106 lines
3.1 KiB
Python
Executable File
106 lines
3.1 KiB
Python
Executable File
import cv2
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import nltk
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import urllib
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import random
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import numpy as np
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from pathlib import Path
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import urllib.parse
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import uuid
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from copy import deepcopy
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import sys, os
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cur_dir = str(Path(__file__).parents[2])
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sys.path.append(cur_dir)
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nltk.data.path.append(os.path.join((os.getcwd() + '/nltk_data')))
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from modules.sdsvkvu import load_engine, process_img
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from configs.sdsap_sbt import device, ocr_cfg, kvu_cfg
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print("OCR engine configfs: \n", ocr_cfg)
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print("KVU configfs: \n", kvu_cfg)
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# ocr_engine = load_ocr_engine(ocr_cfg)
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# kvu_cfg['ocr_engine'] = ocr_engine
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kvu_cfg['ocr_configs'] = ocr_cfg
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option = kvu_cfg['option']
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kvu_cfg.pop("option") # pop option
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sbt_engine = load_engine(kvu_cfg)
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kvu_cfg["option"] = option
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def sbt_predict(image_url, engine, metadata={}) -> None:
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req = urllib.request.urlopen(image_url)
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arr = np.asarray(bytearray(req.read()), dtype=np.uint8)
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img = cv2.imdecode(arr, -1)
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save_dir = "./tmp_results"
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try:
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parsed_url = urllib.parse.urlparse(image_url)
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query_params = urllib.parse.parse_qs(parsed_url.query)
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file_name = query_params['file_name'][0]
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except Exception as e:
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print(f"[ERROR]: Error extracting file name from url: {image_url}")
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file_name = f"{uuid.uuid4()}.jpg"
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os.makedirs(save_dir, exist_ok=True)
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# image_path = os.path.join(save_dir, f"{image_url}.jpg")
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os.makedirs(save_dir, exist_ok = True)
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tmp_image_path = os.path.join(save_dir, file_name)
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cv2.imwrite(tmp_image_path, img)
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extra_params = {'sub': metadata.get("subsidiary", None)} # example of 'AU'
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outputs = process_img(img=tmp_image_path,
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save_dir=save_dir,
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engine=engine,
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export_all=False, # False
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option=option,
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extra_params=extra_params)
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os.remove(tmp_image_path)
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return outputs
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def predict(page_numb, image_url, metadata={}):
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"""
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module predict function
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Args:
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image_url (str): image url
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Returns:
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example output:
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"data": {
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"document_type": "invoice",
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"fields": [
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{
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"label": "Invoice Number",
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"value": "INV-12345",
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"box": [0, 0, 0, 0],
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"confidence": 0.98
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},
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...
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]
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}
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dict: output of model
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"""
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sbt_result = sbt_predict(image_url, engine=sbt_engine, metadata=metadata)
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output_dict = {
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"document_type": "invoice",
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"document_class": " ",
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"page_number": page_numb,
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"fields": []
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}
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for key in sbt_result.keys():
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field = {
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"label": key,
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"value": sbt_result[key],
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"box": [0, 0, 0, 0],
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"confidence": random.uniform(0.9, 1.0),
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"page": page_numb
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}
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output_dict['fields'].append(field)
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return output_dict
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if __name__ == "__main__":
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image_url = "/root/thucpd/20230322144639VUzu_16794962527791962785161104697882.jpg"
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output = predict(0, image_url)
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print(output) |