 100644 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/requires.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/requires.txt deleted file mode 100644 index 89816c8..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/requires.txt +++ /dev/null @@ -1,6 +0,0 @@ -paddleocr>=2.0.1 -opencv-contrib-python -opencv-python -numpy -gdown==3.13.0 -imgaug==0.4.0 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt deleted file mode 100644 index a5ce4e8..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -sdsv_dewarp diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/api.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/api.py deleted file mode 100644 index d71ddc5..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/api.py +++ /dev/null @@ -1,200 +0,0 @@ -import math -import numpy as np -from typing import List -import cv2 -import collections -import logging -import imgaug.augmenters as iaa -from imgaug.augmentables.polys import Polygon, PolygonsOnImage - -from sdsv_dewarp.models import PaddleTextClassifier, PaddleTextDetector -from sdsv_dewarp.config import Cfg -from .utils import * - - -MIN_LONG_EDGE = 40**2 -NUMBER_BOX_FOR_ALIGNMENT = 200 -MAX_ANGLE = 180 -MIN_ANGLE = 1 -MIN_NUM_BOX_TEXT = 3 -CROP_SIZE = 3000 - -logging.basicConfig(level=logging.INFO) -LOGGER = logging.getLogger(__name__) - - -class AlignImage: - """Rotate image to 0 degree - Args: - text_detector (deepmodel): Text detection model - text_cls (deepmodel): Text classification model (0 or 180) - - Return: - is_blank (bool): Blank image when haven't boxes text - image_align: Image after alignment - angle_align: Degree of angle alignment - """ - - def __init__(self, text_detector: dict, text_cls: dict, device: str = 'cpu'): - self.text_detector = None - self.text_cls = None - self.use_gpu = True if device != 'cpu' else False - - self._init_model(text_detector, text_cls) - - def _init_model(self, text_detector, text_cls): - det_config = Cfg.load_config_from_file(text_detector['config']) - det_config['model_dir'] = text_detector['weight'] - cls_config = Cfg.load_config_from_file(text_cls['config']) - cls_config['model_dir'] = text_cls['weight'] - - self.text_detector = PaddleTextDetector(config=det_config, use_gpu=self.use_gpu) - self.text_cls = PaddleTextClassifier(config=cls_config, use_gpu=self.use_gpu) - - def _cal_width(self, poly_box): - """Calculate width of a polygon [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]""" - tl, tr, br, bl = poly_box - edge_s, edge_l = distance(tl, tr), distance(tr, br) - - return max(edge_s, edge_l) - - def _get_most_frequent(self, values): - values = np.array(values) - # create the histogram - hist, bins = np.histogram(values, bins=np.arange(0, 181, 10)) - - # get the index of the most frequent angle - index = np.argmax(hist) - - # get the most frequent angle - most_frequent_angle = (bins[index] + bins[index + 1]) / 2 - - return most_frequent_angle - - def _cal_angle(self, poly_box): - """Calculate the angle between two point""" - a = poly_box[0] - b = poly_box[1] - c = poly_box[2] - - # Get the longer edge - if distance(a, b) >= distance(b, c): - x, y = a, b - else: - x, y = b, c - - angle = math.degrees(math.atan2(-(y[1] - x[1]), y[0] - x[0])) - - if angle < 0: - angle = 180 - abs(angle) - - return angle - - def _reject_outliers(self, data, m=5.0): - """Remove noise angle""" - list_index = np.arange(len(data)) - d = np.abs(data - np.median(data)) - mdev = np.median(d) - s = d / (mdev if mdev else 1.0) - - return list_index[s < m], data[s < m] - - def __call__(self, image): - """image (np.ndarray): BGR image""" - - # Crop center image to increase speed of text detection - - image_resized = crop_image(image, crop_size=CROP_SIZE).copy() if max(image.shape) > CROP_SIZE else image.copy() - poly_box_texts = self.text_detector(image_resized) - - # draw_img = vis_ocr( - # image_resized, - # poly_box_texts, - # ) - # cv2.imwrite("draw_img.jpg", draw_img) - - is_blank = False - - # Check image is blank - if len(poly_box_texts) <= MIN_NUM_BOX_TEXT: - is_blank = True - return image, is_blank, 0 - - # # Crop document - # poly_np = np.array(poly_box_texts) - # min_x = poly_box_texts[:, 0].min() - # max_x = poly_box_texts[:, 2].max() - # min_y = poly_box_texts[:, 1].min() - # max_y = poly_box_texts[:, 3].max() - - # Filter small poly - poly_box_areas = [ - [self._cal_width(poly_box), id] - for id, poly_box in enumerate(poly_box_texts) - ] - - poly_box_areas = sorted(poly_box_areas)[-NUMBER_BOX_FOR_ALIGNMENT:] - poly_box_areas = [poly_box_texts[id[1]] for id in poly_box_areas] - - # Calculate angle - list_angle = [self._cal_angle(poly_box) for poly_box in poly_box_areas] - list_angle = [angle if angle >= MIN_ANGLE else 180 for angle in list_angle] - - # LOGGER.info(f"List angle before reject outlier: {list_angle}") - list_angle = np.array(list_angle) - list_index, list_angle = self._reject_outliers(list_angle) - # LOGGER.info(f"List angle after reject outlier: {list_angle}") - - if len(list_angle): - - frequent_angle = self._get_most_frequent(list_angle) - list_angle = [angle for angle in list_angle if abs(angle - frequent_angle) <= 45] - # LOGGER.info(f"List angle after reject angle: {list_angle}") - angle = np.mean(list_angle) - else: - angle = 0 - - # LOGGER.info(f"Avg angle: {angle}") - - # Reuse poly boxes detected by text detection - polys_org = PolygonsOnImage( - [Polygon(poly_box_areas[index]) for index in list_index], - shape=image_resized.shape, - ) - seq_augment = iaa.Sequential([iaa.Rotate(angle, fit_output=True, order=3)]) - - # Rotate image by degree - if angle >= MIN_ANGLE and angle <= MAX_ANGLE: - image_resized, polys_aug = seq_augment( - image=image_resized, polygons=polys_org - ) - else: - angle = 0 - image_resized, polys_aug = image_resized, polys_org - - # cv2.imwrite("image_resized.jpg", image_resized) - - # Classify image 0 or 180 degree - list_poly = [poly.coords for poly in polys_aug] - - image_crop_list = [ - dewarp_by_polygon(image_resized, poly)[0] for poly in list_poly - ] - - cls_res = self.text_cls(image_crop_list) - cls_labels = [cls_[0] for cls_ in cls_res[1]] - # LOGGER.info(f"Angle lines: {cls_labels}") - counter = collections.Counter(cls_labels) - - angle_align = angle - if counter["0"] <= counter["180"]: - aug = iaa.Rotate(angle + 180, fit_output=True, order=3) - angle_align = angle + 180 - else: - aug = iaa.Rotate(angle, fit_output=True, order=3) - - # Rotate the image by degree - image = aug.augment_image(image) - - return image, is_blank, angle_align - # return image diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/config.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/config.py deleted file mode 100644 index 204c2c0..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/config.py +++ /dev/null @@ -1,41 +0,0 @@ -import yaml -import pprint -import os -import json - - -def load_from_yaml(fname): - with open(fname, encoding='utf-8') as f: - base_config = yaml.safe_load(f) - return base_config - -def load_from_json(fname): - with open(fname, "r", encoding='utf-8') as f: - base_config = json.load(f) - return base_config - -class Cfg(dict): - def __init__(self, config_dict): - super(Cfg, self).__init__(**config_dict) - self.__dict__ = self - - @staticmethod - def load_config_from_file(fname, download_base=False): - if not os.path.exists(fname): - raise FileNotFoundError("Not found config at {}".format(fname)) - if fname.endswith(".yaml") or fname.endswith(".yml"): - return Cfg(load_from_yaml(fname)) - elif fname.endswith(".json"): - return Cfg(load_from_json(fname)) - else: - raise Exception(f"{fname} not supported") - - - def save(self, fname): - with open(fname, 'w', encoding='utf-8') as outfile: - yaml.dump(dict(self), outfile, default_flow_style=False, allow_unicode=True) - - # @property - def pretty_text(self): - return pprint.PrettyPrinter().pprint(self) - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/factory.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/factory.py deleted file mode 100644 index 65e4bbd..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/factory.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import shutil -import hashlib -import warnings - -def sha256sum(filename): - h = hashlib.sha256() - b = bytearray(128*1024) - mv = memoryview(b) - with open(filename, 'rb', buffering=0) as f: - for n in iter(lambda : f.readinto(mv), 0): - h.update(mv[:n]) - return h.hexdigest() - - -online_model_factory = { - 'yolox-s-general-text-pretrain-20221226': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/62j266xm8r.pth', - 'hash': '89bff792685af454d0cfea5d6d673be6914d614e4c2044e786da6eddf36f8b50'}, - 'yolox-s-checkbox-20220726': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/1647d7eys7.pth', - 'hash': '7c1e188b7375dcf0b7b9d317675ebd92a86fdc29363558002249867249ee10f8'}, - 'yolox-s-idcard-5c-20221027': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/jr0egad3ix.pth', - 'hash': '73a7772594c1f6d3f6d6a98b6d6e4097af5026864e3bd50531ad9e635ae795a7'}, - 'yolox-s-handwritten-text-line-20230228': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/rb07rtwmgi.pth', - 'hash': 'a31d1bf8fc880479d2e11463dad0b4081952a13e553a02919109b634a1190ef1'} -} - -__hub_available_versions__ = online_model_factory.keys() - -def _get_from_hub(file_path, version, version_url): - os.system(f'wget -O {file_path} {version_url}') - assert os.path.exists(file_path), \ - 'wget failed while trying to retrieve from hub.' - downloaded_hash = sha256sum(file_path) - if downloaded_hash != online_model_factory[version]['hash']: - os.remove(file_path) - raise ValueError('sha256 hash doesnt match for version retrieved from hub.') - -def _get(version): - use_online = version in __hub_available_versions__ - - if not use_online and not os.path.exists(version): - raise ValueError(f'Model version {version} not found online and not found local.') - - hub_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'hub') - if not os.path.exists(hub_path): - os.makedirs(hub_path) - if use_online: - version_url = online_model_factory[version]['url'] - file_path = os.path.join(hub_path, os.path.basename(version_url)) - else: - file_path = os.path.join(hub_path, os.path.basename(version)) - - if not os.path.exists(file_path): - if use_online: - _get_from_hub(file_path, version, version_url) - else: - shutil.copy2(version, file_path) - else: - if use_online: - downloaded_hash = sha256sum(file_path) - if downloaded_hash != online_model_factory[version]['hash']: - os.remove(file_path) - warnings.warn('existing hub version sha256 hash doesnt match, now re-download from hub.') - _get_from_hub(file_path, version, version_url) - else: - if sha256sum(file_path) != sha256sum(version): - os.remove(file_path) - warnings.warn('existing local version sha256 hash doesnt match, now replace with new local version.') - shutil.copy2(version, file_path) - - return \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/models.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/models.py deleted file mode 100644 index 64cb88c..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/models.py +++ /dev/null @@ -1,73 +0,0 @@ - -from paddleocr.tools.infer.predict_det import TextDetector -from paddleocr.tools.infer.predict_cls import TextClassifier -from paddleocr.paddleocr import parse_args -from sdsv_dewarp.config import Cfg - -class PaddleTextDetector(object): - def __init__( - self, - # config_path: str, - config: dict, - use_gpu=False - ): - # config = Cfg.load_config_from_file(config_path) - - self.args = parse_args(mMain=False) - self.args.__dict__.update( - det_model_dir=config['model_dir'], - gpu_mem=config['gpu_mem'], - use_gpu=use_gpu, - use_zero_copy_run=True, - max_batch_size=1, - det_limit_side_len=config['det_limit_side_len'], #960 - det_limit_type=config['det_limit_type'], #'max' - det_db_unclip_ratio=config['det_db_unclip_ratio'], - det_db_thresh=config['det_db_thresh'], - det_db_box_thresh=config['det_db_box_thresh'], - det_db_score_mode=config['det_db_score_mode'], - ) - self.text_detector = TextDetector(self.args) - - def __call__(self, image): - """ - - Args: - image (np.ndarray): BGR images - - Returns: - np.ndarray: numpy array of poly boxes - shape 4x2 - """ - dt_boxes, time_infer = self.text_detector(image) - return dt_boxes - - -class PaddleTextClassifier(object): - def __init__( - self, - # config_path: str, - config: str, - use_gpu=False - ): - # config = Cfg.load_config_from_file(config_path) - - self.args = parse_args(mMain=False) - self.args.__dict__.update( - cls_model_dir=config['model_dir'], - gpu_mem=config['gpu_mem'], - use_gpu=use_gpu, - use_zero_copy_run=True, - cls_batch_num=config['max_batch_size'], - ) - self.text_classifier = TextClassifier(self.args) - - def __call__(self, images): - """ - Args: - images (np.ndarray): list of BGR images - - Returns: - img_list, cls_res, elapse : cls_res format = (label, conf) - """ - out= self.text_classifier(images) - return out \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/utils.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/utils.py deleted file mode 100644 index ae02cc1..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/utils.py +++ /dev/null @@ -1,212 +0,0 @@ -import math -import cv2 -import numpy as np -from PIL import Image, ImageDraw, ImageFont -import random - - -def distance(p1, p2): - """Calculate Euclid distance""" - x1, y1 = p1 - x2, y2 = p2 - dist = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) - - return dist - - -def crop_image(image, crop_size=1280): - """Crop center image""" - h, w = image.shape[:2] - x_center, y_center = w // 2, h // 2 - half_size = crop_size // 2 - - xmin, ymin = x_center - half_size, y_center - half_size - xmax, ymax = x_center + half_size, y_center + half_size - - xmin = max(xmin, 0) - ymin = max(ymin, 0) - xmax = min(xmax, w) - ymax = min(ymax, h) - - return image[ymin:ymax, xmin:xmax] - - -def _closest_point(corners, A): - """Find closest A in corrers point""" - distances = [distance(A, p) for p in corners] - return corners[np.argmin(distances)] - - -def _re_order_corners(image_size, corners) -> list: - """Order by corners by clockwise angle""" - h, w = image_size - tl = _closest_point(corners, (0, 0)) - tr = _closest_point(corners, (w, 0)) - br = _closest_point(corners, (w, h)) - bl = _closest_point(corners, (0, h)) - - return [tl, tr, br, bl] - - -def _validate_corner(corners, ratio_thres=0.5, epsilon=1e-3) -> bool: - """Check corners is valid - Invalid: 3 points, duplicate points, .... - """ - c_tl, c_tr, c_br, c_bl = corners - e_top = distance(c_tl, c_tr) - e_right = distance(c_tr, c_br) - e_bottom = distance(c_br, c_bl) - e_left = distance(c_bl, c_tl) - - min_tb = min(e_top, e_bottom) - max_tb = max(e_top, e_bottom) - min_lr = min(e_left, e_right) - max_lr = max(e_left, e_right) - - # Nếu các điểm trùng nhau thì độ dài các cạnh sẽ bằng 0 - if min(max_tb, max_lr) < epsilon: - return False - - ratio = min(min_tb / max_tb, min_lr / max_lr) - if ratio < ratio_thres: - return False - - return True - - -def dewarp_by_polygon( - image, corners, need_validate=False, need_reorder=True, trace_trans=None -): - """Crop and dewarp from 4 corners of images - - Args: - image (np.array) - corners (list): Ex : [(3347, 512), (3379, 2427), (638, 2524), (647, 495)] - need_validate (bool, optional): validate 4 points. Defaults to False. - need_reorder (bool, optional): validate 4 points. Defaults to True. - - Returns: - dewarped: image after dewarp - corners: location of 4 corners after reorder - """ - h, w = image.shape[:2] - - if need_reorder: - corners = _re_order_corners((h, w), corners) - - dewarped = image - - if need_validate: - validate = _validate_corner(corners) - else: - validate = True - - if validate: - # perform dewarp - target_w = int( - max(distance(corners[0], corners[1]), distance(corners[2], corners[3])) - ) - target_h = int( - max(distance(corners[0], corners[3]), distance(corners[1], corners[2])) - ) - target_corners = [ - [0, 0], - [target_w, 0], - [target_w, target_h], - [0, target_h], - ] - - pts1 = np.float32(corners) - pts2 = np.float32(target_corners) - transform_matrix = cv2.getPerspectiveTransform(pts1, pts2) - - dewarped = cv2.warpPerspective(image, transform_matrix, (target_w, target_h)) - if trace_trans is not None: - trace_trans["dewarp_method"]["polygon"][ - "transform_matrix" - ] = transform_matrix - - return (dewarped, corners, trace_trans) - - -def vis_ocr(image, boxes, txts=[], scores=None, drop_score=0.5): - """ - Args: - image (np.ndarray / PIL): BGR image or PIL image - boxes (list / np.ndarray): list of polygon boxes - txts (list): list of text labels - scores (list, optional): probality. Defaults to None. - drop_score (float, optional): . Defaults to 0.5. - font_path (str, optional): Path of font. Defaults to "test/fonts/latin.ttf". - Returns: - np.ndarray: BGR image - """ - - if len(txts) == 0: - txts = [""] * len(boxes) - - if isinstance(image, np.ndarray): - image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - if isinstance(boxes, list): - boxes = np.array(boxes) - - h, w = image.height, image.width - img_left = image.copy() - img_right = Image.new("RGB", (w, h), (255, 255, 255)) - draw_left = ImageDraw.Draw(img_left) - draw_right = ImageDraw.Draw(img_right) - for idx, (box, txt) in enumerate(zip(boxes, txts)): - if scores is not None and scores[idx] < drop_score: - continue - color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) - draw_left.polygon( - [ - box[0][0], - box[0][1], - box[1][0], - box[1][1], - box[2][0], - box[2][1], - box[3][0], - box[3][1], - ], - fill=color, - ) - draw_right.polygon( - [ - box[0][0], - box[0][1], - box[1][0], - box[1][1], - box[2][0], - box[2][1], - box[3][0], - box[3][1], - ], - outline=color, - ) - box_height = math.sqrt( - (box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2 - ) - box_width = math.sqrt( - (box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2 - ) - if box_height > 2 * box_width: - font_size = max(int(box_width * 0.9), 10) - font = ImageFont.load_default() - cur_y = box[0][1] - for c in txt: - char_size = font.getsize(c) - draw_right.text((box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font) - cur_y += char_size[1] - else: - font_size = max(int(box_height * 0.8), 10) - font = ImageFont.load_default() - draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font) - img_left = Image.blend(image, img_left, 0.5) - - img_show = Image.new("RGB", (w * 2, h), (255, 255, 255)) - img_show.paste(img_left, (0, 0, w, h)) - img_show.paste(img_right, (w, 0, w * 2, h)) - img_show = cv2.cvtColor(np.array(img_show), cv2.COLOR_RGB2BGR) - return img_show diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/version.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/version.py deleted file mode 100644 index a1570ac..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/sdsv_dewarp/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__="1.0.0" \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/setup.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/setup.py deleted file mode 100644 index 7887e8f..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/setup.py +++ /dev/null @@ -1,187 +0,0 @@ -import os -import os.path as osp -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -version_file = 'sdsv_dewarp/version.py' -is_windows = sys.platform == 'win32' - - -def add_mim_extention(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmocr', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - import sys - - # return short version for sdist - if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - return locals()['short_version'] - else: - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strip - specific version information. - - Args: - fname (str): Path to requirements file. - with_version (bool, default=False): If True, include version specs. - Returns: - info (list[str]): List of requirements items. - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, 'r') as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -if __name__ == '__main__': - setup( - name='sdsv_dewarp', - version=get_version(), - description='Dewarp document', - long_description=readme(), - long_description_content_type='text/markdown', - packages=find_packages(exclude=('configs', 'tools', 'demo')), - include_package_data=True, - url='', - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements.txt'), - zip_safe=False) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/test.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/test.py deleted file mode 100644 index 4bfdbc7..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsv_dewarp/test.py +++ /dev/null @@ -1,47 +0,0 @@ -from sdsv_dewarp.api import AlignImage -import cv2 -import glob -import os -import tqdm -import time -import argparse - - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--input") - parser.add_argument("--out") - parser.add_argument("--device", type=str, default="cuda:1") - - args = parser.parse_args() - model = AlignImage(device=args.device) - - - img_dir = args.input - out_dir = args.out - if not os.path.exists(out_dir): - os.makedirs(out_dir) - - img_paths = glob.glob(img_dir + "/*") - - times = [] - for img_path in tqdm.tqdm(img_paths): - t1 = time.time() - img = cv2.imread(img_path) - if img is None: - print(img_path) - continue - - aligned_img, is_blank, angle_align = model(img) - - times.append(time.time() - t1) - - if not is_blank: - cv2.imwrite(os.path.join(out_dir, os.path.basename(img_path)), aligned_img) - else: - cv2.imwrite(os.path.join(out_dir, os.path.basename(img_path)), img) - - - times = times[1:] - print("Avg time: ", sum(times) / len(times)) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/.gitignore b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/.gitignore deleted file mode 100644 index a8c1759..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -# Builds -*.egg-info -__pycache__ - -# Checkpoint -hub \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/LICENSE b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/LICENSE deleted file mode 100644 index 9e419e0..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. By contrast, -the GNU General Public License is intended to guarantee your freedom to -share and change all versions of a program--to make sure it remains free -software for all its users. We, the Free Software Foundation, use the -GNU General Public License for most of our software; it applies also to -any other work released this way by its authors. You can apply it to -your programs, too. - - When we speak of free software, we are referring to freedom, not -price. Our General Public Licenses are designed to make sure that you -have the freedom to distribute copies of free software (and charge for -them if you wish), that you receive source code or can get it if you -want it, that you can change the software or use pieces of it in new -free programs, and that you know you can do these things. - - To protect your rights, we need to prevent others from denying you -these rights or asking you to surrender the rights. Therefore, you have -certain responsibilities if you distribute copies of the software, or if -you modify it: responsibilities to respect the freedom of others. - - For example, if you distribute copies of such a program, whether -gratis or for a fee, you must pass on to the recipients the same -freedoms that you received. You must make sure that they, too, receive -or can get the source code. And you must show them these terms so they -know their rights. - - Developers that use the GNU GPL protect your rights with two steps: -(1) assert copyright on the software, and (2) offer you this License -giving you legal permission to copy, distribute and/or modify it. - - For the developers' and authors' protection, the GPL clearly explains -that there is no warranty for this free software. For both users' and -authors' sake, the GPL requires that modified versions be marked as -changed, so that their problems will not be attributed erroneously to -authors of previous versions. - - Some devices are designed to deny users access to install or run -modified versions of the software inside them, although the manufacturer -can do so. This is fundamentally incompatible with the aim of -protecting users' freedom to change the software. The systematic -pattern of such abuse occurs in the area of products for individuals to -use, which is precisely where it is most unacceptable. Therefore, we -have designed this version of the GPL to prohibit the practice for those -products. 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But first, please read -. \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/README.md b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/README.md deleted file mode 100644 index 4de145b..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/README.md +++ /dev/null @@ -1,76 +0,0 @@ -## Introduction -This repo serve as source code storage for the Standalone YoloX Detection packages. -Installing this package requires 2 additional packages: PyTorch and MMCV. - - -## Installation -```shell -conda create -n sdsvtd-env python=3.8 -conda activate sdsvtd-env -conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia -pip install mmcv-full -git clone https://github.com/moewiee/sdsvtd.git -cd sdsvtd -pip install -v -e . -``` - -## Basic Usage -```python -from sdsvtd import StandaloneYOLOXRunner -runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu') -``` - -The `version` parameter accepts version names declared in `sdsvtd.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run: -```python -import sdsvtd -print(sdsvtd.__hub_available_versions__) -``` - -Naturally, a `StandaloneYOLOXRunner` instance assumes the input to be an instance of `np.ndarray` or an instance of `str` (batch inferece is not supported), for examples: -```python -import numpy as np -from sdsvtd import StandaloneYOLOXRunner -runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu') - -dummy_input = np.ndarray(500, 500, 3) -result = runner(dummy_input) -``` - -**Note:** Output of `StandaloneYOLOXRunner` instance will be in format `List[np.ndarray]` with each list element corresponds to one class. Each `np.ndarray` will be a 5-d vector `[x1 y1 x2 y2 confident]` with coordinates rescaled to fit the original image size. - -**AutoRotation:** -From version 0.1.0, `sdsvtd` support *AutoRotation* feature which accept a `np.ndarray/str` as input and return an straight rotated image (only available rotation degrees are 90, 180 and 270) of type `np.ndarray` and its bounding boxes of type `List[np.ndarray]`. Usage: -```python -import numpy as np -from sdsvtd import StandaloneYOLOXRunner -runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu', auto_rotate=True) - -rotated_image, result = runner(cv2.imread('path-to-image')) # or -rotated_image, result = runner(np.ndarray) -``` - -## Version Changelog -* **[0.0.1]** -Initial version with specified features. - -* **[0.0.2]** -Update more versions in model hub. - -* **[0.0.3]** -Update feature to specify running device while initialize runner. [Issue](https://github.com/moewiee/sdsvtd/issues/2) - -* **[0.0.4]** -Fix a minor bugs when existing hub/local version != current hub/local version. - -* **[0.0.5]** -Update model hub with handwritten text line detection version. - -* **[0.1.0]** -Update new feature: Auto Rotation. - -* **[0.1.1]** -Fix a bug in API inference class where return double result with auto_rotate=False. - -* **[0.1.2]** -Fix a bug in rotate feature where rotator_version was ignored. - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/requirements.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/requirements.txt deleted file mode 100644 index 10f4b05..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -torch -mmcv-full \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/__init__.py deleted file mode 100644 index a3fb997..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .api import StandaloneYOLOXRunner -from .version import __version__ -from .factory import __hub_available_versions__ \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/api.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/api.py deleted file mode 100644 index 7deca24..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/api.py +++ /dev/null @@ -1,35 +0,0 @@ -from .model import SingleStageDetector, AutoRotateDetector -import numpy as np - -class StandaloneYOLOXRunner: - - def __init__(self, - version, - device, - auto_rotate=False, - rotator_version=None): - self.model = SingleStageDetector(version, - device) - self.auto_rotate = auto_rotate - if self.auto_rotate: - if rotator_version is None: - rotator_version = version - self.rotator = AutoRotateDetector(rotator_version, - device) - - self.warmup_() - - def warmup_(self): - ''' Call on dummpy input to warm up instance ''' - x = np.ndarray((1280, 1280, 3)).astype(np.uint8) - self.model(x) - if self.auto_rotate: - self.rotator(x) - - - def __call__(self, img): - if self.auto_rotate: - img = self.rotator(img) - result = self.model(img) - - return result if not self.auto_rotate else (img, result) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/backbone.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/backbone.py deleted file mode 100644 index 5aadded..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/backbone.py +++ /dev/null @@ -1,395 +0,0 @@ -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule -from torch.nn.modules.batchnorm import _BatchNorm - - -class DarknetBottleneck(nn.Module): - """The basic bottleneck block used in Darknet. - - Each ResBlock consists of two ConvModules and the input is added to the - final output. Each ConvModule is composed of Conv, BN, and LeakyReLU. - The first convLayer has filter size of 1x1 and the second one has the - filter size of 3x3. - - Args: - in_channels (int): The input channels of this Module. - out_channels (int): The output channels of this Module. - expansion (int): The kernel size of the convolution. Default: 0.5 - add_identity (bool): Whether to add identity to the out. - Default: True - use_depthwise (bool): Whether to use depthwise separable convolution. - Default: False - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN'). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish'). - """ - - def __init__(self, - in_channels, - out_channels, - expansion=0.5, - add_identity=True, - use_depthwise=False, - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish')): - super().__init__() - hidden_channels = int(out_channels * expansion) - self.conv1 = ConvModule( - in_channels, - hidden_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.conv2 = ConvModule( - hidden_channels, - out_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.add_identity = \ - add_identity and in_channels == out_channels - - def forward(self, x): - identity = x - out = self.conv1(x) - out = self.conv2(out) - - if self.add_identity: - return out + identity - else: - return out - - -class CSPLayer(nn.Module): - """Cross Stage Partial Layer. - - Args: - in_channels (int): The input channels of the CSP layer. - out_channels (int): The output channels of the CSP layer. - expand_ratio (float): Ratio to adjust the number of channels of the - hidden layer. Default: 0.5 - num_blocks (int): Number of blocks. Default: 1 - add_identity (bool): Whether to add identity in blocks. - Default: True - use_depthwise (bool): Whether to depthwise separable convolution in - blocks. Default: False - conv_cfg (dict, optional): Config dict for convolution layer. - Default: None, which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN') - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish') - """ - - def __init__(self, - in_channels, - out_channels, - expand_ratio=0.5, - num_blocks=1, - add_identity=True, - use_depthwise=False, - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish')): - super().__init__() - mid_channels = int(out_channels * expand_ratio) - self.main_conv = ConvModule( - in_channels, - mid_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.short_conv = ConvModule( - in_channels, - mid_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.final_conv = ConvModule( - 2 * mid_channels, - out_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - - self.blocks = nn.Sequential(*[ - DarknetBottleneck( - mid_channels, - mid_channels, - 1.0, - add_identity, - use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) for _ in range(num_blocks) - ]) - - def forward(self, x): - x_short = self.short_conv(x) - - x_main = self.main_conv(x) - x_main = self.blocks(x_main) - - x_final = torch.cat((x_main, x_short), dim=1) - return self.final_conv(x_final) - - - -class Focus(nn.Module): - """Focus width and height information into channel space. - - Args: - in_channels (int): The input channels of this Module. - out_channels (int): The output channels of this Module. - kernel_size (int): The kernel size of the convolution. Default: 1 - stride (int): The stride of the convolution. Default: 1 - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN', momentum=0.03, eps=0.001). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish'). - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size=1, - stride=1, - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish')): - super().__init__() - self.conv = ConvModule( - in_channels * 4, - out_channels, - kernel_size, - stride, - padding=(kernel_size - 1) // 2, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - - def forward(self, x): - # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2) - patch_top_left = x[..., ::2, ::2] - patch_top_right = x[..., ::2, 1::2] - patch_bot_left = x[..., 1::2, ::2] - patch_bot_right = x[..., 1::2, 1::2] - x = torch.cat( - ( - patch_top_left, - patch_bot_left, - patch_top_right, - patch_bot_right, - ), - dim=1, - ) - return self.conv(x) - - -class SPPBottleneck(nn.Module): - """Spatial pyramid pooling layer used in YOLOv3-SPP. - - Args: - in_channels (int): The input channels of this Module. - out_channels (int): The output channels of this Module. - kernel_sizes (tuple[int]): Sequential of kernel sizes of pooling - layers. Default: (5, 9, 13). - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN'). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish'). - init_cfg (dict or list[dict], optional): Initialization config dict. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_sizes=(5, 9, 13), - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish')): - super().__init__() - mid_channels = in_channels // 2 - self.conv1 = ConvModule( - in_channels, - mid_channels, - 1, - stride=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.poolings = nn.ModuleList([ - nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2) - for ks in kernel_sizes - ]) - conv2_channels = mid_channels * (len(kernel_sizes) + 1) - self.conv2 = ConvModule( - conv2_channels, - out_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - - def forward(self, x): - x = self.conv1(x) - x = torch.cat([x] + [pooling(x) for pooling in self.poolings], dim=1) - x = self.conv2(x) - return x - - -class CSPDarknet(nn.Module): - """CSP-Darknet backbone used in YOLOv5 and YOLOX. - - Args: - arch (str): Architecture of CSP-Darknet, from {P5, P6}. - Default: P5. - deepen_factor (float): Depth multiplier, multiply number of - blocks in CSP layer by this amount. Default: 1.0. - widen_factor (float): Width multiplier, multiply number of - channels in each layer by this amount. Default: 1.0. - out_indices (Sequence[int]): Output from which stages. - Default: (2, 3, 4). - frozen_stages (int): Stages to be frozen (stop grad and set eval - mode). -1 means not freezing any parameters. Default: -1. - use_depthwise (bool): Whether to use depthwise separable convolution. - Default: False. - arch_ovewrite(list): Overwrite default arch settings. Default: None. - spp_kernal_sizes: (tuple[int]): Sequential of kernel sizes of SPP - layers. Default: (5, 9, 13). - conv_cfg (dict): Config dict for convolution layer. Default: None. - norm_cfg (dict): Dictionary to construct and config norm layer. - Default: dict(type='BN', requires_grad=True). - act_cfg (dict): Config dict for activation layer. - Default: dict(type='LeakyReLU', negative_slope=0.1). - norm_eval (bool): Whether to set norm layers to eval mode, namely, - freeze running stats (mean and var). Note: Effect on Batch Norm - and its variants only. - """ - # From left to right: - # in_channels, out_channels, num_blocks, add_identity, use_spp - arch_settings = { - 'P5': [[64, 128, 3, True, False], [128, 256, 9, True, False], - [256, 512, 9, True, False], [512, 1024, 3, False, True]], - 'P6': [[64, 128, 3, True, False], [128, 256, 9, True, False], - [256, 512, 9, True, False], [512, 768, 3, True, False], - [768, 1024, 3, False, True]] - } - - def __init__(self, - arch='P5', - deepen_factor=1.0, - widen_factor=1.0, - out_indices=(2, 3, 4), - frozen_stages=-1, - use_depthwise=False, - arch_ovewrite=None, - spp_kernal_sizes=(5, 9, 13), - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish'), - norm_eval=False): - super().__init__() - arch_setting = self.arch_settings[arch] - if arch_ovewrite: - arch_setting = arch_ovewrite - assert set(out_indices).issubset( - i for i in range(len(arch_setting) + 1)) - if frozen_stages not in range(-1, len(arch_setting) + 1): - raise ValueError('frozen_stages must be in range(-1, ' - 'len(arch_setting) + 1). But received ' - f'{frozen_stages}') - - self.out_indices = out_indices - self.frozen_stages = frozen_stages - self.use_depthwise = use_depthwise - self.norm_eval = norm_eval - - self.stem = Focus( - 3, - int(arch_setting[0][0] * widen_factor), - kernel_size=3, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - self.layers = ['stem'] - - for i, (in_channels, out_channels, num_blocks, add_identity, - use_spp) in enumerate(arch_setting): - in_channels = int(in_channels * widen_factor) - out_channels = int(out_channels * widen_factor) - num_blocks = max(round(num_blocks * deepen_factor), 1) - stage = [] - conv_layer = ConvModule( - in_channels, - out_channels, - 3, - stride=2, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - stage.append(conv_layer) - if use_spp: - spp = SPPBottleneck( - out_channels, - out_channels, - kernel_sizes=spp_kernal_sizes, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - stage.append(spp) - csp_layer = CSPLayer( - out_channels, - out_channels, - num_blocks=num_blocks, - add_identity=add_identity, - use_depthwise=use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - stage.append(csp_layer) - self.add_module(f'stage{i + 1}', nn.Sequential(*stage)) - self.layers.append(f'stage{i + 1}') - - def _freeze_stages(self): - if self.frozen_stages >= 0: - for i in range(self.frozen_stages + 1): - m = getattr(self, self.layers[i]) - m.eval() - for param in m.parameters(): - param.requires_grad = False - - def train(self, mode=True): - super(CSPDarknet, self).train(mode) - self._freeze_stages() - if mode and self.norm_eval: - for m in self.modules(): - if isinstance(m, _BatchNorm): - m.eval() - - def forward(self, x): - outs = [] - for i, layer_name in enumerate(self.layers): - layer = getattr(self, layer_name) - x = layer(x) - if i in self.out_indices: - outs.append(x) - return tuple(outs) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/bbox_head.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/bbox_head.py deleted file mode 100644 index bb485f2..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/bbox_head.py +++ /dev/null @@ -1,288 +0,0 @@ -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule -from mmcv.ops.nms import batched_nms -from mmcv.runner import force_fp32 -from functools import partial -from .priors import MlvlPointGenerator - - -def multi_apply(func, *args, **kwargs): - """Apply function to a list of arguments. - - Note: - This function applies the ``func`` to multiple inputs and - map the multiple outputs of the ``func`` into different - list. Each list contains the same type of outputs corresponding - to different inputs. - - Args: - func (Function): A function that will be applied to a list of - arguments - - Returns: - tuple(list): A tuple containing multiple list, each list contains \ - a kind of returned results by the function - """ - pfunc = partial(func, **kwargs) if kwargs else func - map_results = map(pfunc, *args) - return tuple(map(list, zip(*map_results))) - - -class YOLOXHead(nn.Module): - """YOLOXHead head used in `YOLOX `_. - - Args: - num_classes (int): Number of categories excluding the background - category. - in_channels (int): Number of channels in the input feature map. - feat_channels (int): Number of hidden channels in stacking convs. - Default: 256 - stacked_convs (int): Number of stacking convs of the head. - Default: 2. - strides (tuple): Downsample factor of each feature map. - use_depthwise (bool): Whether to depthwise separable convolution in - blocks. Default: False - dcn_on_last_conv (bool): If true, use dcn in the last layer of - towers. Default: False. - conv_bias (bool | str): If specified as `auto`, it will be decided by - the norm_cfg. Bias of conv will be set as True if `norm_cfg` is - None, otherwise False. Default: "auto". - conv_cfg (dict): Config dict for convolution layer. Default: None. - norm_cfg (dict): Config dict for normalization layer. Default: None. - act_cfg (dict): Config dict for activation layer. Default: None. - """ - - def __init__(self, - num_classes, - in_channels, - feat_channels=256, - stacked_convs=2, - strides=[8, 16, 32], - use_depthwise=False, - dcn_on_last_conv=False, - conv_bias='auto', - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish'), - nms_score_thr=0.3, - nms_iou_threshold=0.1): - - super().__init__() - self.nms_config = dict(score_thr=nms_score_thr, nms=dict(iou_threshold=nms_iou_threshold)) - self.num_classes = num_classes - self.cls_out_channels = num_classes - self.in_channels = in_channels - self.feat_channels = feat_channels - self.stacked_convs = stacked_convs - self.strides = strides - self.use_depthwise = use_depthwise - self.dcn_on_last_conv = dcn_on_last_conv - assert conv_bias == 'auto' or isinstance(conv_bias, bool) - self.conv_bias = conv_bias - self.use_sigmoid_cls = True - - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - - self.prior_generator = MlvlPointGenerator(strides, offset=0) - - self.fp16_enabled = False - self._init_layers() - - def _init_layers(self): - self.multi_level_cls_convs = nn.ModuleList() - self.multi_level_reg_convs = nn.ModuleList() - self.multi_level_conv_cls = nn.ModuleList() - self.multi_level_conv_reg = nn.ModuleList() - self.multi_level_conv_obj = nn.ModuleList() - for _ in self.strides: - self.multi_level_cls_convs.append(self._build_stacked_convs()) - self.multi_level_reg_convs.append(self._build_stacked_convs()) - conv_cls, conv_reg, conv_obj = self._build_predictor() - self.multi_level_conv_cls.append(conv_cls) - self.multi_level_conv_reg.append(conv_reg) - self.multi_level_conv_obj.append(conv_obj) - - def _build_stacked_convs(self): - """Initialize conv layers of a single level head.""" - conv = ConvModule - stacked_convs = [] - for i in range(self.stacked_convs): - chn = self.in_channels if i == 0 else self.feat_channels - if self.dcn_on_last_conv and i == self.stacked_convs - 1: - conv_cfg = dict(type='DCNv2') - else: - conv_cfg = self.conv_cfg - stacked_convs.append( - conv( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=self.norm_cfg, - act_cfg=self.act_cfg, - bias=self.conv_bias)) - return nn.Sequential(*stacked_convs) - - def _build_predictor(self): - """Initialize predictor layers of a single level head.""" - conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1) - conv_reg = nn.Conv2d(self.feat_channels, 4, 1) - conv_obj = nn.Conv2d(self.feat_channels, 1, 1) - return conv_cls, conv_reg, conv_obj - - def forward_single(self, x, cls_convs, reg_convs, conv_cls, conv_reg, - conv_obj): - """Forward feature of a single scale level.""" - - cls_feat = cls_convs(x) - reg_feat = reg_convs(x) - - cls_score = conv_cls(cls_feat) - bbox_pred = conv_reg(reg_feat) - objectness = conv_obj(reg_feat) - - return cls_score, bbox_pred, objectness - - def forward(self, feats): - """Forward features from the upstream network. - - Args: - feats (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - Returns: - tuple[Tensor]: A tuple of multi-level predication map, each is a - 4D-tensor of shape (batch_size, 5+num_classes, height, width). - """ - - return multi_apply(self.forward_single, feats, - self.multi_level_cls_convs, - self.multi_level_reg_convs, - self.multi_level_conv_cls, - self.multi_level_conv_reg, - self.multi_level_conv_obj) - - @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses')) - def get_bboxes(self, - cls_scores, - bbox_preds, - objectnesses, - cfg=None): - """Transform network outputs of a batch into bbox results. - Args: - cls_scores (list[Tensor]): Classification scores for all - scale levels, each is a 4D-tensor, has shape - (batch_size, num_priors * num_classes, H, W). - bbox_preds (list[Tensor]): Box energies / deltas for all - scale levels, each is a 4D-tensor, has shape - (batch_size, num_priors * 4, H, W). - objectnesses (list[Tensor], Optional): Score factor for - all scale level, each is a 4D-tensor, has shape - (batch_size, 1, H, W). - cfg (mmcv.Config, Optional): Test / postprocessing configuration, - if None, test_cfg would be used. Default None. - rescale (bool): If True, return boxes in original image space. - Default False. - with_nms (bool): If True, do nms before return boxes. - Default True. - Returns: - list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class label of - the corresponding box. - """ - assert len(cls_scores) == len(bbox_preds) == len(objectnesses) - cfg = self.nms_config if cfg is None else cfg - - featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores] - mlvl_priors = self.prior_generator.grid_priors( - featmap_sizes, - dtype=cls_scores[0].dtype, - device=cls_scores[0].device, - with_stride=True) - - # flatten cls_scores, bbox_preds and objectness - flatten_cls_scores = [ - cls_score.permute(0, 2, 3, 1).reshape(1, -1, - self.cls_out_channels) - for cls_score in cls_scores - ] - flatten_bbox_preds = [ - bbox_pred.permute(0, 2, 3, 1).reshape(1, -1, 4) - for bbox_pred in bbox_preds - ] - flatten_objectness = [ - objectness.permute(0, 2, 3, 1).reshape(1, -1) - for objectness in objectnesses - ] - - flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid() - flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1) - flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid() - flatten_priors = torch.cat(mlvl_priors) - - flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds) - - result_list = [] - for img_id in range(1): - cls_scores = flatten_cls_scores[img_id] - score_factor = flatten_objectness[img_id] - bboxes = flatten_bboxes[img_id] - - result_list.append( - self._bboxes_nms(cls_scores, bboxes, score_factor, cfg)) - - return result_list - - def _bbox_decode(self, priors, bbox_preds): - xys = (bbox_preds[..., :2] * priors[:, 2:]) + priors[:, :2] - whs = bbox_preds[..., 2:].exp() * priors[:, 2:] - - tl_x = (xys[..., 0] - whs[..., 0] / 2) - tl_y = (xys[..., 1] - whs[..., 1] / 2) - br_x = (xys[..., 0] + whs[..., 0] / 2) - br_y = (xys[..., 1] + whs[..., 1] / 2) - - decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1) - return decoded_bboxes - - def _bboxes_nms(self, cls_scores, bboxes, score_factor, cfg): - max_scores, labels = torch.max(cls_scores, 1) - valid_mask = score_factor * max_scores >= cfg['score_thr'] - bboxes = bboxes[valid_mask] - scores = max_scores[valid_mask] * score_factor[valid_mask] - labels = labels[valid_mask] - - if labels.numel() == 0: - return bboxes, labels - else: - dets, keep = batched_nms(bboxes.float(), scores.float(), labels, cfg['nms']) - return dets, labels[keep] - - def simple_test_bboxes(self, feats): - """Test det bboxes without test-time augmentation, can be applied in - DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``, - etc. - - Args: - feats (tuple[torch.Tensor]): Multi-level features from the - upstream network, each is a 4D-tensor. - rescale (bool, optional): Whether to rescale the results. - Defaults to False. - - Returns: - list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is ``bboxes`` with shape (n, 5), - where 5 represent (tl_x, tl_y, br_x, br_y, score). - The shape of the second tensor in the tuple is ``labels`` - with shape (n,) - """ - outs = self.forward(feats) - results_list = self.get_bboxes(*outs) - return results_list \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/factory.