From f066814a238ef65140973358da5d11d0e183bd04 Mon Sep 17 00:00:00 2001 From: tuanlv Date: Fri, 5 Jan 2024 16:20:38 +0700 Subject: [PATCH] modify dockerfile: install from sdskvu/external/sdsvocr --- cope2n-ai-fi/Dockerfile | 7 +- cope2n-ai-fi/api/sdsap_sbt/prediction_sbt.py | 15 +- cope2n-ai-fi/configs/sdsap_sbt/configs.py | 4 +- cope2n-ai-fi/modules/ocr_engine/.gitignore | 6 - cope2n-ai-fi/modules/ocr_engine/README.md | 47 - cope2n-ai-fi/modules/ocr_engine/TODO.todo | 10 - cope2n-ai-fi/modules/ocr_engine/__init__.py | 19 - .../externals/sdsv_dewarp/.gitignore | 9 - .../externals/sdsv_dewarp/README.md | 29 - .../externals/sdsv_dewarp/config/cls.yaml | 3 - .../externals/sdsv_dewarp/config/det.yaml | 8 - .../externals/sdsv_dewarp/requirements.txt | 7 - .../sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO | 45 - .../sdsv_dewarp.egg-info/SOURCES.txt | 15 - .../sdsv_dewarp.egg-info/dependency_links.txt | 1 - .../sdsv_dewarp.egg-info/not-zip-safe | 1 - .../sdsv_dewarp.egg-info/requires.txt | 6 - 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.../externals/sdsvtd/sdsvtd/neck.py | 140 --- .../externals/sdsvtd/sdsvtd/priors.py | 225 ----- .../externals/sdsvtd/sdsvtd/transform.py | 81 -- .../externals/sdsvtd/sdsvtd/version.py | 1 - .../ocr_engine/externals/sdsvtd/setup.py | 186 ---- .../ocr_engine/externals/sdsvtr/.gitignore | 6 - .../ocr_engine/externals/sdsvtr/LICENSE | 674 ------------- .../ocr_engine/externals/sdsvtr/README.md | 76 -- .../externals/sdsvtr/requirements.txt | 3 - .../externals/sdsvtr/sdsvtr/__init__.py | 3 - .../ocr_engine/externals/sdsvtr/sdsvtr/api.py | 106 -- .../externals/sdsvtr/sdsvtr/backbone.py | 159 --- .../externals/sdsvtr/sdsvtr/conv.py | 173 ---- .../externals/sdsvtr/sdsvtr/converter.py | 152 --- .../externals/sdsvtr/sdsvtr/decoder.py | 278 ------ .../externals/sdsvtr/sdsvtr/encoder.py | 317 ------ .../externals/sdsvtr/sdsvtr/factory.py | 57 -- .../externals/sdsvtr/sdsvtr/fp16_utils.py | 78 -- .../externals/sdsvtr/sdsvtr/transform.py | 33 - .../externals/sdsvtr/sdsvtr/version.py | 1 - .../ocr_engine/externals/sdsvtr/setup.py | 187 ---- .../ocr_engine/externals/sdsvtr/test.py | 12 - .../modules/ocr_engine/requirements.txt | 82 -- cope2n-ai-fi/modules/ocr_engine/run.py | 200 ---- .../modules/ocr_engine/scripts/run_deskew.sh | 9 - .../modules/ocr_engine/scripts/run_ocr.sh | 49 - cope2n-ai-fi/modules/ocr_engine/settings.yml | 36 - cope2n-ai-fi/modules/ocr_engine/src/dto.py | 534 ----------- cope2n-ai-fi/modules/ocr_engine/src/ocr.py | 258 ----- cope2n-ai-fi/modules/ocr_engine/src/utils.py | 369 ------- .../modules/ocr_engine/src/word_formation.py | 903 ------------------ docker-compose-dev.yml | 9 +- 69 files changed, 14 insertions(+), 8157 deletions(-) delete mode 100755 cope2n-ai-fi/modules/ocr_engine/.gitignore delete mode 100755 cope2n-ai-fi/modules/ocr_engine/README.md delete mode 100755 cope2n-ai-fi/modules/ocr_engine/TODO.todo delete mode 100755 cope2n-ai-fi/modules/ocr_engine/__init__.py delete mode 100755 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100755 cope2n-ai-fi/modules/ocr_engine/src/dto.py delete mode 100755 cope2n-ai-fi/modules/ocr_engine/src/ocr.py delete mode 100755 cope2n-ai-fi/modules/ocr_engine/src/utils.py delete mode 100755 cope2n-ai-fi/modules/ocr_engine/src/word_formation.py diff --git a/cope2n-ai-fi/Dockerfile b/cope2n-ai-fi/Dockerfile index 13ba941..2a1f54d 100755 --- a/cope2n-ai-fi/Dockerfile +++ b/cope2n-ai-fi/Dockerfile @@ -22,9 +22,10 @@ RUN python -m pip install 'git+https://github.com/facebookresearch/detectron2.gi # Install SDSV packages COPY . /workspace/cope2n-ai-fi -RUN cd /workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp && pip3 install -v -e . --no-cache-dir -RUN cd /workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd && pip3 install -v -e . --no-cache-dir -RUN cd /workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr && pip3 install -v -e . --no-cache-dir +RUN cd /workspace/cope2n-ai-fi/modules/sdsvkvu/sdsvkvu/externals/sdsvocr/externals/sdsv_dewarp && pip3 install -v -e . --no-cache-dir +RUN cd /workspace/cope2n-ai-fi/modules/sdsvkvu/sdsvkvu/externals/sdsvocr/externals/sdsvtd && pip3 install -v -e . --no-cache-dir +RUN cd /workspace/cope2n-ai-fi/modules/sdsvkvu/sdsvkvu/externals/sdsvocr/externals/sdsvtr && pip3 install -v -e . --no-cache-dir + # COPY ./modules/sdsvkvu/sdsvkvu/externals/sdsvocr/externals/sdsv_dewarp /tmp/sdsv_dewarp # RUN cd /tmp/sdsv_dewarp && pip install -v -e . --no-cache-dir diff --git a/cope2n-ai-fi/api/sdsap_sbt/prediction_sbt.py b/cope2n-ai-fi/api/sdsap_sbt/prediction_sbt.py index 7039c2b..49038d9 100755 --- a/cope2n-ai-fi/api/sdsap_sbt/prediction_sbt.py +++ b/cope2n-ai-fi/api/sdsap_sbt/prediction_sbt.py @@ -12,17 +12,8 @@ sys.path.append(cur_dir) nltk.data.path.append(os.path.join((os.getcwd() + '/nltk_data'))) from modules.sdsvkvu import load_engine, process_img -from modules.ocr_engine import OcrEngine from configs.sdsap_sbt import device, ocr_cfg, kvu_cfg - -def load_ocr_engine(opt) -> OcrEngine: - print("[INFO] Loading engine...") - engine = OcrEngine(**opt) - print("[INFO] Engine loaded") - return engine - - print("OCR engine configfs: \n", ocr_cfg) print("KVU configfs: \n", kvu_cfg) @@ -43,13 +34,13 @@ def sbt_predict(image_url, engine) -> None: save_dir = "./tmp_results" # image_path = os.path.join(save_dir, f"{image_url}.jpg") + os.makedirs(save_dir, exist_ok = True) tmp_image_path = os.path.join(save_dir, f"{uuid.uuid4()}.jpg") - cv2.imwrite(tmp_image_path, img) - + cv2.imwrite(tmp_image_path, img) outputs = process_img(img_path=tmp_image_path, save_dir=save_dir, engine=engine, - export_all=False, + export_all=False, # False option=option) os.remove(tmp_image_path) return outputs diff --git a/cope2n-ai-fi/configs/sdsap_sbt/configs.py b/cope2n-ai-fi/configs/sdsap_sbt/configs.py index 3a41be0..402c836 100755 --- a/cope2n-ai-fi/configs/sdsap_sbt/configs.py +++ b/cope2n-ai-fi/configs/sdsap_sbt/configs.py @@ -12,11 +12,11 @@ ocr_engine = { "deskew": { "enable": True, "text_detector": { - "config": "/workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/det.yaml", + "config": "/workspace/cope2n-ai-fi/weights/models/ocr_engine/sdsv_dewarp/det.yaml", "weight": "/workspace/cope2n-ai-fi/weights/models/ocr_engine/sdsv_dewarp/ch_PP-OCRv3_det_infer" }, "text_cls": { - "config": "/workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/cls.yaml", + "config": "/workspace/cope2n-ai-fi/weights/models/ocr_engine/sdsv_dewarp/cls.yaml", "weight": "/workspace/cope2n-ai-fi/weights/models/ocr_engine/sdsv_dewarp/ch_ppocr_mobile_v2.0_cls_infer" }, "device": device diff --git a/cope2n-ai-fi/modules/ocr_engine/.gitignore b/cope2n-ai-fi/modules/ocr_engine/.gitignore deleted file mode 100755 index 1250e95..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/.gitignore +++ /dev/null @@ -1,6 +0,0 @@ -__pycache__/ -visualize/ -results/ -*.jpeg -*.jpg -*.png diff --git a/cope2n-ai-fi/modules/ocr_engine/README.md b/cope2n-ai-fi/modules/ocr_engine/README.md deleted file mode 100755 index ca1b349..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/TODO.todo b/cope2n-ai-fi/modules/ocr_engine/TODO.todo deleted file mode 100755 index 34df095..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/__init__.py b/cope2n-ai-fi/modules/ocr_engine/__init__.py deleted file mode 100755 index 433d5ff..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# # Define package-level variables -# __version__ = '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, "externals")) - -# Import modules -from .run import load_engine -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/ocr_engine/externals/sdsv_dewarp/.gitignore b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/.gitignore deleted file mode 100755 index e56dbb2..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/README.md b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/README.md deleted file mode 100755 index d1b9637..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/config/cls.yaml b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/cls.yaml deleted file mode 100755 index 4c4cfdc..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/config/det.yaml b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/det.yaml deleted file mode 100755 index f218ef1..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/requirements.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/requirements.txt deleted file mode 100755 index 768fde9..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/PKG-INFO deleted file mode 100755 index 634708d..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/SOURCES.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/SOURCES.txt deleted file mode 100755 index 953a123..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt deleted file mode 100755 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/dependency_links.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe deleted file mode 100755 index 8b13789..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/not-zip-safe +++ /dev/null @@ -1 +0,0 @@ - diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/requires.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/requires.txt deleted file mode 100755 index 89816c8..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt deleted file mode 100755 index a5ce4e8..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp.egg-info/top_level.txt +++ /dev/null @@ -1 +0,0 @@ -sdsv_dewarp diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/__init__.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/__init__.py deleted file mode 100755 index e69de29..0000000 diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/api.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/api.py deleted file mode 100755 index d71ddc5..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/config.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/config.py deleted file mode 100755 index 204c2c0..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/factory.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/factory.py deleted file mode 100755 index 65e4bbd..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/models.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/models.py deleted file mode 100755 index 64cb88c..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/utils.