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/factory.py deleted file mode 100644 index 2b6068f..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/factory.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import shutil -import hashlib -import warnings - -def sha256sum(filename): - h = hashlib.sha256() - b = bytearray(128*1024) - mv = memoryview(b) - with open(filename, 'rb', buffering=0) as f: - for n in iter(lambda : f.readinto(mv), 0): - h.update(mv[:n]) - return h.hexdigest() - - -online_model_factory = { - 'yolox-s-general-text-pretrain-20221226': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/62j266xm8r.pth', - 'hash': '89bff792685af454d0cfea5d6d673be6914d614e4c2044e786da6eddf36f8b50'}, - 'yolox-s-checkbox-20220726': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/1647d7eys7.pth', - 'hash': '7c1e188b7375dcf0b7b9d317675ebd92a86fdc29363558002249867249ee10f8'}, - 'yolox-s-idcard-5c-20221027': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/jr0egad3ix.pth', - 'hash': '73a7772594c1f6d3f6d6a98b6d6e4097af5026864e3bd50531ad9e635ae795a7'}, - 'yolox-s-handwritten-text-line-20230228': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/rb07rtwmgi.pth', - 'hash': 'a31d1bf8fc880479d2e11463dad0b4081952a13e553a02919109b634a1190ef1'} -} - -__hub_available_versions__ = online_model_factory.keys() - -def _get_from_hub(file_path, version, version_url): - os.system(f'wget -O {file_path} {version_url}') - assert os.path.exists(file_path), \ - 'wget failed while trying to retrieve from hub.' - downloaded_hash = sha256sum(file_path) - if downloaded_hash != online_model_factory[version]['hash']: - os.remove(file_path) - raise ValueError('sha256 hash doesnt match for version retrieved from hub.') - -def _get(version): - use_online = version in __hub_available_versions__ - - if not use_online and not os.path.exists(version): - raise ValueError(f'Model version {version} not found online and not found local.') - - hub_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'hub') - if not os.path.exists(hub_path): - os.makedirs(hub_path) - if use_online: - version_url = online_model_factory[version]['url'] - file_path = os.path.join(hub_path, os.path.basename(version_url)) - else: - file_path = os.path.join(hub_path, os.path.basename(version)) - - if not os.path.exists(file_path): - if use_online: - _get_from_hub(file_path, version, version_url) - else: - shutil.copy2(version, file_path) - else: - if use_online: - downloaded_hash = sha256sum(file_path) - if downloaded_hash != online_model_factory[version]['hash']: - os.remove(file_path) - warnings.warn('existing hub version sha256 hash doesnt match, now re-download from hub.') - _get_from_hub(file_path, version, version_url) - else: - if sha256sum(file_path) != sha256sum(version): - os.remove(file_path) - warnings.warn('existing local version sha256 hash doesnt match, now replace with new local version.') - shutil.copy2(version, file_path) - - return file_path \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/model.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/model.py deleted file mode 100644 index 69a26ef..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/model.py +++ /dev/null @@ -1,151 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -from .backbone import CSPDarknet -from .neck import YOLOXPAFPN -from .bbox_head import YOLOXHead -from .transform import DetectorDataPipeline, AutoRotateDetectorDataPipeline -from .factory import _get as get_version - - -def bbox2result(bboxes, labels, num_classes): - """Convert detection results to a list of numpy arrays. - - Args: - bboxes (torch.Tensor | np.ndarray): shape (n, 5) - labels (torch.Tensor | np.ndarray): shape (n, ) - num_classes (int): class number, including background class - - Returns: - list(ndarray): bbox results of each class - """ - if bboxes.shape[0] == 0: - return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)] - else: - if isinstance(bboxes, torch.Tensor): - bboxes = bboxes.detach().cpu().numpy() - labels = labels.detach().cpu().numpy() - return [bboxes[labels == i, :] for i in range(num_classes)] - - -def normalize_bbox(bboxes, scale): - for i in range(len(bboxes)): - bboxes[i][:,:4] /= scale - return bboxes - - -class SingleStageDetector(nn.Module): - - def __init__(self, - version, - device): - super(SingleStageDetector, self).__init__() - - assert 'cpu' in device or 'cuda' in device - - checkpoint = get_version(version) - pt = torch.load(checkpoint, 'cpu') - - self.pipeline = DetectorDataPipeline(**pt['pipeline_args'], device=device) - self.backbone = CSPDarknet(**pt['backbone_args']) - self.neck = YOLOXPAFPN(**pt['neck_args']) - self.bbox_head = YOLOXHead(**pt['bbox_head_args']) - self.load_state_dict(pt['state_dict'], strict=True) - - self.eval() - for param in self.parameters(): - param.requires_grad = False - - self = self.to(device=device) - - print(f'Text detection load from version {version}.') - - def extract_feat(self, img): - """Directly extract features from the backbone + neck.""" - - x = self.backbone(img) - x = self.neck(x) - return x - - def forward(self, img): - """Test function without test-time augmentation. - - Args: - img (np.ndarray): Images with shape (H, W, C) or - img (str): Path to image. - - Returns: - list[list[np.ndarray]]: BBox results of each image and classes. - The list corresponds to each class. - """ - img, origin_shape, new_shape = self.pipeline(img) - scale = min(new_shape / origin_shape) - feat = self.extract_feat(img) - results_list = self.bbox_head.simple_test_bboxes(feat) - bbox_results = [ - bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) - for det_bboxes, det_labels in results_list - ][0] - bbox_results = normalize_bbox(bbox_results, scale) - return bbox_results - - -class AutoRotateDetector(nn.Module): - def __init__(self, - version, - device): - super(AutoRotateDetector, self).__init__() - - assert 'cpu' in device or 'cuda' in device - - checkpoint = get_version(version) - pt = torch.load(checkpoint, 'cpu') - - self.pipeline = AutoRotateDetectorDataPipeline(**pt['pipeline_args'], device=device) - self.backbone = CSPDarknet(**pt['backbone_args']) - self.neck = YOLOXPAFPN(**pt['neck_args']) - self.bbox_head = YOLOXHead(**pt['bbox_head_args'], nms_score_thr=0.8) - self.load_state_dict(pt['state_dict'], strict=True) - - self.eval() - for param in self.parameters(): - param.requires_grad = False - - self = self.to(device=device) - - print(f'Auto rotate detector load from version {version}.') - - def extract_feat(self, img): - """Directly extract features from the backbone + neck.""" - - x = self.backbone(img) - x = self.neck(x) - return x - - def forward(self, img): - """Test function without test-time augmentation. - - Args: - img (np.ndarray): Images with shape (H, W, C) or - img (str): Path to image. - - Returns: - np.ndarray: Straight rotated image. - """ - imgs, imgs_np = self.pipeline(img) - maxCount = -1 - maxCountRot = None - for idx, img in enumerate(imgs): - currentCount = 0 - feat = self.extract_feat(img) - results_list = self.bbox_head.simple_test_bboxes(feat) - bbox_results = [ - bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) - for det_bboxes, det_labels in results_list - ][0] - for class_result in bbox_results: - currentCount += len(class_result) - if currentCount > maxCount: - maxCount = currentCount - maxCountRot = idx - return imgs_np[maxCountRot] \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/neck.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/neck.py deleted file mode 100644 index 2b11e36..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/neck.py +++ /dev/null @@ -1,140 +0,0 @@ -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule -from .backbone import CSPLayer - - -class YOLOXPAFPN(nn.Module): - """Path Aggregation Network used in YOLOX. - - Args: - in_channels (List[int]): Number of input channels per scale. - out_channels (int): Number of output channels (used at each scale) - num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 3 - use_depthwise (bool): Whether to depthwise separable convolution in - blocks. Default: False - upsample_cfg (dict): Config dict for interpolate layer. - Default: `dict(scale_factor=2, mode='nearest')` - conv_cfg (dict, optional): Config dict for convolution layer. - Default: None, which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. - Default: dict(type='BN') - act_cfg (dict): Config dict for activation layer. - Default: dict(type='Swish') - init_cfg (dict or list[dict], optional): Initialization config dict. - Default: None. - """ - - def __init__(self, - in_channels, - out_channels, - num_csp_blocks=3, - use_depthwise=False, - upsample_cfg=dict(scale_factor=2, mode='nearest'), - conv_cfg=None, - norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), - act_cfg=dict(type='Swish')): - super(YOLOXPAFPN, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - - # build top-down blocks - self.upsample = nn.Upsample(**upsample_cfg) - self.reduce_layers = nn.ModuleList() - self.top_down_blocks = nn.ModuleList() - for idx in range(len(in_channels) - 1, 0, -1): - self.reduce_layers.append( - ConvModule( - in_channels[idx], - in_channels[idx - 1], - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - self.top_down_blocks.append( - CSPLayer( - in_channels[idx - 1] * 2, - in_channels[idx - 1], - num_blocks=num_csp_blocks, - add_identity=False, - use_depthwise=use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - # build bottom-up blocks - self.downsamples = nn.ModuleList() - self.bottom_up_blocks = nn.ModuleList() - for idx in range(len(in_channels) - 1): - self.downsamples.append( - ConvModule( - in_channels[idx], - in_channels[idx], - 3, - stride=2, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - self.bottom_up_blocks.append( - CSPLayer( - in_channels[idx] * 2, - in_channels[idx + 1], - num_blocks=num_csp_blocks, - add_identity=False, - use_depthwise=use_depthwise, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - self.out_convs = nn.ModuleList() - for i in range(len(in_channels)): - self.out_convs.append( - ConvModule( - in_channels[i], - out_channels, - 1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg)) - - def forward(self, inputs): - """ - Args: - inputs (tuple[Tensor]): input features. - - Returns: - tuple[Tensor]: YOLOXPAFPN features. - """ - assert len(inputs) == len(self.in_channels) - - # top-down path - inner_outs = [inputs[-1]] - for idx in range(len(self.in_channels) - 1, 0, -1): - feat_heigh = inner_outs[0] - feat_low = inputs[idx - 1] - feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx]( - feat_heigh) - inner_outs[0] = feat_heigh - - upsample_feat = self.upsample(feat_heigh) - - inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx]( - torch.cat([upsample_feat, feat_low], 1)) - inner_outs.insert(0, inner_out) - - # bottom-up path - outs = [inner_outs[0]] - for idx in range(len(self.in_channels) - 1): - feat_low = outs[-1] - feat_height = inner_outs[idx + 1] - downsample_feat = self.downsamples[idx](feat_low) - out = self.bottom_up_blocks[idx]( - torch.cat([downsample_feat, feat_height], 1)) - outs.append(out) - - # out convs - for idx, conv in enumerate(self.out_convs): - outs[idx] = conv(outs[idx]) - - return tuple(outs) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/priors.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/priors.py deleted file mode 100644 index 8eb2fde..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/priors.py +++ /dev/null @@ -1,225 +0,0 @@ -import torch -import numpy as np -from torch.nn.modules.utils import _pair - - -class MlvlPointGenerator: - """Standard points generator for multi-level (Mlvl) feature maps in 2D - points-based detectors. - - Args: - strides (list[int] | list[tuple[int, int]]): Strides of anchors - in multiple feature levels in order (w, h). - offset (float): The offset of points, the value is normalized with - corresponding stride. Defaults to 0.5. - """ - - def __init__(self, strides, offset=0.5): - self.strides = [_pair(stride) for stride in strides] - self.offset = offset - - @property - def num_levels(self): - """int: number of feature levels that the generator will be applied""" - return len(self.strides) - - @property - def num_base_priors(self): - """list[int]: The number of priors (points) at a point - on the feature grid""" - return [1 for _ in range(len(self.strides))] - - def _meshgrid(self, x, y, row_major=True): - yy, xx = torch.meshgrid(y, x) - if row_major: - # warning .flatten() would cause error in ONNX exporting - # have to use reshape here - return xx.flatten(), yy.flatten() - - else: - return yy.flatten(), xx.flatten() - - def grid_priors(self, - featmap_sizes, - dtype=torch.float32, - device='cuda', - with_stride=False): - """Generate grid points of multiple feature levels. - - Args: - featmap_sizes (list[tuple]): List of feature map sizes in - multiple feature levels, each size arrange as - as (h, w). - dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. - device (str): The device where the anchors will be put on. - with_stride (bool): Whether to concatenate the stride to - the last dimension of points. - - Return: - list[torch.Tensor]: Points of multiple feature levels. - The sizes of each tensor should be (N, 2) when with stride is - ``False``, where N = width * height, width and height - are the sizes of the corresponding feature level, - and the last dimension 2 represent (coord_x, coord_y), - otherwise the shape should be (N, 4), - and the last dimension 4 represent - (coord_x, coord_y, stride_w, stride_h). - """ - - assert self.num_levels == len(featmap_sizes) - multi_level_priors = [] - for i in range(self.num_levels): - priors = self.single_level_grid_priors( - featmap_sizes[i], - level_idx=i, - dtype=dtype, - device=device, - with_stride=with_stride) - multi_level_priors.append(priors) - return multi_level_priors - - def single_level_grid_priors(self, - featmap_size, - level_idx, - dtype=torch.float32, - device='cuda', - with_stride=False): - """Generate grid Points of a single level. - - Note: - This function is usually called by method ``self.grid_priors``. - - Args: - featmap_size (tuple[int]): Size of the feature maps, arrange as - (h, w). - level_idx (int): The index of corresponding feature map level. - dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32. - device (str, optional): The device the tensor will be put on. - Defaults to 'cuda'. - with_stride (bool): Concatenate the stride to the last dimension - of points. - - Return: - Tensor: Points of single feature levels. - The shape of tensor should be (N, 2) when with stride is - ``False``, where N = width * height, width and height - are the sizes of the corresponding feature level, - and the last dimension 2 represent (coord_x, coord_y), - otherwise the shape should be (N, 4), - and the last dimension 4 represent - (coord_x, coord_y, stride_w, stride_h). - """ - feat_h, feat_w = featmap_size - stride_w, stride_h = self.strides[level_idx] - shift_x = (torch.arange(0, feat_w, device=device) + - self.offset) * stride_w - # keep featmap_size as Tensor instead of int, so that we - # can convert to ONNX correctly - shift_x = shift_x.to(dtype) - - shift_y = (torch.arange(0, feat_h, device=device) + - self.offset) * stride_h - # keep featmap_size as Tensor instead of int, so that we - # can convert to ONNX correctly - shift_y = shift_y.to(dtype) - shift_xx, shift_yy = self._meshgrid(shift_x, shift_y) - if not with_stride: - shifts = torch.stack([shift_xx, shift_yy], dim=-1) - else: - # use `shape[0]` instead of `len(shift_xx)` for ONNX export - stride_w = shift_xx.new_full((shift_xx.shape[0], ), - stride_w).to(dtype) - stride_h = shift_xx.new_full((shift_yy.shape[0], ), - stride_h).to(dtype) - shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], - dim=-1) - all_points = shifts.to(device) - return all_points - - def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): - """Generate valid flags of points of multiple feature levels. - - Args: - featmap_sizes (list(tuple)): List of feature map sizes in - multiple feature levels, each size arrange as - as (h, w). - pad_shape (tuple(int)): The padded shape of the image, - arrange as (h, w). - device (str): The device where the anchors will be put on. - - Return: - list(torch.Tensor): Valid flags of points of multiple levels. - """ - assert self.num_levels == len(featmap_sizes) - multi_level_flags = [] - for i in range(self.num_levels): - point_stride = self.strides[i] - feat_h, feat_w = featmap_sizes[i] - h, w = pad_shape[:2] - valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h) - valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w) - flags = self.single_level_valid_flags((feat_h, feat_w), - (valid_feat_h, valid_feat_w), - device=device) - multi_level_flags.append(flags) - return multi_level_flags - - def single_level_valid_flags(self, - featmap_size, - valid_size, - device='cuda'): - """Generate the valid flags of points of a single feature map. - - Args: - featmap_size (tuple[int]): The size of feature maps, arrange as - as (h, w). - valid_size (tuple[int]): The valid size of the feature maps. - The size arrange as as (h, w). - device (str, optional): The device where the flags will be put on. - Defaults to 'cuda'. - - Returns: - torch.Tensor: The valid flags of each points in a single level \ - feature map. - """ - feat_h, feat_w = featmap_size - valid_h, valid_w = valid_size - assert valid_h <= feat_h and valid_w <= feat_w - valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device) - valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device) - valid_x[:valid_w] = 1 - valid_y[:valid_h] = 1 - valid_xx, valid_yy = self._meshgrid(valid_x, valid_y) - valid = valid_xx & valid_yy - return valid - - def sparse_priors(self, - prior_idxs, - featmap_size, - level_idx, - dtype=torch.float32, - device='cuda'): - """Generate sparse points according to the ``prior_idxs``. - - Args: - prior_idxs (Tensor): The index of corresponding anchors - in the feature map. - featmap_size (tuple[int]): feature map size arrange as (w, h). - level_idx (int): The level index of corresponding feature - map. - dtype (obj:`torch.dtype`): Date type of points. Defaults to - ``torch.float32``. - device (obj:`torch.device`): The device where the points is - located. - Returns: - Tensor: Anchor with shape (N, 2), N should be equal to - the length of ``prior_idxs``. And last dimension - 2 represent (coord_x, coord_y). - """ - height, width = featmap_size - x = (prior_idxs % width + self.offset) * self.strides[level_idx][0] - y = ((prior_idxs // width) % height + - self.offset) * self.strides[level_idx][1] - prioris = torch.stack([x, y], 1).to(dtype) - prioris = prioris.to(device) - return prioris \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/transform.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/transform.py deleted file mode 100644 index fda7ab9..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/transform.py +++ /dev/null @@ -1,81 +0,0 @@ -import mmcv -import numpy as np -import cv2 -import torch - - -class DetectorDataPipeline: - - def __init__(self, - img_scale, - device): - self.scale = img_scale - self.device = device - - def load(self, img): - if isinstance(img, str): - return cv2.imread(img) - elif isinstance(img, np.ndarray): - return img - else: - raise ValueError(f'img input must be a str/np.ndarray, got {type(img)}') - - def resize(self, img): - origin_shape = img.shape[:2] - img = mmcv.imrescale(img, - self.scale, - return_scale=False, - interpolation='bilinear', - backend='cv2') - - return img, origin_shape, np.array(self.scale) - - def pad(self, img): - if self.scale is not None: - width = max(self.scale[1] - img.shape[1], 0) - height = max(self.scale[0] - img.shape[0], 0) - padding = (0, 0, width, height) - - img = cv2.copyMakeBorder(img, - padding[1], - padding[3], - padding[0], - padding[2], - cv2.BORDER_CONSTANT, - value=(114, 114, 114,)) - - return img - - def to_tensor(self, img): - - img = torch.from_numpy(img.astype(np.float32)).permute(2,0,1).unsqueeze(0) - img = img.to(device=self.device) - - return img - - def __call__(self, img): - img = self.load(img) - img, origin_shape, new_shape = self.resize(img) - img = self.pad(img) - img = self.to_tensor(img) - - return img, origin_shape, new_shape - - -class AutoRotateDetectorDataPipeline(DetectorDataPipeline): - - def __call__(self, img): - img = self.load(img) - imgs = [] - imgs_np = [] - - for flag in (None, cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_180, cv2.ROTATE_90_COUNTERCLOCKWISE): - img_t = img if flag is None else cv2.rotate(img, flag) - imgs_np.append(img_t) - img_t, _, _ = self.resize(img_t) - img_t = self.pad(img_t) - img_t = self.to_tensor(img_t) - - imgs.append(img_t) - - return imgs, imgs_np \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/version.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/version.py deleted file mode 100644 index 10939f0..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/sdsvtd/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '0.1.2' diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/setup.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/setup.py deleted file mode 100644 index a2e04db..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtd/setup.py +++ /dev/null @@ -1,186 +0,0 @@ -import os -import os.path as osp -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -version_file = 'sdsvtd/version.py' -is_windows = sys.platform == 'win32' - - -def add_mim_extention(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmocr', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - import sys - - # return short version for sdist - if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - return locals()['short_version'] - else: - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strip - specific version information. - - Args: - fname (str): Path to requirements file. - with_version (bool, default=False): If True, include version specs. - Returns: - info (list[str]): List of requirements items. - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, 'r') as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -if __name__ == '__main__': - setup( - name='sdsvtd', - version=get_version(), - description='SDSV OCR Team Text Detection', - long_description=readme(), - long_description_content_type='text/markdown', - packages=find_packages(exclude=('configs', 'tools', 'demo')), - include_package_data=True, - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements.txt'), - zip_safe=False) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/.gitignore b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/.gitignore deleted file mode 100644 index a8c1759..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -# Builds -*.egg-info -__pycache__ - -# Checkpoint -hub \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/LICENSE b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/LICENSE deleted file mode 100644 index 92b370f..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/README.md b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/README.md deleted file mode 100644 index 2cabc83..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/README.md +++ /dev/null @@ -1,76 +0,0 @@ -## Introduction -This repo serve as source code storage for the Standalone SATRN Text Recognizer packages. -Installing this package requires 3 additional packages: PyTorch, MMCV, and colorama. - - -## Installation -```shell -conda create -n sdsvtr-env python=3.8 -conda activate sdsvtr-env -conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia -pip install -U openmim -mim install mmcv-full -pip install colorama -git clone https://github.com/moewiee/sdsvtr.git -cd sdsvtr -pip install -v -e . -``` - -## Basic Usage -```python -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, use_cuda=False) -``` - -The `version` parameter accepts version names declared in `sdsvtr.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run: -```python -import sdsvtr -print(sdsvtr.__hub_available_versions__) -``` - -Naturally, a `StandaloneSATRNRunner` instance assumes the input to be one of the following: an instance of `np.ndarray`, an instance of `str`, a list of `np.ndarray`, or a list of `str`, for examples: -```python -import numpy as np -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, use_cuda=False) - -dummy_list = [np.ndarray((32,128,3)) for _ in range(100)] -result = runner(dummy_list) -``` - -To run with a specific batchsize, try: -```python -import numpy as np -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:0') - -dummy_list = [np.ndarray(1,3,32,128) for _ in range(100)] -bs = min(32, len(imageFiles)) # batchsize = 32 - -all_results = [] -while len(dummy_list) > 0: - dummy_batch = dummy_list[:bs] - dummy_list = dummy_list[bs:] - all_results += runner(dummy_batch) -``` - -## Version Changelog -* **[0.0.1]** -Initial version with specified features. - - -* **[0.0.2]** -Update online model hub. - - -* **[0.0.3]** -Update API now able to inference with 4 types of inputs: list/instance of `np.ndarray`/`str` -Update API interface with `return_confident` parameter. -Update `wget` check and `sha256` check for model hub retrieval. - -* **[0.0.4]** -Update decoder module with EarlyStopping mechanism to possibly improve inference speed on short sequences. -Update API interface with optional argument `max_seq_len_overwrite` to overwrite checkpoint's `max_seq_len` config. - -* **[0.0.5]** -Allow inference on a specific device diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/requirements.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/requirements.txt deleted file mode 100644 index f4a74db..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -torch -colorama -mmcv-full \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/__init__.py deleted file mode 100644 index dd8470d..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .api import StandaloneSATRNRunner -from .version import __version__ -from .factory import __hub_available_versions__ \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/api.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/api.py deleted file mode 100644 index 566a904..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/api.py +++ /dev/null @@ -1,106 +0,0 @@ - -import torch -import torch.nn as nn - -from colorama import Fore, Style - -from .converter import AttnConvertor -from .backbone import ResNetABI -from .encoder import SatrnEncoder -from .decoder import NRTRDecoder -from .transform import DataPipelineSATRN -from .fp16_utils import patch_norm_fp32 -from .factory import _get as get_version - - -class SATRN(nn.Module): - """Standalone implementation for SATRN encode-decode recognizer.""" - - def __init__(self, - version, - return_confident=False, - device='cpu', - max_seq_len_overwrite=None): - - super().__init__() - - checkpoint = get_version(version) - - pt = torch.load(checkpoint, 'cpu') - if device == 'cpu': - print(Fore.RED + 'Warning: You are using CPU inference method. Init with device=cuda: to run with CUDA method.' + Style.RESET_ALL) - - self.pipeline = DataPipelineSATRN(**pt['pipeline_args'], device=device) - - # Convertor - self.label_convertor = AttnConvertor(**pt['label_convertor_args'], return_confident=return_confident) - - # Backbone - self.backbone = ResNetABI(**pt['backbone_args']) - - # Encoder module - self.encoder = SatrnEncoder(**pt['encoder_args']) - - # Decoder module - decoder_max_seq_len = max_seq_len_overwrite if max_seq_len_overwrite is not None else pt['max_seq_len'] - self.decoder = NRTRDecoder( - **pt['decoder_args'], - max_seq_len=decoder_max_seq_len, - num_classes=self.label_convertor.num_classes(), - start_idx=self.label_convertor.start_idx, - padding_idx=self.label_convertor.padding_idx, - return_confident=return_confident, - end_idx=self.label_convertor.end_idx - ) - - self.load_state_dict(pt['state_dict'], strict=True) - print(f'Text recognition from version {version}.') - - if device != 'cpu': - self = self.to(device) - self = self.half() - patch_norm_fp32(self) - - self.eval() - for param in self.parameters(): - param.requires_grad = False - - def extract_feat(self, img): - x = self.backbone(img) - - return x - - def forward(self, img): - """Test function with test time augmentation. - - Args: - imgs (torch.Tensor): Image input tensor. - - Returns: - list[str]: Text label result of each image. - """ - img = self.pipeline(img) - feat = self.extract_feat(img) - out_enc = self.encoder(feat) - out_dec = self.decoder(out_enc).cpu().numpy() - label_strings = self.label_convertor(out_dec) - - return label_strings - - -class StandaloneSATRNRunner: - def __init__(self, - version, - return_confident, - device='cpu', - max_seq_len_overwrite=None): - self.device = device - self.model = SATRN(version=version, - return_confident=return_confident, - device=self.device, - max_seq_len_overwrite=max_seq_len_overwrite) - - def __call__(self, imgs): - results = self.model(imgs) - - return results \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/backbone.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/backbone.py deleted file mode 100644 index 29c2509..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/backbone.py +++ /dev/null @@ -1,159 +0,0 @@ -import torch.nn as nn - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding.""" - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=1, - bias=False) - - -def conv1x1(in_planes, out_planes): - """1x1 convolution with padding.""" - return nn.Conv2d( - in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False) - - -class BasicBlock(nn.Module): - - expansion = 1 - - def __init__(self, - inplanes, - planes, - stride=1, - downsample=None, - use_conv1x1=False): - super(BasicBlock, self).__init__() - - if use_conv1x1: - self.conv1 = conv1x1(inplanes, planes) - self.conv2 = conv3x3(planes, planes * self.expansion, stride) - else: - self.conv1 = conv3x3(inplanes, planes, stride) - self.conv2 = conv3x3(planes, planes * self.expansion) - - self.planes = planes - self.bn1 = nn.BatchNorm2d(planes) - self.relu = nn.ReLU(inplace=True) - self.bn2 = nn.BatchNorm2d(planes * self.expansion) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - out = self.conv2(out) - out = self.bn2(out) - if self.downsample is not None: - residual = self.downsample(x) - out += residual - out = self.relu(out) - - return out - -class ResNetABI(nn.Module): - """Implement ResNet backbone for text recognition, modified from `ResNet. - - `_ and - ``_ - - Args: - in_channels (int): Number of channels of input image tensor. - stem_channels (int): Number of stem channels. - base_channels (int): Number of base channels. - arch_settings (list[int]): List of BasicBlock number for each stage. - strides (Sequence[int]): Strides of the first block of each stage. - out_indices (None | Sequence[int]): Indices of output stages. If not - specified, only the last stage will be returned. - last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage. - """ - - def __init__(self, - in_channels=3, - stem_channels=32, - base_channels=32, - arch_settings=[3, 4, 6, 6, 3], - strides=[2, 1, 2, 1, 1], - out_indices=None, - last_stage_pool=False): - super().__init__() - - self.out_indices = out_indices - self.last_stage_pool = last_stage_pool - self.block = BasicBlock - self.inplanes = stem_channels - - self._make_stem_layer(in_channels, stem_channels) - - self.res_layers = [] - planes = base_channels - for i, num_blocks in enumerate(arch_settings): - stride = strides[i] - res_layer = self._make_layer( - block=self.block, - inplanes=self.inplanes, - planes=planes, - blocks=num_blocks, - stride=stride) - self.inplanes = planes * self.block.expansion - planes *= 2 - layer_name = f'layer{i + 1}' - self.add_module(layer_name, res_layer) - self.res_layers.append(layer_name) - - def _make_layer(self, block, inplanes, planes, blocks, stride=1): - layers = [] - downsample = None - if stride != 1 or inplanes != planes: - downsample = nn.Sequential( - nn.Conv2d(inplanes, planes, 1, stride, bias=False), - nn.BatchNorm2d(planes), - ) - layers.append( - block( - inplanes, - planes, - use_conv1x1=True, - stride=stride, - downsample=downsample)) - inplanes = planes - for _ in range(1, blocks): - layers.append(block(inplanes, planes, use_conv1x1=True)) - - return nn.Sequential(*layers) - - def _make_stem_layer(self, in_channels, stem_channels): - self.conv1 = nn.Conv2d( - in_channels, stem_channels, kernel_size=3, stride=1, padding=1) - self.bn1 = nn.BatchNorm2d(stem_channels) - self.relu1 = nn.ReLU(inplace=True) - - def forward(self, x): - """ - Args: - x (Tensor): Image tensor of shape :math:`(N, 3, H, W)`. - - Returns: - Tensor or list[Tensor]: Feature tensor. Its shape depends on - ResNetABI's config. It can be a list of feature outputs at specific - layers if ``out_indices`` is specified. - """ - - x = self.conv1(x) - x = self.bn1(x) - x = self.relu1(x) - - outs = [] - for i, layer_name in enumerate(self.res_layers): - res_layer = getattr(self, layer_name) - x = res_layer(x) - if self.out_indices and i in self.out_indices: - outs.append(x) - - return tuple(outs) if self.out_indices else x diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/conv.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/conv.py deleted file mode 100644 index 950ec0c..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/conv.py +++ /dev/null @@ -1,173 +0,0 @@ -import warnings -import torch.nn as nn -from torch.nn.modules.instancenorm import _InstanceNorm -from torch.nn.modules.batchnorm import _BatchNorm -from mmcv.cnn import build_padding_layer, build_conv_layer, build_norm_layer, build_activation_layer - -class ConvModule(nn.Module): - """A conv block that bundles conv/norm/activation layers. - - This block simplifies the usage of convolution layers, which are commonly - used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). - It is based upon three build methods: `build_conv_layer()`, - `build_norm_layer()` and `build_activation_layer()`. - - Besides, we add some additional features in this module. - 1. Automatically set `bias` of the conv layer. - 2. Spectral norm is supported. - 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only - supports zero and circular padding, and we add "reflect" padding mode. - - Args: - in_channels (int): Number of channels in the input feature map. - Same as that in ``nn._ConvNd``. - out_channels (int): Number of channels produced by the convolution. - Same as that in ``nn._ConvNd``. - kernel_size (int | tuple[int]): Size of the convolving kernel. - Same as that in ``nn._ConvNd``. - stride (int | tuple[int]): Stride of the convolution. - Same as that in ``nn._ConvNd``. - padding (int | tuple[int]): Zero-padding added to both sides of - the input. Same as that in ``nn._ConvNd``. - dilation (int | tuple[int]): Spacing between kernel elements. - Same as that in ``nn._ConvNd``. - groups (int): Number of blocked connections from input channels to - output channels. Same as that in ``nn._ConvNd``. - bias (bool | str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise - False. Default: "auto". - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. Default: None. - act_cfg (dict): Config dict for activation layer. - Default: dict(type='ReLU'). - inplace (bool): Whether to use inplace mode for activation. - Default: True. - with_spectral_norm (bool): Whether use spectral norm in conv module. - Default: False. - padding_mode (str): If the `padding_mode` has not been supported by - current `Conv2d` in PyTorch, we will use our own padding layer - instead. Currently, we support ['zeros', 'circular'] with official - implementation and ['reflect'] with our own implementation. - Default: 'zeros'. - order (tuple[str]): The order of conv/norm/activation layers. It is a - sequence of "conv", "norm" and "act". Common examples are - ("conv", "norm", "act") and ("act", "conv", "norm"). - Default: ('conv', 'norm', 'act'). - """ - - _abbr_ = 'conv_block' - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias='auto', - conv_cfg=None, - norm_cfg=None, - act_cfg=dict(type='ReLU'), - inplace=True, - with_spectral_norm=False, - padding_mode='zeros', - order=('conv', 'norm', 'act')): - super(ConvModule, self).__init__() - assert conv_cfg is None or isinstance(conv_cfg, dict) - assert norm_cfg is None or isinstance(norm_cfg, dict) - assert act_cfg is None or isinstance(act_cfg, dict) - official_padding_mode = ['zeros', 'circular'] - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.inplace = inplace - self.with_spectral_norm = with_spectral_norm - self.with_explicit_padding = padding_mode not in official_padding_mode - self.order = order - assert isinstance(self.order, tuple) and len(self.order) == 3 - assert set(order) == set(['conv', 'norm', 'act']) - - self.with_norm = norm_cfg is not None - self.with_activation = act_cfg is not None - # if the conv layer is before a norm layer, bias is unnecessary. - if bias == 'auto': - bias = not self.with_norm - self.with_bias = bias - - if self.with_explicit_padding: - pad_cfg = dict(type=padding_mode) - self.padding_layer = build_padding_layer(pad_cfg, padding) - - # reset padding to 0 for conv module - conv_padding = 0 if self.with_explicit_padding else padding - # build convolution layer - self.conv = build_conv_layer( - conv_cfg, - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=conv_padding, - dilation=dilation, - groups=groups, - bias=bias) - # export the attributes of self.conv to a higher level for convenience - self.in_channels = self.conv.in_channels - self.out_channels = self.conv.out_channels - self.kernel_size = self.conv.kernel_size - self.stride = self.conv.stride - self.padding = padding - self.dilation = self.conv.dilation - self.transposed = self.conv.transposed - self.output_padding = self.conv.output_padding - self.groups = self.conv.groups - - if self.with_spectral_norm: - self.conv = nn.utils.spectral_norm(self.conv) - - # build normalization layers - if self.with_norm: - # norm layer is after conv layer - if order.index('norm') > order.index('conv'): - norm_channels = out_channels - else: - norm_channels = in_channels - self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels) - self.add_module(self.norm_name, norm) - if self.with_bias: - if isinstance(norm, (_BatchNorm, _InstanceNorm)): - warnings.warn( - 'Unnecessary conv bias before batch/instance norm') - else: - self.norm_name = None - - # build activation layer - if self.with_activation: - act_cfg_ = act_cfg.copy() - # nn.Tanh has no 'inplace' argument - if act_cfg_['type'] not in [ - 'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish' - ]: - act_cfg_.setdefault('inplace', inplace) - self.activate = build_activation_layer(act_cfg_) - - @property - def norm(self): - if self.norm_name: - return getattr(self, self.norm_name) - else: - return None - - def forward(self, x, activate=True, norm=True): - for layer in self.order: - if layer == 'conv': - if self.with_explicit_padding: - x = self.padding_layer(x) - x = self.conv(x) - elif layer == 'norm' and norm and self.with_norm: - x = self.norm(x) - elif layer == 'act' and activate and self.with_activation: - x = self.activate(x) - return x \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/converter.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/converter.py deleted file mode 100644 index 4d9bf82..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/converter.py +++ /dev/null @@ -1,152 +0,0 @@ -import torch -import numpy as np - -class BaseConvertor: - """Convert between text, index and tensor for text recognize pipeline. - - Args: - dict_type (str): Type of dict, options are 'DICT36', 'DICT37', 'DICT90' - and 'DICT91'. - dict_file (None|str): Character dict file path. If not none, - the dict_file is of higher priority than dict_type. - dict_list (None|list[str]): Character list. If not none, the list - is of higher priority than dict_type, but lower than dict_file. - """ - start_idx = end_idx = padding_idx = 0 - unknown_idx = None - lower = False - - DICT36 = tuple('0123456789abcdefghijklmnopqrstuvwxyz') - DICT63 = tuple('0123456789abcdefghijklmnopqrstuvwxyz' - 'ABCDEFGHIJKLMNOPQRSTUVWXYZ') - DICT90 = tuple('0123456789abcdefghijklmnopqrstuvwxyz' - 'ABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()' - '*+,-./:;<=>?@[\\]_`~') - DICT131 = tuple('0123456789abcdefghijklmnopqrstuvwxyz' - '!"#$%&\'()' - '*+,-./:;<=>?@[\\]_`~' - 'ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ') - DICT224 = tuple('0123456789abcdefghijklmnopqrstuvwxyz' - 'ABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()' - '*+,-./:;<=>?@[\\]_`~{}|^ ̂' - 'ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ' - 'ẠẢÃÀÁÂẬẦẤẨẪĂẮẰẶẲẴÓÒỌÕỎÔỘỔỖỒỐƠỜỚỢỞỠÉÈẺẸẼÊẾỀỆỂỄÚÙỤỦŨƯỰỮỬỪỨÍÌỊỈĨÝỲỶỴỸĐ✪') - - def __init__(self, dict_type='DICT90'): - assert dict_type in ('DICT36', 'DICT63', 'DICT90', 'DICT131', 'DICT224') - self.idx2char = [] - - if dict_type == 'DICT36': - self.idx2char = list(self.DICT36) - elif dict_type == 'DICT63': - self.idx2char = list(self.DICT63) - elif dict_type == 'DICT90': - self.idx2char = list(self.DICT90) - elif dict_type == 'DICT131': - self.idx2char = list(self.DICT131) - elif dict_type == 'DICT224': - self.idx2char = list(self.DICT224) - else: - raise ('Dictonary not implemented') - - assert len(set(self.idx2char)) == len(self.idx2char), \ - 'Invalid dictionary: Has duplicated characters.' - - self.char2idx = {char: idx for idx, char in enumerate(self.idx2char)} - - def num_classes(self): - """Number of output classes.""" - return len(self.idx2char) - - -class AttnConvertor(BaseConvertor): - """Convert between text, index and tensor for encoder-decoder based - pipeline. - - Args: - dict_type (str): Type of dict, should be one of {'DICT36', 'DICT90'}. - dict_file (None|str): Character dict file path. If not none, - higher priority than dict_type. - dict_list (None|list[str]): Character list. If not none, higher - priority than dict_type, but lower than dict_file. - with_unknown (bool): If True, add `UKN` token to class. - max_seq_len (int): Maximum sequence length of label. - lower (bool): If True, convert original string to lower case. - start_end_same (bool): Whether use the same index for - start and end token or not. Default: True. - """ - - def __init__(self, - dict_type='DICT90', - with_unknown=True, - max_seq_len=40, - lower=False, - start_end_same=True, - return_confident=False, - **kwargs): - super().__init__(dict_type) - assert isinstance(with_unknown, bool) - assert isinstance(max_seq_len, int) - assert isinstance(lower, bool) - - self.with_unknown = with_unknown - self.max_seq_len = max_seq_len - self.lower = lower - self.start_end_same = start_end_same - self.return_confident = return_confident - - self.update_dict() - - def update_dict(self): - start_end_token = '' - unknown_token = '' - padding_token = '' - - # unknown - self.unknown_idx = None - if self.with_unknown: - self.idx2char.append(unknown_token) - self.unknown_idx = len(self.idx2char) - 1 - - # BOS/EOS - self.idx2char.append(start_end_token) - self.start_idx = len(self.idx2char) - 1 - if not self.start_end_same: - self.idx2char.append(start_end_token) - self.end_idx = len(self.idx2char) - 1 - - # padding - self.idx2char.append(padding_token) - self.padding_idx = len(self.idx2char) - 1 - - # update char2idx - self.char2idx = {} - for idx, char in enumerate(self.idx2char): - self.char2idx[char] = idx - - def __call__(self, indexes): - strings = [] - confidents = [] - if self.return_confident: - b,sq,_ = indexes.shape - for idx in range(b): - index = indexes[idx, :, :] - chars = index.argmax(-1) - confident = index.max(-1) - i = 0 - while i < sq and chars[i] != self.end_idx and chars[i] != self.padding_idx: i += 1 - chars = chars[:i] - confident = confident[:i].min() - string = [self.idx2char[i] for i in chars] - strings.append(''.join(string)) - confidents.append(confident) - - return strings, confidents - else: - for index in indexes: - i, l = 0, len(index) - while i < l and index[i] != self.end_idx and index[i] != self.padding_idx: i += 1 - index = index[:i] - string = [self.idx2char[i] for i in index] - strings.append(''.join(string)) - return strings \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/decoder.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/decoder.py deleted file mode 100644 index b4ec51a..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/decoder.py +++ /dev/null @@ -1,278 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import math -import numpy as np -from .encoder import MultiHeadAttention - - -class PositionwiseFeedForward(nn.Module): - """Two-layer feed-forward module. - - Args: - d_in (int): The dimension of the input for feedforward - network model. - d_hid (int): The dimension of the feedforward - network model. - dropout (float): Dropout layer on feedforward output. - act_cfg (dict): Activation cfg for feedforward module. - """ - - def __init__(self, d_in, d_hid, dropout=0.1): - super().__init__() - self.w_1 = nn.Linear(d_in, d_hid) - self.w_2 = nn.Linear(d_hid, d_in) - self.act = nn.GELU() - self.dropout = nn.Dropout(dropout) - - def forward(self, x): - x = self.w_1(x) - x = self.act(x) - x = self.w_2(x) - x = self.dropout(x) - - return x - - -class PositionalEncoding(nn.Module): - """Fixed positional encoding with sine and cosine functions.""" - - def __init__(self, d_hid=512, n_position=200): - super().__init__() - - # Not a parameter - # Position table of shape (1, n_position, d_hid) - self.register_buffer( - 'position_table', - self._get_sinusoid_encoding_table(n_position, d_hid)) - - def _get_sinusoid_encoding_table(self, n_position, d_hid): - """Sinusoid position encoding table.""" - denominator = torch.Tensor([ - 1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid) - for hid_j in range(d_hid) - ]) - denominator = denominator.view(1, -1) - pos_tensor = torch.arange(n_position).unsqueeze(-1).float() - sinusoid_table = pos_tensor * denominator - sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2]) - sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2]) - - return sinusoid_table.unsqueeze(0) - - def forward(self, x): - """ - Args: - x (Tensor): Tensor of shape (batch_size, pos_len, d_hid, ...) - """ - self.device = x.device - x = x + self.position_table[:, :x.size(1)].clone().detach() - return x - - - -class TFDecoderLayer(nn.Module): - """Transformer Decoder Layer. - - Args: - d_model (int): The number of expected features - in the decoder inputs (default=512). - d_inner (int): The dimension of the feedforward - network model (default=256). - n_head (int): The number of heads in the - multiheadattention models (default=8). - d_k (int): Total number of features in key. - d_v (int): Total number of features in value. - dropout (float): Dropout layer on attn_output_weights. - qkv_bias (bool): Add bias in projection layer. Default: False. - act_cfg (dict): Activation cfg for feedforward module. - operation_order (tuple[str]): The execution order of operation - in transformer. Such as ('self_attn', 'norm', 'enc_dec_attn', - 'norm', 'ffn', 'norm') or ('norm', 'self_attn', 'norm', - 'enc_dec_attn', 'norm', 'ffn'). - Default:None. - """ - - def __init__(self, - d_model=512, - d_inner=256, - n_head=8, - d_k=64, - d_v=64, - dropout=0.1, - qkv_bias=False): - super().__init__() - - self.norm1 = nn.LayerNorm(d_model) - self.norm2 = nn.LayerNorm(d_model) - self.norm3 = nn.LayerNorm(d_model) - - self.self_attn = MultiHeadAttention( - n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias) - - self.enc_attn = MultiHeadAttention( - n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias) - - self.mlp = PositionwiseFeedForward( - d_model, d_inner, dropout=dropout) - - def forward(self, - dec_input, - enc_output, - self_attn_mask=None, - dec_enc_attn_mask=None): - dec_input_norm = self.norm1(dec_input) - dec_attn_out = self.self_attn(dec_input_norm, dec_input_norm, - dec_input_norm, self_attn_mask) - dec_attn_out += dec_input - - enc_dec_attn_in = self.norm2(dec_attn_out) - enc_dec_attn_out = self.enc_attn(enc_dec_attn_in, enc_output, - enc_output, dec_enc_attn_mask) - enc_dec_attn_out += dec_attn_out - - mlp_out = self.mlp(self.norm3(enc_dec_attn_out)) - mlp_out += enc_dec_attn_out - - return mlp_out - -class NRTRDecoder(nn.Module): - """Transformer Decoder block with self attention mechanism. - - Args: - n_layers (int): Number of attention layers. - d_embedding (int): Language embedding dimension. - n_head (int): Number of parallel attention heads. - d_k (int): Dimension of the key vector. - d_v (int): Dimension of the value vector. - d_model (int): Dimension :math:`D_m` of the input from previous model. - d_inner (int): Hidden dimension of feedforward layers. - n_position (int): Length of the positional encoding vector. Must be - greater than ``max_seq_len``. - dropout (float): Dropout rate. - num_classes (int): Number of output classes :math:`C`. - max_seq_len (int): Maximum output sequence length :math:`T`. - start_idx (int): The index of ``. - padding_idx (int): The index of ``. - init_cfg (dict or list[dict], optional): Initialization configs. - - Warning: - This decoder will not predict the final class which is assumed to be - ``. Therefore, its output size is always :math:`C - 1`. `` - is also ignored by loss as specified in - :obj:`mmocr.models.textrecog.recognizer.EncodeDecodeRecognizer`. - """ - - def __init__(self, - n_layers=6, - d_embedding=512, - n_head=8, - d_k=64, - d_v=64, - d_model=512, - d_inner=256, - n_position=200, - dropout=0.1, - num_classes=93, - max_seq_len=40, - start_idx=1, - padding_idx=92, - return_confident=False, - end_idx=None, - **kwargs): - super().__init__() - - self.padding_idx = padding_idx - self.start_idx = start_idx - self.max_seq_len = max_seq_len - - self.trg_word_emb = nn.Embedding( - num_classes, d_embedding, padding_idx=padding_idx) - - self.position_enc = PositionalEncoding( - d_embedding, n_position=n_position) - - self.layer_stack = nn.ModuleList([ - TFDecoderLayer( - d_model, d_inner, n_head, d_k, d_v, dropout=dropout, **kwargs) - for _ in range(n_layers) - ]) - self.layer_norm = nn.LayerNorm(d_model, eps=1e-6) - - pred_num_class = num_classes - 1 # ignore padding_idx - self.classifier = nn.Linear(d_model, pred_num_class) - - self.return_confident = return_confident - self.end_idx = end_idx - - @staticmethod - def get_pad_mask(seq, pad_idx): - - return (seq != pad_idx).unsqueeze(-2) - - @staticmethod - def get_subsequent_mask(seq): - """For masking out the subsequent info.""" - len_s = seq.size(1) - subsequent_mask = 1 - torch.triu( - torch.ones((len_s, len_s), device=seq.device), diagonal=1) - subsequent_mask = subsequent_mask.unsqueeze(0).bool() - - return subsequent_mask - - def _attention(self, trg_seq, src, src_mask=None): - trg_embedding = self.trg_word_emb(trg_seq) - trg_pos_encoded = self.position_enc(trg_embedding) - trg_mask = self.get_pad_mask( - trg_seq, - pad_idx=self.padding_idx) & self.get_subsequent_mask(trg_seq) - output = trg_pos_encoded - for dec_layer in self.layer_stack: - output = dec_layer( - output, - src, - self_attn_mask=trg_mask, - dec_enc_attn_mask=src_mask) - output = self.layer_norm(output) - - return output - - def _get_mask(self, logit): - N, T, _ = logit.size() - mask = logit.new_ones((N, T)) - - return mask - - def forward(self, out_enc): - src_mask = self._get_mask(out_enc) - N = out_enc.size(0) - init_target_seq = torch.full((N, self.max_seq_len + 1), - self.padding_idx, - device=out_enc.device, - dtype=torch.long) - # bsz * seq_len - init_target_seq[:, 0] = self.start_idx - - outputs = [] - for step in range(0, self.max_seq_len): - decoder_output = self._attention( - trg_seq=init_target_seq, - src=out_enc, - src_mask=src_mask) - if self.return_confident: - step_result = torch.softmax(self.classifier(decoder_output[:, step, :]), -1) - next_step_init = step_result.argmax(-1) - init_target_seq[:, step + 1] = next_step_init - if next_step_init.min() >= self.end_idx: - break - else: - step_result = self.classifier(decoder_output[:, step, :]).argmax(-1) - init_target_seq[:, step + 1] = step_result - if step_result.min() >= self.end_idx: - break - - outputs.append(step_result) - - outputs = torch.stack(outputs, dim=1) - - return outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/encoder.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/encoder.py deleted file mode 100644 index 6ccb612..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/encoder.py +++ /dev/null @@ -1,317 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -import math -from .conv import ConvModule - - -class LocalityAwareFeedforward(nn.Module): - """Locality-aware feedforward layer in SATRN, see `SATRN. - - `_ - """ - - def __init__(self, - d_in, - d_hid, - dropout=0.1): - super().__init__() - self.conv1 = ConvModule( - d_in, - d_hid, - kernel_size=1, - padding=0, - bias=False, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU')) - - self.depthwise_conv = ConvModule( - d_hid, - d_hid, - kernel_size=3, - padding=1, - bias=False, - groups=d_hid, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU')) - - self.conv2 = ConvModule( - d_hid, - d_in, - kernel_size=1, - padding=0, - bias=False, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU')) - - def forward(self, x): - x = self.conv1(x) - x = self.depthwise_conv(x) - x = self.conv2(x) - - return x - - -class ScaledDotProductAttention(nn.Module): - """Scaled Dot-Product Attention Module. This code is adopted from - https://github.com/jadore801120/attention-is-all-you-need-pytorch. - - Args: - temperature (float): The scale factor for softmax input. - attn_dropout (float): Dropout layer on attn_output_weights. - """ - - def __init__(self, temperature, attn_dropout=0.1): - super().__init__() - self.temperature = temperature - self.dropout = nn.Dropout(attn_dropout) - - def forward(self, q, k, v, mask=None): - - attn = torch.matmul(q / self.temperature, k.transpose(2, 3)) - - if mask is not None: - attn = attn.masked_fill(mask == 0, float('-inf')) - - attn = self.dropout(F.softmax(attn, dim=-1)) - output = torch.matmul(attn, v) - - return output, attn - - -class MultiHeadAttention(nn.Module): - """Multi-Head Attention module. - - Args: - n_head (int): The number of heads in the - multiheadattention models (default=8). - d_model (int): The number of expected features - in the decoder inputs (default=512). - d_k (int): Total number of features in key. - d_v (int): Total number of features in value. - dropout (float): Dropout layer on attn_output_weights. - qkv_bias (bool): Add bias in projection layer. Default: False. - """ - - def __init__(self, - n_head=8, - d_model=512, - d_k=64, - d_v=64, - dropout=0.1, - qkv_bias=False): - super().__init__() - self.n_head = n_head - self.d_k = d_k - self.d_v = d_v - - self.dim_k = n_head * d_k - self.dim_v = n_head * d_v - - self.linear_q = nn.Linear(self.dim_k, self.dim_k, bias=qkv_bias) - self.linear_k = nn.Linear(self.dim_k, self.dim_k, bias=qkv_bias) - self.linear_v = nn.Linear(self.dim_v, self.dim_v, bias=qkv_bias) - - self.attention = ScaledDotProductAttention(d_k**0.5, dropout) - - self.fc = nn.Linear(self.dim_v, d_model, bias=qkv_bias) - self.proj_drop = nn.Dropout(dropout) - - def forward(self, q, k, v, mask=None): - batch_size, len_q, _ = q.size() - _, len_k, _ = k.size() - - q = self.linear_q(q).view(batch_size, len_q, self.n_head, self.d_k) - k = self.linear_k(k).view(batch_size, len_k, self.n_head, self.d_k) - v = self.linear_v(v).view(batch_size, len_k, self.n_head, self.d_v) - - q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2) - - if mask is not None: - if mask.dim() == 3: - mask = mask.unsqueeze(1) - elif mask.dim() == 2: - mask = mask.unsqueeze(1).unsqueeze(1) - - attn_out, _ = self.attention(q, k, v, mask=mask) - - attn_out = attn_out.transpose(1, 2).contiguous().view( - batch_size, len_q, self.dim_v) - - attn_out = self.fc(attn_out) - attn_out = self.proj_drop(attn_out) - - return attn_out - - -class SatrnEncoderLayer(nn.Module): - """""" - - def __init__(self, - d_model=512, - d_inner=512, - n_head=8, - d_k=64, - d_v=64, - dropout=0.1, - qkv_bias=False): - super().__init__() - self.norm1 = nn.LayerNorm(d_model) - self.attn = MultiHeadAttention( - n_head, d_model, d_k, d_v, qkv_bias=qkv_bias, dropout=dropout) - self.norm2 = nn.LayerNorm(d_model) - self.feed_forward = LocalityAwareFeedforward( - d_model, d_inner, dropout=dropout) - - def forward(self, x, h, w, mask=None): - n, hw, c = x.size() - residual = x - x = self.norm1(x) - x = residual + self.attn(x, x, x, mask) - residual = x - x = self.norm2(x) - x = x.transpose(1, 2).contiguous().view(n, c, h, w) - x = self.feed_forward(x) - x = x.view(n, c, hw).transpose(1, 2) - x = residual + x - return x - - -class Adaptive2DPositionalEncoding(nn.Module): - """Implement Adaptive 2D positional encoder for SATRN, see - `SATRN `_ - Modified from https://github.com/Media-Smart/vedastr - Licensed under the Apache License, Version 2.0 (the "License"); - Args: - d_hid (int): Dimensions of hidden layer. - n_height (int): Max height of the 2D feature output. - n_width (int): Max width of the 2D feature output. - dropout (int): Size of hidden layers of the model. - """ - - def __init__(self, - d_hid=512, - n_height=100, - n_width=100, - dropout=0.1): - super().__init__() - - h_position_encoder = self._get_sinusoid_encoding_table(n_height, d_hid) - h_position_encoder = h_position_encoder.transpose(0, 1) - h_position_encoder = h_position_encoder.view(1, d_hid, n_height, 1) - - w_position_encoder = self._get_sinusoid_encoding_table(n_width, d_hid) - w_position_encoder = w_position_encoder.transpose(0, 1) - w_position_encoder = w_position_encoder.view(1, d_hid, 1, n_width) - - self.register_buffer('h_position_encoder', h_position_encoder) - self.register_buffer('w_position_encoder', w_position_encoder) - - self.h_scale = self.scale_factor_generate(d_hid) - self.w_scale = self.scale_factor_generate(d_hid) - self.pool = nn.AdaptiveAvgPool2d(1) - self.dropout = nn.Dropout(p=dropout) - - def _get_sinusoid_encoding_table(self, n_position, d_hid): - """Sinusoid position encoding table.""" - denominator = torch.Tensor([ - 1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid) - for hid_j in range(d_hid) - ]) - denominator = denominator.view(1, -1) - pos_tensor = torch.arange(n_position).unsqueeze(-1).float() - sinusoid_table = pos_tensor * denominator - sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2]) - sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2]) - - return sinusoid_table - - def scale_factor_generate(self, d_hid): - scale_factor = nn.Sequential( - nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.ReLU(inplace=True), - nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.Sigmoid()) - - return scale_factor - - def forward(self, x): - b, c, h, w = x.size() - - avg_pool = self.pool(x) - - h_pos_encoding = \ - self.h_scale(avg_pool) * self.h_position_encoder[:, :, :h, :] - w_pos_encoding = \ - self.w_scale(avg_pool) * self.w_position_encoder[:, :, :, :w] - - out = x + h_pos_encoding + w_pos_encoding - - out = self.dropout(out) - - return out - - -class SatrnEncoder(nn.Module): - """Implement encoder for SATRN, see `SATRN. - - `_. - - Args: - n_layers (int): Number of attention layers. - n_head (int): Number of parallel attention heads. - d_k (int): Dimension of the key vector. - d_v (int): Dimension of the value vector. - d_model (int): Dimension :math:`D_m` of the input from previous model. - n_position (int): Length of the positional encoding vector. Must be - greater than ``max_seq_len``. - d_inner (int): Hidden dimension of feedforward layers. - dropout (float): Dropout rate. - init_cfg (dict or list[dict], optional): Initialization configs. - """ - - def __init__(self, - n_layers=12, - n_head=8, - d_k=64, - d_v=64, - d_model=512, - n_position=100, - d_inner=256, - dropout=0.1, - **kwargs): - super().__init__() - self.d_model = d_model - self.position_enc = Adaptive2DPositionalEncoding( - d_hid=d_model, - n_height=n_position, - n_width=n_position, - dropout=dropout) - self.layer_stack = nn.ModuleList([ - SatrnEncoderLayer( - d_model, d_inner, n_head, d_k, d_v, dropout=dropout) - for _ in range(n_layers) - ]) - self.layer_norm = nn.LayerNorm(d_model) - - def forward(self, feat): - """ - Args: - feat (Tensor): Feature tensor of shape :math:`(N, D_m, H, W)`. - - Returns: - Tensor: A tensor of shape :math:`(N, T, D_m)`. - """ - feat = feat + self.position_enc(feat) - n, c, h, w = feat.size() - mask = feat.new_zeros((n, h, w)) - mask[:,:,:w] = 1 - - mask = mask.view(n, h * w) - feat = feat.view(n, c, h * w) - - output = feat.permute(0, 2, 1).contiguous() - for enc_layer in self.layer_stack: - output = enc_layer(output, h, w, mask) - output = self.layer_norm(output) - - return output \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/factory.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/factory.py deleted file mode 100644 index 773d5a0..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/factory.py +++ /dev/null @@ -1,57 +0,0 @@ -import os -import shutil -import colorama -import hashlib - -def sha256sum(filename): - h = hashlib.sha256() - b = bytearray(128*1024) - mv = memoryview(b) - with open(filename, 'rb', buffering=0) as f: - for n in iter(lambda : f.readinto(mv), 0): - h.update(mv[:n]) - return h.hexdigest() - - -online_model_factory = { - 'satrn-lite-general-pretrain-20230106': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/jxqhbem4to.pth', - 'hash': 'b0069a72bf6fc080ad5d431d5ce650c3bfbab535141adef1631fce331cb1471c'}, - 'satrn-lite-captcha-finetune-20230108': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/l27vitogmc.pth', - 'hash': 'efcbcf2955b6b21125b073f83828d2719e908c7303b0d9901e94be5a8efba916'}, - 'satrn-lite-handwritten-finetune-20230108': { - 'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/lj9gkwelns.pth', - 'hash': 'bccd8e985b131fcd4701114af5ceaef098f2eac50654bbb1d828e7f829e711dd'}, -} - -__hub_available_versions__ = online_model_factory.keys() - -def _get(version): - use_online = version in __hub_available_versions__ - - if not use_online and not os.path.exists(version): - raise ValueError(f'Model version {version} not found online and not found local.') - - hub_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'hub') - if not os.path.exists(hub_path): - os.makedirs(hub_path) - if use_online: - version_url = online_model_factory[version]['url'] - file_path = os.path.join(hub_path, os.path.basename(version_url)) - else: - file_path = os.path.join(hub_path, os.path.basename(version)) - - if not os.path.exists(file_path): - if use_online: - os.system(f'wget -O {file_path} {version_url}') - assert os.path.exists(file_path), \ - colorama.Fore.RED + 'wget failed while trying to retrieve from hub.' + colorama.Style.RESET_ALL - downloaded_hash = sha256sum(file_path) - if downloaded_hash != online_model_factory[version]['hash']: - os.remove(file_path) - raise ValueError(colorama.Fore.RED + 'sha256 hash doesnt match for version retrieved from hub.' + colorama.Style.RESET_ALL) - else: - shutil.copy2(version, file_path) - - return file_path \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/fp16_utils.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/fp16_utils.py deleted file mode 100644 index 126ff02..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/fp16_utils.py +++ /dev/null @@ -1,78 +0,0 @@ -import torch -import torch.nn as nn -import numpy as np -from collections import abc - - -def cast_tensor_type(inputs, src_type, dst_type): - """Recursively convert Tensor in inputs from src_type to dst_type. - - Args: - inputs: Inputs that to be casted. - src_type (torch.dtype): Source type.. - dst_type (torch.dtype): Destination type. - - Returns: - The same type with inputs, but all contained Tensors have been cast. - """ - if isinstance(inputs, nn.Module): - return inputs - elif isinstance(inputs, torch.Tensor): - return inputs.to(dst_type) - elif isinstance(inputs, str): - return inputs - elif isinstance(inputs, np.ndarray): - return inputs - elif isinstance(inputs, abc.Mapping): - return type(inputs)({ - k: cast_tensor_type(v, src_type, dst_type) - for k, v in inputs.items() - }) - elif isinstance(inputs, abc.Iterable): - return type(inputs)( - cast_tensor_type(item, src_type, dst_type) for item in inputs) - else: - return inputs - - -def patch_forward_method(func, src_type, dst_type, convert_output=True): - """Patch the forward method of a module. - - Args: - func (callable): The original forward method. - src_type (torch.dtype): Type of input arguments to be converted from. - dst_type (torch.dtype): Type of input arguments to be converted to. - convert_output (bool): Whether to convert the output back to src_type. - - Returns: - callable: The patched forward method. - """ - - def new_forward(*args, **kwargs): - output = func(*cast_tensor_type(args, src_type, dst_type), - **cast_tensor_type(kwargs, src_type, dst_type)) - if convert_output: - output = cast_tensor_type(output, dst_type, src_type) - return output - - return new_forward - - -def patch_norm_fp32(module): - """Recursively convert normalization layers from FP16 to FP32. - - Args: - module (nn.Module): The modules to be converted in FP16. - - Returns: - nn.Module: The converted module, the normalization layers have been - converted to FP32. - """ - if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)): - module.float() - if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3': - module.forward = patch_forward_method(module.forward, torch.half, - torch.float) - for child in module.children(): - patch_norm_fp32(child) - return module \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/transform.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/transform.py deleted file mode 100644 index e366a4c..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/transform.py +++ /dev/null @@ -1,33 +0,0 @@ -import torchvision.transforms.functional as TF -import cv2 -import torch - -class DataPipelineSATRN: - def __init__(self, - resize_height, - resize_width, - norm_mean, - norm_std, - device='cpu'): - self.resize_height = resize_height - self.resize_width = resize_width - self.norm_mean = norm_mean - self.norm_std = norm_std - self.device = device - - def __call__(self, imgs): - if not isinstance(imgs, list): - imgs = [imgs] - datas = [] - for img in imgs: - if isinstance(img, str): - img = cv2.imread(img) - data = torch.from_numpy(cv2.resize(img, (self.resize_width, self.resize_height), interpolation=cv2.INTER_LINEAR)) - datas.append(data) - - data = torch.stack(datas, dim=0) - data = data.to(self.device) - data = data.float().div_(255.).permute((0,3,1,2)) - TF.normalize(data, mean=self.norm_mean, std=self.norm_std, inplace=True) - - return data.half() if self.device != 'cpu' else data \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/version.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/version.py deleted file mode 100644 index a55ccd2..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '0.0.5' \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/setup.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/setup.py deleted file mode 100644 index 4df1557..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/setup.py +++ /dev/null @@ -1,187 +0,0 @@ -import os -import os.path as osp -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - - -version_file = 'sdsvtr/version.py' -is_windows = sys.platform == 'win32' - - -def add_mim_extention(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmocr', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - import sys - - # return short version for sdist - if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - return locals()['short_version'] - else: - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strip - specific version information. - - Args: - fname (str): Path to requirements file. - with_version (bool, default=False): If True, include version specs. - Returns: - info (list[str]): List of requirements items. - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, 'r') as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - - -if __name__ == '__main__': - setup( - name='sdsvtr', - version=get_version(), - description='SDSV OCR Team Text Recognizer', - long_description=readme(), - long_description_content_type='text/markdown', - packages=find_packages(exclude=('configs', 'tools', 'demo')), - include_package_data=True, - url='https://github.com/open-mmlab/mmocr', - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements.txt'), - zip_safe=False) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/test.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/test.py deleted file mode 100644 index b929e7e..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/test.py +++ /dev/null @@ -1,12 +0,0 @@ -import numpy as np -from sdsvtr import StandaloneSATRNRunner -runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:1') - -dummy_list = [np.ndarray((32,128,3)) for _ in range(100)] -bs = 32 - -all_results = [] -while len(dummy_list) > 0: - dummy_batch = dummy_list[:bs] - dummy_list = dummy_list[bs:] - all_results += runner(dummy_batch) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/requirements.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/requirements.txt deleted file mode 100644 index b247e52..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/requirements.txt +++ /dev/null @@ -1,82 +0,0 @@ -addict==2.4.0 -asttokens==2.2.1 -autopep8==1.6.0 -backcall==0.2.0 -backports.functools-lru-cache==1.6.4 -brotlipy==0.7.0 -certifi==2022.12.7 -cffi==1.15.1 -charset-normalizer==2.0.4 -click==8.1.3 -colorama==0.4.6 -cryptography==39.0.1 -debugpy==1.5.1 -decorator==5.1.1 -docopt==0.6.2 -entrypoints==0.4 -executing==1.2.0 -flit_core==3.6.0 -idna==3.4 -importlib-metadata==6.0.0 -ipykernel==6.15.0 -ipython==8.11.0 -jedi==0.18.2 -jupyter-client==7.0.6 -jupyter_core==4.12.0 -Markdown==3.4.1 -markdown-it-py==2.2.0 -matplotlib-inline==0.1.6 -mdurl==0.1.2 -mkl-fft==1.3.1 -mkl-random==1.2.2 -mkl-service==2.4.0 -mmcv-full==1.7.1 -model-index==0.1.11 -nest-asyncio==1.5.6 -numpy==1.23.5 -opencv-python==4.7.0.72 -openmim==0.3.6 -ordered-set==4.1.0 -packaging==23.0 -pandas==1.5.3 -parso==0.8.3 -pexpect==4.8.0 -pickleshare==0.7.5 -Pillow==9.4.0 -pip==22.3.1 -pipdeptree==2.5.2 -prompt-toolkit==3.0.38 -psutil==5.9.0 -ptyprocess==0.7.0 -pure-eval==0.2.2 -pycodestyle==2.10.0 -pycparser==2.21 -Pygments==2.14.0 -pyOpenSSL==23.0.0 -PySocks==1.7.1 -python-dateutil==2.8.2 -pytz==2022.7.1 -PyYAML==6.0 -pyzmq==19.0.2 -requests==2.28.1 -rich==13.3.1 -sdsvtd==0.1.1 -sdsvtr==0.0.5 -setuptools==65.6.3 -Shapely==1.8.4 -six==1.16.0 -stack-data==0.6.2 -tabulate==0.9.0 -toml==0.10.2 -torch==1.13.1 -torchvision==0.14.1 -tornado==6.1 -tqdm==4.65.0 -traitlets==5.9.0 -typing_extensions==4.4.0 -urllib3==1.26.14 -wcwidth==0.2.6 -wheel==0.38.4 -yapf==0.32.0 -yarg==0.1.9 -zipp==3.15.0 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/run.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/run.py deleted file mode 100644 index 3b19879..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/run.py +++ /dev/null @@ -1,200 +0,0 @@ -""" -see scripts/run_ocr.sh to run -""" -# from pathlib import Path # add parent path to run debugger -# import sys -# FILE = Path(__file__).absolute() -# sys.path.append(FILE.parents[2].as_posix()) - - -from src.utils import construct_file_path, ImageReader -from src.dto import Line -from src.ocr import OcrEngine -import argparse -import tqdm -import pandas as pd -from pathlib import Path -import json -import os -import numpy as np -from typing import Union, Tuple, List, Optional -from collections import defaultdict - -current_dir = os.getcwd() - - -def get_args(): - parser = argparse.ArgumentParser() - # parser image - parser.add_argument( - "--image", - type=str, - required=True, - help="path to input image/directory/csv file", - ) - parser.add_argument( - "--save_dir", type=str, required=True, help="path to save directory" - ) - parser.add_argument( - "--include", type=str, nargs="+", default=[], help="files/folders to include" - ) - parser.add_argument( - "--exclude", type=str, nargs="+", default=[], help="files/folders to exclude" - ) - parser.add_argument( - "--base_dir", - type=str, - required=False, - default=current_dir, - help="used when --image and --save_dir are relative paths to a base directory, default to current directory", - ) - parser.add_argument( - "--export_csv", - type=str, - required=False, - default="", - help="used when --image is a directory. If set, a csv file contains image_path, ocr_path and label will be exported to save_dir.", - ) - parser.add_argument( - "--export_img", - type=bool, - required=False, - default=False, - help="whether to save the visualize img", - ) - parser.add_argument("--ocr_kwargs", type=str, required=False, default="") - opt = parser.parse_args() - return opt - - -def load_engine(opt) -> OcrEngine: - print("[INFO] Loading engine...") - kw = json.loads(opt.ocr_kwargs) if opt.ocr_kwargs else {} - engine = OcrEngine(**kw) - print("[INFO] Engine loaded") - return engine - - -def convert_relative_path_to_positive_path(tgt_dir: Path, base_dir: Path) -> Path: - return tgt_dir if tgt_dir.is_absolute() else base_dir.joinpath(tgt_dir) - - -def get_paths_from_opt(opt) -> Tuple[Path, Path]: - # BC\ kiem\ tra\ y\ te -> BC kiem tra y te - img_path = opt.image.replace("\\ ", " ").strip() - save_dir = opt.save_dir.replace("\\ ", " ").strip() - base_dir = opt.base_dir.replace("\\ ", " ").strip() - input_image = convert_relative_path_to_positive_path(Path(img_path), Path(base_dir)) - save_dir = convert_relative_path_to_positive_path(Path(save_dir), Path(base_dir)) - if not save_dir.exists(): - save_dir.mkdir() - print("[INFO]: Creating folder ", save_dir) - return input_image, save_dir - - -def process_img( - img: Union[str, np.ndarray], - save_dir_or_path: str, - engine: OcrEngine, - export_img: bool, - save_path_deskew: Optional[str] = None, -) -> None: - save_dir_or_path = Path(save_dir_or_path) - if isinstance(img, np.ndarray): - if save_dir_or_path.is_dir(): - raise ValueError("numpy array input require a save path, not a save dir") - page = engine(img) - save_path = ( - str(save_dir_or_path.joinpath(Path(img).stem + ".txt")) - if save_dir_or_path.is_dir() - else str(save_dir_or_path) - ) - page.write_to_file("word", save_path) - if export_img: - page.save_img( - save_path.replace(".txt", ".jpg"), - is_vnese=True, - save_path_deskew=save_path_deskew, - ) - - -def process_dir( - dir_path: str, - save_dir: str, - engine: OcrEngine, - export_img: bool, - lexcludes: List[str] = [], - lincludes: List[str] = [], - ddata=defaultdict(list), -) -> None: - pdir_path = Path(dir_path) - print(pdir_path) - # save_dir_sub = Path(construct_file_path(save_dir, dir_path, ext="")) - psave_dir = Path(save_dir) - psave_dir.mkdir(exist_ok=True) - for img_path in (pbar := tqdm.tqdm(pdir_path.iterdir())): - pbar.set_description(f"Processing {pdir_path}") - if (lincludes and img_path.name not in lincludes) or ( - img_path.name in lexcludes - ): - continue # only process desired files/foders - if img_path.is_dir(): - psave_dir_sub = psave_dir.joinpath(img_path.stem) - process_dir(img_path, str(psave_dir_sub), engine, ddata) - elif img_path.suffix.lower() in ImageReader.supported_ext: - simg_path = str(img_path) - # try: - img = ( - ImageReader.read(simg_path) - if img_path.suffix != ".pdf" - else ImageReader.read(simg_path)[0] - ) - save_path = str(Path(psave_dir).joinpath(img_path.stem + ".txt")) - save_path_deskew = str( - Path(psave_dir).joinpath(img_path.stem + "_deskewed.jpg") - ) - process_img(img, save_path, engine, export_img, save_path_deskew) - # except Exception as e: - # print('[ERROR]: ', e, ' at ', simg_path) - # continue - ddata["img_path"].append(simg_path) - ddata["ocr_path"].append(save_path) - if Path(save_path_deskew).exists(): - ddata["save_path_deskew"].append(save_path) - ddata["label"].append(pdir_path.stem) - # ddata.update({"img_path": img_path, "save_path": save_path, "label": dir_path.stem}) - return ddata - - -def process_csv(csv_path: str, engine: OcrEngine) -> None: - df = pd.read_csv(csv_path) - if not "image_path" in df.columns or not "ocr_path" in df.columns: - raise AssertionError("Cannot fing image_path in df headers") - for row in df.iterrows(): - process_img(row.image_path, row.ocr_path, engine) - - -if __name__ == "__main__": - opt = get_args() - engine = load_engine(opt) - print("[INFO]: OCR engine settings:", engine.settings) - img, save_dir = get_paths_from_opt(opt) - - lskip_dir = [] - if img.is_dir(): - ddata = process_dir( - img, save_dir, engine, opt.export_img, opt.exclude, opt.include - ) - if opt.export_csv: - pd.DataFrame.from_dict(ddata).to_csv( - Path(save_dir).joinpath(opt.export_csv) - ) - elif img.suffix in ImageReader.supported_ext: - process_img(str(img), save_dir, engine, opt.export_img) - elif img.suffix == ".csv": - print( - "[WARNING]: Running with csv file will ignore the save_dir argument. Instead, the ocr_path in the csv would be used" - ) - process_csv(img, engine) - else: - raise NotImplementedError("[ERROR]: Unsupported file {}".format(img)) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_deskew.sh b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_deskew.sh deleted file mode 100644 index 34ecd8c..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_deskew.sh +++ /dev/null @@ -1,9 +0,0 @@ -export CUDA_VISIBLE_DEVICES=1 -# export PATH=/usr/local/cuda-11.6/bin${PATH:+:${PATH}} -# export LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64\ {LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} -# export CUDA_HOME=/usr/local/cuda-11.6 -# export PATH=/usr/local/cuda-11.6/bin:$PATH -# export CPATH=/usr/local/cuda-11.6/include:$CPATH -# export LIBRARY_PATH=/usr/local/cuda-11.6/lib64:$LIBRARY_PATH -# export LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64:/usr/local/cuda-11.6/extras/CUPTI/lib64:$LD_LIBRARY_PATH -python test/test_deskew_dir.py \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_ocr.sh b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_ocr.sh deleted file mode 100644 index 8ade432..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/scripts/run_ocr.sh +++ /dev/null @@ -1,49 +0,0 @@ - - -#bash scripts/run_ocr.sh -i /mnt/hdd2T/AICR/Projects/2023/FWD/Forms/PDFs/ -o /mnt/ssd1T/hungbnt/DocumentClassification/results/ocr -e out.csv -k "{\"device\":\"cuda:1\"}" -p True -n Passport 'So\ HK' -#bash scripts/run_ocr.sh -i '/mnt/hdd2T/AICR/Projects/2023/FWD/Forms/PDFs/So\ HK' -o /mnt/ssd1T/hungbnt/DocumentClassification/results/ocr -e out.csv -k "{\"device\":\"cuda:1\"}" -p True -#-n and -x do not accept multiple argument currently - - -# bash scripts/run_ocr.sh -i /mnt/hdd4T/OCR/hoangdc/End_to_end/ICDAR2013/data/images_receipt_5images/ -o visualize/ -e out.csv -k "{\"device\":\"cuda:1\"}" -p True - -export PYTHONWARNINGS="ignore" - -while getopts i:o:b:e:p:k:n:x: flag -do - case "${flag}" in - i) img=${OPTARG};; - o) out_dir=${OPTARG};; - b) base_dir=${OPTARG};; - e) export_csv=${OPTARG};; - p) export_img=${OPTARG};; - k) ocr_kwargs=${OPTARG};; - n) include=("${OPTARG[@]}");; - x) exclude=("${OPTARG[@]}");; - esac -done - -cmd="python run.py \ - --image $img \ - --save_dir $out_dir \ - --export_csv $export_csv \ - --export_img $export_img \ - --ocr_kwargs $ocr_kwargs" - -if [ ${#include[@]} -gt 0 ]; then - cmd+=" --include" - for item in "${include[@]}"; do - cmd+=" $item" - done -fi - -if [ ${#exclude[@]} -gt 0 ]; then - cmd+=" --exclude" - for item in "${exclude[@]}"; do - cmd+=" $item" - done -fi - - -echo $cmd -exec $cmd diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/settings.yml b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/settings.yml deleted file mode 100644 index 9107bb1..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/settings.yml +++ /dev/null @@ -1,35 +0,0 @@ -device: &device cuda:0 -max_img_size: [1920,1920] #text det default size: 1280x1280 #[] = originla size, TODO: fix the deskew code to resize the image only for detecting the angle, we want to feed the original size image to the text detection pipeline so that the bounding boxes would be mapped back to the original size -extend_bbox: [0, 0.0, 0.0, 0.0] # left, top, right, bottom -batch_size: 1 #1 means batch_mode = False -detector: - # version: /mnt/hdd2T/datnt/datnt_from_ssd1T/mmdetection/wild_receipt_finetune_weights_c_lite.pth - version: /mnt/hdd4T/OCR/datnt/mmdetection/logs/textdet-baseline-Oct04-wildreceiptv4-sdsapv1-mcocr-ssreceipt/epoch_100_params.pth - auto_rotate: True - rotator_version: /mnt/hdd2T/datnt/datnt_from_ssd1T/mmdetection/logs/textdet-with-rotate-20230317/best_bbox_mAP_epoch_30_lite.pth - device: *device - -recognizer: - version: satrn-lite-general-pretrain-20230106 - max_seq_len_overwrite: 24 #default = 12 - return_confident: True - device: *device -#extend the bbox to avoid losing accent mark in vietnames, if using ocr for only english, disable it - -deskew: - enable: True - text_detector: - config: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/config/det.yaml - weight: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_PP-OCRv3_det_infer - text_cls: - config: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/config/cls.yaml - weight: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_ppocr_mobile_v2.0_cls_infer - device: *device - - -words_to_lines: - gradient: 0.6 - max_x_dist: 20 - max_running_y_shift_degree: 10 #degrees - y_overlap_threshold: 0.5 - word_formation_mode: line diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/dto.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/dto.py deleted file mode 100644 index 8dae901..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/dto.py +++ /dev/null @@ -1,534 +0,0 @@ -import numpy as np -from typing import Optional, List, Union -import cv2 -from PIL import Image -from pathlib import Path -from .utils import visualize_bbox_and_label - - -class Box: - def __init__( - self, x1: int, y1: int, x2: int, y2: int, conf: float = -1.0, label: str = "" - ): - self._x1 = x1 - self._y1 = y1 - self._x2 = x2 - self._y2 = y2 - self._conf = conf - self._label = label - - def __repr__(self) -> str: - return str(self.bbox) - - def __str__(self) -> str: - return str(self.bbox) - - def get(self, return_confidence=False) -> Union[list[int], list[Union[float, int]]]: - return self.bbox if not return_confidence else self.xyxyc - - def __getitem__(self, key): - return self.bbox[key] - - @property - def width(self): - return max(self._x2 - self._x1, -1) - - @property - def height(self): - return max(self._y2 - self._y1, -1) - - @property - def bbox(self) -> list[int]: - return [self._x1, self._y1, self._x2, self._y2] - - @bbox.setter - def bbox(self, bbox_: list[int]): - self._x1, self._y1, self._x2, self._y2 = bbox_ - - @property - def xyxyc(self) -> list[Union[float, int]]: - return [self._x1, self._y1, self._x2, self._y2, self._conf] - - @staticmethod - def normalize_bbox(bbox: list[int]) -> list[int]: - return [int(b) for b in bbox] - - def to_int(self): - self._x1, self._y1, self._x2, self._y2 = self.normalize_bbox( - [self._x1, self._y1, self._x2, self._y2] - ) - return self - - @staticmethod - def clamp_bbox_by_img_wh(bbox: list, width: int, height: int) -> list[int]: - x1, y1, x2, y2 = bbox - x1 = min(max(0, x1), width) - x2 = min(max(0, x2), width) - y1 = min(max(0, y1), height) - y2 = min(max(0, y2), height) - return [x1, y1, x2, y2] - - def clamp_by_img_wh(self, width: int, height: int): - self._x1, self._y1, self._x2, self._y2 = self.clamp_bbox_by_img_wh( - [self._x1, self._y1, self._x2, self._y2], width, height - ) - return self - - @staticmethod - def extend_bbox(bbox: list, margin: list): # -> Self (python3.11) - margin_l, margin_t, margin_r, margin_b = margin - l, t, r, b = bbox # left, top, right, bottom - t = t - (b - t) * margin_t - b = b + (b - t) * margin_b - l = l - (r - l) * margin_l - r = r + (r - l) * margin_r - return [l, t, r, b] - - def get_extend_bbox(self, margin: list): - extended_bbox = self.extend_bbox(self.bbox, margin) - return Box(*extended_bbox, label=self._label) - - @staticmethod - def bbox_is_valid(bbox: list[int]) -> bool: - if bbox == [-1, -1, -1, -1]: - raise ValueError("Empty bounding box found") - l, t, r, b = bbox # left, top, right, bottom - return True if (b - t) * (r - l) > 0 else False - - def is_valid(self) -> bool: - return self.bbox_is_valid(self.bbox) - - @staticmethod - def crop_img_by_bbox(img: np.ndarray, bbox: list) -> np.ndarray: - l, t, r, b = bbox - return img[t:b, l:r] - - def crop_img(self, img: np.ndarray) -> np.ndarray: - return self.crop_img_by_bbox(img, self.bbox) - - -class Word: - def __init__( - self, - image=None, - text="", - conf_cls=-1.0, - bbox_obj: Box = Box(-1, -1, -1, -1), - conf_detect=-1.0, - kie_label="", - ): - # self.type = "word" - self._text = text - self._image = image - self._conf_det = conf_detect - self._conf_cls = conf_cls - # [left, top,right,bot] coordinate of top-left and bottom-right point - self._bbox_obj = bbox_obj - # self.word_id = 0 # id of word - # self.word_group_id = 0 # id of word_group which instance belongs to - # self.line_id = 0 # id of line which instance belongs to - # self.paragraph_id = 0 # id of line which instance belongs to - self._kie_label = kie_label - - @property - def bbox(self) -> list[int]: - return self._bbox_obj.bbox - - @property - def text(self) -> str: - return self._text - - @property - def height(self): - return self._bbox_obj.height - - @property - def width(self): - return self._bbox_obj.width - - def __repr__(self) -> str: - return self._text - - def __str__(self) -> str: - return self._text - - def is_valid(self) -> bool: - return self._bbox_obj.is_valid() - - # def is_special_word(self): - # if not self._text: - # raise ValueError("Cannot validatie size of empty bounding box") - - # # if len(text) > 7: - # # return True - # if len(self._text) >= 7: - # no_digits = sum(c.isdigit() for c in text) - # return no_digits / len(text) >= 0.3 - - # return False - - -class WordGroup: - def __init__( - self, - list_words: List[Word] = list(), - text: str = "", - boundingbox: Box = Box(-1, -1, -1, -1), - conf_cls: float = -1, - conf_det: float = -1, - ): - # self.type = "word_group" - self._list_words = list_words # dict of word instances - # self.word_group_id = 0 # word group id - # self.line_id = 0 # id of line which instance belongs to - # self.paragraph_id = 0 # id of paragraph which instance belongs to - self._text = text - self._bbox_obj = boundingbox - self._kie_label = "" - self._conf_cls = conf_cls - self._conf_det = conf_det - - @property - def bbox(self) -> list[int]: - return self._bbox_obj.bbox - - @property - def text(self) -> str: - return self._text - - @property - def list_words(self) -> list[Word]: - return self._list_words - - def __repr__(self) -> str: - return self._text - - def __str__(self) -> str: - return self._text - - # def add_word(self, word: Word): # add a word instance to the word_group - # if word._text != "✪": - # for w in self._list_words: - # if word.word_id == w.word_id: - # print("Word id collision") - # return False - # word.word_group_id = self.word_group_id # - # word.line_id = self.line_id - # word.paragraph_id = self.paragraph_id - # self._list_words.append(word) - # self._text += " " + word._text - # if self.bbox_obj == [-1, -1, -1, -1]: - # self.bbox_obj = word._bbox_obj - # else: - # self.bbox_obj = [ - # min(self.bbox_obj[0], word._bbox_obj[0]), - # min(self.bbox_obj[1], word._bbox_obj[1]), - # max(self.bbox_obj[2], word._bbox_obj[2]), - # max(self.bbox_obj[3], word._bbox_obj[3]), - # ] - # return True - # else: - # return False - - # def update_word_group_id(self, new_word_group_id): - # self.word_group_id = new_word_group_id - # for i in range(len(self._list_words)): - # self._list_words[i].word_group_id = new_word_group_id - - # def update_kie_label(self): - # list_kie_label = [word._kie_label for word in self._list_words] - # dict_kie = dict() - # for label in list_kie_label: - # if label not in dict_kie: - # dict_kie[label] = 1 - # else: - # dict_kie[label] += 1 - # total = len(list(dict_kie.values())) - # max_value = max(list(dict_kie.values())) - # list_keys = list(dict_kie.keys()) - # list_values = list(dict_kie.values()) - # self.kie_label = list_keys[list_values.index(max_value)] - - # def update_text(self): # update text after changing positions of words in list word - # text = "" - # for word in self._list_words: - # text += " " + word._text - # self._text = text - - -class Line: - def __init__( - self, - list_word_groups: List[WordGroup] = [], - text: str = "", - boundingbox: Box = Box(-1, -1, -1, -1), - conf_cls: float = -1, - conf_det: float = -1, - ): - # self.type = "line" - self._list_word_groups = ( - list_word_groups # list of Word_group instances in the line - ) - # self.line_id = 0 # id of line in the paragraph - # self.paragraph_id = 0 # id of paragraph which instance belongs to - self._text = text - self._bbox_obj = boundingbox - self._conf_cls = conf_cls - self._conf_det = conf_det - - @property - def bbox(self) -> list[int]: - return self._bbox_obj.bbox - - @property - def text(self) -> str: - return self._text - - @property - def list_word_groups(self) -> List[WordGroup]: - return self._list_word_groups - - @property - def list_words(self) -> list[Word]: - return [ - word - for word_group in self._list_word_groups - for word in word_group.list_words - ] - - def __repr__(self) -> str: - return self._text - - def __str__(self) -> str: - return self._text - - # def add_group(self, word_group: WordGroup): # add a word_group instance - # if word_group._list_words is not None: - # for wg in self.list_word_groups: - # if word_group.word_group_id == wg.word_group_id: - # print("Word_group id collision") - # return False - - # self.list_word_groups.append(word_group) - # self.text += word_group._text - # word_group.paragraph_id = self.paragraph_id - # word_group.line_id = self.line_id - - # for i in range(len(word_group._list_words)): - # word_group._list_words[ - # i - # ].paragraph_id = self.paragraph_id # set paragraph_id for word - # word_group._list_words[i].line_id = self.line_id # set line_id for word - # return True - # return False - - # def update_line_id(self, new_line_id): - # self.line_id = new_line_id - # for i in range(len(self.list_word_groups)): - # self.list_word_groups[i].line_id = new_line_id - # for j in range(len(self.list_word_groups[i]._list_words)): - # self.list_word_groups[i]._list_words[j].line_id = new_line_id - - # def merge_word(self, word): # word can be a Word instance or a Word_group instance - # if word.text != "✪": - # if self.boundingbox == [-1, -1, -1, -1]: - # self.boundingbox = word.boundingbox - # else: - # self.boundingbox = [ - # min(self.boundingbox[0], word.boundingbox[0]), - # min(self.boundingbox[1], word.boundingbox[1]), - # max(self.boundingbox[2], word.boundingbox[2]), - # max(self.boundingbox[3], word.boundingbox[3]), - # ] - # self.list_word_groups.append(word) - # self.text += " " + word.text - # return True - # return False - - # def __cal_ratio(self, top1, bottom1, top2, bottom2): - # sorted_vals = sorted([top1, bottom1, top2, bottom2]) - # intersection = sorted_vals[2] - sorted_vals[1] - # min_height = min(bottom1 - top1, bottom2 - top2) - # if min_height == 0: - # return -1 - # ratio = intersection / min_height - # return ratio - - # def __cal_ratio_height(self, top1, bottom1, top2, bottom2): - - # height1, height2 = top1 - bottom1, top2 - bottom2 - # ratio_height = float(max(height1, height2)) / float(min(height1, height2)) - # return ratio_height - - # def in_same_line(self, input_line, thresh=0.7): - # # calculate iou in vertical direction - # _, top1, _, bottom1 = self.boundingbox - # _, top2, _, bottom2 = input_line.boundingbox - - # ratio = self.