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/utils.py deleted file mode 100755 index ae02cc1..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/version.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/sdsv_dewarp/version.py deleted file mode 100755 index a1570ac..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/setup.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/setup.py deleted file mode 100755 index 7887e8f..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsv_dewarp/test.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/test.py deleted file mode 100755 index 4bfdbc7..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/.gitignore b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/.gitignore deleted file mode 100755 index a8c1759..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/LICENSE b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/LICENSE deleted file mode 100755 index 9e419e0..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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. 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But first, please read -. \ No newline at end of file diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/README.md b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/README.md deleted file mode 100755 index 4de145b..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/requirements.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/requirements.txt deleted file mode 100755 index 10f4b05..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/__init__.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/__init__.py deleted file mode 100755 index a3fb997..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/api.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/api.py deleted file mode 100755 index 7deca24..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/backbone.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/backbone.py deleted file mode 100755 index 5aadded..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/bbox_head.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/bbox_head.py deleted file mode 100755 index bb485f2..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/factory.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/factory.py deleted file mode 100755 index 2b6068f..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/model.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/model.py deleted file mode 100755 index 69a26ef..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/neck.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/neck.py deleted file mode 100755 index 2b11e36..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/priors.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/priors.py deleted file mode 100755 index 8eb2fde..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/transform.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/transform.py deleted file mode 100755 index fda7ab9..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtd/sdsvtd/version.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/version.py deleted file mode 100755 index 10939f0..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/sdsvtd/version.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = '0.1.2' diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/setup.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtd/setup.py deleted file mode 100755 index a2e04db..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/.gitignore b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/.gitignore deleted file mode 100755 index a8c1759..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/LICENSE b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/LICENSE deleted file mode 100755 index 92b370f..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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. 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. 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But first, please read -. diff --git a/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/README.md b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/README.md deleted file mode 100755 index 2cabc83..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/requirements.txt b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/requirements.txt deleted file mode 100755 index f4a74db..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/__init__.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/__init__.py deleted file mode 100755 index dd8470d..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/api.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/api.py deleted file mode 100755 index 566a904..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/backbone.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/backbone.py deleted file mode 100755 index 29c2509..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/conv.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/conv.py deleted file mode 100755 index 950ec0c..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/converter.