__cal_ratio(top1, bottom1, top2, bottom2) - # ratio_height = self.__cal_ratio_height(top1, bottom1, top2, bottom2) - - # if ( - # (top2 <= top1 <= bottom2) or (top1 <= top2 <= bottom1) - # and ratio >= thresh - # and (ratio_height < 2) - # ): - # return True - # return False - - -# class Paragraph: -# def __init__(self, id=0, lines=None): -# self.list_lines = lines if lines is not None else [] # list of all lines in the paragraph -# self.paragraph_id = id # index of paragraph in the ist of paragraph -# self.text = "" -# self.boundingbox = [-1, -1, -1, -1] - -# @property -# def bbox(self): -# return self.boundingbox - -# def __repr__(self) -> str: -# return self.text - -# def __str__(self) -> str: -# return self.text - -# def add_line(self, line: Line): # add a line instance -# if line.list_word_groups is not None: -# for l in self.list_lines: -# if line.line_id == l.line_id: -# print("Line id collision") -# return False -# for i in range(len(line.list_word_groups)): -# line.list_word_groups[ -# i -# ].paragraph_id = ( -# self.paragraph_id -# ) # set paragraph id for every word group in line -# for j in range(len(line.list_word_groups[i]._list_words)): -# line.list_word_groups[i]._list_words[ -# j -# ].paragraph_id = ( -# self.paragraph_id -# ) # set paragraph id for every word in word groups -# line.paragraph_id = self.paragraph_id # set paragraph id for line -# self.list_lines.append(line) # add line to paragraph -# self.text += " " + line.text -# return True -# else: -# return False - -# def update_paragraph_id( -# self, new_paragraph_id -# ): # update new paragraph_id for all lines, word_groups, words inside paragraph -# self.paragraph_id = new_paragraph_id -# for i in range(len(self.list_lines)): -# self.list_lines[ -# i -# ].paragraph_id = new_paragraph_id # set new paragraph_id for line -# for j in range(len(self.list_lines[i].list_word_groups)): -# self.list_lines[i].list_word_groups[ -# j -# ].paragraph_id = new_paragraph_id # set new paragraph_id for word_group -# for k in range(len(self.list_lines[i].list_word_groups[j].list_words)): -# self.list_lines[i].list_word_groups[j].list_words[ -# k -# ].paragraph_id = new_paragraph_id # set new paragraph id for word -# return True - - -class Page: - def __init__( - self, - word_segments: Union[List[WordGroup], List[Line]], - image: np.ndarray, - deskewed_image: Optional[np.ndarray] = None, - ) -> None: - self._word_segments = word_segments - self._image = image - self._deskewed_image = deskewed_image - self._drawed_image: Optional[np.ndarray] = None - - @property - def word_segments(self): - return self._word_segments - - @property - def list_words(self) -> list[Word]: - return [ - word - for word_segment in self._word_segments - for word in word_segment.list_words - ] - - @property - def image(self): - return self._image - - @property - def PIL_image(self): - return Image.fromarray(self._image) - - @property - def drawed_image(self): - return self._drawed_image - - @property - def deskewed_image(self): - return self._deskewed_image - - def visualize_bbox_and_label(self, **kwargs: dict): - if self._drawed_image is not None: - return self._drawed_image - bboxes = list() - texts = list() - for word in self.list_words: - bboxes.append([int(float(b)) for b in word.bbox]) - texts.append(word._text) - img = visualize_bbox_and_label( - self._deskewed_image if self._deskewed_image is not None else self._image, - bboxes, - texts, - **kwargs - ) - self._drawed_image = img - return self._drawed_image - - def save_img(self, save_path: str, **kwargs: dict) -> None: - save_path_deskew = kwargs.pop("save_path_deskew", Path(save_path).with_stem(Path(save_path).stem + "_deskewed").as_posix()) - if self._deskewed_image is not None: - # save_path_deskew: str = kwargs.pop("save_path_deskew", Path(save_path).with_stem(Path(save_path).stem + "_deskewed").as_posix()) # type: ignore - cv2.imwrite(save_path_deskew, self._deskewed_image) - - img = self.visualize_bbox_and_label(**kwargs) - cv2.imwrite(save_path, img) - - - def write_to_file(self, mode: str, save_path: str) -> None: - f = open(save_path, "w+", encoding="utf-8") - for word_segment in self._word_segments: - if mode == "segment": - xmin, ymin, xmax, ymax = word_segment.bbox - f.write( - "{}\t{}\t{}\t{}\t{}\n".format( - xmin, ymin, xmax, ymax, word_segment._text - ) - ) - elif mode == "word": - for word in word_segment.list_words: - # xmin, ymin, xmax, ymax = word.bbox - xmin, ymin, xmax, ymax = [int(float(b)) for b in word.bbox] - f.write( - "{}\t{}\t{}\t{}\t{}\n".format( - xmin, ymin, xmax, ymax, word._text - ) - ) - else: - raise NotImplementedError("Unknown mode: {}".format(mode)) - f.close() - - -class Document: - def __init__(self, lpages: List[Page]) -> None: - self.lpages = lpages diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/ocr.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/ocr.py deleted file mode 100644 index 280d0b2..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/ocr.py +++ /dev/null @@ -1,258 +0,0 @@ -from typing import Union, overload, List, Optional, Tuple -from PIL import Image -import torch -import numpy as np -import yaml -from pathlib import Path -import mmcv -from sdsvtd import StandaloneYOLOXRunner -from sdsvtr import StandaloneSATRNRunner -from sdsv_dewarp.api import AlignImage - -from .utils import ImageReader, chunks, Timer, post_process_recog # rotate_bbox - -# from .utils import jdeskew as deskew -# from externals.deskew.sdsv_dewarp import pdeskew as deskew -# from .utils import deskew -from .dto import Word, Line, Page, Document, Box, WordGroup - -# from .word_formation import words_to_lines as words_to_lines -# from .word_formation import wo rds_to_lines_mmocr as words_to_lines -from .word_formation import words_formation_mmocr_tesseract as word_formation - -DEFAULT_SETTING_PATH = str(Path(__file__).parents[1]) + "/settings.yml" - - -class OcrEngine: - def __init__(self, settings_file: str = DEFAULT_SETTING_PATH, **kwargs): - """Warper of text detection and text recognition - :param settings_file: path to default setting file - :param kwargs: keyword arguments to overwrite the default settings file - """ - with open(settings_file) as f: - # use safe_load instead load - self._settings = yaml.safe_load(f) - self._update_configs(kwargs) - - self._ensure_device() - self._detector = StandaloneYOLOXRunner(**self._settings["detector"]) - self._recognizer = StandaloneSATRNRunner(**self._settings["recognizer"]) - self._deskewer = self._load_deskewer() - - def _update_configs(self, params): - for key, para in params.items(): # overwrite default settings by keyword arguments - if key not in self._settings: - raise ValueError("Invalid setting found in OcrEngine: ", k) - if key == "device": - self._settings[key] = para - self._settings["detector"][key] = para - self._settings["recognizer"][key] = para - self._settings["deskew"][key] = para - else: - for k, v in para.items(): - if isinstance(v, dict): - for sub_key, sub_value in v.items(): - self._settings[key][k][sub_key] = sub_value - else: - self._settings[key][k] = v - - def _load_deskewer(self) -> Optional[AlignImage]: - if self._settings["deskew"]["enable"]: - deskewer = AlignImage( - **{k: v for k, v in self._settings["deskew"].items() if k != "enable"} - ) - print( - "[WARNING]: Deskew is enabled. The bounding boxes prediction may not be aligned with the original image. In case of using these predictions for pseudo-label, turn on save_deskewed option and use the saved deskewed images instead for further proceed." - ) - return deskewer - return None - - def _ensure_device(self): - if "cuda" in self._settings["device"]: - if not torch.cuda.is_available(): - print("[WARNING]: CUDA is not available, running with cpu instead") - self._settings["device"] = "cpu" - - @property - def version(self): - return { - "detector": self._settings["detector"], - "recognizer": self._settings["recognizer"], - } - - @property - def settings(self): - return self._settings - - # @staticmethod - # def xyxyc_to_xyxy_c(xyxyc: np.ndarray) -> Tuple[List[list], list]: - # ''' - # convert sdsvtd yoloX detection output to list of bboxes and list of confidences - # @param xyxyc: array of shape (n, 5) - # ''' - # xyxy = xyxyc[:, :4].tolist() - # confs = xyxyc[:, 4].tolist() - # return xyxy, confs - # -> Tuple[np.ndarray, List[Box]]: - - def preprocess(self, img: np.ndarray) -> tuple[np.ndarray, bool, float]: - img_ = img.copy() - if self._settings["max_img_size"]: - img_ = mmcv.imrescale( - img, - tuple(self._settings["max_img_size"]), - return_scale=False, - interpolation="bilinear", - backend="cv2", - ) - is_blank = False - if self._deskewer: - with Timer("deskew"): - img_, is_blank, angle = self._deskewer(img_) - return img, is_blank, angle # replace img_ to img - # for i, bbox in enumerate(bboxes): - # rotated_bbox = rotate_bbox(bbox, angle, img.shape[:2]) - # bboxes[i].bbox = rotated_bbox - return img, is_blank, 0 - - def run_detect( - self, img: np.ndarray, return_raw: bool = False - ) -> Tuple[np.ndarray, Union[List[Box], List[list]]]: - """ - run text detection and return list of xyxyc if return_confidence is True, otherwise return a list of xyxy - """ - pred_det = self._detector(img) - if self._settings["detector"]["auto_rotate"]: - img, pred_det = pred_det - pred_det = pred_det[0] # only image at a time - return ( - (img, pred_det.tolist()) - if return_raw - else (img, [Box(*xyxyc) for xyxyc in pred_det.tolist()]) - ) - - def run_recog( - self, imgs: List[np.ndarray] - ) -> Union[List[str], List[Tuple[str, float]]]: - if len(imgs) == 0: - return list() - pred_rec = self._recognizer(imgs) - return [ - (post_process_recog(word), conf) - for word, conf in zip(pred_rec[0], pred_rec[1]) - ] - - def read_img(self, img: str) -> np.ndarray: - return ImageReader.read(img) - - def get_cropped_imgs( - self, img: np.ndarray, bboxes: Union[List[Box], List[list]] - ) -> Tuple[List[np.ndarray], List[bool]]: - """ - img: np image - bboxes: list of xyxy - """ - lcropped_imgs = list() - mask = list() - for bbox in bboxes: - bbox = Box(*bbox) if isinstance(bbox, list) else bbox - bbox = bbox.get_extend_bbox(self._settings["extend_bbox"]) - - bbox.clamp_by_img_wh(img.shape[1], img.shape[0]) - bbox.to_int() - if not bbox.is_valid(): - mask.append(False) - continue - cropped_img = bbox.crop_img(img) - lcropped_imgs.append(cropped_img) - mask.append(True) - return lcropped_imgs, mask - - def read_page( - self, img: np.ndarray, bboxes: Union[List[Box], List[list]] - ) -> Union[List[WordGroup], List[Line]]: - if len(bboxes) == 0: # no bbox found - return list() - with Timer("cropped imgs"): - lcropped_imgs, mask = self.get_cropped_imgs(img, bboxes) - with Timer("recog"): - # batch_mode for efficiency - pred_recs = self.run_recog(lcropped_imgs) - with Timer("construct words"): - lwords = list() - for i in range(len(pred_recs)): - if not mask[i]: - continue - text, conf_rec = pred_recs[i][0], pred_recs[i][1] - bbox = Box(*bboxes[i]) if isinstance(bboxes[i], list) else bboxes[i] - lwords.append( - Word( - image=img, - text=text, - conf_cls=conf_rec, - bbox_obj=bbox, - conf_detect=bbox._conf, - ) - ) - with Timer("word formation"): - return word_formation( - lwords, img.shape[1], **self._settings["words_to_lines"] - )[0] - - # https://stackoverflow.com/questions/48127642/incompatible-types-in-assignment-on-union - - @overload - def __call__(self, img: Union[str, np.ndarray, Image.Image]) -> Page: - ... - - @overload - def __call__(self, img: List[Union[str, np.ndarray, Image.Image]]) -> Document: - ... - - def __call__(self, img): # type: ignore #ignoring type before implementing batch_mode - """ - Accept an image or list of them, return ocr result as a page or document - """ - with Timer("read image"): - img = ImageReader.read(img) - if self._settings["batch_size"] == 1: - if isinstance(img, list): - if len(img) == 1: - img = img[0] # in case input type is a 1 page pdf - else: - raise AssertionError( - "list input can only be used with batch_mode enabled" - ) - img_deskewed, is_blank, angle = self.preprocess(img) - - if is_blank: - print( - "[WARNING]: Blank image detected" - ) # TODO: should we stop the execution here? - with Timer("detect"): - img_deskewed, bboxes = self.run_detect(img_deskewed) - with Timer("read_page"): - lsegments = self.read_page(img_deskewed, bboxes) - return Page(lsegments, img, img_deskewed if angle != 0 else None) - else: - # lpages = [] - # # chunks to reduce memory footprint - # for imgs in chunks(img, self._batch_size): - # # pred_dets = self._detector(imgs) - # # TEMP: use list comprehension because sdsvtd do not support batch mode of text detection - # img = self.preprocess(img) - # img, bboxes = self.run_detect(img) - # for img_, bboxes_ in zip(imgs, bboxes): - # llines = self.read_page(img, bboxes_) - # page = Page(llines, img) - # lpages.append(page) - # return Document(lpages) - raise NotImplementedError("Batch mode is currently not supported") - - -if __name__ == "__main__": - img_path = "/mnt/ssd1T/hungbnt/Cello/data/PH/Sea7/Sea_7_1.jpg" - engine = OcrEngine(device="cuda:0") - # https://stackoverflow.com/questions/66435480/overload-following-optional-argument - page = engine(img_path) # type: ignore - print(page._word_segments) diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/utils.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/utils.py deleted file mode 100644 index 3c4332e..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/utils.py +++ /dev/null @@ -1,369 +0,0 @@ -from PIL import ImageFont, ImageDraw, Image, ImageOps -# import matplotlib.pyplot as plt -import numpy as np -import cv2 -import os -import time -from typing import Generator, Union, List, overload, Tuple, Callable -import glob -import math -from pathlib import Path -from pdf2image import convert_from_path -# from deskew import determine_skew -# from jdeskew.estimator import get_angle -# from jdeskew.utility import rotate as jrotate - - -def post_process_recog(text: str) -> str: - text = text.replace("✪", " ") - return text - - -def find_maximum_without_outliers(lst: list[int], threshold: float = 1.): - ''' - To find the maximum number in a list while excluding its outlier values, you can follow these steps: - Determine the range within which you consider values as outliers. This can be based on a specific threshold or a statistical measure such as the interquartile range (IQR). - Iterate through the list and filter out the outlier values based on the defined range. Keep track of the non-outlier values. - Find the maximum value among the non-outlier values. - ''' - # Calculate the lower and upper boundaries for outliers - q1 = np.percentile(lst, 25) - q3 = np.percentile(lst, 75) - iqr = q3 - q1 - lower_bound = q1 - threshold * iqr - upper_bound = q3 + threshold * iqr - - # Filter out outlier values - non_outliers = [x for x in lst if lower_bound <= x <= upper_bound] - - # Find the maximum value among non-outliers - max_value = max(non_outliers) - - return max_value - - -class Timer: - def __init__(self, name: str) -> None: - self.name = name - - def __enter__(self): - self.start_time = time.perf_counter() - return self - - def __exit__(self, func: Callable, *args): - self.end_time = time.perf_counter() - self.elapsed_time = self.end_time - self.start_time - print(f"[INFO]: {self.name} took : {self.elapsed_time:.6f} seconds") - - -# def rotate( -# image: np.ndarray, angle: float, background: Union[int, Tuple[int, int, int]] -# ) -> np.ndarray: -# old_width, old_height = image.shape[:2] -# angle_radian = math.radians(angle) -# width = abs(np.sin(angle_radian) * old_height) + abs(np.cos(angle_radian) * old_width) -# height = abs(np.sin(angle_radian) * old_width) + abs(np.cos(angle_radian) * old_height) -# image_center = tuple(np.array(image.shape[1::-1]) / 2) -# rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0) -# rot_mat[1, 2] += (width - old_width) / 2 -# rot_mat[0, 2] += (height - old_height) / 2 -# return cv2.warpAffine(image, rot_mat, (int(round(height)), int(round(width))), borderValue=background) - - -# def rotate_bbox(bbox: list, angle: float) -> list: -# # Compute the center point of the bounding box -# cx = bbox[0] + bbox[2] / 2 -# cy = bbox[1] + bbox[3] / 2 - -# # Define the scale factor for the rotated bounding box -# scale = 1.0 # following the deskew and jdeskew function -# angle_radian = math.radians(angle) - -# # Obtain the rotation matrix using cv2.getRotationMatrix2D() -# M = cv2.getRotationMatrix2D((cx, cy), angle_radian, scale) - -# # Apply the rotation matrix to the four corners of the bounding box -# corners = np.array([[bbox[0], bbox[1]], -# [bbox[0] + bbox[2], bbox[1]], -# [bbox[0] + bbox[2], bbox[1] + bbox[3]], -# [bbox[0], bbox[1] + bbox[3]]], dtype=np.float32) -# rotated_corners = cv2.transform(np.array([corners]), M)[0] - -# # Compute the bounding box of the rotated corners -# x = int(np.min(rotated_corners[:, 0])) -# y = int(np.min(rotated_corners[:, 1])) -# w = int(np.max(rotated_corners[:, 0]) - np.min(rotated_corners[:, 0])) -# h = int(np.max(rotated_corners[:, 1]) - np.min(rotated_corners[:, 1])) -# rotated_bbox = [x, y, w, h] - -# return rotated_bbox - -# def rotate_bbox(bbox: List[int], angle: float, old_shape: Tuple[int, int]) -> List[int]: -# # https://medium.com/@pokomaru/image-and-bounding-box-rotation-using-opencv-python-2def6c39453 -# bbox_ = [bbox[0], bbox[1], bbox[2], bbox[1], bbox[2], bbox[3], bbox[0], bbox[3]] -# h, w = old_shape -# cx, cy = (int(w / 2), int(h / 2)) - -# bbox_tuple = [ -# (bbox_[0], bbox_[1]), -# (bbox_[2], bbox_[3]), -# (bbox_[4], bbox_[5]), -# (bbox_[6], bbox_[7]), -# ] # put x and y coordinates in tuples, we will iterate through the tuples and perform rotation - -# rotated_bbox = [] - -# for i, coord in enumerate(bbox_tuple): -# M = cv2.getRotationMatrix2D((cx, cy), angle, 1.0) -# cos, sin = abs(M[0, 0]), abs(M[0, 1]) -# newW = int((h * sin) + (w * cos)) -# newH = int((h * cos) + (w * sin)) -# M[0, 2] += (newW / 2) - cx -# M[1, 2] += (newH / 2) - cy -# v = [coord[0], coord[1], 1] -# adjusted_coord = np.dot(M, v) -# rotated_bbox.insert(i, (adjusted_coord[0], adjusted_coord[1])) -# result = [int(x) for t in rotated_bbox for x in t] -# return [result[i] for i in [0, 1, 2, -1]] # reformat to xyxy - - -# def deskew(image: np.ndarray) -> Tuple[np.ndarray, float]: -# grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) -# angle = 0. -# try: -# angle = determine_skew(grayscale) -# except Exception: -# pass -# rotated = rotate(image, angle, (0, 0, 0)) if angle else image -# return rotated, angle - - -# def jdeskew(image: np.ndarray) -> Tuple[np.ndarray, float]: -# angle = 0. -# try: -# angle = get_angle(image) -# except Exception: -# pass -# # TODO: change resize = True and scale the bounding box -# rotated = jrotate(image, angle, resize=False) if angle else image -# return rotated, angle -# def deskew() - -class ImageReader: - """ - accept anything, return numpy array image - """ - supported_ext = [".png", ".jpg", ".jpeg", ".pdf", ".gif"] - - @staticmethod - def validate_img_path(img_path: str) -> None: - if not os.path.exists(img_path): - raise FileNotFoundError(img_path) - if os.path.isdir(img_path): - raise IsADirectoryError(img_path) - if not Path(img_path).suffix.lower() in ImageReader.supported_ext: - raise NotImplementedError("Not supported extension at {}".format(img_path)) - - @overload - @staticmethod - def read(img: Union[str, np.ndarray, Image.Image]) -> np.ndarray: ... - - @overload - @staticmethod - def read(img: List[Union[str, np.ndarray, Image.Image]]) -> List[np.ndarray]: ... - - @overload - @staticmethod - def read(img: str) -> List[np.ndarray]: ... # for pdf or directory - - @staticmethod - def read(img): - if isinstance(img, list): - return ImageReader.from_list(img) - elif isinstance(img, str) and os.path.isdir(img): - return ImageReader.from_dir(img) - elif isinstance(img, str) and img.endswith(".pdf"): - return ImageReader.from_pdf(img) - else: - return ImageReader._read(img) - - @staticmethod - def from_dir(dir_path: str) -> List[np.ndarray]: - if os.path.isdir(dir_path): - image_files = glob.glob(os.path.join(dir_path, "*")) - return ImageReader.from_list(image_files) - else: - raise NotADirectoryError(dir_path) - - @staticmethod - def from_str(img_path: str) -> np.ndarray: - ImageReader.validate_img_path(img_path) - return ImageReader.from_PIL(Image.open(img_path)) - - @staticmethod - def from_np(img_array: np.ndarray) -> np.ndarray: - return img_array - - @staticmethod - def from_PIL(img_pil: Image.Image, transpose=True) -> np.ndarray: - # if img_pil.is_animated: - # raise NotImplementedError("Only static images are supported, animated image found") - if transpose: - img_pil = ImageOps.exif_transpose(img_pil) - if img_pil.mode != "RGB": - img_pil = img_pil.convert("RGB") - - return np.array(img_pil) - - @staticmethod - def from_list(img_list: List[Union[str, np.ndarray, Image.Image]]) -> List[np.ndarray]: - limgs = list() - for img_path in img_list: - try: - if isinstance(img_path, str): - ImageReader.validate_img_path(img_path) - limgs.append(ImageReader._read(img_path)) - except (FileNotFoundError, NotImplementedError, IsADirectoryError) as e: - print("[ERROR]: ", e) - print("[INFO]: Skipping image {}".format(img_path)) - return limgs - - @staticmethod - def from_pdf(pdf_path: str, start_page: int = 0, end_page: int = 0) -> List[np.ndarray]: - pdf_file = convert_from_path(pdf_path) - if end_page is not None: - end_page = min(len(pdf_file), end_page + 1) - limgs = [np.array(pdf_page) for pdf_page in pdf_file[start_page:end_page]] - return limgs - - @staticmethod - def _read(img: Union[str, np.ndarray, Image.Image]) -> np.ndarray: - if isinstance(img, str): - return ImageReader.from_str(img) - elif isinstance(img, Image.Image): - return ImageReader.from_PIL(img) - elif isinstance(img, np.ndarray): - return ImageReader.from_np(img) - else: - raise ValueError("Invalid img argument type: ", type(img)) - - -def get_name(file_path, ext: bool = True): - file_path_ = os.path.basename(file_path) - return file_path_ if ext else os.path.splitext(file_path_)[0] - - -def construct_file_path(dir, file_path, ext=''): - ''' - args: - dir: /path/to/dir - file_path /example_path/to/file.txt - ext = '.json' - return - /path/to/dir/file.json - ''' - return os.path.join( - dir, get_name(file_path, - True)) if ext == '' else os.path.join( - dir, get_name(file_path, - False)) + ext - - -def chunks(lst: list, n: int) -> Generator: - """ - Yield successive n-sized chunks from lst. - https://stackoverflow.com/questions/312443/how-do-i-split-a-list-into-equally-sized-chunks - """ - for i in range(0, len(lst), n): - yield lst[i:i + n] - - -def read_ocr_result_from_txt(file_path: str) -> Tuple[list, list]: - ''' - return list of bounding boxes, list of words - ''' - with open(file_path, 'r') as f: - lines = f.read().splitlines() - boxes, words = [], [] - for line in lines: - if line == "": - continue - x1, y1, x2, y2, text = line.split("\t") - x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) - if text and text != " ": - words.append(text) - boxes.append((x1, y1, x2, y2)) - return boxes, words - - -def get_xyxywh_base_on_format(bbox, format): - if format == "xywh": - x1, y1, w, h = bbox[0], bbox[1], bbox[2], bbox[3] - x2, y2 = x1 + w, y1 + h - elif format == "xyxy": - x1, y1, x2, y2 = bbox - w, h = x2 - x1, y2 - y1 - else: - raise NotImplementedError("Invalid format {}".format(format)) - return (x1, y1, x2, y2, w, h) - - -def get_dynamic_params_for_bbox_of_label(text, x1, y1, w, h, img_h, img_w, font, font_scale_offset=1): - font_scale_factor = img_h / (img_w + img_h) * font_scale_offset - font_scale = w / (w + h) * font_scale_factor # adjust font scale by width height - thickness = int(font_scale_factor) + 1 - (text_width, text_height) = cv2.getTextSize(text, font, fontScale=font_scale, thickness=thickness)[0] - text_offset_x = x1 - text_offset_y = y1 - thickness - box_coords = ((text_offset_x, text_offset_y + 1), (text_offset_x + text_width - 2, text_offset_y - text_height - 2)) - return (font_scale, thickness, text_height, box_coords) - - -def visualize_bbox_and_label( - img, bboxes, texts, bbox_color=(200, 180, 60), - text_color=(0, 0, 0), - format="xyxy", is_vnese=False, draw_text=True): - ori_img_type = type(img) - if is_vnese: - img = Image.fromarray(img) if ori_img_type is np.ndarray else img - draw = ImageDraw.Draw(img) - img_w, img_h = img.size - font_pil_str = "fonts/arial.ttf" - font_cv2 = cv2.FONT_HERSHEY_SIMPLEX - else: - img_h, img_w = img.shape[0], img.shape[1] - font_cv2 = cv2.FONT_HERSHEY_SIMPLEX - for i in range(len(bboxes)): - text = texts[i] # text = "{}: {:.0f}%".format(LABELS[classIDs[i]], confidences[i]*100) - x1, y1, x2, y2, w, h = get_xyxywh_base_on_format(bboxes[i], format) - font_scale, thickness, text_height, box_coords = get_dynamic_params_for_bbox_of_label( - text, x1, y1, w, h, img_h, img_w, font=font_cv2) - if is_vnese: - font_pil = ImageFont.truetype(font_pil_str, size=text_height) # type: ignore - fdraw_text = draw.text # type: ignore - fdraw_bbox = draw.rectangle # type: ignore - # Pil use different coordinate => y = y+thickness = y-thickness + 2*thickness - arg_text = ((box_coords[0][0], box_coords[1][1]), text) - kwarg_text = {"font": font_pil, "fill": text_color, "width": thickness} - arg_rec = ((x1, y1, x2, y2),) - kwarg_rec = {"outline": bbox_color, "width": thickness} - arg_rec_text = ((box_coords[0], box_coords[1]),) - kwarg_rec_text = {"fill": bbox_color, "width": thickness} - else: - # cv2.rectangle(img, box_coords[0], box_coords[1], color, cv2.FILLED) - # cv2.putText(img, text, (text_offset_x, text_offset_y), font, fontScale=font_scale, color=(50, 0,0), thickness=thickness) - # cv2.rectangle(img, (x1, y1), (x2, y2), color, thickness) - fdraw_text = cv2.putText - fdraw_bbox = cv2.rectangle - arg_text = (img, text, box_coords[0]) - kwarg_text = {"fontFace": font_cv2, "fontScale": font_scale, "color": text_color, "thickness": thickness} - arg_rec = (img, (x1, y1), (x2, y2)) - kwarg_rec = {"color": bbox_color, "thickness": thickness} - arg_rec_text = (img, box_coords[0], box_coords[1]) - kwarg_rec_text = {"color": bbox_color, "thickness": cv2.FILLED} - # draw a bounding box rectangle and label on the img - fdraw_bbox(*arg_rec, **kwarg_rec) # type: ignore - if draw_text: - fdraw_bbox(*arg_rec_text, **kwarg_rec_text) # type: ignore - fdraw_text(*arg_text, **kwarg_text) # type: ignore # text have to put in front of rec_text - return np.array(img) if ori_img_type is np.ndarray and is_vnese else img diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/word_formation.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/word_formation.py deleted file mode 100644 index 3e64b97..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/src/word_formation.py +++ /dev/null @@ -1,903 +0,0 @@ -from builtins import dict -from .dto import Word, Line, WordGroup, Box -from .utils import find_maximum_without_outliers -import numpy as np -from typing import Optional, List, Tuple, Union - -############################################################################################################################################################################################################################ -############################################################################################################################################################################################################################ -### WORDS TO LINES ALGORITHMS FROM MMOCR AND TESSERACT ############################################################################################################################################################################### -############################################################################################################################################################################################################################ -############################################################################################################################################################################################################################ - -DEGREE_TO_RADIAN_COEF = np.pi / 180 -MAX_INT = int(2e10 + 9) -MIN_INT = -MAX_INT - - -def is_on_same_line(box_a, box_b, min_y_overlap_ratio=0.8): - """Check if two boxes are on the same line by their y-axis coordinates. - - Two boxes are on the same line if they overlap vertically, and the length - of the overlapping line segment is greater than min_y_overlap_ratio * the - height of either of the boxes. - - Args: - box_a (list), box_b (list): Two bounding boxes to be checked - min_y_overlap_ratio (float): The minimum vertical overlapping ratio - allowed for boxes in the same line - - Returns: - The bool flag indicating if they are on the same line - """ - a_y_min = np.min(box_a[1::2]) - b_y_min = np.min(box_b[1::2]) - a_y_max = np.max(box_a[1::2]) - b_y_max = np.max(box_b[1::2]) - - # Make sure that box a is always the box above another - if a_y_min > b_y_min: - a_y_min, b_y_min = b_y_min, a_y_min - a_y_max, b_y_max = b_y_max, a_y_max - - if b_y_min <= a_y_max: - if min_y_overlap_ratio is not None: - sorted_y = sorted([b_y_min, b_y_max, a_y_max]) - overlap = sorted_y[1] - sorted_y[0] - min_a_overlap = (a_y_max - a_y_min) * min_y_overlap_ratio - min_b_overlap = (b_y_max - b_y_min) * min_y_overlap_ratio - return overlap >= min_a_overlap or \ - overlap >= min_b_overlap - else: - return True - return False - - -def merge_bboxes_to_group(bboxes_group, x_sorted_boxes): - merged_bboxes = [] - for box_group in bboxes_group: - merged_box = {} - merged_box['text'] = ' '.join( - [x_sorted_boxes[idx]['text'] for idx in box_group]) - x_min, y_min = float('inf'), float('inf') - x_max, y_max = float('-inf'), float('-inf') - for idx in box_group: - x_max = max(np.max(x_sorted_boxes[idx]['box'][::2]), x_max) - x_min = min(np.min(x_sorted_boxes[idx]['box'][::2]), x_min) - y_max = max(np.max(x_sorted_boxes[idx]['box'][1::2]), y_max) - y_min = min(np.min(x_sorted_boxes[idx]['box'][1::2]), y_min) - merged_box['box'] = [ - x_min, y_min, x_max, y_min, x_max, y_max, x_min, y_max - ] - merged_box['list_words'] = [x_sorted_boxes[idx]['word'] - for idx in box_group] - merged_bboxes.append(merged_box) - return merged_bboxes - - -def stitch_boxes_into_lines(boxes, max_x_dist=10, min_y_overlap_ratio=0.3): - """Stitch fragmented boxes of words into lines. - - Note: part of its logic is inspired by @Johndirr - (https://github.com/faustomorales/keras-ocr/issues/22) - - Args: - boxes (list): List of ocr results to be stitched - max_x_dist (int): The maximum horizontal distance between the closest - edges of neighboring boxes in the same line - min_y_overlap_ratio (float): The minimum vertical overlapping ratio - allowed for any pairs of neighboring boxes in the same line - - Returns: - merged_boxes(List[dict]): List of merged boxes and texts - """ - - if len(boxes) <= 1: - if len(boxes) == 1: - boxes[0]["list_words"] = [boxes[0]["word"]] - return boxes - - # merged_groups = [] - merged_lines = [] - - # sort groups based on the x_min coordinate of boxes - x_sorted_boxes = sorted(boxes, key=lambda x: np.min(x['box'][::2])) - # store indexes of boxes which are already parts of other lines - skip_idxs = set() - - i = 0 - # locate lines of boxes starting from the leftmost one - for i in range(len(x_sorted_boxes)): - if i in skip_idxs: - continue - # the rightmost box in the current line - rightmost_box_idx = i - line = [rightmost_box_idx] - for j in range(i + 1, len(x_sorted_boxes)): - if j in skip_idxs: - continue - if is_on_same_line(x_sorted_boxes[rightmost_box_idx]['box'], - x_sorted_boxes[j]['box'], min_y_overlap_ratio): - line.append(j) - skip_idxs.add(j) - rightmost_box_idx = j - - # split line into lines if the distance between two neighboring - # sub-lines' is greater than max_x_dist - # groups = [] - # line_idx = 0 - # groups.append([line[0]]) - # for k in range(1, len(line)): - # curr_box = x_sorted_boxes[line[k]] - # prev_box = x_sorted_boxes[line[k - 1]] - # dist = np.min(curr_box['box'][::2]) - np.max(prev_box['box'][::2]) - # if dist > max_x_dist: - # line_idx += 1 - # groups.append([]) - # groups[line_idx].append(line[k]) - - # # Get merged boxes - merged_line = merge_bboxes_to_group([line], x_sorted_boxes) - merged_lines.extend(merged_line) - # merged_group = merge_bboxes_to_group(groups,x_sorted_boxes) - # merged_groups.extend(merged_group) - - merged_lines = sorted(merged_lines, key=lambda x: np.min(x['box'][1::2])) - # merged_groups = sorted(merged_groups, key=lambda x: np.min(x['box'][1::2])) - return merged_lines # , merged_groups - -# REFERENCE -# https://vigneshgig.medium.com/bounding-box-sorting-algorithm-for-text-detection-and-object-detection-from-left-to-right-and-top-cf2c523c8a85 -# https://huggingface.co/spaces/tomofi/MMOCR/blame/main/mmocr/utils/box_util.py - - -def words_to_lines_mmocr(words: List[Word], *args) -> Tuple[List[Line], Optional[int]]: - bboxes = [{"box": [w.bbox[0], w.bbox[1], w.bbox[2], w.bbox[1], w.bbox[2], w.bbox[3], w.bbox[0], w.bbox[3]], - "text":w._text, "word":w} for w in words] - merged_lines = stitch_boxes_into_lines(bboxes) - merged_groups = merged_lines # TODO: fix code to return both word group and line - lwords_groups = [WordGroup(list_words=merged_box["list_words"], - text=merged_box["text"], - boundingbox=[merged_box["box"][i] for i in [0, 1, 2, -1]]) - for merged_box in merged_groups] - - llines = [Line(text=word_group._text, list_word_groups=[word_group], boundingbox=word_group._bbox_obj) - for word_group in lwords_groups] - - return llines, None # same format with the origin words_to_lines - # lines = [Line() for merged] - - -# def most_overlapping_row(rows, top, bottom, y_shift): -# max_overlap = -1 -# max_overlap_idx = -1 -# for i, row in enumerate(rows): -# row_top, row_bottom = row -# overlap = min(top + y_shift, row_top) - max(bottom + y_shift, row_bottom) -# if overlap > max_overlap: -# max_overlap = overlap -# max_overlap_idx = i -# return max_overlap_idx -def most_overlapping_row(rows, row_words, bottom, top, y_shift, max_row_size, y_overlap_threshold=0.5): - max_overlap = -1 - max_overlap_idx = -1 - overlapping_rows = [] - - for i, row in enumerate(rows): - row_bottom, row_top = row - overlap = min(bottom - y_shift[i], row_bottom) - \ - max(top - y_shift[i], row_top) - - if overlap > max_overlap: - max_overlap = overlap - max_overlap_idx = i - - # if at least overlap 1 pixel and not (overlap too much and overlap too little) - if (row_top <= bottom and row_bottom >= top) and not (bottom - top - max_overlap > max_row_size * y_overlap_threshold) and not (max_overlap < max_row_size * y_overlap_threshold): - overlapping_rows.append(i) - - # Merge overlapping rows if necessary - if len(overlapping_rows) > 1: - merge_bottom = max(rows[i][0] for i in overlapping_rows) - merge_top = min(rows[i][1] for i in overlapping_rows) - - if merge_bottom - merge_top <= max_row_size: - # Merge rows - merged_row = (merge_bottom, merge_top) - merged_words = [] - # Remove other overlapping rows - - for row_idx in overlapping_rows[:0:-1]: # [1,2,3] -> 3,2 - merged_words.extend(row_words[row_idx]) - del rows[row_idx] - del row_words[row_idx] - - rows[overlapping_rows[0]] = merged_row - row_words[overlapping_rows[0]].extend(merged_words[::-1]) - max_overlap_idx = overlapping_rows[0] - - if bottom - top - max_overlap > max_row_size * y_overlap_threshold and max_overlap < max_row_size * y_overlap_threshold: - max_overlap_idx = -1 - return max_overlap_idx - - -def stitch_boxes_into_lines_tesseract(words: list[Word], max_running_y_shift: int, - gradient: float, y_overlap_threshold: float) -> Tuple[list[list[Word]], float]: - sorted_words = sorted(words, key=lambda x: x.bbox[0]) - rows = [] - row_words = [] - max_row_size = find_maximum_without_outliers([word.height for word in sorted_words]) - running_y_shift = [] - for _i, word in enumerate(sorted_words): - bbox, _text = word.bbox, word._text - _x1, y1, _x2, y2 = bbox - bottom, top = y2, y1 - max_row_size = max(max_row_size, bottom - top) - overlap_row_idx = most_overlapping_row( - rows, row_words, bottom, top, running_y_shift, max_row_size, y_overlap_threshold) - - if overlap_row_idx == -1: # No overlapping row found - new_row = (bottom, top) - rows.append(new_row) - row_words.append([word]) - running_y_shift.append(0) - else: # Overlapping row found - row_bottom, row_top = rows[overlap_row_idx] - new_bottom = max(row_bottom, bottom) - new_top = min(row_top, top) - rows[overlap_row_idx] = (new_bottom, new_top) - row_words[overlap_row_idx].append(word) - new_shift = (top + bottom) / 2 - (row_top + row_bottom) / 2 - running_y_shift[overlap_row_idx] = min( - gradient * running_y_shift[overlap_row_idx] + (1 - gradient) * new_shift, max_running_y_shift) # update and clamp - - # Sort rows and row_texts based on the top y-coordinate - sorted_rows_data = sorted(zip(rows, row_words), key=lambda x: x[0][1]) - _sorted_rows_idx, sorted_row_words = zip(*sorted_rows_data) - # /_|<- the perpendicular line of the horizontal line and the skew line of the page - page_skew_dist = sum(running_y_shift) / len(running_y_shift) - return sorted_row_words, page_skew_dist - - -def construct_word_groups_tesseract(sorted_row_words: list[list[Word]], - max_x_dist: int, page_skew_dist: float) -> list[list[list[Word]]]: - # approximate page_skew_angle by page_skew_dist - corrected_max_x_dist = max_x_dist * abs(np.cos(page_skew_dist * DEGREE_TO_RADIAN_COEF)) - constructed_row_word_groups = [] - for row_words in sorted_row_words: - lword_groups = [] - line_idx = 0 - lword_groups.append([row_words[0]]) - for k in range(1, len(row_words)): - curr_box = row_words[k].bbox - prev_box = row_words[k - 1].bbox - dist = curr_box[0] - prev_box[2] - if dist > corrected_max_x_dist: - line_idx += 1 - lword_groups.append([]) - lword_groups[line_idx].append(row_words[k]) - constructed_row_word_groups.append(lword_groups) - return constructed_row_word_groups - - -def group_bbox_and_text(lwords: Union[list[Word], list[WordGroup]]) -> tuple[Box, tuple[str, float]]: - text = ' '.join([word._text for word in lwords]) - x_min, y_min = MAX_INT, MAX_INT - x_max, y_max = MIN_INT, MIN_INT - conf_det = 0 - conf_cls = 0 - for word in lwords: - x_max = int(max(np.max(word.bbox[::2]), x_max)) - x_min = int(min(np.min(word.bbox[::2]), x_min)) - y_max = int(max(np.max(word.bbox[1::2]), y_max)) - y_min = int(min(np.min(word.bbox[1::2]), y_min)) - conf_det += word._conf_det - conf_cls += word._conf_cls - bbox = Box(x_min, y_min, x_max, y_max, conf=conf_det / len(lwords)) - return bbox, (text, conf_cls / len(lwords)) - - -def words_to_lines_tesseract(words: List[Word], - page_width: int, max_running_y_shift_degree: int, gradient: float, max_x_dist: int, - y_overlap_threshold: float) -> Tuple[List[Line], - Optional[float]]: - max_running_y_shift = page_width * np.tan(max_running_y_shift_degree * DEGREE_TO_RADIAN_COEF) - sorted_row_words, page_skew_dist = stitch_boxes_into_lines_tesseract( - words, max_running_y_shift, gradient, y_overlap_threshold) - constructed_row_word_groups = construct_word_groups_tesseract( - sorted_row_words, max_x_dist, page_skew_dist) - llines = [] - for row in constructed_row_word_groups: - lwords_row = [] - lword_groups = [] - for word_group in row: - bbox_word_group, text_word_group = group_bbox_and_text(word_group) - lwords_row.extend(word_group) - lword_groups.append( - WordGroup( - list_words=word_group, text=text_word_group[0], - conf_cls=text_word_group[1], - boundingbox=bbox_word_group)) - bbox_line, text_line = group_bbox_and_text(lwords_row) - llines.append( - Line( - list_word_groups=lword_groups, text=text_line[0], - boundingbox=bbox_line, conf_cls=text_line[1])) - return llines, page_skew_dist - - - - -### WORDS TO WORDGROUPS ######################################################################################################################################################################################################################### - - -def merge_overlapping_word_groups( - rows: list[list[int]], - row_words: list[list[Word]], - overlapping_rows: list[int], - max_row_size: int) -> bool: - # Merge found overlapping rows if necessary - merge_top = max(rows[i][1] for i in overlapping_rows) - merge_bottom = min(rows[i][3] for i in overlapping_rows) - merge_left = min(rows[i][0] for i in overlapping_rows) - merge_right = max(rows[i][2] for i in overlapping_rows) - - if merge_top - merge_bottom <= max_row_size: - # Merge rows - merged_row = [merge_left, merge_top, merge_right, merge_bottom] - merged_words = [] - # Remove other overlapping rows - - for row_idx in overlapping_rows[:0:-1]: # [1,2,3] -> 3,2 - merged_words.extend(row_words[row_idx]) - del rows[row_idx] - del row_words[row_idx] - - rows[overlapping_rows[0]] = merged_row - row_words[overlapping_rows[0]].extend(merged_words[::-1]) - return True - return False - - -def most_overlapping_word_groups( - rows, row_words, curr_word_bbox, y_shift, max_row_size, y_overlap_threshold, max_x_dist): - max_overlap = -1 - max_overlap_idx = -1 - overlapping_rows = [] - left, top, right, bottom = curr_word_bbox - for i, row in enumerate(rows): - row_left, row_top, row_right, row_bottom = row - top_shift = top - y_shift[i] - bottom_shift = bottom - y_shift[i] - - # find the most overlapping row - overlap = min(bottom_shift, row_bottom) - max(top_shift, row_top) - if overlap > max_overlap and min(right - row_left, left - row_right) < max_x_dist: - max_overlap = overlap - max_overlap_idx = i - - # exclusive process to handle cases where there are multiple satisfying overlapping rows. For example some rows are not initially overlapping but as the appended words constantly get skewer, there is a change that the end of 1 row would reạch the beginning other row - # if (row_top <= bottom and row_bottom >= top) and not (bottom - top - max_overlap > max_row_size * y_overlap_threshold) and not (max_overlap < max_row_size * y_overlap_threshold): - if (row_top <= bottom_shift and row_bottom >= top_shift) \ - and min(right - row_left, left - row_right) < max_x_dist \ - and not (bottom - top - overlap > max_row_size * y_overlap_threshold) \ - and not (overlap < max_row_size * y_overlap_threshold): - # explain: - # (row_top <= bottom_shift and row_bottom >= top_shift) -> overlap at least 1 pixel - # not (bottom - top - overlap > max_row_size * y_overlap_threshold) -> curr_word is not too big too overlap (to exclude figures containing words) - # not (overlap < max_row_size * y_overlap_threshold) -> overlap too little should not be merged - # min(right - row_left, row_right - left) < max_x_dist -> either the curr_word is close enough to left or right of the curr_row - overlapping_rows.append(i) - - if len(overlapping_rows) > 1 and merge_overlapping_word_groups(rows, row_words, overlapping_rows, max_row_size): - max_overlap_idx = overlapping_rows[0] - if bottom - top - max_overlap > max_row_size * y_overlap_threshold and max_overlap < max_row_size * y_overlap_threshold: - max_overlap_idx = -1 - return max_overlap_idx - - -def update_overlapping_word_group_bbox(rows: list[list[int]], overlap_row_idx: int, curr_word_bbox: list[int]) -> None: - left, top, right, bottom = curr_word_bbox - row_left, row_top, row_right, row_bottom = rows[overlap_row_idx] - new_bottom = max(row_bottom, bottom) - new_top = min(row_top, top) - new_left = min(row_left, left) - new_right = max(row_right, right) - rows[overlap_row_idx] = [new_left, new_top, new_right, new_bottom] - - -def update_word_group_running_y_shift( - running_y_shift: list[float], - overlap_row_idx: int, curr_row_bbox: list[int], - curr_word_bbox: list[int], - gradient: float, max_running_y_shift: float) -> None: - _, top, _, bottom = curr_word_bbox - _, row_top, _, row_bottom = curr_row_bbox - new_shift = (top + bottom) / 2 - (row_top + row_bottom) / 2 - running_y_shift[overlap_row_idx] = min( - gradient * running_y_shift[overlap_row_idx] + (1 - gradient) * new_shift, max_running_y_shift) # update and clamp - - -def stitch_boxes_into_word_groups_tesseract(words: list[Word], - max_running_y_shift: int, gradient: float, y_overlap_threshold: float, - max_x_dist: int) -> Tuple[list[WordGroup], float]: - sorted_words = sorted(words, key=lambda x: x.bbox[0]) - rows = [] - row_words = [] - max_row_size = sorted_words[0].height - running_y_shift = [] - for word in sorted_words: - bbox: list[int] = word.bbox - max_row_size = max(max_row_size, bbox[3] - bbox[1]) - if bbox[-1] < 200 and word.text == "Nguyễn": - print("DEBUGING") - overlap_row_idx = most_overlapping_word_groups( - rows, row_words, bbox, running_y_shift, max_row_size, y_overlap_threshold, max_x_dist) - if overlap_row_idx == -1: # No overlapping row found - rows.append(bbox) # new row - row_words.append([word]) # new row_word - running_y_shift.append(0) - else: # Overlapping row found - # row_bottom, row_top = rows[overlap_row_idx] - update_overlapping_word_group_bbox(rows, overlap_row_idx, bbox) - row_words[overlap_row_idx].append(word) # update row_words - update_word_group_running_y_shift( - running_y_shift, overlap_row_idx, rows[overlap_row_idx], - bbox, gradient, max_running_y_shift) - - # Sort rows and row_texts based on the top y-coordinate - sorted_rows_data = sorted(zip(rows, row_words), key=lambda x: x[0][1]) - _sorted_rows_idx, sorted_row_words = zip(*sorted_rows_data) - lword_groups = [] - for word_group in sorted_row_words: - bbox_word_group, text_word_group = group_bbox_and_text(word_group) - lword_groups.append( - WordGroup( - list_words=word_group, text=text_word_group[0], - conf_cls=text_word_group[1], - boundingbox=bbox_word_group)) - # /_|<- the perpendicular line of the horizontal line and the skew line of the page - page_skew_dist = sum(running_y_shift) / len(running_y_shift) - - return lword_groups, page_skew_dist - - -def is_on_same_line_mmocr_tesseract(box_a: list[int], box_b: list[int], min_y_overlap_ratio: float) -> bool: - a_y_min = box_a[1] - b_y_min = box_b[1] - a_y_max = box_a[3] - b_y_max = box_b[3] - - # Make sure that box a is always the box above another - if a_y_min > b_y_min: - a_y_min, b_y_min = b_y_min, a_y_min - a_y_max, b_y_max = b_y_max, a_y_max - - if b_y_min <= a_y_max: - if min_y_overlap_ratio is not None: - sorted_y = sorted([b_y_min, b_y_max, a_y_max]) - overlap = sorted_y[1] - sorted_y[0] - min_a_overlap = (a_y_max - a_y_min) * min_y_overlap_ratio - min_b_overlap = (b_y_max - b_y_min) * min_y_overlap_ratio - return overlap >= min_a_overlap or \ - overlap >= min_b_overlap - else: - return True - return False - - -def stitch_word_groups_into_lines_mmocr_tesseract( - lword_groups: list[WordGroup], - min_y_overlap_ratio: float) -> list[Line]: - merged_lines = [] - - # sort groups based on the x_min coordinate of boxes - # store indexes of boxes which are already parts of other lines - sorted_word_groups = sorted(lword_groups, key=lambda x: x.bbox[0]) - skip_idxs = set() - - i = 0 - # locate lines of boxes starting from the leftmost one - for i in range(len(sorted_word_groups)): - if i in skip_idxs: - continue - # the rightmost box in the current line - rightmost_box_idx = i - line = [rightmost_box_idx] - for j in range(i + 1, len(sorted_word_groups)): - if j in skip_idxs: - continue - if is_on_same_line_mmocr_tesseract(sorted_word_groups[rightmost_box_idx].bbox, - sorted_word_groups[j].bbox, min_y_overlap_ratio): - line.append(j) - skip_idxs.add(j) - rightmost_box_idx = j - - lword_groups_in_line = [sorted_word_groups[k] for k in line] - bbox_line, text_line = group_bbox_and_text(lword_groups_in_line) - merged_lines.append( - Line( - list_word_groups=lword_groups_in_line, text=text_line[0], - conf_cls=text_line[1], - boundingbox=bbox_line)) - merged_lines = sorted(merged_lines, key=lambda x: x.bbox[1]) - return merged_lines - - -def words_formation_mmocr_tesseract(words: List[Word], page_width: int, word_formation_mode: str, max_running_y_shift_degree: int, gradient: float, - max_x_dist: int, y_overlap_threshold: float) -> Tuple[Union[List[WordGroup], list[Line]], - Optional[float]]: - if len(words) == 0: - return [], 0 - max_running_y_shift = page_width * np.tan(max_running_y_shift_degree * DEGREE_TO_RADIAN_COEF) - lword_groups, page_skew_dist = stitch_boxes_into_word_groups_tesseract( - words, max_running_y_shift, gradient, y_overlap_threshold, max_x_dist) - if word_formation_mode == "word_group": - return lword_groups, page_skew_dist - elif word_formation_mode == "line": - llines = stitch_word_groups_into_lines_mmocr_tesseract(lword_groups, y_overlap_threshold) - return llines, page_skew_dist - else: - raise NotImplementedError("Word formation mode not supported: {}".format(word_formation_mode)) - -############################################################################################################################################################################################################################ -############################################################################################################################################################################################################################ -### END WORDS TO LINES ALGORITHMS FROM MMOCR AND TESSERACT ############################################################################################################################################################################### -############################################################################################################################################################################################################################ -############################################################################################################################################################################################################################ - -# MIN_IOU_HEIGHT = 0.7 -# MIN_WIDTH_LINE_RATIO = 0.05 - - -# def resize_to_original( -# boundingbox, scale -# ): # resize coordinates to match size of original image -# left, top, right, bottom = boundingbox -# left *= scale[1] -# right *= scale[1] -# top *= scale[0] -# bottom *= scale[0] -# return [left, top, right, bottom] - - -# def check_iomin(word: Word, word_group: Word_group): -# min_height = min( -# word.boundingbox[3] - word.boundingbox[1], -# word_group.boundingbox[3] - word_group.boundingbox[1], -# ) -# intersect = min(word.boundingbox[3], word_group.boundingbox[3]) - max( -# word.boundingbox[1], word_group.boundingbox[1] -# ) -# if intersect / min_height > 0.7: -# return True -# return False - - -# def prepare_line(words): -# lines = [] -# visited = [False] * len(words) -# for id_word, word in enumerate(words): -# if word.invalid_size() == 0: -# continue -# new_line = True -# for i in range(len(lines)): -# if ( -# lines[i].in_same_line(word) and not visited[id_word] -# ): # check if word is in the same line with lines[i] -# lines[i].merge_word(word) -# new_line = False -# visited[id_word] = True - -# if new_line == True: -# new_line = Line() -# new_line.merge_word(word) -# lines.append(new_line) - -# # print(len(lines)) -# # sort line from top to bottom according top coordinate -# lines.sort(key=lambda x: x.boundingbox[1]) -# return lines - - -# def __create_word_group(word, word_group_id): -# new_word_group_ = Word_group() -# new_word_group_.list_words = list() -# new_word_group_.word_group_id = word_group_id -# new_word_group_.add_word(word) - -# return new_word_group_ - - -# def __sort_line(line): -# line.list_word_groups.sort( -# key=lambda x: x.boundingbox[0] -# ) # sort word in lines from left to right - -# return line - - -# def __merge_text_for_line(line): -# line.text = "" -# for word in line.list_word_groups: -# line.text += " " + word.text - -# return line - - -# def __update_list_word_groups(line, word_group_id, word_id, line_width): - -# old_list_word_group = line.list_word_groups -# list_word_groups = [] - -# inital_word_group = __create_word_group( -# old_list_word_group[0], word_group_id) -# old_list_word_group[0].word_id = word_id -# list_word_groups.append(inital_word_group) -# word_group_id += 1 -# word_id += 1 - -# for word in old_list_word_group[1:]: -# check_word_group = True -# word.word_id = word_id -# word_id += 1 - -# if ( -# (not list_word_groups[-1].text.endswith(":")) -# and ( -# (word.boundingbox[0] - list_word_groups[-1].boundingbox[2]) -# / line_width -# < MIN_WIDTH_LINE_RATIO -# ) -# and check_iomin(word, list_word_groups[-1]) -# ): -# list_word_groups[-1].add_word(word) -# check_word_group = False - -# if check_word_group: -# new_word_group = __create_word_group(word, word_group_id) -# list_word_groups.append(new_word_group) -# word_group_id += 1 -# line.list_word_groups = list_word_groups -# return line, word_group_id, word_id - - -# def construct_word_groups_in_each_line(lines): -# line_id = 0 -# word_group_id = 0 -# word_id = 0 -# for i in range(len(lines)): -# if len(lines[i].list_word_groups) == 0: -# continue - -# # left, top ,right, bottom -# line_width = lines[i].boundingbox[2] - \ -# lines[i].boundingbox[0] # right - left -# line_width = 1 # TODO: to remove -# lines[i] = __sort_line(lines[i]) - -# # update text for lines after sorting -# lines[i] = __merge_text_for_line(lines[i]) - -# lines[i], word_group_id, word_id = __update_list_word_groups( -# lines[i], -# word_group_id, -# word_id, -# line_width) -# lines[i].update_line_id(line_id) -# line_id += 1 -# return lines - - -# def words_to_lines(words, check_special_lines=True): # words is list of Word instance -# # sort word by top -# words.sort(key=lambda x: (x.boundingbox[1], x.boundingbox[0])) -# # words.sort(key=lambda x: (sum(x.bbox))) -# number_of_word = len(words) -# # print(number_of_word) -# # sort list words to list lines, which have not contained word_group yet -# lines = prepare_line(words) - -# # construct word_groups in each line -# lines = construct_word_groups_in_each_line(lines) -# return lines, number_of_word - - -# def near(word_group1: Word_group, word_group2: Word_group): -# min_height = min( -# word_group1.boundingbox[3] - word_group1.boundingbox[1], -# word_group2.boundingbox[3] - word_group2.boundingbox[1], -# ) -# overlap = min(word_group1.boundingbox[3], word_group2.boundingbox[3]) - max( -# word_group1.boundingbox[1], word_group2.boundingbox[1] -# ) - -# if overlap > 0: -# return True -# if abs(overlap / min_height) < 1.5: -# print("near enough", abs(overlap / min_height), overlap, min_height) -# return True -# return False - - -# def calculate_iou_and_near(wg1: Word_group, wg2: Word_group): -# min_height = min( -# wg1.boundingbox[3] - -# wg1.boundingbox[1], wg2.boundingbox[3] - wg2.boundingbox[1] -# ) -# overlap = min(wg1.boundingbox[3], wg2.boundingbox[3]) - max( -# wg1.boundingbox[1], wg2.boundingbox[1] -# ) -# iou = overlap / min_height -# distance = min( -# abs(wg1.boundingbox[0] - wg2.boundingbox[2]), -# abs(wg1.boundingbox[2] - wg2.boundingbox[0]), -# ) -# if iou > 0.7 and distance < 0.5 * (wg1.boundingboxp[2] - wg1.boundingbox[0]): -# return True -# return False - - -# def construct_word_groups_to_kie_label(list_word_groups: list): -# kie_dict = dict() -# for wg in list_word_groups: -# if wg.kie_label == "other": -# continue -# if wg.kie_label not in kie_dict: -# kie_dict[wg.kie_label] = [wg] -# else: -# kie_dict[wg.kie_label].append(wg) - -# new_dict = dict() -# for key, value in kie_dict.items(): -# if len(value) == 1: -# new_dict[key] = value -# continue - -# value.sort(key=lambda x: x.boundingbox[1]) -# new_dict[key] = value -# return new_dict - - -# def invoice_construct_word_groups_to_kie_label(list_word_groups: list): -# kie_dict = dict() - -# for wg in list_word_groups: -# if wg.kie_label == "other": -# continue -# if wg.kie_label not in kie_dict: -# kie_dict[wg.kie_label] = [wg] -# else: -# kie_dict[wg.kie_label].append(wg) - -# return kie_dict - - -# def postprocess_total_value(kie_dict): -# if "total_in_words_value" not in kie_dict: -# return kie_dict - -# for k, value in kie_dict.items(): -# if k == "total_in_words_value": -# continue -# l = [] -# for v in value: -# if v.boundingbox[3] <= kie_dict["total_in_words_value"][0].boundingbox[3]: -# l.append(v) - -# if len(l) != 0: -# kie_dict[k] = l - -# return kie_dict - - -# def postprocess_tax_code_value(kie_dict): -# if "buyer_tax_code_value" in kie_dict or "seller_tax_code_value" not in kie_dict: -# return kie_dict - -# kie_dict["buyer_tax_code_value"] = [] -# for v in kie_dict["seller_tax_code_value"]: -# if "buyer_name_key" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_key"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_key"][0]) -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# continue - -# if "buyer_name_value" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_value"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_value"][0]) -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# continue - -# if "buyer_address_value" in kie_dict and near( -# kie_dict["buyer_address_value"][0], v -# ): -# kie_dict["buyer_tax_code_value"].append(v) -# return kie_dict - - -# def postprocess_tax_code_key(kie_dict): -# if "buyer_tax_code_key" in kie_dict or "seller_tax_code_key" not in kie_dict: -# return kie_dict -# kie_dict["buyer_tax_code_key"] = [] -# for v in kie_dict["seller_tax_code_key"]: -# if "buyer_name_key" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_key"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_key"][0]) -# ): -# kie_dict["buyer_tax_code_key"].append(v) -# continue - -# if "buyer_name_value" in kie_dict and ( -# v.boundingbox[3] > kie_dict["buyer_name_value"][0].boundingbox[3] -# or near(v, kie_dict["buyer_name_value"][0]) -# ): -# kie_dict["buyer_tax_code_key"].append(v) -# continue - -# if "buyer_address_value" in kie_dict and near( -# kie_dict["buyer_address_value"][0], v -# ): -# kie_dict["buyer_tax_code_key"].append(v) - -# return kie_dict - - -# def invoice_postprocess(kie_dict: dict): -# # all keys or values which are below total_in_words_value will be thrown away -# kie_dict = postprocess_total_value(kie_dict) -# kie_dict = postprocess_tax_code_value(kie_dict) -# kie_dict = postprocess_tax_code_key(kie_dict) -# return kie_dict - - -# def throw_overlapping_words(list_words): -# new_list = [list_words[0]] -# for word in list_words: -# overlap = False -# area = (word.boundingbox[2] - word.boundingbox[0]) * ( -# word.boundingbox[3] - word.boundingbox[1] -# ) -# for word2 in new_list: -# area2 = (word2.boundingbox[2] - word2.boundingbox[0]) * ( -# word2.boundingbox[3] - word2.boundingbox[1] -# ) -# xmin_intersect = max(word.boundingbox[0], word2.boundingbox[0]) -# xmax_intersect = min(word.boundingbox[2], word2.boundingbox[2]) -# ymin_intersect = max(word.boundingbox[1], word2.boundingbox[1]) -# ymax_intersect = min(word.boundingbox[3], word2.boundingbox[3]) -# if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect: -# continue - -# area_intersect = (xmax_intersect - xmin_intersect) * ( -# ymax_intersect - ymin_intersect -# ) -# if area_intersect / area > 0.7 or area_intersect / area2 > 0.7: -# overlap = True -# if overlap == False: -# new_list.append(word) -# return new_list - - -# def check_iou(box1: Word, box2: Box, threshold=0.9): -# area1 = (box1.boundingbox[2] - box1.boundingbox[0]) * ( -# box1.boundingbox[3] - box1.boundingbox[1] -# ) -# area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin) -# xmin_intersect = max(box1.boundingbox[0], box2.xmin) -# ymin_intersect = max(box1.boundingbox[1], box2.ymin) -# xmax_intersect = min(box1.boundingbox[2], box2.xmax) -# ymax_intersect = min(box1.boundingbox[3], box2.ymax) -# if xmax_intersect < xmin_intersect or ymax_intersect < ymin_intersect: -# area_intersect = 0 -# else: -# area_intersect = (xmax_intersect - xmin_intersect) * ( -# ymax_intersect - ymin_intersect -# ) -# union = area1 + area2 - area_intersect -# iou = area_intersect / union -# if iou > threshold: -# return True -# return False diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/main.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/main.py deleted file mode 100644 index db5f3b6..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/main.py +++ /dev/null @@ -1,147 +0,0 @@ -import os -import glob -import cv2 -import json -import argparse -import numpy as np -from tqdm import tqdm -from PIL import Image -from datetime import datetime -from sdsvkvu.sources.kvu import KVUEngine -from sdsvkvu.sources.utils import export_kvu_outputs, export_sbt_outputs, draw_kvu_outputs -from sdsvkvu.utils.utils import create_dir, write_to_json, pdf2img -from sdsvkvu.utils.query.vat import export_kvu_for_VAT_invoice, merged_kvu_for_VAT_invoice_for_multi_pages -from sdsvkvu.utils.query.sbt import export_kvu_for_SDSAP, merged_kvu_for_SDSAP_for_multi_pages -from sdsvkvu.utils.query.vtb import export_kvu_for_vietin, merged_kvu_for_vietin_for_multi_pages -from sdsvkvu.utils.query.all import export_kvu_for_all, merged_kvu_for_all_for_multi_pages -from sdsvkvu.utils.query.manulife import export_kvu_for_manulife, merged_kvu_for_manulife_for_multi_pages -from sdsvkvu.utils.query.sbt_v2 import export_kvu_for_SBT, merged_kvu_for_SBT_for_multi_pages - - -def get_args(): - args = argparse.ArgumentParser(description='Main file') - args.add_argument('--img_dir', type=str, required=True, - help='path to input image/directory file') - args.add_argument('--save_dir', type=str, required=True, - help='path to save directory') - args.add_argument('--doc_type', type=str, default="vat", - help='type of document') - args.add_argument('--export_img', type=bool, default=False, - help='export image of output visualization') - args.add_argument('--kvu_params', type=str, required=False, default="") - return args.parse_args() - - -def load_engine(kwargs) -> KVUEngine: - print('[INFO] Loading Key-Value Understanding model ...') - if not isinstance(kwargs, dict): - kwargs = json.loads(kwargs) if kwargs else {} - engine = KVUEngine(**kwargs) - print("[INFO] Loaded model") - print("[INFO] KVU engine settings: \n", engine._settings) - return engine - - -def process_img(img_path: str, save_dir: str, engine: KVUEngine, export_all: bool, option: str) -> dict: - assert (engine._settings.mode == 4 and option == "sbt_v2") \ - or (engine._settings.mode != 4 and option != "sbt_v2"), \ - "[ERROR] Mode (4) has just supported option \"sbt_v2\"" - - print("="*5, os.path.basename(img_path)) - create_dir(save_dir) - fname, img_ext = os.path.splitext(os.path.basename(img_path)) - out_ext = ".json" - image, lbbox, lwords, pr_class_words, pr_relations = engine.predict(img_path) - - if len(lbbox) != 1: - raise ValueError( - f"Not support to predict each separated window: {len(lbbox)}" - ) - - for i in range(len(lbbox)): - if engine._settings.mode in range(4): - raw_outputs = export_kvu_outputs(lwords[i], lbbox[i], pr_class_words[i], pr_relations[i], engine._settings.class_names) - elif engine._settings.mode == 4: - raw_outputs = export_sbt_outputs(lwords[i], lbbox[i], pr_class_words[i], pr_relations[i], engine._settings.class_names) - - if export_all: - save_path = os.path.join(save_dir, 'kvu_results') - create_dir(save_path) - write_to_json(os.path.join(save_path, fname + out_ext), raw_outputs) - # image = Image.open(img_path) - image = np.array(image) - image = draw_kvu_outputs(image, lbbox[i], pr_class_words[i], pr_relations[i], class_names=engine._settings.class_names) - cv2.imwrite(os.path.join(save_path, fname + img_ext), image) - - - if option == "vat": - outputs = export_kvu_for_VAT_invoice(raw_outputs) - elif option == "sbt": - outputs = export_kvu_for_SDSAP(raw_outputs) - elif option == "vtb": - outputs = export_kvu_for_vietin(raw_outputs) - elif option == "manulife": - outputs = export_kvu_for_manulife(raw_outputs) - elif option == "sbt_v2": - outputs = export_kvu_for_SBT(raw_outputs) - else: - outputs = export_kvu_for_all(raw_outputs) - write_to_json(os.path.join(save_dir, fname + out_ext), outputs) - return outputs - - -def process_pdf(pdf_path: str, save_dir: str, engine: KVUEngine, export_all: bool, option: str, n_pages: int = -1) -> dict: - out_ext = ".json" - fname, pdf_ext = os.path.splitext(os.path.basename(pdf_path)) - img_dirname = '_'.join([os.path.basename(os.path.dirname(pdf_path)), fname]) - img_save_dir = os.path.join(save_dir, img_dirname) - create_dir(img_save_dir) - list_img_files = pdf2img(pdf_path, img_save_dir, n_pages=n_pages, return_fname=True) - outputs = [] - for img_path in list_img_files: - print("=====", os.path.basename(img_path)) - _outputs = process_img(img_path, img_save_dir, engine, export_all=export_all, option=option) - outputs.append(_outputs) - if option == "vat": - outputs = merged_kvu_for_VAT_invoice_for_multi_pages(outputs) - elif option == "sbt": - outputs = merged_kvu_for_SDSAP_for_multi_pages(outputs) - elif option == "vtb": - outputs = merged_kvu_for_vietin_for_multi_pages(outputs) - elif option == "manulife": - outputs = merged_kvu_for_manulife_for_multi_pages(outputs) - elif option == "sbt_v2": - outputs = merged_kvu_for_SBT_for_multi_pages(outputs) - else: - outputs = merged_kvu_for_all_for_multi_pages(outputs) - write_to_json(os.path.join(save_dir, fname + out_ext), outputs) - return outputs - - -def process_dir(dir_path: str, save_dir: str, engine: KVUEngine, export_all: bool, option: str, dir_level: int = 0) -> None: - list_images = [] - for ext in ['JPG', 'PNG', 'jpeg', 'jpg', 'png', 'pdf']: - list_images += glob.glob(os.path.join(dir_path, f"{'*/'*dir_level}*.{ext}")) - print('No. images:', len(list_images)) - for file_path in tqdm(list_images): - if os.path.splitext(file_path)[1] == ".pdf": - outputs = process_pdf(file_path, save_dir, engine, export_all=export_all, option=option, n_pages=-1) - else: - outputs = process_img(file_path, save_dir, engine, export_all=export_all, option=option) - - -def Predictor_KVU(img: str, save_dir: str, engine: KVUEngine) -> dict: - curr_datetime = datetime.now().strftime('%Y-%m-%d %H-%M-%S') - image_path = "/home/thucpd/thucpd/PV2-2023/tmp_image/{}.jpg".format(curr_datetime) - cv2.imwrite(image_path, img) - vat_outputs = process_img(image_path, save_dir, engine, export_all=False, option="vat") - return vat_outputs - - -if __name__ == "__main__": - args = get_args() - engine = load_engine(args.kvu_params) - # vat_outputs = process_img(args.img_dir, args.save_dir, engine, export_all=True, option="vat") - # vat_outputs = process_pdf(args.img_dir, args.save_dir, engine, export_all=True, option="vat") - process_dir(args.img_dir, args.save_dir, engine, export_all=args.export_img, option=args.doc_type) - print('[INFO] Done') diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/__init__.py deleted file mode 100644 index d4e48aa..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/__init__.py +++ /dev/null @@ -1,45 +0,0 @@ - -import os -import torch -from sdsvkvu.model.kvu_model import KVUModel -from sdsvkvu.model.combined_model import ComKVUModel -from sdsvkvu.model.document_kvu_model import DocKVUModel -from sdsvkvu.model.sbt_model import SBTModel - -def get_model(cfg): - if cfg.mode == 0 or cfg.mode == 1: - model = ComKVUModel(cfg=cfg) - elif cfg.mode == 2: - model = KVUModel(cfg=cfg) - elif cfg.mode == 3: - model = DocKVUModel(cfg=cfg) - elif cfg.mode == 4: - model = SBTModel(cfg=cfg) - else: - raise ValueError(f'[ERROR] Model mode of {cfg.mode} is not supported') - return model - -def load_checkpoint(ckpt_path, model, key_include): - assert os.path.exists(ckpt_path) == True, f"Ckpt path at {ckpt_path} not exist!" - state_dict = torch.load(ckpt_path, 'cpu')['state_dict'] - for key in list(state_dict.keys()): - if f'.{key_include}.' not in key: - del state_dict[key] - else: - state_dict[key[4:].replace(key_include + '.', "")] = state_dict[key] # remove net.something. - del state_dict[key] - model.load_state_dict(state_dict, strict=True) - print(f"Load checkpoint at {ckpt_path}") - return model - -def load_model_weight(net, pretrained_model_file): - pretrained_model_state_dict = torch.load(pretrained_model_file, map_location="cpu")[ - "state_dict" - ] - new_state_dict = {} - for k, v in pretrained_model_state_dict.items(): - new_k = k - if new_k.startswith("net."): - new_k = new_k[len("net.") :] - new_state_dict[new_k] = v - net.load_state_dict(new_state_dict) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/combined_model.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/combined_model.py deleted file mode 100644 index 5c32797..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/combined_model.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import torch -from torch import nn - -from sdsvkvu.model.kvu_model import KVUModel -# from model import load_checkpoint - - -class ComKVUModel(KVUModel): - def __init__(self, cfg): - super().__init__(cfg) - - self.model_cfg = cfg.model - self.freeze = cfg.train.freeze - self.finetune_only = cfg.train.finetune_only - - self._get_backbones(self.model_cfg.backbone) - self._create_head() - - # if os.path.exists(self.model_cfg.ckpt_model_file): - # self.backbone_layoutxlm = load_checkpoint(self.model_cfg.ckpt_model_file, self.backbone_layoutxlm, 'backbone_layoutxlm') - # self.itc_layer = load_checkpoint(self.model_cfg.ckpt_model_file, self.itc_layer, 'itc_layer') - # self.stc_layer = load_checkpoint(self.model_cfg.ckpt_model_file, self.stc_layer, 'stc_layer') - # self.relation_layer = load_checkpoint(self.model_cfg.ckpt_model_file, self.relation_layer, 'relation_layer') - # self.relation_layer_from_key = load_checkpoint(self.model_cfg.ckpt_model_file, self.relation_layer_from_key, 'relation_layer_from_key') - - self.loss_func = nn.CrossEntropyLoss() - - # if self.freeze: - # for name, param in self.named_parameters(): - # if 'backbone' in name: - # param.requires_grad = False - # if self.finetune_only == 'EE': - # for name, param in self.named_parameters(): - # if 'itc_layer' not in name and 'stc_layer' not in name: - # param.requires_grad = False - # if self.finetune_only == 'EL': - # for name, param in self.named_parameters(): - # if 'relation_layer' not in name or 'relation_layer_from_key' in name: - # param.requires_grad = False - # if self.finetune_only == 'ELK': - # for name, param in self.named_parameters(): - # if 'relation_layer_from_key' not in name: - # param.requires_grad = False - - - def forward(self, batch): - image = batch["image"] - input_ids_layoutxlm = batch["input_ids_layoutxlm"] - bbox = batch["bbox"] - attention_mask_layoutxlm = batch["attention_mask_layoutxlm"] - - backbone_outputs_layoutxlm = self.backbone_layoutxlm( - image=image, input_ids=input_ids_layoutxlm, bbox=bbox, attention_mask=attention_mask_layoutxlm) - - last_hidden_states = backbone_outputs_layoutxlm.last_hidden_state[:, :512, :] - last_hidden_states = last_hidden_states.transpose(0, 1).contiguous() - - itc_outputs = self.itc_layer(last_hidden_states).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer(last_hidden_states, last_hidden_states).squeeze(0) - el_outputs = self.relation_layer(last_hidden_states, last_hidden_states).squeeze(0) - el_outputs_from_key = self.relation_layer_from_key(last_hidden_states, last_hidden_states).squeeze(0) - head_outputs = {"itc_outputs": itc_outputs, "stc_outputs": stc_outputs, - "el_outputs": el_outputs, "el_outputs_from_key": el_outputs_from_key} - - loss = 0.0 - if any(['labels' in key for key in batch.keys()]): - loss = self._get_loss(head_outputs, batch) - - return head_outputs, loss - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/document_kvu_model.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/document_kvu_model.py deleted file mode 100644 index b278fb5..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/document_kvu_model.py +++ /dev/null @@ -1,162 +0,0 @@ -import torch -from torch import nn -from sdsvkvu.model.relation_extractor import RelationExtractor -from sdsvkvu.model.kvu_model import KVUModel -# from model import load_checkpoint - - -class DocKVUModel(KVUModel): - def __init__(self, cfg): - super().__init__(cfg) - - self.model_cfg = cfg.model - self.freeze = cfg.train.freeze - self.train_cfg = cfg.train - self.n_classes = len(self.model_cfg.class_names) - - self._get_backbones(self.model_cfg.backbone) - - self._create_head() - - self.loss_func = nn.CrossEntropyLoss() - - def _create_head(self): - self.backbone_hidden_size = self.backbone_config.hidden_size - self.head_hidden_size = self.model_cfg.head_hidden_size - self.head_p_dropout = self.model_cfg.head_p_dropout - # self.n_classes = self.model_cfg.n_classes + 1 - self.repr_hiddent_size = self.backbone_hidden_size - - # (1) Initial token classification - self.itc_layer = nn.Sequential( - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.backbone_hidden_size), - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.n_classes), - ) - # (2) Subsequent token classification - self.stc_layer = RelationExtractor( - n_relations=1, #1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # (3) Linking token classification - self.relation_layer = RelationExtractor( - n_relations=1, #1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # (4) Linking token classification - self.relation_layer_from_key = RelationExtractor( - n_relations=1, #1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # Classfication Layer for whole document - # (1) Initial token classification - self.itc_layer_document = nn.Sequential( - nn.Dropout(self.head_p_dropout), - nn.Linear(self.repr_hiddent_size, self.repr_hiddent_size), - nn.Dropout(self.head_p_dropout), - nn.Linear(self.repr_hiddent_size, self.n_classes), - ) - # (2) Subsequent token classification - self.stc_layer_document = RelationExtractor( - n_relations=1, - backbone_hidden_size=self.repr_hiddent_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - # (3) Linking token classification - self.relation_layer_document = RelationExtractor( - n_relations=1, - backbone_hidden_size=self.repr_hiddent_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - # (4) Linking token classification - self.relation_layer_from_key_document = RelationExtractor( - n_relations=1, - backbone_hidden_size=self.repr_hiddent_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - self.itc_layer.apply(self._init_weight) - self.stc_layer.apply(self._init_weight) - self.relation_layer.apply(self._init_weight) - self.relation_layer_from_key.apply(self._init_weight) - - self.itc_layer_document.apply(self._init_weight) - self.stc_layer_document.apply(self._init_weight) - self.relation_layer_document.apply(self._init_weight) - self.relation_layer_from_key_document.apply(self._init_weight) - - - def forward(self, batches): - head_outputs_list = [] - loss = 0.0 - for batch in batches["windows"]: - image = batch["image"] - input_ids = batch["input_ids_layoutxlm"] - bbox = batch["bbox"] - attention_mask = batch["attention_mask_layoutxlm"] - - if self.freeze: - for param in self.backbone.parameters(): - param.requires_grad = False - - if self.model_cfg.backbone == 'layoutxlm': - backbone_outputs = self.backbone( - image=image, input_ids=input_ids, bbox=bbox, attention_mask=attention_mask - ) - else: - backbone_outputs = self.backbone(input_ids, attention_mask=attention_mask) - - last_hidden_states = backbone_outputs.last_hidden_state[:, :512, :] - last_hidden_states = last_hidden_states.transpose(0, 1).contiguous() - - itc_outputs = self.itc_layer(last_hidden_states).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer(last_hidden_states, last_hidden_states).squeeze(0) - el_outputs = self.relation_layer(last_hidden_states, last_hidden_states).squeeze(0) - el_outputs_from_key = self.relation_layer_from_key(last_hidden_states, last_hidden_states).squeeze(0) - - window_repr = last_hidden_states.transpose(0, 1).contiguous() - - head_outputs = {"window_repr": window_repr, - "itc_outputs": itc_outputs, - "stc_outputs": stc_outputs, - "el_outputs": el_outputs, - "el_outputs_from_key": el_outputs_from_key} - - if any(['labels' in key for key in batch.keys()]): - loss += self._get_loss(head_outputs, batch) - - head_outputs_list.append(head_outputs) - - batch = batches["documents"] - - document_repr = torch.cat([w['window_repr'] for w in head_outputs_list], dim=1) - document_repr = document_repr.transpose(0, 1).contiguous() - - itc_outputs = self.itc_layer_document(document_repr).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer_document(document_repr, document_repr).squeeze(0) - el_outputs = self.relation_layer_document(document_repr, document_repr).squeeze(0) - el_outputs_from_key = self.relation_layer_from_key_document(document_repr, document_repr).squeeze(0) - - head_outputs = {"itc_outputs": itc_outputs, - "stc_outputs": stc_outputs, - "el_outputs": el_outputs, - "el_outputs_from_key": el_outputs_from_key} - - if any(['labels' in key for key in batch.keys()]): - loss += self._get_loss(head_outputs, batch) - - return head_outputs, loss - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/kvu_model.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/kvu_model.py deleted file mode 100644 index 0277d1b..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/kvu_model.py +++ /dev/null @@ -1,300 +0,0 @@ -import os -import torch -from torch import nn -from pathlib import Path -from transformers import ( - LayoutLMConfig, - LayoutLMModel, - LayoutLMTokenizer, -) -from transformers import ( - LayoutLMv2Config, - LayoutLMv2Model, - LayoutLMv2FeatureExtractor, - LayoutXLMTokenizer, -) -from transformers import ( - XLMRobertaConfig, - AutoTokenizer, - XLMRobertaModel -) - -# from model import load_checkpoint -from sdsvkvu.sources.utils import merged_token_embeddings -from sdsvkvu.model.relation_extractor import RelationExtractor - - -class KVUModel(nn.Module): - def __init__(self, cfg): - super().__init__() - - self.model_cfg = cfg.model - self.freeze = cfg.train.freeze - self.finetune_only = cfg.train.finetune_only - self.n_classes = len(self.model_cfg.class_names) - - self._get_backbones(self.model_cfg.backbone) - self._create_head() - - # if (cfg.stage == 2) and (os.path.exists(self.model_cfg.ckpt_model_file)): - # self.backbone_layoutxlm = load_checkpoint(self.model_cfg.ckpt_model_file, self.backbone_layoutxlm, 'backbone_layoutxlm') - - self._create_head() - self.loss_func = nn.CrossEntropyLoss() - - if self.freeze: - for name, param in self.named_parameters(): - if "backbone" in name: - param.requires_grad = False - - def _create_head(self): - self.backbone_hidden_size = 768 - self.head_hidden_size = self.model_cfg.head_hidden_size - self.head_p_dropout = self.model_cfg.head_p_dropout - # self.n_classes = self.model_cfg.n_classes + 1 - - # (1) Initial token classification - self.itc_layer = nn.Sequential( - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.backbone_hidden_size), - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.n_classes), - ) - # (2) Subsequent token classification - self.stc_layer = RelationExtractor( - n_relations=1, # 1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # (3) Linking token classification - self.relation_layer = RelationExtractor( - n_relations=1, # 1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # (4) Linking token classification - self.relation_layer_from_key = RelationExtractor( - n_relations=1, # 1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - self.itc_layer.apply(self._init_weight) - self.stc_layer.apply(self._init_weight) - self.relation_layer.apply(self._init_weight) - - # def _get_backbones(self, config_type): - # self.tokenizer_layoutxlm = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base') - # self.feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False) - # self.backbone_layoutxlm = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base') - - def _get_backbones(self, config_type): - configs = { - "layoutlm": { - "config": LayoutLMConfig, - "tokenizer": LayoutLMTokenizer, - "backbone": LayoutLMModel, - "feature_extrator": LayoutLMv2FeatureExtractor, - }, - "layoutxlm": { - "config": LayoutLMv2Config, - "tokenizer": LayoutXLMTokenizer, - "backbone": LayoutLMv2Model, - "feature_extrator": LayoutLMv2FeatureExtractor, - }, - "xlm-roberta": { - "config": XLMRobertaConfig, - "tokenizer": AutoTokenizer, - "backbone": XLMRobertaModel, - "feature_extrator": LayoutLMv2FeatureExtractor, - }, - } - - self.backbone_config = configs[config_type]["config"].from_pretrained( - self.model_cfg.pretrained_model_path - ) - if config_type != "xlm-roberta": - self.tokenizer = configs[config_type]["tokenizer"].from_pretrained( - self.model_cfg.pretrained_model_path - ) - else: - self.tokenizer = configs[config_type]["tokenizer"].from_pretrained( - self.model_cfg.pretrained_model_path, use_fast=False - ) - self.feature_extractor = configs[config_type]["feature_extrator"]( - apply_ocr=False - ) - self.backbone = configs[config_type]["backbone"].from_pretrained( - self.model_cfg.pretrained_model_path - ) - - @staticmethod - def _init_weight(module): - init_std = 0.02 - if isinstance(module, nn.Linear): - nn.init.normal_(module.weight, 0.0, init_std) - if module.bias is not None: - nn.init.constant_(module.bias, 0.0) - elif isinstance(module, nn.LayerNorm): - nn.init.normal_(module.weight, 1.0, init_std) - if module.bias is not None: - nn.init.constant_(module.bias, 0.0) - - def forward(self, lbatches): - windows = lbatches["windows"] - token_embeddings_windows = [] - lvalids = [] - loverlaps = [] - - for i, batch in enumerate(windows): - batch = { - k: v.cuda() for k, v in batch.items() if k not in ("img_path", "words") - } - image = batch["image"] - input_ids_layoutxlm = batch["input_ids_layoutxlm"] - bbox = batch["bbox"] - attention_mask_layoutxlm = batch["attention_mask_layoutxlm"] - - backbone_outputs_layoutxlm = self.backbone_layoutxlm( - image=image, - input_ids=input_ids_layoutxlm, - bbox=bbox, - attention_mask=attention_mask_layoutxlm, - ) - - last_hidden_states_layoutxlm = backbone_outputs_layoutxlm.last_hidden_state[ - :, :512, : - ] - - lvalids.append(batch["len_valid_tokens"]) - loverlaps.append(batch["len_overlap_tokens"]) - token_embeddings_windows.append(last_hidden_states_layoutxlm) - - token_embeddings = merged_token_embeddings( - token_embeddings_windows, loverlaps, lvalids, average=False - ) - - token_embeddings = token_embeddings.transpose(0, 1).contiguous().cuda() - itc_outputs = self.itc_layer(token_embeddings).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer(token_embeddings, token_embeddings).squeeze(0) - el_outputs = self.relation_layer(token_embeddings, token_embeddings).squeeze(0) - el_outputs_from_key = self.relation_layer_from_key( - token_embeddings, token_embeddings - ).squeeze(0) - head_outputs = { - "itc_outputs": itc_outputs, - "stc_outputs": stc_outputs, - "el_outputs": el_outputs, - "el_outputs_from_key": el_outputs_from_key, - "embedding_tokens": token_embeddings.transpose(0, 1) - .contiguous() - .detach() - .cpu() - .numpy(), - } - - loss = 0.0 - if any(["labels" in key for key in lbatches.keys()]): - labels = { - k: v.cuda() - for k, v in lbatches["documents"].items() - if k not in ("img_path") - } - loss = self._get_loss(head_outputs, labels) - - return head_outputs, loss - - def _get_loss(self, head_outputs, batch): - itc_outputs = head_outputs["itc_outputs"] - stc_outputs = head_outputs["stc_outputs"] - el_outputs = head_outputs["el_outputs"] - el_outputs_from_key = head_outputs["el_outputs_from_key"] - - itc_loss = self._get_itc_loss(itc_outputs, batch) - stc_loss = self._get_stc_loss(stc_outputs, batch) - el_loss = self._get_el_loss(el_outputs, batch) - el_loss_from_key = self._get_el_loss(el_outputs_from_key, batch, from_key=True) - - loss = itc_loss + stc_loss + el_loss + el_loss_from_key - - return loss - - def _get_itc_loss(self, itc_outputs, batch): - itc_mask = batch["are_box_first_tokens"].view(-1) - - itc_logits = itc_outputs.view(-1, self.model_cfg.n_classes + 1) - itc_logits = itc_logits[itc_mask] - - itc_labels = batch["itc_labels"].view(-1) - itc_labels = itc_labels[itc_mask] - - itc_loss = self.loss_func(itc_logits, itc_labels) - - return itc_loss - - def _get_stc_loss(self, stc_outputs, batch): - inv_attention_mask = 1 - batch["attention_mask_layoutxlm"] - - bsz, max_seq_length = inv_attention_mask.shape - device = inv_attention_mask.device - - invalid_token_mask = torch.cat( - [inv_attention_mask, torch.zeros([bsz, 1]).to(device)], axis=1 - ).bool() - - stc_outputs.masked_fill_(invalid_token_mask[:, None, :], -10000.0) - - self_token_mask = ( - torch.eye(max_seq_length, max_seq_length + 1).to(device).bool() - ) - stc_outputs.masked_fill_(self_token_mask[None, :, :], -10000.0) - - stc_mask = batch["attention_mask_layoutxlm"].view(-1).bool() - stc_logits = stc_outputs.view(-1, max_seq_length + 1) - stc_logits = stc_logits[stc_mask] - - stc_labels = batch["stc_labels"].view(-1) - stc_labels = stc_labels[stc_mask] - - stc_loss = self.loss_func(stc_logits, stc_labels) - - return stc_loss - - def _get_el_loss(self, el_outputs, batch, from_key=False): - bsz, max_seq_length = batch["attention_mask_layoutxlm"].shape - - device = batch["attention_mask_layoutxlm"].device - - self_token_mask = ( - torch.eye(max_seq_length, max_seq_length + 1).to(device).bool() - ) - - box_first_token_mask = torch.cat( - [ - (batch["are_box_first_tokens"] == False), - torch.zeros([bsz, 1], dtype=torch.bool).to(device), - ], - axis=1, - ) - el_outputs.masked_fill_(box_first_token_mask[:, None, :], -10000.0) - el_outputs.masked_fill_(self_token_mask[None, :, :], -10000.0) - - mask = batch["are_box_first_tokens"].view(-1) - - logits = el_outputs.view(-1, max_seq_length + 1) - logits = logits[mask] - - if from_key: - el_labels = batch["el_labels_from_key"] - else: - el_labels = batch["el_labels"] - labels = el_labels.view(-1) - labels = labels[mask] - - loss = self.loss_func(logits, labels) - return loss diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/relation_extractor.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/relation_extractor.py deleted file mode 100644 index 40a169e..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/relation_extractor.py +++ /dev/null @@ -1,48 +0,0 @@ -import torch -from torch import nn - - -class RelationExtractor(nn.Module): - def __init__( - self, - n_relations, - backbone_hidden_size, - head_hidden_size, - head_p_dropout=0.1, - ): - super().