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/converter.py deleted file mode 100755 index 4d9bf82..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/decoder.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/decoder.py deleted file mode 100755 index b4ec51a..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/encoder.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/encoder.py deleted file mode 100755 index 6ccb612..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/factory.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/factory.py deleted file mode 100755 index 773d5a0..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/fp16_utils.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/fp16_utils.py deleted file mode 100755 index 126ff02..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/transform.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/transform.py deleted file mode 100755 index e366a4c..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/sdsvtr/version.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/sdsvtr/version.py deleted file mode 100755 index a55ccd2..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/setup.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/setup.py deleted file mode 100755 index 4df1557..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/externals/sdsvtr/test.py b/cope2n-ai-fi/modules/ocr_engine/externals/sdsvtr/test.py deleted file mode 100755 index b929e7e..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/requirements.txt b/cope2n-ai-fi/modules/ocr_engine/requirements.txt deleted file mode 100755 index 7bfd9c5..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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==1.3.6 -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==1.24.3 -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/ocr_engine/run.py b/cope2n-ai-fi/modules/ocr_engine/run.py deleted file mode 100755 index 3b19879..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/scripts/run_deskew.sh b/cope2n-ai-fi/modules/ocr_engine/scripts/run_deskew.sh deleted file mode 100755 index 34ecd8c..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/scripts/run_ocr.sh b/cope2n-ai-fi/modules/ocr_engine/scripts/run_ocr.sh deleted file mode 100755 index 8ade432..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/settings.yml b/cope2n-ai-fi/modules/ocr_engine/settings.yml deleted file mode 100755 index 055e16f..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/settings.yml +++ /dev/null @@ -1,36 +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: /workspace/cope2n-ai-fi/weights/models/sdsap_sbt/ocr_engine/sdsvtd/epoch_100_params.pth - auto_rotate: True - rotator_version: /workspace/cope2n-ai-fi/weights/models/sdsap_sbt/ocr_engine/sdsvtd/best_bbox_mAP_epoch_30_lite.pth - device: *device - -recognizer: - # version: satrn-lite-general-pretrain-20230106 - version: /workspace/cope2n-ai-fi/weights/models/sdsvtr/hub/jxqhbem4to.pth - 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: /workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/det.yaml - weight: /workspace/cope2n-ai-fi/weights/models/sdsap_sbt/ocr_engine/sdsv_dewarp/ch_PP-OCRv3_det_infer - text_cls: - config: /workspace/cope2n-ai-fi/modules/ocr_engine/externals/sdsv_dewarp/config/cls.yaml - weight: /workspace/cope2n-ai-fi/weights/models/sdsap_sbt/ocr_engine/sdsv_dewarp/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/ocr_engine/src/dto.py b/cope2n-ai-fi/modules/ocr_engine/src/dto.py deleted file mode 100755 index 8dae901..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/src/ocr.py b/cope2n-ai-fi/modules/ocr_engine/src/ocr.py deleted file mode 100755 index 280d0b2..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/src/utils.py b/cope2n-ai-fi/modules/ocr_engine/src/utils.py deleted file mode 100755 index 3c4332e..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/ocr_engine/src/word_formation.py b/cope2n-ai-fi/modules/ocr_engine/src/word_formation.py deleted file mode 100755 index 3e64b97..0000000 --- a/cope2n-ai-fi/modules/ocr_engine/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/docker-compose-dev.yml b/docker-compose-dev.yml index 5638fee..5244ab2 100755 --- a/docker-compose-dev.yml +++ b/docker-compose-dev.yml @@ -18,10 +18,11 @@ services: privileged: true image: sidp/cope2n-ai-fi-sbt environment: + - PYTHONPATH=${PYTHONPATH}:/workspace/cope2n-ai-fi # For import module - CELERY_BROKER=amqp://${RABBITMQ_DEFAULT_USER}:${RABBITMQ_DEFAULT_PASS}@rabbitmq-sbt:5672 - CUDA_VISIBLE_DEVICES=0 - # volumes: - # - ./cope2n-ai-fi:/workspace/cope2n-ai-fi # for dev container only + volumes: + - ./cope2n-ai-fi:/workspace/cope2n-ai-fi # for dev container only working_dir: /workspace/cope2n-ai-fi # deploy: # resources: @@ -74,7 +75,7 @@ services: networks: - ctel-sbt volumes: - - ${HOST_MEDIA_FOLDER}:${MEDIA_ROOT} + # - ${HOST_MEDIA_FOLDER}:${MEDIA_ROOT} - BE_static:/app/static - ./cope2n-api:/app working_dir: /app @@ -164,7 +165,7 @@ services: rabbitmq-sbt: condition: service_started volumes: - - ${HOST_MEDIA_FOLDER}:${MEDIA_ROOT} + # - ${HOST_MEDIA_FOLDER}:${MEDIA_ROOT} - ./cope2n-api:/app working_dir: /app