__init__() - - self.n_relations = n_relations - self.backbone_hidden_size = backbone_hidden_size - self.head_hidden_size = head_hidden_size - self.head_p_dropout = head_p_dropout - - self.drop = nn.Dropout(head_p_dropout) - self.q_net = nn.Linear( - self.backbone_hidden_size, self.n_relations * self.head_hidden_size - ) - - self.k_net = nn.Linear( - self.backbone_hidden_size, self.n_relations * self.head_hidden_size - ) - - self.dummy_node = nn.Parameter(torch.Tensor(1, self.backbone_hidden_size)) - nn.init.normal_(self.dummy_node) - - def forward(self, h_q, h_k): - h_q = self.q_net(self.drop(h_q)) - - dummy_vec = self.dummy_node.unsqueeze(0).repeat(1, h_k.size(1), 1) - h_k = torch.cat([h_k, dummy_vec], axis=0) - h_k = self.k_net(self.drop(h_k)) - - head_q = h_q.view( - h_q.size(0), h_q.size(1), self.n_relations, self.head_hidden_size - ) - head_k = h_k.view( - h_k.size(0), h_k.size(1), self.n_relations, self.head_hidden_size - ) - - relation_score = torch.einsum("ibnd,jbnd->nbij", (head_q, head_k)) - - return relation_score diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/sbt_model.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/sbt_model.py deleted file mode 100644 index 736d223..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/model/sbt_model.py +++ /dev/null @@ -1,156 +0,0 @@ -import torch -from torch import nn -from transformers import LayoutLMConfig, LayoutLMModel, LayoutLMTokenizer, LayoutLMv2FeatureExtractor -from transformers import LayoutLMv2Config, LayoutLMv2Model -from sdsvkvu.model.relation_extractor import RelationExtractor -from sdsvkvu.model.kvu_model import KVUModel -# from utils import load_checkpoint - - -class SBTModel(KVUModel): - def __init__(self, cfg): - super().__init__(cfg=cfg) - - self.model_cfg = cfg.model - self.freeze = cfg.train.freeze - self.train_cfg = cfg.train - self.n_classes = len(self.model_cfg.class_names) - - self._get_backbones(self.model_cfg.backbone) - self._create_head() - - self.loss_func = nn.CrossEntropyLoss() - - - def _create_head(self): - self.backbone_hidden_size = self.backbone_config.hidden_size - self.head_hidden_size = self.model_cfg.head_hidden_size - self.head_p_dropout = self.model_cfg.head_p_dropout - # self.n_classes = self.model_cfg.n_classes + 1 - # self.relations = self.model_cfg.n_relations - self.repr_hiddent_size = self.backbone_hidden_size - - # (1) Initial token classification - self.itc_layer = nn.Sequential( - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.backbone_hidden_size), - nn.Dropout(self.head_p_dropout), - nn.Linear(self.backbone_hidden_size, self.n_classes), - ) - # (2) Subsequent token classification - self.stc_layer = RelationExtractor( - n_relations=1, #1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # (3) Linking token classification - self.relation_layer = RelationExtractor( - n_relations=1, #1 - backbone_hidden_size=self.backbone_hidden_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - # Classfication Layer for whole document - self.itc_layer_document = nn.Sequential( - nn.Dropout(self.head_p_dropout), - nn.Linear(self.repr_hiddent_size, self.repr_hiddent_size), - nn.Dropout(self.head_p_dropout), - nn.Linear(self.repr_hiddent_size, self.n_classes), - ) - - self.stc_layer_document = RelationExtractor( - n_relations=1, - backbone_hidden_size=self.repr_hiddent_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - self.relation_layer_document = RelationExtractor( - n_relations=1, - backbone_hidden_size=self.repr_hiddent_size, - head_hidden_size=self.head_hidden_size, - head_p_dropout=self.head_p_dropout, - ) - - self.itc_layer.apply(self._init_weight) - self.stc_layer.apply(self._init_weight) - self.relation_layer.apply(self._init_weight) - - self.itc_layer_document.apply(self._init_weight) - self.stc_layer_document.apply(self._init_weight) - self.relation_layer_document.apply(self._init_weight) - - - - def forward(self, batches): - head_outputs_list = [] - loss = 0. - for batch in batches["windows"]: - image = batch["image"] - input_ids = batch["input_ids_layoutxlm"] - bbox = batch["bbox"] - attention_mask = batch["attention_mask_layoutxlm"] - - if self.freeze: - for param in self.backbone.parameters(): - param.requires_grad = False - - if self.model_cfg.backbone == 'layoutxlm': - backbone_outputs = self.backbone( - image=image, input_ids=input_ids, bbox=bbox, attention_mask=attention_mask - ) - else: - backbone_outputs = self.backbone(input_ids, attention_mask=attention_mask) - - last_hidden_states = backbone_outputs.last_hidden_state[:, :512, :] - last_hidden_states = last_hidden_states.transpose(0, 1).contiguous() - - itc_outputs = self.itc_layer(last_hidden_states).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer(last_hidden_states, last_hidden_states).squeeze(0) - el_outputs = self.relation_layer(last_hidden_states, last_hidden_states).squeeze(0) - - window_repr = last_hidden_states.transpose(0, 1).contiguous() - - head_outputs = {"window_repr": window_repr, - "itc_outputs": itc_outputs, - "stc_outputs": stc_outputs, - "el_outputs": el_outputs,} - - if any(['labels' in key for key in batch.keys()]): - loss += self._get_loss(head_outputs, batch) - - head_outputs_list.append(head_outputs) - - batch = batches["documents"] - - document_repr = torch.cat([w['window_repr'] for w in head_outputs_list], dim=1) - document_repr = document_repr.transpose(0, 1).contiguous() - - itc_outputs = self.itc_layer_document(document_repr).transpose(0, 1).contiguous() - stc_outputs = self.stc_layer_document(document_repr, document_repr).squeeze(0) - el_outputs = self.relation_layer_document(document_repr, document_repr).squeeze(0) - - head_outputs = {"itc_outputs": itc_outputs, - "stc_outputs": stc_outputs, - "el_outputs": el_outputs} - - if any(['labels' in key for key in batch.keys()]): - loss += self._get_loss(head_outputs, batch) - - return head_outputs, loss - - def _get_loss(self, head_outputs, batch): - itc_outputs = head_outputs["itc_outputs"] - stc_outputs = head_outputs["stc_outputs"] - el_outputs = head_outputs["el_outputs"] - - itc_loss = self._get_itc_loss(itc_outputs, batch) - stc_loss = self._get_stc_loss(stc_outputs, batch) - el_loss = self._get_el_loss(el_outputs, batch, from_key=False) - - loss = itc_loss + stc_loss + el_loss - - return loss diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/predictor.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/predictor.py deleted file mode 100644 index 2861a31..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/predictor.py +++ /dev/null @@ -1,225 +0,0 @@ -import torch -from pathlib import Path -from omegaconf import OmegaConf - -import os -from sdsvkvu.sources.utils import parse_initial_words, parse_subsequent_words, parse_relations -from sdsvkvu.model import get_model, load_model_weight - - -class KVUPredictor: - def __init__(self, configs): - self.mode = configs.mode - self.device = configs.device - self.pretrained_model_path = configs.model.pretrained_model_path - net, cfg = self._load_model(configs.model.config, - configs.model.checkpoint) - - self.model = net - self.class_names = cfg.model.class_names - self.max_seq_length = cfg.train.max_seq_length - self.backbone_type = cfg.model.backbone - - if self.mode in (3, 4): - self.slice_interval = 0 - self.window_size = cfg.train.window_size - self.max_window_count = cfg.train.max_window_count - self.dummy_idx = self.max_seq_length * self.max_window_count - - else: - self.slice_interval = cfg.train.slice_interval - self.window_size = cfg.train.max_num_words - self.max_window_count = 1 - if self.mode == 2: - self.dummy_idx = 0 # dynamic dummy - else: - self.dummy_idx = self.max_seq_length # 512 - - - def get_process_configs(self): - _settings = { - # "tokenizer_layoutxlm": self.model.tokenizer_layoutxlm, - # "feature_extractor": self.model.feature_extractor, - "class_names": self.class_names, - "backbone_type": self.backbone_type, - "window_size": self.window_size, - "slice_interval": self.slice_interval, - "max_window_count": self.max_window_count, - "max_seq_length": self.max_seq_length, - "device": self.device, - "mode": self.mode - } - - feature_extractor = self.model.feature_extractor - if self.mode in (3, 4): - tokenizer_layoutxlm = self.model.tokenizer - else: - tokenizer_layoutxlm = self.model.tokenizer_layoutxlm - - return OmegaConf.create(_settings), tokenizer_layoutxlm, feature_extractor - - - def _load_model(self, cfg_path, ckpt_path): - cfg = OmegaConf.load(cfg_path) - - if self.pretrained_model_path is not None and os.path.exists(self.pretrained_model_path): - cfg.model.pretrained_model_path = self.pretrained_model_path - print("[INFO] Load pretrained backbone at:", cfg.model.pretrained_model_path) - - cfg.mode = self.mode - net = get_model(cfg) - load_model_weight(net, ckpt_path) - net.to(self.device) - net.eval() - return net, cfg - - def predict(self, input_sample): - if self.mode == 0: # Normal - bbox, lwords, pr_class_words, pr_relations = self.com_predict(input_sample) - return [bbox], [lwords], [pr_class_words], [pr_relations] - - elif self.mode == 1: # Full - tokens - bbox, lwords, pr_class_words, pr_relations = self.cat_predict(input_sample) - return [bbox], [lwords], [pr_class_words], [pr_relations] - - elif self.mode == 2: # Sliding - bbox, lwords, pr_class_words, pr_relations = [], [], [], [] - for window in input_sample['windows']: - _bbox, _lwords, _pr_class_words, _pr_relations = self.com_predict(window) - bbox.append(_bbox) - lwords.append(_lwords) - pr_class_words.append(_pr_class_words) - pr_relations.append(_pr_relations) - return bbox, lwords, pr_class_words, pr_relations - - elif self.mode == 3: # Document - bbox, lwords, pr_class_words, pr_relations = self.doc_predict(input_sample) - return [bbox], [lwords], [pr_class_words], [pr_relations] - - elif self.mode == 4: # SBT - bbox, lwords, pr_class_words, pr_relations = self.sbt_predict(input_sample) - return [bbox], [lwords], [pr_class_words], [pr_relations] - else: - raise ValueError(f"Not supported mode: {self.mode }") - - def doc_predict(self, input_sample): - lwords = input_sample['documents']['words'] - for idx, window in enumerate(input_sample['windows']): - input_sample['windows'][idx] = {k: v.unsqueeze(0).to(self.device) for k, v in window.items() if k not in ('words', 'n_empty_windows')} - - with torch.no_grad(): - head_outputs, _ = self.model(input_sample) - - input_sample = input_sample['documents'] - head_outputs = {k: v.detach().cpu() for k, v in head_outputs.items()} - # input_sample = {k: v.detach().cpu() for k, v in input_sample.items()} - - bbox = input_sample['bbox'].squeeze(0) - pr_class_words, pr_relations = self.kvu_parser(input_sample, head_outputs) - - return bbox, lwords, pr_class_words, pr_relations - - - def com_predict(self, input_sample): - lwords = input_sample['words'] - input_sample = {k: v.unsqueeze(0) for k, v in input_sample.items() if k not in ('words', 'img_path')} - input_sample = {k: v.to(self.device) for k, v in input_sample.items()} - - with torch.no_grad(): - head_outputs, _ = self.model(input_sample) - - head_outputs = {k: v.detach().cpu() for k, v in head_outputs.items()} - input_sample = {k: v.detach().cpu() for k, v in input_sample.items()} - - - bbox = input_sample['bbox'].squeeze(0) - pr_class_words, pr_relations = self.kvu_parser(input_sample, head_outputs) - - return bbox, lwords, pr_class_words, pr_relations - - - def cat_predict(self, input_sample): - lwords = input_sample['documents']['words'] - inputs = [] - for window in input_sample['windows']: - inputs.append({k: v.unsqueeze(0).cuda() for k, v in window.items() if k not in ('words', 'img_path')}) - input_sample['windows'] = inputs - - with torch.no_grad(): - head_outputs, _ = self.model(input_sample) - - head_outputs = {k: v.detach().cpu() for k, v in head_outputs.items() if k not in ('embedding_tokens')} - - - input_sample = {k: v.unsqueeze(0) for k, v in input_sample["documents"].items()} - - bbox = input_sample['bbox'].squeeze(0) - self.dummy_idx = bbox.shape[0] - pr_class_words, pr_relations = self.kvu_parser(input_sample, head_outputs) - return bbox, lwords, pr_class_words, pr_relations - - - def kvu_parser(self, input_sample, head_outputs): - itc_outputs = head_outputs["itc_outputs"] - stc_outputs = head_outputs["stc_outputs"] - el_outputs = head_outputs["el_outputs"] - el_outputs_from_key = head_outputs["el_outputs_from_key"] - - pr_itc_label = torch.argmax(itc_outputs, -1).squeeze(0) - pr_stc_label = torch.argmax(stc_outputs, -1).squeeze(0) - pr_el_label = torch.argmax(el_outputs, -1).squeeze(0) - pr_el_from_key = torch.argmax(el_outputs_from_key, -1).squeeze(0) - - box_first_token_mask = input_sample['are_box_first_tokens'].squeeze(0) - attention_mask = input_sample['attention_mask_layoutxlm'].squeeze(0) - - pr_init_words = parse_initial_words(pr_itc_label, box_first_token_mask, self.class_names) - pr_class_words = parse_subsequent_words( - pr_stc_label, attention_mask, pr_init_words, self.dummy_idx - ) - - pr_relations_from_header = parse_relations(pr_el_label, box_first_token_mask, self.dummy_idx) - pr_relations_from_key = parse_relations(pr_el_from_key, box_first_token_mask, self.dummy_idx) - pr_relations = pr_relations_from_header | pr_relations_from_key - - return pr_class_words, pr_relations - - - def sbt_predict(self, input_sample): - lwords = input_sample['documents']['words'] - for idx, window in enumerate(input_sample['windows']): - input_sample['windows'][idx] = {k: v.unsqueeze(0).to(self.device) for k, v in window.items() if k not in ('words', 'n_empty_windows')} - - with torch.no_grad(): - head_outputs, _ = self.model(input_sample) - - input_sample = input_sample['documents'] - head_outputs = {k: v.detach().cpu() for k, v in head_outputs.items()} - # input_sample = {k: v.detach().cpu() for k, v in input_sample.items()} - - bbox = input_sample['bbox'].squeeze(0) - pr_class_words, pr_relations = self.sbt_parser(input_sample, head_outputs) - - return bbox, lwords, pr_class_words, pr_relations - - - def sbt_parser(self, input_sample, head_outputs): - itc_outputs = head_outputs["itc_outputs"] - stc_outputs = head_outputs["stc_outputs"] - el_outputs = head_outputs["el_outputs"] - - pr_itc_label = torch.argmax(itc_outputs, -1).squeeze(0) - pr_stc_label = torch.argmax(stc_outputs, -1).squeeze(0) - pr_el_label = torch.argmax(el_outputs, -1).squeeze(0) - - box_first_token_mask = input_sample['are_box_first_tokens'].squeeze(0) - attention_mask = input_sample['attention_mask_layoutxlm'].squeeze(0) - - pr_init_words = parse_initial_words(pr_itc_label, box_first_token_mask, self.class_names) - pr_class_words = parse_subsequent_words( - pr_stc_label, attention_mask, pr_init_words, self.dummy_idx - ) - - pr_relations = parse_relations(pr_el_label, box_first_token_mask, self.dummy_idx) - - return pr_class_words, pr_relations \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/preprocess.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/preprocess.py deleted file mode 100644 index c499a61..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/preprocess.py +++ /dev/null @@ -1,479 +0,0 @@ -import torch -import itertools -import numpy as np - -from sdsvkvu.sources.utils import sliding_windows - - -class KVUProcessor: - def __init__( - self, - tokenizer_layoutxlm, - feature_extractor, - backbone_type, - class_names, - slice_interval, - window_size, - max_seq_length, - mode, - **kwargs, - ): - self.mode = mode - self.class_names = class_names - self.backbone_type = backbone_type - - self.window_size = window_size - self.slice_interval = slice_interval - self.max_seq_length = max_seq_length - - self.tokenizer_layoutxlm = tokenizer_layoutxlm - self.feature_extractor = feature_extractor - - self.pad_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids( - tokenizer_layoutxlm._pad_token - ) - self.cls_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids( - tokenizer_layoutxlm._cls_token - ) - self.sep_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids( - tokenizer_layoutxlm._sep_token - ) - self.unk_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids( - self.tokenizer_layoutxlm._unk_token - ) - - self.class_idx_dic = dict( - [(class_name, idx) for idx, class_name in enumerate(self.class_names)] - ) - - def __call__(self, lbboxes: list, lwords: list, image, width, height) -> dict: - image = torch.from_numpy( - self.feature_extractor(image)["pixel_values"][0].copy() - ) - - bbox_windows = sliding_windows(lbboxes, self.window_size, self.slice_interval) - word_windows = sliding_windows(lwords, self.window_size, self.slice_interval) - assert len(bbox_windows) == len(word_windows), \ - f"Shape of lbboxes and lwords after sliding window is not the same {len(bbox_windows)} # {len(word_windows)}" - - if self.mode == 0: # First 512 tokens - output = self.preprocess_window( - bounding_boxes=lbboxes, - words=lwords, - image_features={"image": image, "width": width, "height": height}, - max_seq_length=self.max_seq_length, - ) - elif self.mode == 1: # Get full tokens - output = {} - windows = [] - for i in range(len(bbox_windows)): - windows.append( - self.preprocess_window( - bounding_boxes=bbox_windows[i], - words=word_windows[i], - image_features={"image": image, "width": width, "height": height}, - max_seq_length=self.max_seq_length, - ) - ) - - output["windows"] = windows - elif self.mode == 2: # Sliding window - output = {} - windows = [] - output["doduments"] = self.preprocess_window( - bounding_boxes=lbboxes, - words=lwords, - image_features={"image": image, "width": width, "height": height}, - max_seq_length=2048, - ) - for i in range(len(bbox_windows)): - windows.append( - self.preprocess( - bounding_boxes=bbox_windows[i], - words=word_windows[i], - image_features={"image": image, "width": width, "height": height}, - max_seq_length=self.max_seq_length, - ) - ) - - output["windows"] = windows - else: - raise ValueError(f"Not supported mode: {self.mode }") - return output - - def preprocess_window(self, bounding_boxes, words, image_features, max_seq_length): - list_word_objects = [] - for bb, text in zip(bounding_boxes, words): - boundingBox = [ - [bb[0], bb[1]], - [bb[2], bb[1]], - [bb[2], bb[3]], - [bb[0], bb[3]], - ] - tokens = self.tokenizer_layoutxlm.convert_tokens_to_ids( - self.tokenizer_layoutxlm.tokenize(text) - ) - list_word_objects.append( - {"layoutxlm_tokens": tokens, "boundingBox": boundingBox, "text": text} - ) - - ( - bbox, - input_ids, - attention_mask, - are_box_first_tokens, - box_to_token_indices, - box2token_span_map, - lwords, - len_valid_tokens, - len_non_overlap_tokens, - len_list_tokens, - ) = self.parser_words( - list_word_objects, - self.max_seq_length, - image_features["width"], - image_features["height"], - ) - - assert len_list_tokens == len_valid_tokens + 2 - len_overlap_tokens = len_valid_tokens - len_non_overlap_tokens - - ntokens = max_seq_length if max_seq_length == 512 else len_valid_tokens + 2 - - input_ids = input_ids[:ntokens] - attention_mask = attention_mask[:ntokens] - bbox = bbox[:ntokens] - are_box_first_tokens = are_box_first_tokens[:ntokens] - - input_ids = torch.from_numpy(input_ids) - attention_mask = torch.from_numpy(attention_mask) - bbox = torch.from_numpy(bbox) - are_box_first_tokens = torch.from_numpy(are_box_first_tokens) - - len_valid_tokens = torch.tensor(len_valid_tokens) - len_overlap_tokens = torch.tensor(len_overlap_tokens) - return_dict = { - "words": lwords, - "len_overlap_tokens": len_overlap_tokens, - "len_valid_tokens": len_valid_tokens, - "image": image_features["image"], - "input_ids_layoutxlm": input_ids, - "attention_mask_layoutxlm": attention_mask, - "are_box_first_tokens": are_box_first_tokens, - "bbox": bbox, - } - return return_dict - - def parser_words(self, words, max_seq_length, width, height): - list_bbs = [] - list_words = [] - list_tokens = [] - cls_bbs = [0.0] * 8 - box2token_span_map = [] - box_to_token_indices = [] - lwords = [""] * max_seq_length - - cum_token_idx = 0 - len_valid_tokens = 0 - len_non_overlap_tokens = 0 - - input_ids = np.ones(max_seq_length, dtype=int) * self.pad_token_id_layoutxlm - bbox = np.zeros((max_seq_length, 8), dtype=np.float32) - attention_mask = np.zeros(max_seq_length, dtype=int) - are_box_first_tokens = np.zeros(max_seq_length, dtype=np.bool_) - - for word_idx, word in enumerate(words): - this_box_token_indices = [] - - tokens = word["layoutxlm_tokens"] - bb = word["boundingBox"] - text = word["text"] - - len_valid_tokens += len(tokens) - if word_idx < self.slice_interval: - len_non_overlap_tokens += len(tokens) - - if len(tokens) == 0: - tokens.append(self.unk_token_id) - - if len(list_tokens) + len(tokens) > max_seq_length - 2: - break - - box2token_span_map.append( - [len(list_tokens) + 1, len(list_tokens) + len(tokens) + 1] - ) # including st_idx - list_tokens += tokens - - # min, max clipping - for coord_idx in range(4): - bb[coord_idx][0] = max(0.0, min(bb[coord_idx][0], width)) - bb[coord_idx][1] = max(0.0, min(bb[coord_idx][1], height)) - - bb = list(itertools.chain(*bb)) - bbs = [bb for _ in range(len(tokens))] - texts = [text for _ in range(len(tokens))] - - for _ in tokens: - cum_token_idx += 1 - this_box_token_indices.append(cum_token_idx) - - list_bbs.extend(bbs) - list_words.extend(texts) #### - box_to_token_indices.append(this_box_token_indices) - - sep_bbs = [width, height] * 4 - - # For [CLS] and [SEP] - list_tokens = ( - [self.cls_token_id_layoutxlm] - + list_tokens[: max_seq_length - 2] - + [self.sep_token_id_layoutxlm] - ) - if len(list_bbs) == 0: - # When len(json_obj["words"]) == 0 (no OCR result) - list_bbs = [cls_bbs] + [sep_bbs] - else: # len(list_bbs) > 0 - list_bbs = [cls_bbs] + list_bbs[: max_seq_length - 2] + [sep_bbs] - # list_words = ['CLS'] + list_words[: max_seq_length - 2] + ['SEP'] ### - # if len(list_words) < 510: - # list_words.extend(['

' for _ in range(510 - len(list_words))]) - list_words = ( - [self.tokenizer_layoutxlm._cls_token] - + list_words[: max_seq_length - 2] - + [self.tokenizer_layoutxlm._sep_token] - ) - - len_list_tokens = len(list_tokens) - input_ids[:len_list_tokens] = list_tokens - attention_mask[:len_list_tokens] = 1 - - bbox[:len_list_tokens, :] = list_bbs - lwords[:len_list_tokens] = list_words - - # Normalize bbox -> 0 ~ 1 - bbox[:, [0, 2, 4, 6]] = bbox[:, [0, 2, 4, 6]] / width - bbox[:, [1, 3, 5, 7]] = bbox[:, [1, 3, 5, 7]] / height - - if self.backbone_type in ("layoutlm", "layoutxlm"): - bbox = bbox[:, [0, 1, 4, 5]] - bbox = bbox * 1000 - bbox = bbox.astype(int) - else: - assert False - - st_indices = [ - indices[0] - for indices in box_to_token_indices - if indices[0] < max_seq_length - ] - are_box_first_tokens[st_indices] = True - - return ( - bbox, - input_ids, - attention_mask, - are_box_first_tokens, - box_to_token_indices, - box2token_span_map, - lwords, - len_valid_tokens, - len_non_overlap_tokens, - len_list_tokens, - ) - - -class DocKVUProcessor(KVUProcessor): - def __init__( - self, - tokenizer_layoutxlm, - feature_extractor, - backbone_type, - class_names, - max_window_count, - slice_interval, - window_size, - max_seq_length, - mode, - **kwargs, - ): - super().__init__( - tokenizer_layoutxlm=tokenizer_layoutxlm, - feature_extractor=feature_extractor, - backbone_type=backbone_type, - class_names=class_names, - slice_interval=slice_interval, - window_size=window_size, - max_seq_length=max_seq_length, - mode=mode, - ) - - self.max_window_count = max_window_count - self.pad_token_id = self.pad_token_id_layoutxlm - self.cls_token_id = self.cls_token_id_layoutxlm - self.sep_token_id = self.sep_token_id_layoutxlm - self.unk_token_id = self.unk_token_id_layoutxlm - self.tokenizer = self.tokenizer_layoutxlm - - def __call__(self, lbboxes: list, lwords: list, images, width, height) -> dict: - image_features = torch.from_numpy( - self.feature_extractor(images)["pixel_values"][0].copy() - ) - output = self.preprocess_document( - bounding_boxes=lbboxes, - words=lwords, - image_features={"image": image_features, "width": width, "height": height}, - max_seq_length=self.max_seq_length, - ) - return output - - def preprocess_document(self, bounding_boxes, words, image_features, max_seq_length): - n_words = len(words) - output_dicts = {"windows": [], "documents": []} - n_empty_windows = 0 - - for i in range(self.max_window_count): - input_ids = np.ones(max_seq_length, dtype=int) * self.pad_token_id - bbox = np.zeros((max_seq_length, 8), dtype=np.float32) - attention_mask = np.zeros(max_seq_length, dtype=int) - are_box_first_tokens = np.zeros(max_seq_length, dtype=np.bool_) - - if n_words == 0: - n_empty_windows += 1 - output_dicts["windows"].append( - { - "image": image_features["image"], - "input_ids_layoutxlm": torch.from_numpy(input_ids), - "bbox": torch.from_numpy(bbox), - "words": [], - "attention_mask_layoutxlm": torch.from_numpy(attention_mask), - "are_box_first_tokens": torch.from_numpy(are_box_first_tokens), - } - ) - continue - - start_word_idx = i * self.window_size - stop_word_idx = min(n_words, (i + 1) * self.window_size) - - if start_word_idx >= stop_word_idx: - n_empty_windows += 1 - output_dicts["windows"].append(output_dicts["windows"][-1]) - continue - - list_word_objects = [] - for bb, text in zip( - bounding_boxes[start_word_idx:stop_word_idx], - words[start_word_idx:stop_word_idx], - ): - boundingBox = [ - [bb[0], bb[1]], - [bb[2], bb[1]], - [bb[2], bb[3]], - [bb[0], bb[3]], - ] - tokens = self.tokenizer_layoutxlm.convert_tokens_to_ids( - self.tokenizer_layoutxlm.tokenize(text) - ) - list_word_objects.append( - { - "layoutxlm_tokens": tokens, - "boundingBox": boundingBox, - "text": text, - } - ) - - ( - bbox, - input_ids, - attention_mask, - are_box_first_tokens, - box_to_token_indices, - box2token_span_map, - lwords, - len_valid_tokens, - len_non_overlap_tokens, - len_list_layoutxlm_tokens, - ) = self.parser_words( - list_word_objects, - self.max_seq_length, - image_features["width"], - image_features["height"], - ) - - input_ids = torch.from_numpy(input_ids) - bbox = torch.from_numpy(bbox) - attention_mask = torch.from_numpy(attention_mask) - are_box_first_tokens = torch.from_numpy(are_box_first_tokens) - - return_dict = { - "bbox": bbox, - "words": lwords, - "image": image_features["image"], - "input_ids_layoutxlm": input_ids, - "attention_mask_layoutxlm": attention_mask, - "are_box_first_tokens": are_box_first_tokens, - } - output_dicts["windows"].append(return_dict) - - attention_mask = torch.cat( - [o["attention_mask_layoutxlm"] for o in output_dicts["windows"]] - ) - are_box_first_tokens = torch.cat( - [o["are_box_first_tokens"] for o in output_dicts["windows"]] - ) - if n_empty_windows > 0: - attention_mask[ - self.max_seq_length * (self.max_window_count - n_empty_windows) : - ] = torch.from_numpy( - np.zeros(self.max_seq_length * n_empty_windows, dtype=int) - ) - are_box_first_tokens[ - self.max_seq_length * (self.max_window_count - n_empty_windows) : - ] = torch.from_numpy( - np.zeros(self.max_seq_length * n_empty_windows, dtype=np.bool_) - ) - bbox = torch.cat([o["bbox"] for o in output_dicts["windows"]]) - words = [] - for o in output_dicts["windows"]: - words.extend(o["words"]) - - return_dict = { - "bbox": bbox, - "words": words, - "attention_mask_layoutxlm": attention_mask, - "are_box_first_tokens": are_box_first_tokens, - "n_empty_windows": n_empty_windows, - } - output_dicts["documents"] = return_dict - - return output_dicts - - -class SBTProcessor(DocKVUProcessor): - def __init__( - self, - tokenizer_layoutxlm, - feature_extractor, - backbone_type, - class_names, - max_window_count, - slice_interval, - window_size, - max_seq_length, - mode, - **kwargs, - ): - super().__init__( - tokenizer_layoutxlm, - feature_extractor, - backbone_type, - class_names, - max_window_count, - slice_interval, - window_size, - max_seq_length, - mode, - **kwargs, - ) - - def __call__(self, lbboxes: list, lwords: list, images, width, height) -> dict: - return super().__call__(lbboxes, lwords, images, width, height) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/run_ocr.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/run_ocr.py deleted file mode 100644 index 8bce812..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/modules/run_ocr.py +++ /dev/null @@ -1,25 +0,0 @@ -import numpy as np -from pathlib import Path -from typing import Union, Tuple, List -from sdsvkvu.externals.basic_ocr.src.ocr import OcrEngine - - -def load_ocr_engine(opt) -> OcrEngine: - print("[INFO] Loading engine...") - engine = OcrEngine(**opt) - print("[INFO] Engine loaded") - return engine - - -def process_img(img: Union[str, np.ndarray], engine: OcrEngine) -> List: # For OCR integrated deskew using paddle - page = engine(img) - bboxes = [] - texts = [] - for word_segment in page._word_segments: - for word in word_segment.list_words: - bboxes.append(word.bbox[:]) - texts.append(word.text) - - image = page.deskewed_image if page.deskewed_image is not None else page.image - return bboxes, texts, image - \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/settings.yml b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/settings.yml deleted file mode 100644 index 685f9d3..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/settings.yml +++ /dev/null @@ -1,21 +0,0 @@ -device: "cuda:0" # "cuda:0" -mode: 4 # best option to infer -model: - pretrained_model_path: /mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/microsoft/layoutxlm-base # default: "" - config: /home/sds/tuanlv/02-KVU/03-KVU_sbt/experiments/key_value_understanding_for_sbt-20231121-085847/base.yaml - checkpoint: /home/sds/tuanlv/02-KVU/03-KVU_sbt/experiments/key_value_understanding_for_sbt-20231121-085847/checkpoints/best_model.pth -ocr_engine: - detector: - # version: /home/sds/datnt/mmdetection/wild_receipt_finetune_weights_c_lite.pth - version: /mnt/hdd4T/OCR/datnt/mmdetection/logs/textdet-baseline-Nov3-wildreceiptv4-sdsapv1-mcocr-ssreceipt1_Imei/epoch_100_params.pth - rotator_version: /home/sds/datnt/mmdetection/logs/textdet-with-rotate-20230317/best_bbox_mAP_epoch_30_lite.pth - recognizer: - version: satrn-lite-general-pretrain-20230106 - deskew: - enable: True - text_detector: - config: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/config/det.yaml - weight: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_PP-OCRv3_det_infer - text_cls: - config: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/config/cls.yaml - weight: /mnt/hdd4T/OCR/tuanlv/01-BasicOCR/ocr-engine-deskew/externals/sdsv_dewarp/weights/ch_ppocr_mobile_v2.0_cls_infer diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/kvu.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/kvu.py deleted file mode 100644 index 1b560b0..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/kvu.py +++ /dev/null @@ -1,73 +0,0 @@ -import imagesize -from PIL import Image -from pathlib import Path -from omegaconf import OmegaConf - -from sdsvkvu.modules.predictor import KVUPredictor -from sdsvkvu.modules.preprocess import KVUProcessor, DocKVUProcessor, SBTProcessor -from sdsvkvu.modules.run_ocr import load_ocr_engine, process_img -from sdsvkvu.utils.utils import post_process_basic_ocr -from sdsvkvu.sources.utils import revert_scale_bbox, Timer - -DEFAULT_SETTING_PATH = str(Path(__file__).parents[1]) + "/settings.yml" - - -class KVUEngine: - def __init__(self, setting_file: str = DEFAULT_SETTING_PATH, ocr_engine=None, **kwargs) -> None: - configs = OmegaConf.load(setting_file) - for key, param in kwargs.items(): # overwrite default settings by keyword arguments - if key not in configs: - raise ValueError("Invalid setting found in KVUEngine: ", key) - if isinstance(param, dict): - for k, v in param.items(): - if k not in configs[key]: - raise ValueError("Invalid setting found in KVUEngine: ", key, k) - configs[key][k] = v - else: - configs[key] = param - - self.predictor = KVUPredictor(configs) - self._settings, tokenizer_layoutxlm, feature_extractor = self.predictor.get_process_configs() - mode = self._settings.mode - if mode in (0, 1, 2): - self.processor = KVUProcessor(tokenizer_layoutxlm=tokenizer_layoutxlm, - feature_extractor=feature_extractor, - **self._settings) - elif mode == 3: - self.processor = DocKVUProcessor(tokenizer_layoutxlm=tokenizer_layoutxlm, - feature_extractor=feature_extractor, - **self._settings) - elif mode == 4: - self.processor = SBTProcessor(tokenizer_layoutxlm=tokenizer_layoutxlm, - feature_extractor=feature_extractor, - **self._settings) - else: - raise ValueError(f'[ERROR] Inferencing mode of {mode} is not supported') - - if ocr_engine is None: - print("[INFO] Load internal OCR Engine") - configs.ocr_engine.device = configs.device - self.ocr_engine = load_ocr_engine(configs.ocr_engine) - else: - print("[INFO] Load external OCR Engine") - self.ocr_engine = ocr_engine - - def predict(self, img_path): - lbboxes, lwords, image = process_img(img_path, self.ocr_engine) - lwords = post_process_basic_ocr(lwords) - - if len(lbboxes) == 0: - print("[WARNING] Empty document") - return image, [[]], [[]], [[]], [[]] - - height, width, _ = image.shape - image = Image.fromarray(image) - - inputs = self.processor(lbboxes, lwords, image, width=width, height=height) - - with Timer("kvu"): - lbbox, lwords, pr_class_words, pr_relations = self.predictor.predict(inputs) - - for i in range(len(lbbox)): - lbbox[i] = [revert_scale_bbox(bb, width=width, height=height) for bb in lbbox[i]] - return image, lbbox, lwords, pr_class_words, pr_relations \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/utils.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/utils.py deleted file mode 100644 index 53bc12e..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/sources/utils.py +++ /dev/null @@ -1,610 +0,0 @@ -import os -import cv2 -import copy -import time -import torch -import math -import numpy as np -from typing import Callable -from sdsvkvu.utils.post_processing import get_string, get_string_by_deduplicate_bbox, get_string_with_word2line - -# def get_colormap(): -# return { -# 'others': (0, 0, 255), # others: red -# 'title': (0, 255, 255), # title: yellow -# 'key': (255, 0, 0), # key: blue -# 'value': (0, 255, 0), # value: green -# 'header': (233, 197, 15), # header -# 'group': (0, 128, 128), # group -# 'relation': (0, 0, 255)# (128, 128, 128), # relation -# } - - -class Timer: - def __init__(self, name: str) -> None: - self.name = name - - def __enter__(self): - self.start_time = time.perf_counter() - return self - - def __exit__(self, func: Callable, *args): - self.end_time = time.perf_counter() - self.elapsed_time = self.end_time - self.start_time - print(f"[INFO]: {self.name} took : {self.elapsed_time:.6f} seconds") - -def get_colormap(): - return { - "others": (0, 0, 255), # others: red - "title": (0, 255, 255), # title: yellow - "key": (255, 0, 0), # key: blue - "value": (0, 255, 0), # value: green - "header": (233, 197, 15), # header - "group": (0, 128, 128), # group - "relation": (0, 0, 255), # (128, 128, 128), # relation - - # "others": (187, 125, 250), # pink - "seller": (183, 50, 255), # bold pink - "date_key": (128, 51, 115), # orange - "date_value": (55, 250, 250), # yellow - "product_name": (245, 61, 61), # blue - "product_code": (233, 197, 17), # header - "quantity": (102, 255, 102), # green - "sn_key": (179, 134, 89), - "sn_value": (51, 153, 204), - "invoice_number_key": (40, 90, 144), - "invoice_number_value": (162, 239, 204), - "sold_key": (74, 180, 150), - "sold_value": (14, 184, 53), - "voucher": (39, 86, 103), - "website": (207, 19, 85), - "hotline": (153, 224, 56), - # "group": (0, 128, 128), # brown - # "relation": (0, 0, 255), # (128, 128, 128), # red - } - -def convert_image(image): - exif = image._getexif() - orientation = None - if exif is not None: - orientation = exif.get(0x0112) - # Convert the PIL image to OpenCV format - image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) - # Rotate the image in OpenCV if necessary - if orientation == 3: - image = cv2.rotate(image, cv2.ROTATE_180) - elif orientation == 6: - image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE) - elif orientation == 8: - image = cv2.rotate(image, cv2.ROTATE_90_COUNTERCLOCKWISE) - else: - image = np.asarray(image) - - if len(image.shape) == 2: - image = np.repeat(image[:, :, np.newaxis], 3, axis=2) - assert len(image.shape) == 3 - - return image, orientation - -def visualize(image, bbox, pr_class_words, pr_relations, color_map, labels=['others', 'title', 'key', 'value', 'header'], thickness=1): - # image, orientation = convert_image(image) - - # if orientation is not None and orientation == 6: - # width, height, _ = image.shape - # else: - # height, width, _ = image.shape - - if len(pr_class_words) > 0: - id2label = {k: labels[k] for k in range(len(labels))} - for lb, groups in enumerate(pr_class_words): - if lb == 0: - continue - for group_id, group in enumerate(groups): - for i, word_id in enumerate(group): - # x0, y0, x1, y1 = revert_scale_bbox(bbox[word_id], width, height) - x0, y0, x1, y1 = bbox[word_id] - cv2.rectangle(image, (x0, y0), (x1, y1), color=color_map[id2label[lb]], thickness=thickness) - - if i == 0: - x_center0, y_center0 = int((x0+x1)/2), int((y0+y1)/2) - else: - x_center1, y_center1 = int((x0+x1)/2), int((y0+y1)/2) - cv2.line(image, (x_center0, y_center0), (x_center1, y_center1), color=color_map['group'], thickness=thickness) - x_center0, y_center0 = x_center1, y_center1 - - if len(pr_relations) > 0: - for pair in pr_relations: - # xyxy0 = revert_scale_bbox(bbox[pair[0]], width, height) - # xyxy1 = revert_scale_bbox(bbox[pair[1]], width, height) - xyxy0 = bbox[pair[0]] - xyxy1 = bbox[pair[1]] - - x_center0, y_center0 = int((xyxy0[0] + xyxy0[2])/2), int((xyxy0[1] + xyxy0[3])/2) - x_center1, y_center1 = int((xyxy1[0] + xyxy1[2])/2), int((xyxy1[1] + xyxy1[3])/2) - - cv2.line(image, (x_center0, y_center0), (x_center1, y_center1), color=color_map['relation'], thickness=thickness) - - return image - -def revert_scale_bbox(box, width, height): - return [ - int((box[0] / 1000) * width), - int((box[1] / 1000) * height), - int((box[2] / 1000) * width), - int((box[3] / 1000) * height) - ] - - -def draw_kvu_outputs(image: np.ndarray, bbox: list, pr_class_words: list, pr_relations: list, class_names: list = ['others', 'title', 'key', 'value', 'header'], thickness: int = 1): - color_map = get_colormap() - image = visualize(image, bbox, pr_class_words, pr_relations, color_map, class_names, thickness) - if (image.shape[2] == 2): - image = cv2.cvtColor(image, cv2.COLOR_BGR5652BGR) - return cv2.cvtColor(image, cv2.COLOR_RGB2BGR) - - -def sliding_windows(elements: list, window_size: int, slice_interval: int) -> list: - element_windows = [] - - if len(elements) > window_size: - max_step = math.ceil((len(elements) - window_size)/slice_interval) - - for i in range(0, max_step + 1): - if (i*slice_interval+window_size) >= len(elements): - _window = copy.deepcopy(elements[i*slice_interval:]) - else: - _window = copy.deepcopy(elements[i*slice_interval: i*slice_interval+window_size]) - element_windows.append(_window) - return element_windows - else: - return [elements] - - -def merged_token_embeddings(lpatches: list, loverlaps:list, lvalids: list, average: bool) -> torch.tensor: - start_pos = 1 - end_pos = start_pos + lvalids[0] - embedding_tokens = copy.deepcopy(lpatches[0][:, start_pos:end_pos, ...]) - cls_token = copy.deepcopy(lpatches[0][:, :1, ...]) - sep_token = copy.deepcopy(lpatches[0][:, -1:, ...]) - - for i in range(1, len(lpatches)): - start_pos = 1 - end_pos = start_pos + lvalids[i] - - overlap_gap = copy.deepcopy(loverlaps[i-1]) - window = copy.deepcopy(lpatches[i][:, start_pos:end_pos, ...]) - - if overlap_gap != 0: - prev_overlap = copy.deepcopy(embedding_tokens[:, -overlap_gap:, ...]) - curr_overlap = copy.deepcopy(window[:, :overlap_gap, ...]) - assert prev_overlap.shape == curr_overlap.shape, f"{prev_overlap.shape} # {curr_overlap.shape} with overlap: {overlap_gap}" - - if average: - avg_overlap = ( - prev_overlap + curr_overlap - ) / 2. - embedding_tokens = torch.cat( - [embedding_tokens[:, :-overlap_gap, ...], avg_overlap, window[:, overlap_gap:, ...]], dim=1 - ) - else: - embedding_tokens = torch.cat( - [embedding_tokens[:, :-overlap_gap, ...], curr_overlap, window[:, overlap_gap:, ...]], dim=1 - ) - else: - embedding_tokens = torch.cat( - [embedding_tokens, window], dim=1 - ) - return torch.cat([cls_token, embedding_tokens, sep_token], dim=1) - - -def parse_initial_words(itc_label, box_first_token_mask, class_names): - itc_label_np = itc_label.cpu().numpy() - box_first_token_mask_np = box_first_token_mask.cpu().numpy() - - outputs = [[] for _ in range(len(class_names))] - - for token_idx, label in enumerate(itc_label_np): - if box_first_token_mask_np[token_idx] and label != 0: - outputs[label].append(token_idx) - - return outputs - - -def parse_subsequent_words(stc_label, attention_mask, init_words, dummy_idx): - max_connections = 50 - - valid_stc_label = stc_label * attention_mask.bool() - valid_stc_label = valid_stc_label.cpu().numpy() - stc_label_np = stc_label.cpu().numpy() - - valid_token_indices = np.where( - (valid_stc_label != dummy_idx) * (valid_stc_label != 0) - ) - - next_token_idx_dict = {} - for token_idx in valid_token_indices[0]: - next_token_idx_dict[stc_label_np[token_idx]] = token_idx - - outputs = [] - for init_token_indices in init_words: - sub_outputs = [] - for init_token_idx in init_token_indices: - cur_token_indices = [init_token_idx] - for _ in range(max_connections): - if cur_token_indices[-1] in next_token_idx_dict: - if ( - next_token_idx_dict[cur_token_indices[-1]] - not in init_token_indices - ): - cur_token_indices.append( - next_token_idx_dict[cur_token_indices[-1]] - ) - else: - break - else: - break - sub_outputs.append(tuple(cur_token_indices)) - - outputs.append(sub_outputs) - - return outputs - -def parse_relations(el_label, box_first_token_mask, dummy_idx): - valid_el_labels = el_label * box_first_token_mask - valid_el_labels = valid_el_labels.cpu().numpy() - el_label_np = el_label.cpu().numpy() - - max_token = box_first_token_mask.shape[0] - 1 - - valid_token_indices = np.where( - ((valid_el_labels != dummy_idx) * (valid_el_labels != 0)) ### - ) - - link_map_tuples = [] - for token_idx in valid_token_indices[0]: - link_map_tuples.append((el_label_np[token_idx], token_idx)) - - return set(link_map_tuples) - - -def get_pairs(json: list, rel_from: str, rel_to: str) -> dict: - outputs = {} - for pair in json: - is_rel = {rel_from: {'status': 0}, rel_to: {'status': 0}} - for element in pair: - if element['class'] in (rel_from, rel_to): - is_rel[element['class']]['status'] = 1 - is_rel[element['class']]['value'] = element - if all([v['status'] == 1 for _, v in is_rel.items()]): - outputs[is_rel[rel_to]['value']['group_id']] = [is_rel[rel_from]['value']['group_id'], is_rel[rel_to]['value']['group_id']] - return outputs - -def get_table_relations(json: list, header_key_pairs: dict, rel_from="key", rel_to="value") -> dict: - list_keys = list(header_key_pairs.keys()) - relations = {k: [] for k in list_keys} - for pair in json: - is_rel = {rel_from: {'status': 0}, rel_to: {'status': 0}} - for element in pair: - if element['class'] == rel_from and element['group_id'] in list_keys: - is_rel[rel_from]['status'] = 1 - is_rel[rel_from]['value'] = element - if element['class'] == rel_to: - is_rel[rel_to]['status'] = 1 - is_rel[rel_to]['value'] = element - if all([v['status'] == 1 for _, v in is_rel.items()]): - relations[is_rel[rel_from]['value']['group_id']].append(is_rel[rel_to]['value']['group_id']) - return relations - -def get_key2values_relations(key_value_pairs: dict): - triple_linkings = {} - for value_group_id, key_value_pair in key_value_pairs.items(): - key_group_id = key_value_pair[0] - if key_group_id not in list(triple_linkings.keys()): - triple_linkings[key_group_id] = [] - triple_linkings[key_group_id].append(value_group_id) - return triple_linkings - - -def get_wordgroup_bbox(lbbox: list, lword_ids: list) -> list: - points = [lbbox[i] for i in lword_ids] - x_min, y_min = min(points, key=lambda x: x[0])[0], min(points, key=lambda x: x[1])[1] - x_max, y_max = max(points, key=lambda x: x[2])[2], max(points, key=lambda x: x[3])[3] - return [x_min, y_min, x_max, y_max] - - -def merged_token_to_wordgroup(class_words: list, lwords: list, lbbox: list, labels: list) -> dict: - word_groups = {} - id2class = {i: labels[i] for i in range(len(labels))} - for class_id, lwgroups_in_class in enumerate(class_words): - for ltokens_in_wgroup in lwgroups_in_class: - group_id = ltokens_in_wgroup[0] - ltokens_to_ltexts = [lwords[token] for token in ltokens_in_wgroup] - ltokens_to_lbboxes = [lbbox[token] for token in ltokens_in_wgroup] - # text_string = get_string(ltokens_to_ltexts) - text_string = get_string_by_deduplicate_bbox(ltokens_to_ltexts, ltokens_to_lbboxes) - # text_string = get_string_with_word2line(ltokens_to_ltexts, ltokens_to_lbboxes) - group_bbox = get_wordgroup_bbox(lbbox, ltokens_in_wgroup) - word_groups[group_id] = { - 'group_id': group_id, - 'text': text_string, - 'class': id2class[class_id], - 'tokens': ltokens_in_wgroup, - 'bbox': group_bbox - } - return word_groups - -def verify_linking_id(word_groups: dict, linking_id: int) -> int: - if linking_id not in list(word_groups): - for wg_id, _word_group in word_groups.items(): - if linking_id in _word_group['tokens']: - return wg_id - return linking_id - -def matched_wordgroup_relations(word_groups:dict, lrelations: list) -> list: - outputs = [] - for pair in lrelations: - wg_from = verify_linking_id(word_groups, pair[0]) - wg_to = verify_linking_id(word_groups, pair[1]) - try: - outputs.append([word_groups[wg_from], word_groups[wg_to]]) - except Exception as e: - print('Not valid pair:', wg_from, wg_to) - return outputs - - -def get_single_entity(word_groups: dict, lrelations: list, labels: list) -> list: - # single_entity = {'title': [], 'key': [], 'value': [], 'header': []} - single_entity = {lb: [] for lb in labels} - list_linked_ids = [] - for pair in lrelations: - list_linked_ids.extend(pair) - - for word_group_id, word_group in word_groups.items(): - if word_group_id not in list_linked_ids: - single_entity[word_group['class']].append(word_group) - return single_entity - - -def export_kvu_outputs(lwords, lbbox, class_words, lrelations, labels=['others', 'title', 'key', 'value', 'header']): - word_groups = merged_token_to_wordgroup(class_words, lwords, lbbox, labels) - linking_pairs = matched_wordgroup_relations(word_groups, lrelations) - - header_key = get_pairs(linking_pairs, rel_from='header', rel_to='key') # => {key_group_id: [header_group_id, key_group_id]} - header_value = get_pairs(linking_pairs, rel_from='header', rel_to='value') # => {value_group_id: [header_group_id, value_group_id]} - key_value = get_pairs(linking_pairs, rel_from='key', rel_to='value') # => {value_group_id: [key_group_id, value_group_id]} - - single_entity = get_single_entity(word_groups, lrelations, labels=labels) - - # table_relations = get_table_relations(linking_pairs, header_key) # => {key_group_id: [value_group_id1, value_groupid2, ...]} - key2values_relations = get_key2values_relations(key_value) # => {key_group_id: [value_group_id1, value_groupid2, ...]} - - triplet_pairs = [] - single_pairs = [] - table = [] - # print('key2values_relations', key2values_relations) - for key_group_id, list_value_group_ids in key2values_relations.items(): - if len(list_value_group_ids) == 0: continue - elif len(list_value_group_ids) == 1: - value_group_id = list_value_group_ids[0] - single_pairs.append({word_groups[key_group_id]['text']: { - 'id': value_group_id, - 'class': "value", - 'text': word_groups[value_group_id]['text'], - 'bbox': word_groups[value_group_id]['bbox'], - "key_bbox": word_groups[key_group_id]["bbox"], - }}) - else: - item = [] - for value_group_id in list_value_group_ids: - if value_group_id not in header_value.keys(): - header_name_for_value = "non-header" - else: - header_group_id = header_value[value_group_id][0] - header_name_for_value = word_groups[header_group_id]['text'] - item.append({ - 'id': value_group_id, - 'class': 'value', - 'header': header_name_for_value, - 'text': word_groups[value_group_id]['text'], - 'bbox': word_groups[value_group_id]['bbox'], - "key_bbox": word_groups[key_group_id]["bbox"], - "header_bbox": word_groups[header_group_id]["bbox"] - if header_group_id != -1 else [0, 0, 0, 0], - }) - if key_group_id not in list(header_key.keys()): - triplet_pairs.append({ - word_groups[key_group_id]['text']: item - }) - else: - header_group_id = header_key[key_group_id][0] - header_name_for_key = word_groups[header_group_id]['text'] - item.append({ - 'id': key_group_id, - 'class': 'key', - 'header': header_name_for_key, - 'text': word_groups[key_group_id]['text'], - 'bbox': word_groups[key_group_id]['bbox'], - "key_bbox": word_groups[key_group_id]["bbox"], - "header_bbox": word_groups[header_group_id]["bbox"], - }) - table.append({key_group_id: item}) - - - # Add entity without linking - single_entity_dict = {} - for class_name, single_items in single_entity.items(): - single_entity_dict[class_name] = [] - for single_item in single_items: - single_entity_dict[class_name].append({ - 'text': single_item['text'], - 'id': single_item['group_id'], - 'class': class_name, - 'bbox': single_item['bbox'] - }) - - - if len(table) > 0: - table = sorted(table, key=lambda x: list(x.keys())[0]) - table = [v for item in table for k, v in item.items()] - - outputs = {} - outputs['title'] = sorted( - single_entity_dict["title"], key=lambda x: x["id"] - ) - outputs['key'] = sorted( - single_entity_dict["key"], key=lambda x: x["id"] - ) - outputs['value'] = sorted( - single_entity_dict["value"], key=lambda x: x["id"] - ) - outputs['single'] = sorted(single_pairs, key=lambda x: int(float(list(x.values())[0]['id']))) - outputs['triplet'] = triplet_pairs - outputs['table'] = table - return outputs - - - -def export_sbt_outputs( - lwords, - lbboxes, - class_words, - lrelations, - labels, -): - word_groups = merged_token_to_wordgroup(class_words, lwords, lbboxes, labels) - linking_pairs = matched_wordgroup_relations(word_groups, lrelations) - - date_key_value_pairs = get_pairs( - linking_pairs, rel_from="date_key", rel_to="date_value" - ) # => {date_value_group_id: [date_key_group_id, date_value_group_id]} - # product_name_code_pairs = get_pairs( - # linking_pairs, rel_to="product_name", rel_from="product_code" - # ) # => {product_name_group_id: [product_code_group_id, product_name_group_id]} - # product_name_quantity_pairs = get_pairs( - # linking_pairs, rel_to="product_name", rel_from="quantity" - # ) # => {product_name_group_id: [quantity_group_id, product_name_group_id]} - serial_key_value_pairs = get_pairs( - linking_pairs, rel_from="sn_key", rel_to="sn_value" - ) # => {sn_value_group_id: [sn_key_group_id, sn_value_group_id]} - - sold_key_value_pairs = get_pairs( - linking_pairs, rel_from="sold_key", rel_to="sold_value" - ) # => {sold_value_group_id: [sold_key_group_id, sold_value_group_id]} - - single_entity = get_single_entity(word_groups, lrelations, labels=labels) - - date_value = [] - sold_value = [] - serial_imei = [] - table = [] - # print('key2values_relations', key2values_relations) - date_relations = get_key2values_relations(date_key_value_pairs) - for key_group_id, list_value_group_id in date_relations.items(): - for value_group_id in list_value_group_id: - date_value.append( - { - "text": word_groups[value_group_id]["text"], - "id": value_group_id, - "class": "date_value", - "bbox": word_groups[value_group_id]["bbox"], - "key_bbox": word_groups[key_group_id]["bbox"], - "raw_key_name": word_groups[key_group_id]["text"], - } - ) - - sold_relations = get_key2values_relations(sold_key_value_pairs) - for key_group_id, list_value_group_id in sold_relations.items(): - for value_group_id in list_value_group_id: - sold_value.append( - { - "text": word_groups[value_group_id]["text"], - "id": value_group_id, - "class": "sold_value", - "bbox": word_groups[value_group_id]["bbox"], - "key_bbox": word_groups[key_group_id]["bbox"], - "raw_key_name": word_groups[key_group_id]["text"], - } - ) - - - serial_relations = get_key2values_relations(serial_key_value_pairs) - for key_group_id, list_value_group_id in serial_relations.items(): - for value_group_id in list_value_group_id: - serial_imei.append( - { - "text": word_groups[value_group_id]["text"], - "id": value_group_id, - "class": "sn_value", - "bbox": word_groups[value_group_id]["bbox"], - "key_bbox": word_groups[key_group_id]["bbox"], - "raw_key_name": word_groups[key_group_id]["text"], - } - ) - - - single_entity_dict = {} - for class_name, single_items in single_entity.items(): - single_entity_dict[class_name] = [] - for single_item in single_items: - single_entity_dict[class_name].append( - { - "text": single_item["text"], - "id": single_item["group_id"], - "class": class_name, - "bbox": single_item["bbox"], - } - ) - - # list_product_name_group_ids = set( - # list(product_name_code_pairs.keys()) - # + list(product_name_quantity_pairs.keys()) - # + [x["id"] for x in single_entity_dict["product_name"]] - # ) - # for product_name_group_id in list_product_name_group_ids: - # item = {"productname": [], "modelnumber": [], "qty": []} - # item["productname"].append( - # { - # "text": word_groups[product_name_group_id]["text"], - # "id": product_name_group_id, - # "class": "product_name", - # "bbox": word_groups[product_name_group_id]["bbox"], - # } - # ) - # if product_name_group_id in product_name_code_pairs: - # product_code_group_id = product_name_code_pairs[product_name_group_id][0] - # item["modelnumber"].append( - # { - # "text": word_groups[product_code_group_id]["text"], - # "id": product_code_group_id, - # "class": "product_code", - # "bbox": word_groups[product_code_group_id]["bbox"], - # } - # ) - # if product_name_group_id in product_name_quantity_pairs: - # quantity_group_id = product_name_quantity_pairs[product_name_group_id][0] - # item["qty"].append( - # { - # "text": word_groups[quantity_group_id]["text"], - # "id": quantity_group_id, - # "class": "quantity", - # "bbox": word_groups[quantity_group_id]["bbox"], - # } - # ) - # table.append(item) - - # if len(table) > 0: - # table = sorted(table, key=lambda x: x["productname"][0]["id"]) - - if len(serial_imei) > 0: - serial_imei = sorted(serial_imei, key=lambda x: x["id"]) - - outputs = {} - outputs["seller"] = single_entity_dict["seller"] - outputs["voucher"] = single_entity_dict["voucher"] - outputs["website"] = single_entity_dict["website"] - outputs["hotline"] = single_entity_dict["hotline"] - outputs["sold_value"] = sold_value + single_entity_dict["sold_key"] + single_entity_dict["sold_value"] - outputs["date_value"] = date_value + single_entity_dict["date_value"] + single_entity_dict["date_key"] - outputs["serial_imei"] = serial_imei + single_entity_dict["sn_value"] + single_entity_dict["sn_key"] - # outputs["table"] = table - return outputs diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/list_retailers.txt b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/list_retailers.txt deleted file mode 100644 index dbce48a..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/list_retailers.txt +++ /dev/null @@ -1,167 +0,0 @@ -1 FUSION TELECOM -3 Mobile -A STAR MOBILE TRADING -A2Z -ACES MOBILE -ARS DIGITAL WORLD -AV Intelligence -AZ Telecommunication -Active Electronics -Addon Systems -Alpha Telecom -Amazon.sg -Arrow Communication -Audio Ace Electronics -Audio House -BEST DIGITAL LIFESTYLE -BLAZING HANDPHONE SHOP -Best Denki -Best Denki - Great World -Best Denki - Ngee Ann -Best Denki - Online shop -Best Denki - Vivo -C. K. Tang -CALLMOBILE -Hachi -Challenger -Challenger Corporate -Circles.Life -Courts -Courts Heeren -Courts Megastore -Cyber Jip -DV Tech -Easytone -Everjoint Electrical -GIANT MOBILE -GRAPES COMMUNICATION -Gadget Affair -Gain City -Gain City Best-Electric -Gain City - Sungei Kadut -Gain City - Marina Square -Gain City - Sungei Kadut -Garphil Enterprise -Goh Ah Bee -HI MOBILE -Han's Communication -Handphone Shop -Harvey Norman -Harvey Norman - Millenia Walk -Harvey Norman - Pertama Merchandising -Harvey Norman - Millenia Walk -Harvey Norman - Northpoint -Hi-Life -I.COMM MOBILE PLUS -ING Mobile -IT Mobile  -IT TALENT TRADING -Ingram Micro -Isetan -Ivan Mobile & Jewelleries -J2 MOBILE STORE -KASIA Mobile -Kong Tai -Kris Shop -Lazada -Lazada - Samsung Brand Store -Lion City Company -Lucky Store -M1 Exclusive Partners -M1 Shop -MAGNA MOBILE -MEGA TELESHOP -MELA SHOPPE -MG MOBILE COMMUNICATION -MOBILE SQUARE -MOBILE X -MOBILEHUB SERVICE -MOBILERELATION 1 -MOBY -MOHAMED MUSTAFA & SAMSUDDIN -MY MOBILE HOUSE -Magnify -Mega Discount Store -My Mobile -My Mobile House -MyRepublic -NAIN INTERNATIONAL TRADING -NARANJAN ELECTRONICS -NTUC -Naranjan Int Mobile -New Sound Electrical Dept -One Dream Telecom -One2Free Mobile -Onephone Online -PHONEVIBES -POD CONTACT TELECOMMUNICATIONS -PROVIDER STORES -Parisilk Electronics & Computers -Planet Telecoms -Pod Contact -Poorvika (TV) -Popular Book -Provider Stores -RED WHITE MOBILE -REMO COMM -Red White Mobile -Rigel Telecom -SK Mobile -SMART PLAY TRADING -SMART TECH MOBILE -SOLULAR PLUS -SONIC CONNECTION ENTERPRISES -SPRINT - CASS (HQ) -SUMMER TELECOM -Lazada Samsung Brand Store -Shopee Samsung Brand Store -Lazada Samsung Certified Store -Shopee Samsung Certified Store -Samsung Brand Store -Samsung Customer Service Plaza Singapura -Samsung EDU Store -Samsung EPP Store -Samsung Experience Store -Samsung Experience Store - 313 Somerset -Samsung Experience Store - Bedok Mall -Samsung Experience Store - Bugis Junction -Samsung Experience Store - Causeway Point -Samsung Experience Store - ION -Samsung Experience Store - Jurong Point -Samsung Experience Store - Nex -Samsung Experience Store - Northpoint City -Samsung Experience Store - Tampines Mall -Samsung Experience Store - VivoCity -Samsung Experience Store - Westgate -Samsung Experience Store - delivery -Samsung Experience Store - pickup -Samsung Online Shop -Samsung Online Shop -Singtel Exclusive Retailers -Singtel Shop -Sprint-Cass -StarHub Exclusive Partners -StarHub Shop -T2 Electronics -TANGS -Takashimaya -Takashimaya Singapore -Telemobile -Telestation Infocomm -Trends n Trendies -U MOBILE SHOP -U-First Mobile -UNITED MOBILE SERVICES -UWIN - CHEERS COMMUNICATIONS -UWIN COMMUNICATION -Univercell -Urban Republic -VMCS Pte Ltd -VV MOBILE -Vi Mobile -WEN MOBILE TRADING -YOSHI MOBILE -ZYM Official Store -Zalora -i.Comm Mobile -iShopChangi diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/manulife.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/manulife.py deleted file mode 100644 index 6d172ae..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/manulife.py +++ /dev/null @@ -1,69 +0,0 @@ -key_dict = { - "Document type": ["documenttype", "loaichungtu"], - "Document name": ["documentname", "tenchungtu"], - "Patient Name": ["patientname", "tenbenhnhan"], - "Date of Birth/Year of birth": [ - "dateofbirth", - "yearofbirth", - "ngaythangnamsinh", - "namsinh", - ], - "Age": ["age", "tuoi"], - "Gender": ["gender", "gioitinh"], - "Social insurance card No.": ["socialinsurancecardno", "sothebhyt"], - "Medical service provider name": ["medicalserviceprovidername", "tencosoyte"], - "Department name": ["departmentname", "tenkhoadieutri"], - "Diagnosis description": ["diagnosisdescription", "motachandoan"], - "Diagnosis code": ["diagnosiscode", "machandoan"], - "Admission date": ["admissiondate", "ngaynhapvien"], - "Discharge date": ["dischargedate", "ngayxuatvien"], - "Treatment method": ["treatmentmethod", "phuongphapdieutri"], - "Treatment date": ["treatmentdate", "ngaydieutri", "ngaykham"], - "Follow up treatment date": ["followuptreatmentdate", "ngaytaikham"], - # "Name of precribed medicine": [], - # "Quantity of prescribed medicine": [], - # "Dosage for each medicine": [] - "Medical expense": ["Medical expense", "chiphiyte"], - "Invoice No.": ["invoiceno", "sohoadon"], -} - -title_dict = { - "Chứng từ y tế": [ - "giayravien", - "giaychungnhanphauthuat", - "cachthucphauthuat", - "phauthuat", - "tomtathosobenhan", - "donthuoc", - "toathuoc", - "donbosung", - "ketquaconghuongtu" - "ketqua", - "phieuchidinh", - "phieudangkykham", - "giayhenkhamlai", - "phieukhambenh", - "phieukhambenhvaovien", - "phieuxetnghiem", - "phieuketquaxetnghiem", - "phieuchidinhxetnghiem", - "ketquasieuam", - "phieuchidinhxetnghiem" - ], - "Chứng từ thanh toán": [ - "hoadon", - "hoadongiatrigiatang", - "hoadongiatrigiatangchuyendoituhoadondientu", - "bangkechiphibaohiem", - "bienlaithutien", - "bangkechiphidieutrinoitru" - ], -} - -def get_dict(type: str): - if type == "key": - return key_dict - elif type == "title": - return title_dict - else: - raise ValueError(f"[ERROR] Dictionary type of {type} is not supported") \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt.py deleted file mode 100644 index edb33f8..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt.py +++ /dev/null @@ -1,32 +0,0 @@ -header_dict = { - 'productname': ['description', 'productdescription', 'articledescription', 'descriptionofgood', 'itemdescription', - 'brandmodel', 'itemdepartment', 'departmentbrand', 'department', 'certificateno', - 'product', 'modelname', 'paticulars', 'device', 'items', 'itemno'], - 'modelnumber': ['serialno', 'serial', 'articles', 'simimeiserial', 'article', 'articlenumber', 'articleidmaterialcode', - 'itemcode', 'code', 'mcode', 'productcode', 'model', 'product', 'imeiccid', 'transaction'], - 'qty': ['quantity', 'invoicequantity'] -} - -key_dict = { - 'purchase_date': ['date', 'purchasedate', 'datetime', 'orderdate', 'orderdatetime', 'invoicedate', 'dateredeemed', 'issuedate', 'billingdocdate'], - 'retailername': ['retailer', 'retailername', 'ownedoperatedby'], - 'serial_number': ['serialnumber', 'serialno'], - 'imei_number': ['imeiesim', 'imeislot1', 'imeislot2', 'imei', 'imei1', 'imei2'] -} - -extra_dict = { - 'serial_number': ['sn'], - 'imei_number': ['imel', 'imed'], - 'modelnumber': ['sku', 'sn', 'imei'], - 'qty': ['qty'] -} - -def get_dict(type: str): - if type == "key": - return key_dict - elif type == "header": - return header_dict - elif type == "extra": - return extra_dict - else: - raise ValueError(f'[ERROR] Dictionary type of {type} is not supported') \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt_v2.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt_v2.py deleted file mode 100644 index ad3141e..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/sbt_v2.py +++ /dev/null @@ -1,116 +0,0 @@ -import os -from pathlib import Path - -cur_dir = str(Path(__file__).parents[0]) -from sdsvkvu.utils.utils import read_txt -from sdsvkvu.utils.post_processing import preprocessing - - -header_dict = { - "productname": [ - "description", - "productdescription", - "articledescription", - "descriptionofgood", - "itemdescription", - "brandmodel", - "itemdepartment", - "departmentbrand", - "department", - "certificateno", - "product", - "modelname", - "paticulars", - "device", - "items", - "itemno", - ], - "modelnumber": [ - "serialno", - "serial", - "articles", - "simimeiserial", - "article", - "articlenumber", - "articleidmaterialcode", - "itemcode", - "code", - "mcode", - "productcode", - "model", - "product", - "imeiccid", - "transaction", - ], - "qty": ["quantity", "invoicequantity"], -} - -date_dict = { - "purchase_date": [ - "date", - "purchasedate", - "datetime", - "orderdate", - "orderdatetime", - "invoicedate", - "dateredeemed", - "issuedate", - "billingdocdate", - "placedon", - "transactiondatetime", - "creationdate", - "ordertime", - "dateofissue", - ] -} - -imei_dict = { - "serial_number": ["serialnumber", "serialno"], - "imei_number": ["imeiesim", "imeislot1", "imeislot2", "imei", "imei1", "imei2"], -} - -sold_dict = { - "sold_by_party": ["soldtoparty"], - "sold_by": ["soldby"] -} - -extra_dict = { - "serial_number": ["sn"], - "imei_number": ["imel", "imed"], - "modelnumber": ["sku", "sn", "imei"], - "qty": ["qty"], -} - -seller_mapping = { - "Samsung Experience Store": ["G-FORCE NETWORK PTE LTD", "eSmart Mobile", "eSmart Mobile Pte Ltd"], - "Samsung Online Store": ["SAMSUNG ELECTRONICS SINGAPORE PTE LTD"], - "Samsung Brand Store": ["SAMSUNG OFFICIAL STORE"], - "Harvey Norman": ["PERTAMA MERCHANDISING PTE LTD"], - "Shopee": ["shopee mall"], - "Lazada": ["laz mall", "lazmall"], - "LTD": ["limited"], - "PTE": ["private"], -} - - -seller_list = read_txt(os.path.join(cur_dir, "list_retailers.txt")) -seller_dict = {seller.upper(): [preprocessing(seller)] for seller in seller_list} - - -def get_dict(type: str): - if type == "date": - return date_dict - elif type == "imei": - return imei_dict - elif type == "sold_by": - return sold_dict - elif type == "header": - return header_dict - elif type == "extra": - return extra_dict - elif type == "seller": - return seller_dict - elif type == "seller_mapping": - return seller_mapping - else: - raise ValueError(f"[ERROR] Dictionary type of {type} is not supported") diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vat.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vat.py deleted file mode 100644 index 8945200..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vat.py +++ /dev/null @@ -1,69 +0,0 @@ -DKVU2XML = { - "Ký hiệu mẫu hóa đơn": "form_no", - "Ký hiệu hóa đơn": "serial_no", - "Số hóa đơn": "invoice_no", - "Ngày, tháng, năm lập hóa đơn": "issue_date", - "Tên người bán": "seller_name", - "Mã số thuế người bán": "seller_tax_code", - "Thuế suất": "tax_rate", - "Thuế GTGT đủ điều kiện khấu trừ thuế": "VAT_input_amount", - "Mặt hàng": "item", - "Đơn vị tính": "unit", - "Số lượng": "quantity", - "Đơn giá": "unit_price", - "Doanh số mua chưa có thuế": "amount" -} - -key_dict = { - 'Ký hiệu mẫu hóa đơn': ['mausoformno', 'mauso'], - 'Ký hiệu hóa đơn': ['kyhieuserialno', 'kyhieuserial', 'kyhieu'], - 'Số hóa đơn': ['soinvoiceno', 'invoiceno'], - 'Ngày, tháng, năm lập hóa đơn': [], - 'Tên người bán': ['donvibanseller', 'donvibanhangsalesunit', 'donvibanhangseller', 'kyboisignedby'], - 'Mã số thuế người bán': ['masothuetaxcode', 'maxsothuetaxcodenumber', 'masothue'], - 'Thuế suất': ['thuesuatgtgttaxrate', 'thuesuatgtgt'], - 'Thuế GTGT đủ điều kiện khấu trừ thuế': ['tienthuegtgtvatamount', 'tienthuegtgt'], - # 'Ghi chú': [], - # 'Ngày': ['ngayday', 'ngay', 'day'], - # 'Tháng': ['thangmonth', 'thang', 'month'], - # 'Năm': ['namyear', 'nam', 'year'] -} - -header_dict = { - 'Mặt hàng': ['tenhanghoa,dichvu', 'danhmuc,dichvu', 'dichvusudung', 'tenquycachhanghoa','description', 'descriptionofgood', 'itemdescription'], - 'Đơn vị tính': ['donvitinh', 'dvtunit'], - 'Số lượng': ['soluong', 'quantity', 'invoicequantity', 'soluongquantity'], - 'Đơn giá': ['dongia', 'dongiaprice'], - 'Doanh số mua chưa có thuế': ['thanhtien', 'thanhtientruocthuegtgt', 'tienchuathue'], -} - -extra_dict = { - 'Số hóa đơn': ['sono', 'so'], - 'Mã số thuế người bán': ['mst'], - 'Tên người bán': ['kyboi'], - 'Ngày, tháng, năm lập hóa đơn': ['kyngay', 'kyngaydate'], - 'Mặt hàng': ['tenhanghoa','sanpham'], - 'Số lượng': ['sl', 'qty'], - 'Đơn vị tính': ['dvt'], -} - -date_dict = { - 'day': ['ngayday', 'ngaydate', 'ngay', 'day'], - 'month': ['thangmonth', 'thang', 'month'], - 'year': ['namyear', 'nam', 'year'] -} - -def get_dict(type: str): - if type == "key": - return key_dict - elif type == "header": - return header_dict - elif type == "extra": - return extra_dict - elif type == "date": - return date_dict - elif type == "kvu2xml": - return DKVU2XML - else: - raise ValueError(f'[ERROR] Dictionary type of {type} is not supported') - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vtb.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vtb.py deleted file mode 100644 index a4dd15b..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/dictionary/vtb.py +++ /dev/null @@ -1,33 +0,0 @@ -key_dict = { - "number": ["kemtheoquyetdinhso", "quyetdinhso"], - "title": [], - "date": [], - "signee": ['botruong', 'thutruong', 'giamdoc', 'phogiamdoc', 'chunhiem', 'phochunhiem', - 'hieutruong', 'vientruong', 'thuky', 'chutich', 'phochutich', 'bithu', 'chutoa', - 'daidien', 'truongban', 'tongcuctruong', 'photongcuctruong', 'cuctruong', 'cucpho', - 'thuky', 'chanhthanhtra', 'thutruongdonvi', 'thutuong', - 'kiemtoanvien', 'canbokekhai'], - "sender": ['kinhgui'], - "receiver": ['noinhan', 'noigui'] - } - -extra_dict = { - "number": ['so'], - # "sender": ['dien'] -} - -date_dict = { - "day": ['ngayday', 'ngaydate', 'ngay', 'day'], - "month": ['thangmonth', 'thang', 'month'], - "year": ['namyear', 'nam', 'year'] -} - -def get_dict(type: str): - if type == "key": - return key_dict - elif type == "extra": - return extra_dict - elif type == "date": - return date_dict - else: - raise ValueError(f'[ERROR] Dictionary type of {type} is not supported') \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/post_processing.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/post_processing.py deleted file mode 100644 index 14b6bec..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/post_processing.py +++ /dev/null @@ -1,362 +0,0 @@ -import re -import nltk -import string -import tldextract -from dateutil import parser -from datetime import datetime -# nltk.download('words') -try: - nltk.data.find("corpora/words") -except LookupError: - nltk.download('words') -words = set(nltk.corpus.words.words()) - -from sdsvkvu.utils.word2line import Word, words_to_lines - -s1 = u'ÀÁÂÃÈÉÊÌÍÒÓÔÕÙÚÝàáâãèéêìíòóôõùúýĂăĐđĨĩŨũƠơƯưẠạẢảẤấẦầẨẩẪẫẬậẮắẰằẲẳẴẵẶặẸẹẺẻẼẽẾếỀềỂểỄễỆệỈỉỊịỌọỎỏỐốỒồỔổỖỗỘộỚớỜờỞởỠỡỢợỤụỦủỨứỪừỬửỮữỰựỲỳỴỵỶỷỸỹ' -s0 = u'AAAAEEEIIOOOOUUYaaaaeeeiioooouuyAaDdIiUuOoUuAaAaAaAaAaAaAaAaAaAaAaAaEeEeEeEeEeEeEeEeIiIiOoOoOoOoOoOoOoOoOoOoOoOoUuUuUuUuUuUuUuYyYyYyYy' - -# def clean_text(text): -# return re.sub(r"[^A-Za-z(),!?\'\`]", " ", text) - -def get_string(lwords: list): - unique_list = [] - for item in lwords: - if item.isdigit() and len(item) == 1: - unique_list.append(item) - elif item not in unique_list: - unique_list.append(item) - return ' '.join(unique_list) - -def get_string_by_deduplicate_bbox(lwords: list, lbboxes: list): - unique_list = [] - prev_bbox = [-1, -1, -1, -1] - for word, bbox in zip(lwords, lbboxes): - if bbox != prev_bbox: - unique_list.append(word) - prev_bbox = bbox - return ' '.join(unique_list) - -def get_string_with_word2line(lwords: list, lbboxes: list): - list_words = [] - unique_list = [] - list_sorted_words = [] - - prev_bbox = [-1, -1, -1, -1] - for word, bbox in zip(lwords, lbboxes): - if bbox != prev_bbox: - prev_bbox = bbox - list_words.append(Word(image=None, text=word, conf_cls=-1, bndbox=bbox, conf_detect=-1)) - unique_list.append(word) - - llines = words_to_lines(list_words)[0] - - for line in llines: - for _word_group in line.list_word_groups: - for _word in _word_group.list_words: - list_sorted_words.append(_word.text) - - string_from_model = ' '.join(unique_list) - string_after_word2line = ' '.join(list_sorted_words) - - # if string_from_model != string_after_word2line: - # print("[Warning] Word group from model is different with word2line module") - # print("Model: ", ' '.join(unique_list)) - # print("Word2line: ", ' '.join(list_sorted_words)) - - return string_after_word2line - -def date_regexing(inp_str): - patterns = { - 'ngay': r"ngày\d+", - 'thang': r"tháng\d+", - 'nam': r"năm\d+" - } - inp_str = inp_str.replace(" ", "").lower() - outputs = {k: '' for k in patterns} - for key, pattern in patterns.items(): - matches = re.findall(pattern, inp_str) - if len(matches) > 0: - element = set([match[len(key):] for match in matches]) - outputs[key] = list(element)[0] - return outputs['ngay'], outputs['thang'], outputs['nam'] - - -def parse_date1(date_str): - # remove space - date_str = re.sub(r"[\[\]\{\}\(\)\.\,]", " ", date_str) - date_str = re.sub(r"/\s+", "/", date_str) - date_str = re.sub(r"-\s+", "-", date_str) - - is_parser_error = False - try: - date_obj = parser.parse(date_str, fuzzy=True) - year_str = str(date_obj.year) - day_str = str(date_obj.day) - # date_formated = date_obj.strftime("%d/%m/%Y") - date_formated = date_obj.strftime("%Y-%m-%d") - except Exception as err: - # date_str = sorted(date_str.split(" "), key=lambda x: len(x), reverse=True)[0] - # date_str, is_match = date_regexing(date_str) - is_match = False - if is_match: - date_formated = date_str - is_parser_error = False - return date_formated, is_parser_error - else: - print(f"Error parse date: err = {err}, date = {date_str}") - date_formated = date_str - is_parser_error = True - return date_formated, is_parser_error - - if len(normalize_number(date_str)) == 6: - year_str = year_str[-2:] - try: - year_index = date_str.index(str(year_str)) - day_index = date_str.index(str(day_str)) - if year_index > day_index: - date_obj = parser.parse(date_str, fuzzy=True, dayfirst=True) - - # date_formated = date_obj.strftime("%d/%m/%Y") - date_formated = date_obj.strftime("%Y-%m-%d") - except Exception as err: - print(f"Error check dayfirst: err = {err}, date = {date_str}") - - return date_formated, is_parser_error - - -def parse_date(date_str): - # remove space - date_str = re.sub(r"[\[\]\{\}\(\)\.\,]", " ", date_str) - date_str = re.sub(r"/\s+", "/", date_str) - date_str = re.sub(r"-\s+", "-", date_str) - date_str = re.sub(r"\-+", "-", date_str) - date_str = date_str.lower().replace("0ct", "oct") - - is_parser_error = False - try: - date_obj = parser.parse(date_str, fuzzy=True) - except Exception as err: - print(f"1.Error parse date: err = {err}, date = {date_str}") - try: - date_str = sorted(date_str.split(" "), key=lambda x: len(x), reverse=True)[0] - date_obj = parser.parse(date_str, fuzzy=True) - except Exception as err: - print(f"2.Error parse date: err = {err}, date = {date_str}") - is_parser_error = True - return [date_str], is_parser_error - - year_str = int(date_obj.year) - month_str = int(date_obj.month) - day_str = int(date_obj.day) - - current_year = int(datetime.now().year) - if year_str > current_year or year_str < 2010: # invalid year - date_obj = date_obj.replace(year=current_year) - - formated_date = date_obj.strftime("%Y-%m-%d") - revert_formated_date = date_obj.strftime("%Y-%d-%m") - - if any(txt in date_str for txt in ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']): - return [formated_date], is_parser_error - if month_str <= 12 and day_str <= 12: - return [formated_date, revert_formated_date], is_parser_error - return [formated_date], is_parser_error - - - -def normalize_imei(imei): - imei = imei.replace(" ", "") - imei = imei.split("/")[0] - return imei - -def normalize_seller(seller): - # if isinstance(seller, str): - # seller = seller - return seller - -def normalize_website(website): - if isinstance(website, str): - # website = website.lower().replace("www.", "").replace("ww.", "").replace(".com", "") - website = website.lower() - website = website.split(".com")[0] - website = tldextract.extract(website).domain - return website - -def normalize_hotline(hotline): - if isinstance(hotline, str): - hotline = hotline.lower().replace("hotline", "") - return hotline - -def normalize_voucher(voucher): - if isinstance(voucher, str): - voucher = voucher.lower().replace("voucher", "") - return voucher - - - -def normalize_number( - text_str: str, reserve_dot=False, reserve_plus=False, reserve_minus=False -): - """ - Normalize a string of numbers by removing non-numeric characters - - """ - assert isinstance(text_str, str), "input must be str" - reserver_chars = "" - if reserve_dot: - reserver_chars += ".," - if reserve_plus: - reserver_chars += "+" - if reserve_minus: - reserver_chars += "-" - regex_fomula = "[^0-9{}]".format(reserver_chars) - normalized_text_str = re.sub(r"{}".format(regex_fomula), "", text_str) - return normalized_text_str - -def remove_bullet_points_and_punctuation(text): - # Remove bullet points (e.g., • or -) - text = re.sub(r'^\s*[\•\-\*]\s*', '', text, flags=re.MULTILINE) - text = text.strip() - # # Remove end-of-sentence punctuation (e.g., ., !, ?) - # text = re.sub(r'[.!?]', '', text) - if len(text) > 0 and text[0] in (',', '.', ':', ';', '?', '!'): - text = text[1:] - if len(text) > 0 and text[-1] in (',', '.', ':', ';', '?', '!'): - text = text[:-1] - return text.strip() - -def split_key_value_by_colon(key: str, value: str) -> list: - key_string = key if key is not None else "" - value_string = value if value is not None else "" - text_string = key_string + " " + value_string - elements = text_string.split(":") - if len(elements) > 1 and not bool(re.search(r'\d', elements[0])): - return elements[0], text_string[len(elements[0])+1 :].strip() - return key, value - - -def is_string_in_range(s): - try: - num = int(s) - return 0 <= num <= 9 - except ValueError: - return False - -def remove_english_words(text): - _word = [w.lower() for w in nltk.wordpunct_tokenize(text) if w.lower() not in words] - return ' '.join(_word) - -def remove_punctuation(text): - return text.translate(str.maketrans(" ", " ", string.punctuation)) - -def remove_accents(input_str, s0, s1): - s = '' - # print input_str.encode('utf-8') - for c in input_str: - if c in s1: - s += s0[s1.index(c)] - else: - s += c - return s - -def remove_spaces(text): - return text.replace(' ', '') - -def preprocessing(text: str): - # text = remove_english_words(text) if table else text - text = remove_punctuation(text) - text = remove_accents(text, s0, s1) - text = remove_spaces(text) - return text.lower() - -def longestCommonSubsequence(text1: str, text2: str) -> int: - # https://leetcode.com/problems/longest-common-subsequence/discuss/351689/JavaPython-3-Two-DP-codes-of-O(mn)-and-O(min(m-n))-spaces-w-picture-and-analysis - dp = [[0] * (len(text2) + 1) for _ in range(len(text1) + 1)] - for i, c in enumerate(text1): - for j, d in enumerate(text2): - dp[i + 1][j + 1] = 1 + \ - dp[i][j] if c == d else max(dp[i][j + 1], dp[i + 1][j]) - return dp[-1][-1] - - -def longest_common_subsequence_with_idx(X, Y): - """ - This implementation uses dynamic programming to calculate the length of the LCS, and uses a path array to keep track of the characters in the LCS. - The longest_common_subsequence function takes two strings as input, and returns a tuple with three values: - the length of the LCS, - the index of the first character of the LCS in the first string, - and the index of the last character of the LCS in the first string. - """ - m, n = len(X), len(Y) - L = [[0 for i in range(n + 1)] for j in range(m + 1)] - - # Following steps build L[m+1][n+1] in bottom up fashion. Note - # that L[i][j] contains length of LCS of X[0..i-1] and Y[0..j-1] - right_idx = 0 - max_lcs = 0 - for i in range(m + 1): - for j in range(n + 1): - if i == 0 or j == 0: - L[i][j] = 0 - elif X[i - 1] == Y[j - 1]: - L[i][j] = L[i - 1][j - 1] + 1 - if L[i][j] > max_lcs: - max_lcs = L[i][j] - right_idx = i - else: - L[i][j] = max(L[i - 1][j], L[i][j - 1]) - - # Create a string variable to store the lcs string - lcs = L[i][j] - # Start from the right-most-bottom-most corner and - # one by one store characters in lcs[] - i = m - j = n - # right_idx = 0 - while i > 0 and j > 0: - # If current character in X[] and Y are same, then - # current character is part of LCS - if X[i - 1] == Y[j - 1]: - - i -= 1 - j -= 1 - # If not same, then find the larger of two and - # go in the direction of larger value - elif L[i - 1][j] > L[i][j - 1]: - # right_idx = i if not right_idx else right_idx #the first change in L should be the right index of the lcs - i -= 1 - else: - j -= 1 - return lcs, i, max(i + lcs, right_idx) - - - -def longest_common_substring(X, Y): - m = len(X) - n = len(Y) - - # Create a 2D array to store the lengths of common substrings - dp = [[0] * (n + 1) for _ in range(m + 1)] - - # Variables to store the length of the longest common substring - max_length = 0 - end_index = 0 - - # Build the dp array bottom-up - for i in range(1, m + 1): - for j in range(1, n + 1): - if X[i - 1] == Y[j - 1]: - dp[i][j] = dp[i - 1][j - 1] + 1 - - # Update the length and ending index of the common substring - if dp[i][j] > max_length: - max_length = dp[i][j] - end_index = i - 1 - else: - dp[i][j] = 0 - - # The longest common substring is X[end_index - max_length + 1:end_index + 1] - - return len(X[end_index - max_length + 1: end_index + 1]) - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/__init__.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/all.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/all.py deleted file mode 100644 index c1a909a..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/all.py +++ /dev/null @@ -1,75 +0,0 @@ -from sdsvkvu.utils.post_processing import split_key_value_by_colon, remove_bullet_points_and_punctuation - - -def normalize_kvu_output(raw_outputs: dict) -> dict: - outputs = {} - for key, values in raw_outputs.items(): - if key == "table": - table = [] - for row in values: - item = {} - for k, v in row.items(): - k = remove_bullet_points_and_punctuation(k) - if v is not None and len(v) > 0: - v = remove_bullet_points_and_punctuation(v) - item[k] = v - table.append(item) - outputs[key] = table - else: - key = remove_bullet_points_and_punctuation(key) - if isinstance(values, list): - values = [remove_bullet_points_and_punctuation(v) for v in values] - elif values is not None and len(values) > 0: - values = remove_bullet_points_and_punctuation(values) - outputs[key] = values - return outputs - - -def export_kvu_for_all(raw_outputs: dict) -> dict: - outputs = {} - # Title - outputs["title"] = ( - raw_outputs["title"][0]["text"] if len(raw_outputs["title"]) > 0 else None - ) - - # Pairs of key-value - for pair in raw_outputs["single"]: - for key, values in pair.items(): - # outputs[key] = values["text"] - elements = split_key_value_by_colon(key, values["text"]) - outputs[elements[0]] = elements[1] - - # Only key fields - for key in raw_outputs["key"]: - # outputs[key["text"]] = None - elements = split_key_value_by_colon(key["text"], None) - outputs[elements[0]] = elements[1] - - # Triplet data - for triplet in raw_outputs["triplet"]: - for key, list_value in triplet.items(): - outputs[key] = [value["text"] for value in list_value] - - # Table data - table = [] - for row in raw_outputs["table"]: - item = {} - for cell in row: - item[cell["header"]] = cell["text"] - table.append(item) - outputs["table"] = table - outputs = normalize_kvu_output(outputs) - return outputs - - -def merged_kvu_for_all_for_multi_pages(loutputs: list) -> dict: - merged_outputs = {} - table = [] - for outputs in loutputs: - for key, value in outputs.items(): - if key == "table": - table.append(value) - else: - merged_outputs[key] = value - merged_outputs['table'] = table - return merged_outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/manulife.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/manulife.py deleted file mode 100644 index 543b89b..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/manulife.py +++ /dev/null @@ -1,133 +0,0 @@ -from sdsvkvu.utils.dictionary.manulife import get_dict -from sdsvkvu.utils.post_processing import ( - split_key_value_by_colon, - remove_bullet_points_and_punctuation, - longestCommonSubsequence, - preprocessing - ) - - - -def manulife_key_matching(text: str, threshold: float, dict_type: str): - dictionary = get_dict(type=dict_type) - processed_text = preprocessing(text) - - for key, candidates in dictionary.items(): - - if any([txt == processed_text for txt in candidates]): - return True, key, 5 * (1 + len(processed_text)), processed_text - - if any([txt in processed_text for txt in candidates]): - return True, key, 5, processed_text - - scores = {k: 0.0 for k in dictionary} - for k, v in dictionary.items(): - if len(v) == 0: - continue - scores[k] = max( - [ - longestCommonSubsequence(processed_text, key) / len(key) - for key in dictionary[k] - ] - ) - key, score = max(scores.items(), key=lambda x: x[1]) - return score > threshold, key if score > threshold else text, score, processed_text - -def normalize_kvu_output_for_manulife(raw_outputs: dict) -> dict: - outputs = {} - for key, values in raw_outputs.items(): - if key == "tables" and len(values) > 0: - table_list = [] - for table in values: - headers, data = [], [] - headers = [ - remove_bullet_points_and_punctuation(header).upper() - for header in table["headers"] - ] - for row in table["data"]: - item = [] - for k, v in row.items(): - if v is not None and len(v) > 0: - item.append(remove_bullet_points_and_punctuation(v)) - else: - item.append(v) - data.append(item) - table_list.append({"headers": headers, "data": data}) - outputs[key] = table_list - else: - key = remove_bullet_points_and_punctuation(key).capitalize() - if isinstance(values, list): - values = [remove_bullet_points_and_punctuation(v) for v in values] - elif values is not None and len(values) > 0: - values = remove_bullet_points_and_punctuation(values) - outputs[key] = values - return outputs - -def export_kvu_for_manulife(raw_outputs: dict) -> dict: - outputs = {} - # Title - title_list = [] - for title in raw_outputs["title"]: - is_match, title_name, score, proceessed_text = manulife_key_matching(title["text"], threshold=0.6, dict_type="title") - title_list.append({ - 'documment_type': title_name if is_match else "", - 'content': title['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': title['id'] - }) - - if len(title_list) > 0: - selected_element = max(title_list, key=lambda x: x['lcs_score']) - outputs["title"] = f"({selected_element['documment_type']}) {selected_element['content']}" - else: - outputs["title"] = None - - # Pairs of key-value - for pair in raw_outputs["single"]: - for key, values in pair.items(): - # outputs[key] = values["text"] - elements = split_key_value_by_colon(key, values["text"]) - outputs[elements[0]] = elements[1] - - # Only key fields - for key in raw_outputs["key"]: - # outputs[key["text"]] = None - elements = split_key_value_by_colon(key["text"], None) - outputs[elements[0]] = elements[1] - - # Triplet data - for triplet in raw_outputs["triplet"]: - for key, list_value in triplet.items(): - outputs[key] = [value["text"] for value in list_value] - - # Table data - table = [] - header_list = {cell['header']: cell['header_bbox'] for row in raw_outputs['table'] for cell in row} - if header_list: - header_list = dict(sorted(header_list.items(), key=lambda x: int(x[1][0]))) - # print("Header_list:", header_list.keys()) - - for row in raw_outputs["table"]: - item = {header: None for header in list(header_list.keys())} - for cell in row: - item[cell["header"]] = cell["text"] - table.append(item) - outputs["tables"] = [{"headers": list(header_list.keys()), "data": table}] - else: - outputs["tables"] = [] - outputs = normalize_kvu_output_for_manulife(outputs) - return outputs - - -def merged_kvu_for_manulife_for_multi_pages(loutputs: list) -> dict: - merged_outputs = {} - table = [] - for outputs in loutputs: - for key, value in outputs.items(): - if key == "tables": - table.append(value) - else: - merged_outputs[key] = value - merged_outputs['tables'] = table - return merged_outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt.py deleted file mode 100644 index bc7a5e4..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt.py +++ /dev/null @@ -1,186 +0,0 @@ -from sdsvkvu.utils.post_processing import longestCommonSubsequence, preprocessing, is_string_in_range -from sdsvkvu.utils.dictionary.sbt import get_dict - -# For SBT project -def sbt_key_matching(text: str, threshold: float, dict_type: str): - dictionary = get_dict(type=dict_type) - processed_text = preprocessing(text) - - # Step 1: Exactly matching - extra_dict = get_dict("extra") - for key, candidates in dictionary.items(): - candidates = candidates + extra_dict[key] if key in extra_dict.keys() else candidates - - if any([processed_text == txt for txt in candidates]): - return key, 10, processed_text - - # Step 2: LCS score - scores = {k: 0.0 for k in dictionary} - for k, v in dictionary.items(): - scores[k] = max([longestCommonSubsequence(processed_text, key)/len(key) for key in dictionary[k]]) - - key, score = max(scores.items(), key=lambda x: x[1]) - return key if score >= threshold else text, score, processed_text - -def get_sbt_table_info(outputs): - table = [] - for single_item in outputs['table']: - item = {k: [] for k in get_dict("header").keys()} - for cell in single_item: - header_name, score, proceessed_text = sbt_key_matching(cell['header'], threshold=0.8, dict_type="header") - # print(f"{cell['header']} ==> {proceessed_text} ==> {header_name} : {score} - {cell['text']}") - is_header_valid = False - if header_name in list(item.keys()): - if header_name != "productname": - is_header_valid = True - elif cell['class'] == 'key': # Header with name of itemno as productname only when key - is_header_valid = True - _, _, proceessed_text = sbt_key_matching(cell['text'], threshold=0.8, dict_type="header") - if any([txt in proceessed_text for txt in ["originalreceipt", "homeclubvoucher", "ippuob"]]): - # print(proceessed_text) - is_header_valid = False - else: - is_header_valid = False - - if is_header_valid: - item[header_name].append({ - 'content': cell['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': cell['id'] - }) - - for header_name, value in item.items(): - if len(value) == 0: - item[header_name] = None - continue - item[header_name] = max(value, key=lambda x: x['lcs_score'])['content'] # Get max lsc score - - table.append(item) - return table - -def get_sbt_triplet_info(outputs): - triplet_pairs = [] - for single_item in outputs['triplet']: - item = {k: [] for k in get_dict("header").keys()} - is_item_valid = 0 - for key_name, list_value in single_item.items(): - for value in list_value: - if value['header'] == "non-header": - continue - header_name, score, proceessed_text = sbt_key_matching(value['header'], threshold=0.8, dict_type="header") - if header_name in list(item.keys()): - is_item_valid = 1 - item[header_name].append({ - 'content': value['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': value['id'] - }) - - if is_item_valid == 1: - for header_name, value in item.items(): - if len(value) == 0: - item[header_name] = None - continue - item[header_name] = max(value, key=lambda x: x['lcs_score'])['content'] # Get max lsc score - - item['productname'] = key_name - # triplet_pairs.append({key_name: new_item}) - triplet_pairs.append(item) - - # else: ## Triplet => key as productname - # item['productname'] = key_name - # for value in list_value: - # # print(value) - # if is_string_in_range(value['text']): - # item['qty'] = value['text'] - # triplet_pairs.append(item) - return triplet_pairs - - -def get_sbt_info(outputs): - single_pairs = {k: [] for k in get_dict("key").keys()} - for pair in outputs['single']: - for key_name, value in pair.items(): - key_name, score, proceessed_text = sbt_key_matching(key_name, threshold=0.8, dict_type="key") - # print(f"{key} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs): - single_pairs[key_name].append({ - 'content': value['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': value['id'] - }) - - ### Get single_pair of serial_number if it predict as a table (Product Information) - is_product_info = False - for table_row in outputs['table']: - pair = {"key": None, 'value': None} - for cell in table_row: - _, _, proceessed_text = sbt_key_matching(cell['header'], threshold=0.8, dict_type="key") - if any(txt in proceessed_text for txt in ['product', 'information', 'productinformation']): - is_product_info = True - if cell['class'] in pair: - pair[cell['class']] = cell - - if all(v is not None for k, v in pair.items()) and is_product_info == True: - key_name, score, proceessed_text = sbt_key_matching(pair['key']['text'], threshold=0.8, dict_type="key") - # print(f"{pair['key']['text']} ==> {proceessed_text} ==> {key_name} : {score} - {pair['value']['text']}") - - if key_name in list(single_pairs): - single_pairs[key_name].append({ - 'content': pair['value']['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': pair['value']['id'] - }) - ### block_end - - ap_outputs = {k: None for k in list(single_pairs)} - for key_name, list_potential_value in single_pairs.items(): - if len(list_potential_value) == 0: continue - if key_name == "imei_number": - # print('list_potential_value', list_potential_value) - ap_outputs[key_name] = [] - for v in list_potential_value: - imei = v['content'].replace(' ', '') - if imei.isdigit() and len(imei) > 5: # imei is number and have more 5 digits - ap_outputs[key_name].append(imei) - else: - selected_value = max(list_potential_value, key=lambda x: x['lcs_score']) # Get max lsc score - ap_outputs[key_name] = selected_value['content'] - - return ap_outputs - -def export_kvu_for_SDSAP(outputs): - # List of items in table - table = get_sbt_table_info(outputs) - triplet_pairs = get_sbt_triplet_info(outputs) - table = table + triplet_pairs - - ap_outputs = get_sbt_info(outputs) - - ap_outputs['table'] = table - return ap_outputs - -def merged_kvu_for_SDSAP_for_multi_pages(lvat_outputs: list): - merged_outputs = {k: [] for k in get_dict("key").keys()} - merged_outputs['table'] = [] - for outputs in lvat_outputs: - for key_name, value in outputs.items(): - if key_name == "table": - merged_outputs[key_name].extend(value) - else: - merged_outputs[key_name].append(value) - - for key, value in merged_outputs.items(): - if key == "table": - continue - if len(value) == 0: - merged_outputs[key] = None - else: - merged_outputs[key] = value[0] - - return merged_outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt_v2.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt_v2.py deleted file mode 100644 index 31d3002..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/sbt_v2.py +++ /dev/null @@ -1,320 +0,0 @@ -import re -from sdsvkvu.utils.post_processing import ( - longestCommonSubsequence, - longest_common_substring, - preprocessing, - parse_date, - split_key_value_by_colon, - normalize_imei, - normalize_website, - normalize_hotline, - normalize_seller, - normalize_voucher -) -from sdsvkvu.utils.dictionary.sbt_v2 import get_dict - -def post_process_date(list_dates): - if len(list_dates) == 0: - return None - selected_value = max(list_dates, key=lambda x: x["lcs_score"]) # Get max lsc score - if not isinstance(selected_value["content"], str): - is_parser_error = True - date_formated = None - else: - date_formated, is_parser_error = parse_date(selected_value["content"]) - return date_formated - - - -def post_process_serial(list_serials): - if len(list_serials) == 0: - return None - selected_value = max( - list_serials, key=lambda x: x["lcs_score"] - ) # Get max lsc score - return selected_value["content"].strip() - - -def post_process_imei(list_imeis): - imeis = [] - for v in list_imeis: - if not isinstance(v["content"], str): - continue - imei = v["content"].replace(" ", "") - if imei.isdigit() and len(imei) > 5: # imei is number and have more 5 digits - imeis.append({ - "content": imei, - "token_id": v['token_id'] - }) - - if len(imeis) > 0: - return sorted(imeis, key=lambda x: int(x["token_id"]))[0]['content'].strip() - return None - - -def post_process_qty(inp_str: str) -> str: - pattern = r"\d" - match = re.search(pattern, inp_str) - if match: - return match.group() - return inp_str - - -def post_process_seller(list_sellers): - seller_mapping = get_dict(type="seller_mapping") - vote_list = {} - for seller in list_sellers: - seller_name = seller['content'] - if seller_name not in vote_list: - vote_list[seller_name] = 0 - - vote_list[seller_name] += seller['lcs_score'] - - if len(vote_list) > 0: - selected_value = max( - vote_list, key=lambda x: vote_list[x] - ) # Get major voting - - for norm_seller, candidates in seller_mapping.items(): - if any(preprocessing(txt) == preprocessing(selected_value) for txt in candidates): - selected_value = norm_seller - break - - selected_value = selected_value.lower() - for txt in candidates: - txt = txt.lower() - if txt in selected_value: - selected_value = selected_value.replace(txt, norm_seller) - - return selected_value.strip().title() - return None - -def post_process_subsidiary(list_subsidiaries): - if len(list_subsidiaries) > 0: - selected_value = max( - list_subsidiaries, key=lambda x: x["lcs_score"] - ) # Get max lsc score - return selected_value["content"] - return None - -def sbt_key_matching(text: str, threshold: float, dict_type: str): - dictionary = get_dict(type=dict_type) - processed_text = preprocessing(text) - - scores = {k: 0.0 for k in dictionary} - # Step 1: LCS score - for k, v in dictionary.items(): - score1 = max([ - longestCommonSubsequence(processed_text, key) / - max(len(key), len(processed_text)) - for key in dictionary[k]]) - - score2 = max([ - longest_common_substring(processed_text, key) / - max(len(key), len(processed_text)) - for key in dictionary[k]]) - - scores[k] = score1 if score1 > score2 else score2 - - key, score = max(scores.items(), key=lambda x: x[1]) - return key if score >= threshold else text, score, processed_text - - -def get_date_value(list_dates): - date_outputs = [] - for date_obj in list_dates: - if "raw_key_name" in date_obj: - date_key, date_value = split_key_value_by_colon(date_obj['raw_key_name'], date_obj['text']) - else: - date_key, date_value = split_key_value_by_colon( - date_obj['text'] if date_obj['class'] == "date_key" else None, - date_obj['text'] if date_obj['class'] == "date_value" else None - ) - # print(f"======{date_key} : {date_value}") - - if date_key is None and date_obj['class'] == "date_value": - date_value = date_obj['text'] - proceessed_text, score = "", len(date_value) if isinstance(date_value, str) else 0 - else: - key_name, score, proceessed_text = sbt_key_matching( - date_key, threshold=0.8, dict_type="date" - ) - # print(f"{date_key} ==> {proceessed_text} ==> {key_name} : {score} - {date_value}") - date_outputs.append( - { - "content": date_value, - "processed_key_name": proceessed_text, - "lcs_score": score, - "token_id": date_obj["id"], - } - ) - return date_outputs - -def get_serial_imei(list_sn): - sn_outputs = {"serial_number": [], "imei_number": []} - for sn_obj in list_sn: - if "raw_key_name" in sn_obj: - sn_key, sn_value = split_key_value_by_colon(sn_obj['raw_key_name'], sn_obj['text']) - else: - sn_key, sn_value = split_key_value_by_colon( - sn_obj['text'] if sn_obj['class'] == "sn_key" else None, - sn_obj['text'] if sn_obj['class'] == "sn_value" else None - ) - # print(f"====== {sn_key} : {sn_value}") - - if sn_key is None and sn_obj['class'] == "sn_value": - sn_value = sn_obj['text'] - key_name, proceessed_text, score = None, "", 0.8 - else: - key_name, score, proceessed_text = sbt_key_matching( - sn_key, threshold=0.8, dict_type="imei" - ) - # print(f"{sn_key} ==> {proceessed_text} ==> {key_name} : {score} - {sn_value}") - - value = { - "content": sn_value, - "processed_key_name": proceessed_text, - "lcs_score": score, - "token_id": sn_obj["id"], - } - - if key_name is None: - if normalize_imei(sn_value).isdigit(): - sn_outputs['imei_number'].append(value) - else: - sn_outputs['serial_number'].append(value) - elif key_name in ['imei_number', 'serial_number']: - sn_outputs[key_name].append(value) - return sn_outputs - - -def get_product_info(list_items): - table = [] - for row in list_items: - item = {} - for key, value in row.items(): - item[key] = None - if len(value) > 0: - if key == "qty": - item[key] = post_process_qty(value[0]["text"]) - else: - item[key] = value[0]["text"] - table.append(item) - return table - - - -def get_seller(outputs): # Post processing to combine seller and extra information (voucher, hotline, website) - seller_outputs = [] - voucher_info = [] - - for key_field in ["seller", "website", "hotline", "voucher", "sold_by"]: - threshold = 0.7 - func_name = f"normalize_{key_field}" - for potential_seller in outputs[key_field]: - seller_name, score, processed_text = sbt_key_matching(eval(func_name)(potential_seller["text"]), threshold=threshold, dict_type="seller") - print(f"{potential_seller['text']} ==> {processed_text} ==> {seller_name} : {score}") - - if key_field in ("voucher"): - voucher_info.append(potential_seller['text']) - - seller_outputs.append( - { - "content": seller_name, - "raw_seller_name": potential_seller["text"], - "processed_seller_name": processed_text, - "lcs_score": score, - "info": key_field - } - ) - - for voucher in voucher_info: - for i in range(len(seller_outputs)): - if voucher.lower() not in seller_outputs[i]['content'].lower(): - seller_outputs[i]['content'] = f"{voucher} {seller_outputs[i]['content']}" - - return seller_outputs - - -def get_subsidiary(list_subsidiaries): - subsidiary_outputs = [] - sold_by_info = [] - for sold_obj in list_subsidiaries: - if "raw_key_name" in sold_obj: - sold_key, sold_value = split_key_value_by_colon(sold_obj['raw_key_name'], sold_obj['text']) - else: - sold_key, sold_value = split_key_value_by_colon( - sold_obj['text'] if sold_obj['class'] == "sold_key" else None, - sold_obj['text'] if sold_obj['class'] == "sold_value" else None - ) - # print(f"======{sold_key} : {sold_value}") - - - if sold_key is None and sold_obj['class'] == "sold_value": - sold_value = sold_obj['text'] - key_name, proceessed_text, score = "unknown", "", 0.8 - else: - key_name, score, proceessed_text = sbt_key_matching( - sold_key, threshold=0.8, dict_type="sold_by" - ) - # print(f"{sold_key} ==> {proceessed_text} ==> {key_name} : {score} - {sold_value}") - - if key_name == "sold_by": - sold_by_info.append(sold_obj) - else: - subsidiary_outputs.append( - { - "content": sold_value, - "processed_key_name": proceessed_text, - "lcs_score": score, - "token_id": sold_obj["id"], - } - ) - return subsidiary_outputs, sold_by_info - - -def export_kvu_for_SBT(outputs): - # sold to party - list_subsidiaries, sold_by_info = get_subsidiary(outputs['sold_value']) - # seller - outputs['sold_by'] = sold_by_info - list_sellers = get_seller(outputs) - # date - list_dates = get_date_value(outputs["date_value"]) - # serial_number or imei - list_serial_imei = get_serial_imei(outputs["serial_imei"]) - - serial_number = post_process_serial(list_serial_imei["serial_number"]) - imei_number = post_process_imei(list_serial_imei["imei_number"]) - # table - # list_items = get_product_info(outputs["table"]) - - ap_outputs = {} - ap_outputs["retailername"] = post_process_seller(list_sellers) - ap_outputs["sold_to_party"] = post_process_subsidiary(list_subsidiaries) - ap_outputs["purchase_date"] = post_process_date(list_dates) - ap_outputs["imei_number"] = imei_number if imei_number is not None else serial_number - # ap_outputs["table"] = list_items - - return ap_outputs - - -def merged_kvu_for_SBT_for_multi_pages(lvat_outputs: list): - merged_outputs = {k: [] for k in get_dict("key").keys()} - merged_outputs['table'] = [] - for outputs in lvat_outputs: - for key_name, value in outputs.items(): - if key_name == "table": - merged_outputs[key_name].extend(value) - else: - merged_outputs[key_name].append(value) - - for key, value in merged_outputs.items(): - if key == "table": - continue - if len(value) == 0: - merged_outputs[key] = None - else: - merged_outputs[key] = value[0] - - return merged_outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vat.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vat.py deleted file mode 100644 index 424ec35..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vat.py +++ /dev/null @@ -1,237 +0,0 @@ -import re -from sdsvkvu.utils.post_processing import longestCommonSubsequence, preprocessing -from sdsvkvu.utils.dictionary.vat import get_dict - - -# For FI-VAT project -def vat_key_replacing(vat_outputs: dict) -> dict: - outputs = {} - DKVU2XML = get_dict("kvu2xml") - for key, value in vat_outputs.items(): - if key != "table": - outputs[DKVU2XML[key]] = value - else: - list_items = [] - for item in value: - list_items.append({ - DKVU2XML[item_key]: item_value for item_key, item_value in item.items() - }) - outputs['table'] = list_items - return outputs - - -def vat_key_matching(text: str, threshold: float, dict_type: str): - dictionary = get_dict(dict_type) - processed_text = preprocessing(text) - - # Step 1: Exactly matching - date_dict = get_dict("date") - for time_key, candidates in date_dict.items(): - if any([processed_text == txt for txt in candidates]): - return "Ngày, tháng, năm lập hóa đơn", 5, time_key - - extra_dict = get_dict("extra") - for key, candidates in dictionary.items(): - candidates = candidates + extra_dict[key] if key in extra_dict.keys() else candidates - - if key == 'Tên người bán' and processed_text == "kyboi": - return key, 8, processed_text - - if any([processed_text == txt for txt in candidates]): - return key, 10, processed_text - - # Step 2: LCS score - scores = {k: 0.0 for k in dictionary} - for k, v in dictionary.items(): - if k in ("Ngày, tháng, năm lập hóa đơn"): continue - scores[k] = max([longestCommonSubsequence(processed_text, key)/len(key) for key in dictionary[k]]) - key, score = max(scores.items(), key=lambda x: x[1]) - return key if score > threshold else text, score, processed_text - - -def normalize_number_format(s: str) -> float: - s = s.replace(' ', '').replace('O', '0').replace('o', '0') - if s.endswith(",00") or s.endswith(".00"): - s = s[:-3] - if all([delimiter in s for delimiter in [',', '.']]): - s = s.replace('.', '').split(',') - remain_value = s[1].split('0')[0] - return int(s[0]) + int(remain_value) * 1 / (10**len(remain_value)) - else: - s = s.replace(',', '').replace('.', '') - return int(s) - - -def post_process_item(item: dict) -> dict: - check_keys = ['Số lượng', 'Đơn giá', 'Doanh số mua chưa có thuế'] - mis_key = [] - for key in check_keys: - if item[key] in (None, '0'): - mis_key.append(key) - if len(mis_key) == 1: - try: - if mis_key[0] == check_keys[0] and normalize_number_format(item[check_keys[1]]) != 0: - item[mis_key[0]] = round(normalize_number_format(item[check_keys[2]]) / normalize_number_format(item[check_keys[1]])).__str__() - elif mis_key[0] == check_keys[1] and normalize_number_format(item[check_keys[0]]) != 0: - item[mis_key[0]] = (normalize_number_format(item[check_keys[2]]) / normalize_number_format(item[check_keys[0]])).__str__() - elif mis_key[0] == check_keys[2]: - item[mis_key[0]] = (normalize_number_format(item[check_keys[0]]) * normalize_number_format(item[check_keys[1]])).__str__() - except Exception as e: - print("Cannot post process this item with error:", e) - return item - - -def get_vat_table_info(outputs): - table = [] - for single_item in outputs['table']: - item = {k: [] for k in get_dict("header").keys()} - for cell in single_item: - header_name, score, proceessed_text = vat_key_matching(cell['header'], threshold=0.75, dict_type="header") - if header_name in list(item.keys()): - # item[header_name] = value['text'] - item[header_name].append({ - 'content': cell['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': cell['id'] - }) - - for header_name, value in item.items(): - if len(value) == 0: - if header_name in ("Số lượng", "Doanh số mua chưa có thuế"): - item[header_name] = '0' - else: - item[header_name] = None - continue - item[header_name] = max(value, key=lambda x: x['lcs_score'])['content'] # Get max lsc score - - item = post_process_item(item) - - if item["Mặt hàng"] == None: - continue - table.append(item) - return table - -def get_vat_info(outputs): - # VAT Information - single_pairs = {k: [] for k in get_dict("key").keys()} - for pair in outputs['single']: - for raw_key_name, value in pair.items(): - key_name, score, proceessed_text = vat_key_matching(raw_key_name, threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': value['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': value['id'], - }) - - for triplet in outputs['triplet']: - for key, value_list in triplet.items(): - if len(value_list) == 1: - key_name, score, proceessed_text = vat_key_matching(key, threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': value_list[0]['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': value_list[0]['id'] - }) - - for pair in value_list: - key_name, score, proceessed_text = vat_key_matching(pair['header'], threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': pair['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': pair['id'] - }) - - for table_row in outputs['table']: - for pair in table_row: - key_name, score, proceessed_text = vat_key_matching(pair['header'], threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': pair['text'], - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': pair['id'] - }) - - return single_pairs - - -def post_process_tax_code(tax_code_raw: str): - if len(tax_code_raw.replace(' ', '')) not in (10, 13): # to remove the first/last number dupicated - tax_code_raw = tax_code_raw.split(' ') - tax_code_raw = sorted(tax_code_raw, key=lambda x: len(x), reverse=True)[0] - return tax_code_raw.replace(' ', '') - - -def merge_vat_info(single_pairs): - vat_outputs = {k: None for k in list(single_pairs)} - for key_name, list_potential_value in single_pairs.items(): - if key_name in ("Ngày, tháng, năm lập hóa đơn"): - if len(list_potential_value) == 1: - vat_outputs[key_name] = list_potential_value[0]['content'] - else: - date_time = {'day': 'dd', 'month': 'mm', 'year': 'yyyy'} - for value in list_potential_value: - date_time[value['processed_key_name']] = re.sub("[^0-9]", "", value['content']) - vat_outputs[key_name] = f"{date_time['day']}/{date_time['month']}/{date_time['year']}" - else: - if len(list_potential_value) == 0: continue - if key_name in ("Mã số thuế người bán"): - selected_value = min(list_potential_value, key=lambda x: x['token_id']) # Get first tax code - vat_outputs[key_name] = post_process_tax_code(selected_value['content']) - - else: - selected_value = max(list_potential_value, key=lambda x: x['lcs_score']) # Get max lsc score - vat_outputs[key_name] = selected_value['content'] - return vat_outputs - - -def export_kvu_for_VAT_invoice(outputs): - vat_outputs = {} - # List of items in table - table = get_vat_table_info(outputs) - # VAT Information - single_pairs = get_vat_info(outputs) - vat_outputs = merge_vat_info(single_pairs) - # Combine VAT information and table - vat_outputs['table'] = table - return vat_outputs - - -def merged_kvu_for_VAT_invoice_for_multi_pages(lvat_outputs: list): - merged_outputs = {k: [] for k in get_dict("key").keys()} - merged_outputs['table'] = [] - for outputs in lvat_outputs: - for key_name, value in outputs.items(): - if key_name == "table": - merged_outputs[key_name].extend(value) - else: - if value == None or value == "dd/mm/yyyy": - # print(key_name, value) - continue - merged_outputs[key_name].append(value) - - for key, value in merged_outputs.items(): - if key == "table": - continue - if len(value) == 0: - merged_outputs[key] = None - else: - merged_outputs[key] = value[0] - - return merged_outputs - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vtb.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vtb.py deleted file mode 100644 index 14694af..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/query/vtb.py +++ /dev/null @@ -1,153 +0,0 @@ -import re -from sdsvkvu.utils.post_processing import preprocessing, date_regexing, remove_bullet_points_and_punctuation -from sdsvkvu.utils.dictionary.vtb import get_dict - -# For Vietin Bank project -def vietin_key_matching(text: str, threshold: float, dict_type: str): - dictionary = get_dict(type=dict_type) - processed_text = preprocessing(text) - - # Step 1: Exactly matching - date_dict = get_dict("date") - for time_key, candidates in date_dict.items(): - if any([txt in processed_text for txt in candidates]): - return "date", 5, time_key - - extra_dict = get_dict("extra") - for key, candidates in dictionary.items(): - candidates = candidates + extra_dict[key] if key in extra_dict.keys() else candidates - - if processed_text[-4:] == "dien": # EX: Bộ trưởng Bộ GTVT điện: A, B, C - return "sender", 15, processed_text - - if any([txt in processed_text for txt in candidates]): - return key, 10, processed_text - - # Step 2: LCS score - scores = {k: 0.0 for k in dictionary} - ## Disable temporarily - # for k, v in dictionary.items(): - # if k in ("date", "title", "number", 'signee', 'sender', 'receiver'): continue - # scores[k] = max([longestCommonSubsequence(processed_text, key)/len(key) for key in dictionary[k]]) - key, score = max(scores.items(), key=lambda x: x[1]) - return key if score > threshold else text, score, processed_text - - -def get_vietin_info(outputs): - # Vietin Information - single_pairs = {k: [] for k in get_dict(type="key").keys()} - for pair in outputs['single']: - for raw_key_name, value in pair.items(): - key_name, score, proceessed_text = vietin_key_matching(raw_key_name, threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value['text']}") - - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': value['text'], - 'processed_key_name': proceessed_text, - 'raw_key_name': raw_key_name, - 'lcs_score': score, - 'token_id': value['id'], - 'single_entity': False - }) - - - for single_item in outputs['key'] + outputs['value']: - key_name, score, proceessed_text = vietin_key_matching(single_item['text'], threshold=0.8, dict_type="key") - # print(f"{single_item['text']} ==> {proceessed_text} ==> {key_name} : {score} - {single_item['text']}") - - # if key_name not in ('number', 'date'): continue - if key_name in list(single_pairs.keys()): - single_pairs[key_name].append({ - 'content': single_item['text'], - 'processed_key_name': proceessed_text, - 'raw_key_name': single_item['text'], - 'lcs_score': score, - 'token_id': single_item['id'], - 'single_entity': True - }) - - - # Sender and receiver are usually in triplet - for triplet in outputs['triplet']: - for raw_key_name, value_list in triplet.items(): - key_name, score, proceessed_text = vietin_key_matching(raw_key_name, threshold=0.8, dict_type="key") - # print(f"{raw_key_name} ==> {proceessed_text} ==> {key_name} : {score} - {value_list[0]['text']}") - - if key_name in list(single_pairs.keys()): - for pair in value_list: - single_pairs[key_name].append({ - 'content': pair['text'], - 'raw_key_name': raw_key_name, - 'processed_key_name': proceessed_text, - 'lcs_score': score, - 'token_id': pair['id'], - 'single_entity': False - }) - return single_pairs - -def post_process_vietin_info(single_pairs): - vietin_outputs = {k: None for k in get_dict(type="key").keys()} - for key_name, list_potential_value in single_pairs.items(): - if key_name in ("date"): - if len(list_potential_value) == 1: - check_string = list_potential_value[0]['content'].replace(" ", "") - if check_string.replace('/', '').isdigit(): - vietin_outputs[key_name] = check_string - else: - # date_time = {'day': 'dd', 'month': 'mm', 'year': 'yyyy'} - # if len(list_potential_value) == 3: - # for value in list_potential_value: - # date_time[value['processed_key_name']] = re.sub("[^0-9]", "", value['content']) - # vietin_outputs[key_name] = f"{date_time['day']}/{date_time['month']}/{date_time['year']}" - # else: - list_potential_value = sorted(list_potential_value, key=lambda x: x['token_id'], reverse=False) - full_string = ' '.join([v['raw_key_name'] + v['content'] for v in list_potential_value]) - d, m, y = date_regexing(full_string) - vietin_outputs[key_name] = f"{d}/{m}/{y}" - # print(full_string) - # print(d, m, y) - elif key_name in ("receiver", "sender"): - list_potential_value = sorted(list_potential_value, key=lambda x: x['token_id'], reverse=False) - vietin_outputs[key_name] = [remove_bullet_points_and_punctuation(value['content']) for value in list_potential_value] - elif key_name in ("signee"): - list_potential_value = sorted(list_potential_value, key=lambda x: x['token_id'], reverse=False) - vietin_outputs[key_name] = [f"{value['content']} - {value['raw_key_name']}" for value in list_potential_value if value['single_entity'] == False] - else: - if len(list_potential_value) == 0: continue - selected_value = max(list_potential_value, key=lambda x: x['lcs_score']) # Get max lsc score - vietin_outputs[key_name] = selected_value['content'] - if key_name in ("number"): - number = re.sub("[^0-9]", "", selected_value['raw_key_name']) - start_idx = selected_value['content'].find(number) - if start_idx != -1: - vietin_outputs[key_name] = selected_value['content'].replace(" ", "")[start_idx:] - else: - vietin_outputs[key_name] = number + selected_value['content'].replace(" ", "") - - return vietin_outputs - -def export_kvu_for_vietin(outputs): - single_pairs = get_vietin_info(outputs) - vietin_outputs = post_process_vietin_info(single_pairs) - vietin_outputs['title'] = [title['text'] for title in outputs["title"]] - return vietin_outputs - -def merged_kvu_for_vietin_for_multi_pages(lvietin_outputs: list): - merged_outputs = {k: [] for k in get_dict("key").keys()} - for outputs in lvietin_outputs: - for key_name, value in outputs.items(): - if value == None or value == "dd/mm/yyyy": - # print(key_name, value) - continue - merged_outputs[key_name].append(value) - - for key, value in merged_outputs.items(): - if len(value) == 0: - merged_outputs[key] = None - elif key == "receiver": - merged_outputs[key] = value[-1] - elif key in ("number", "title", "date", "signee", "sender"): - merged_outputs[key] = value[0] - - return merged_outputs \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/utils.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/utils.py deleted file mode 100644 index 5019adb..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/utils.py +++ /dev/null @@ -1,129 +0,0 @@ -import os -import json -import glob -from tqdm import tqdm -from pdf2image import convert_from_path -from dicttoxml import dicttoxml - - -def create_dir(save_dir=''): - if not os.path.exists(save_dir): - os.makedirs(save_dir, exist_ok=True) - # else: - # print("DIR already existed.") - # print('Save dir : {}'.format(save_dir)) - -def convert_pdf2img(pdf_dir, save_dir): - pdf_files = glob.glob(f'{pdf_dir}/*.pdf') - print('No. pdf files:', len(pdf_files)) - print(pdf_files) - - for file in tqdm(pdf_files): - pdf2img(file, save_dir, n_pages=-1, return_fname=False) - # pages = convert_from_path(file, 500) - # for i, page in enumerate(pages): - # page.save(os.path.join(save_dir, os.path.basename(file).replace('.pdf', f'_{i}.jpg')), 'JPEG') - print('Done!!!') - -def pdf2img(pdf_path, save_dir, n_pages=-1, return_fname=False): - file_names = [] - pages = convert_from_path(pdf_path) - if n_pages != -1: - pages = pages[:n_pages] - for i, page in enumerate(pages): - _save_path = os.path.join(save_dir, os.path.basename(pdf_path).replace('.pdf', f'_{i}.jpg')) - page.save(_save_path, 'JPEG') - file_names.append(_save_path) - if return_fname: - return file_names - -def xyxy2xywh(bbox): - return [ - float(bbox[0]), - float(bbox[1]), - float(bbox[2]) - float(bbox[0]), - float(bbox[3]) - float(bbox[1]), - ] - -def write_to_json(file_path, content): - with open(file_path, mode='w', encoding='utf8') as f: - json.dump(content, f, ensure_ascii=False) - - -def read_json(file_path): - with open(file_path, 'r') as f: - return json.load(f) - -def read_xml(file_path): - with open(file_path, 'r') as xml_file: - return xml_file.read() - -def write_to_xml(file_path, content): - with open(file_path, mode="w", encoding='utf8') as f: - f.write(content) - -def write_to_xml_from_dict(file_path, content): - xml = dicttoxml(content) - xml = content - xml_decode = xml.decode() - - with open(file_path, mode="w") as f: - f.write(xml_decode) - -def read_txt(ocr_path): - with open(ocr_path, "r") as f: - lines = f.read().splitlines() - return lines - -def load_ocr_result(ocr_path): - with open(ocr_path, 'r') as f: - lines = f.read().splitlines() - - preds = [] - for line in lines: - preds.append(line.split('\t')) - return preds - -def post_process_basic_ocr(lwords: list) -> list: - pp_lwords = [] - for word in lwords: - pp_lwords.append(word.replace("✪", " ")) - return pp_lwords - -def read_ocr_result_from_txt(file_path: str): - ''' - return list of bounding boxes, list of words - ''' - with open(file_path, 'r') as f: - lines = f.read().splitlines() - - boxes, words = [], [] - for line in lines: - if line == "": - continue - word_info = line.split("\t") - if len(word_info) == 6: - x1, y1, x2, y2, text, _ = word_info - elif len(word_info) == 5: - x1, y1, x2, y2, text = word_info - - x1, y1, x2, y2 = int(float(x1)), int(float(y1)), int(float(x2)), int(float(y2)) - if text and text != " ": - words.append(text) - boxes.append((x1, y1, x2, y2)) - return boxes, words - - - - - - - - - - - - - - - diff --git a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/word2line.py b/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/word2line.py deleted file mode 100644 index d8380ef..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/utils/word2line.py +++ /dev/null @@ -1,226 +0,0 @@ -class Word(): - def __init__(self, text="",image=None, conf_detect=0.0, conf_cls=0.0, bndbox = [-1,-1,-1,-1], kie_label =""): - self.type = "word" - self.text =text - self.image = image - self.conf_detect = conf_detect - self.conf_cls = conf_cls - self.boundingbox = bndbox # [left, top,right,bot] coordinate of top-left and bottom-right point - self.word_id = 0 # id of word - self.word_group_id = 0 # id of word_group which instance belongs to - self.line_id = 0 #id of line which instance belongs to - self.paragraph_id = 0 #id of line which instance belongs to - self.kie_label = kie_label - def invalid_size(self): - return (self.boundingbox[2] - self.boundingbox[0]) * (self.boundingbox[3] - self.boundingbox[1]) > 0 - def is_special_word(self): - left, top, right, bottom = self.boundingbox - width, height = right - left, bottom - top - text = self.text - - if text is None: - return True - - # if len(text) > 7: - # return True - if len(text) >= 7: - no_digits = sum(c.isdigit() for c in text) - return no_digits / len(text) >= 0.3 - - return False - -class Word_group(): - def __init__(self): - self.type = "word_group" - self.list_words = [] # dict of word instances - self.word_group_id = 0 # word group id - self.line_id = 0 #id of line which instance belongs to - self.paragraph_id = 0# id of paragraph which instance belongs to - self.text ="" - self.boundingbox = [-1,-1,-1,-1] - self.kie_label ="" - def add_word(self, word:Word): #add a word instance to the word_group - if word.text != "✪": - for w in self.list_words: - if word.word_id == w.word_id: - print("Word id collision") - return False - word.word_group_id = self.word_group_id # - word.line_id = self.line_id - word.paragraph_id = self.paragraph_id - self.list_words.append(word) - self.text += ' '+ word.text - if self.boundingbox == [-1,-1,-1,-1]: - self.boundingbox = word.boundingbox - else: - self.boundingbox = [min(self.boundingbox[0], word.boundingbox[0]), - min(self.boundingbox[1], word.boundingbox[1]), - max(self.boundingbox[2], word.boundingbox[2]), - max(self.boundingbox[3], word.boundingbox[3])] - return True - else: - return False - - def update_word_group_id(self, new_word_group_id): - self.word_group_id = new_word_group_id - for i in range(len(self.list_words)): - self.list_words[i].word_group_id = new_word_group_id - - def update_kie_label(self): - list_kie_label = [word.kie_label for word in self.list_words] - dict_kie = dict() - for label in list_kie_label: - if label not in dict_kie: - dict_kie[label]=1 - else: - dict_kie[label]+=1 - total = len(list(dict_kie.values())) - max_value = max(list(dict_kie.values())) - list_keys = list(dict_kie.keys()) - list_values = list(dict_kie.values()) - self.kie_label = list_keys[list_values.index(max_value)] - -class Line(): - def __init__(self): - self.type = "line" - self.list_word_groups = [] # list of Word_group instances in the line - self.line_id = 0 #id of line in the paragraph - self.paragraph_id = 0 # id of paragraph which instance belongs to - self.text = "" - self.boundingbox = [-1,-1,-1,-1] - def add_group(self, word_group:Word_group): # add a word_group instance - if word_group.list_words is not None: - for wg in self.list_word_groups: - if word_group.word_group_id == wg.word_group_id: - print("Word_group id collision") - return False - - self.list_word_groups.append(word_group) - self.text += word_group.text - word_group.paragraph_id = self.paragraph_id - word_group.line_id = self.line_id - - for i in range(len(word_group.list_words)): - word_group.list_words[i].paragraph_id = self.paragraph_id #set paragraph_id for word - word_group.list_words[i].line_id = self.line_id #set line_id for word - return True - return False - def update_line_id(self, new_line_id): - self.line_id = new_line_id - for i in range(len(self.list_word_groups)): - self.list_word_groups[i].line_id = new_line_id - for j in range(len(self.list_word_groups[i].list_words)): - self.list_word_groups[i].list_words[j].line_id = new_line_id - - - def merge_word(self, word): # word can be a Word instance or a Word_group instance - if word.text != "✪": - if self.boundingbox == [-1,-1,-1,-1]: - self.boundingbox = word.boundingbox - else: - self.boundingbox = [min(self.boundingbox[0], word.boundingbox[0]), - min(self.boundingbox[1], word.boundingbox[1]), - max(self.boundingbox[2], word.boundingbox[2]), - max(self.boundingbox[3], word.boundingbox[3])] - self.list_word_groups.append(word) - self.text += ' ' + word.text - return True - return False - - - def in_same_line(self, input_line, thresh=0.7): - # calculate iou in vertical direction - left1, top1, right1, bottom1 = self.boundingbox - left2, top2, right2, bottom2 = input_line.boundingbox - - sorted_vals = sorted([top1, bottom1, top2, bottom2]) - intersection = sorted_vals[2] - sorted_vals[1] - union = sorted_vals[3]-sorted_vals[0] - min_height = min(bottom1-top1, bottom2-top2) - if min_height==0: - return False - ratio = intersection / min_height - height1, height2 = top1-bottom1, top2-bottom2 - ratio_height = float(max(height1, height2))/float(min(height1, height2)) - # height_diff = (float(top1-bottom1))/(float(top2-bottom2)) - - - if (top1 in range(top2, bottom2) or top2 in range(top1, bottom1)) and ratio >= thresh and (ratio_height<2): - return True - return False - -def check_iomin(word:Word, word_group:Word_group): - min_height = min(word.boundingbox[3]-word.boundingbox[1],word_group.boundingbox[3]-word_group.boundingbox[1]) - intersect = min(word.boundingbox[3],word_group.boundingbox[3]) - max(word.boundingbox[1],word_group.boundingbox[1]) - if intersect/min_height > 0.7: - return True - return False - -def words_to_lines(words, check_special_lines=True): #words is list of Word instance - #sort word by top - words.sort(key = lambda x: (x.boundingbox[1], x.boundingbox[0])) - number_of_word = len(words) - #sort list words to list lines, which have not contained word_group yet - lines = [] - for i, word in enumerate(words): - if word.invalid_size()==0: - continue - new_line = True - for i in range(len(lines)): - if lines[i].in_same_line(word): #check if word is in the same line with lines[i] - lines[i].merge_word(word) - new_line = False - - if new_line ==True: - new_line = Line() - new_line.merge_word(word) - lines.append(new_line) - - # print(len(lines)) - #sort line from top to bottom according top coordinate - lines.sort(key = lambda x: x.boundingbox[1]) - - #construct word_groups in each line - line_id = 0 - word_group_id =0 - word_id = 0 - for i in range(len(lines)): - if len(lines[i].list_word_groups)==0: - continue - #left, top ,right, bottom - line_width = lines[i].boundingbox[2] - lines[i].boundingbox[0] # right - left - # print("line_width",line_width) - lines[i].list_word_groups.sort(key = lambda x: x.boundingbox[0]) #sort word in lines from left to right - - #update text for lines after sorting - lines[i].text ="" - for word in lines[i].list_word_groups: - lines[i].text += " "+word.text - - list_word_groups=[] - inital_word_group = Word_group() - inital_word_group.word_group_id= word_group_id - word_group_id +=1 - lines[i].list_word_groups[0].word_id=word_id - inital_word_group.add_word(lines[i].list_word_groups[0]) - word_id+=1 - list_word_groups.append(inital_word_group) - for word in lines[i].list_word_groups[1:]: #iterate through each word object in list_word_groups (has not been construted to word_group yet) - check_word_group= True - #set id for each word in each line - word.word_id = word_id - word_id+=1 - if (not list_word_groups[-1].text.endswith(':')) and ((word.boundingbox[0]-list_word_groups[-1].boundingbox[2])/line_width <0.05) and check_iomin(word, list_word_groups[-1]): - list_word_groups[-1].add_word(word) - check_word_group=False - if check_word_group ==True: - new_word_group = Word_group() - new_word_group.word_group_id= word_group_id - word_group_id +=1 - new_word_group.add_word(word) - list_word_groups.append(new_word_group) - lines[i].list_word_groups = list_word_groups - # set id for lines from top to bottom - lines[i].update_line_id(line_id) - line_id +=1 - return lines, number_of_word \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/setup.cfg b/cope2n-ai-fi/modules/_sdsvkvu/setup.cfg deleted file mode 100644 index ce66352..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/setup.cfg +++ /dev/null @@ -1,49 +0,0 @@ -[tool:pytest] -norecursedirs = - .git - dist - build -addopts = - --strict - --doctest-modules - --durations=0 - -[coverage:report] -exclude_lines = - pragma: no-cover - pass - -[flake8] -max-line-length = 120 -exclude = .tox,*.egg,build,temp -select = E,W,F -doctests = True -verbose = 2 -# https://pep8.readthedocs.io/en/latest/intro.html#error-codes -format = pylint -# see: https://www.flake8rules.com/ -ignore = - E731 # Do not assign a lambda expression, use a def - W504 # Line break occurred after a binary operator - F401 # Module imported but unused - F841 # Local variable name is assigned to but never used - W605 # Invalid escape sequence 'x' - -# setup.cfg or tox.ini -[check-manifest] -ignore = - *.yml - .github - .github/* - -[metadata] -license_file = LICENSE -description-file = README.md -author = tuanlv -author_email = lv.tuan3@samsung.com -# long_description = file:README.md -# long_description_content_type = text/markdown -[options] -packages = find: -python_requires = >=3.9 -include_package_data = True \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/setup.py b/cope2n-ai-fi/modules/_sdsvkvu/setup.py deleted file mode 100644 index 8fde133..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/setup.py +++ /dev/null @@ -1,181 +0,0 @@ -import os -import os.path as osp -import shutil -import sys -import warnings -from setuptools import find_packages, setup - - -version_file = "sdsvkvu/utils/version.py" -is_windows = sys.platform == 'win32' - -def readme(): - with open('README.md', encoding='utf-8') as f: - content = f.read() - return content - -def add_mim_extention(): - """Add extra files that are required to support MIM into the package. - - These files will be added by creating a symlink to the originals if the - package is installed in `editable` mode (e.g. pip install -e .), or by - copying from the originals otherwise. - """ - - # parse installment mode - if 'develop' in sys.argv: - # installed by `pip install -e .` - mode = 'symlink' - elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - # installed by `pip install .` - # or create source distribution by `python setup.py sdist` - mode = 'copy' - else: - return - - filenames = ['tools', 'configs', 'model-index.yml'] - repo_path = osp.dirname(__file__) - mim_path = osp.join(repo_path, 'mmocr', '.mim') - os.makedirs(mim_path, exist_ok=True) - - for filename in filenames: - if osp.exists(filename): - src_path = osp.join(repo_path, filename) - tar_path = osp.join(mim_path, filename) - - if osp.isfile(tar_path) or osp.islink(tar_path): - os.remove(tar_path) - elif osp.isdir(tar_path): - shutil.rmtree(tar_path) - - if mode == 'symlink': - src_relpath = osp.relpath(src_path, osp.dirname(tar_path)) - try: - os.symlink(src_relpath, tar_path) - except OSError: - # Creating a symbolic link on windows may raise an - # `OSError: [WinError 1314]` due to privilege. If - # the error happens, the src file will be copied - mode = 'copy' - warnings.warn( - f'Failed to create a symbolic link for {src_relpath}, ' - f'and it will be copied to {tar_path}') - else: - continue - - if mode == 'copy': - if osp.isfile(src_path): - shutil.copyfile(src_path, tar_path) - elif osp.isdir(src_path): - shutil.copytree(src_path, tar_path) - else: - warnings.warn(f'Cannot copy file {src_path}.') - else: - raise ValueError(f'Invalid mode {mode}') - - -def get_version(): - with open(version_file, 'r') as f: - exec(compile(f.read(), version_file, 'exec')) - import sys - - # return short version for sdist - if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv: - return locals()['short_version'] - else: - return locals()['__version__'] - - -def parse_requirements(fname='requirements.txt', with_version=True): - """Parse the package dependencies listed in a requirements file but strip - specific version information. - - Args: - fname (str): Path to requirements file. - with_version (bool, default=False): If True, include version specs. - Returns: - info (list[str]): List of requirements items. - CommandLine: - python -c "import setup; print(setup.parse_requirements())" - """ - import re - import sys - from os.path import exists - require_fpath = fname - - def parse_line(line): - """Parse information from a line in a requirements text file.""" - if line.startswith('-r '): - # Allow specifying requirements in other files - target = line.split(' ')[1] - for info in parse_require_file(target): - yield info - else: - info = {'line': line} - if line.startswith('-e '): - info['package'] = line.split('#egg=')[1] - else: - # Remove versioning from the package - pat = '(' + '|'.join(['>=', '==', '>']) + ')' - parts = re.split(pat, line, maxsplit=1) - parts = [p.strip() for p in parts] - - info['package'] = parts[0] - if len(parts) > 1: - op, rest = parts[1:] - if ';' in rest: - # Handle platform specific dependencies - # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies - version, platform_deps = map(str.strip, - rest.split(';')) - info['platform_deps'] = platform_deps - else: - version = rest # NOQA - info['version'] = (op, version) - yield info - - def parse_require_file(fpath): - with open(fpath, 'r') as f: - for line in f.readlines(): - line = line.strip() - if line and not line.startswith('#'): - for info in parse_line(line): - yield info - - def gen_packages_items(): - if exists(require_fpath): - for info in parse_require_file(require_fpath): - parts = [info['package']] - if with_version and 'version' in info: - parts.extend(info['version']) - if not sys.version.startswith('3.4'): - # apparently package_deps are broken in 3.4 - platform_deps = info.get('platform_deps') - if platform_deps is not None: - parts.append(';' + platform_deps) - item = ''.join(parts) - yield item - - packages = list(gen_packages_items()) - return packages - -if __name__ == '__main__': - setup( - name='sdsvkvu', - # version=get_version(), - version="0.0.1", - description='SDSV OCR Team: Key-value understanding', - long_description=readme(), - long_description_content_type='text/markdown', - packages=find_packages(), # exclude=('configs', 'tools', 'demo') - include_package_data=True, - url='https://github.com/open-mmlab/mmocr', - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3.9', - ], - license='Apache License 2.0', - install_requires=parse_requirements('requirements.txt'), - zip_safe=False) \ No newline at end of file diff --git a/cope2n-ai-fi/modules/_sdsvkvu/test.py b/cope2n-ai-fi/modules/_sdsvkvu/test.py deleted file mode 100644 index 5be2845..0000000 --- a/cope2n-ai-fi/modules/_sdsvkvu/test.py +++ /dev/null @@ -1,18 +0,0 @@ -import os -from sdsvkvu import load_engine, process_img, process_pdf, process_dir -from sdsvkvu.modules.run_ocr import load_ocr_engine -os.environ["CUDA_VISIBLE_DEVICES"]="1" -# os.environ["NLTK_DATA"]="/mnt/ssd1T/tuanlv/02-KVU/sdsvkvu/nltk_data" - -if __name__ == "__main__": - # ocr_engine = load_ocr_engine({"device": "cuda:0"}) - kwargs = {"device": "cuda:0", "ocr_engine": None} - img_dir = "/mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/visualize/test_manulife" - save_dir = "/mnt/hdd4T/OCR/tuanlv/02-KVU/sdsvkvu/visualize/test_manulife" - engine = load_engine(kwargs) - # option: "vat" for vat invoice outputs, "sbt": sbt invoice outputs, else for raw outputs - # outputs = process_img(img_dir, save_dir, engine, export_all=False, option="vat") - # outputs = process_pdf(img_dir, save_dir, engine, export_all=True, option="vat") - process_dir(img_dir, save_dir, engine, export_all=True, option="manulife") - # process_dir(img_dir, save_dir, engine, export_all=True, option="") -