# proxychains4 pip install -r PaddleOCR_ali1k_det_rec_300epoch_standalone/requirements.txt # PaddleOCR_ali1k_det_rec_300epoch/tools/infer/predict_system.py __all__ = ['rec'] # 以”白名单“的形式暴露里面定义的符号 import os, sys __dir__ = os.path.dirname(os.path.abspath(__file__)) # sys.path.append(__dir__) sys.path.insert(0, os.path.abspath(os.path.join(__dir__, 'PaddleOCR_ali1k_det_rec_300epoch_standalone'))) os.environ["FLAGS_allocator_strategy"] = 'auto_growth' import cv2 import copy import numpy as np import json import time import logging from PIL import Image import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.utility as utility import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_rec as predict_rec import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_det as predict_det import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_cls as predict_cls from PaddleOCR_ali1k_det_rec_300epoch_standalone.ppocr.utils.utility import get_image_file_list, check_and_read from PaddleOCR_ali1k_det_rec_300epoch_standalone.ppocr.utils.logging import get_logger from PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image logger = get_logger() class TextSystem(object): def __init__(self, args): if not args.show_log: logger.setLevel(logging.INFO) self.text_detector = predict_det.TextDetector(args) self.text_recognizer = predict_rec.TextRecognizer(args) self.use_angle_cls = args.use_angle_cls self.drop_score = args.drop_score if self.use_angle_cls: self.text_classifier = predict_cls.TextClassifier(args) self.args = args self.crop_image_res_index = 0 def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res): os.makedirs(output_dir, exist_ok=True) bbox_num = len(img_crop_list) for bno in range(bbox_num): cv2.imwrite( os.path.join(output_dir, f"mg_crop_{bno+self.crop_image_res_index}.jpg"), img_crop_list[bno]) logger.debug(f"{bno}, {rec_res[bno]}") self.crop_image_res_index += bbox_num def __call__(self, img, cls=True): time_dict = {'det': 0, 'rec': 0, 'csl': 0, 'all': 0} start = time.time() ori_im = img.copy() dt_boxes, elapse = self.text_detector(img) time_dict['det'] = elapse logger.debug("dt_boxes num : {}, elapse : {}".format( len(dt_boxes), elapse)) if dt_boxes is None: return None, None img_crop_list = [] dt_boxes = sorted_boxes(dt_boxes) for bno in range(len(dt_boxes)): tmp_box = copy.deepcopy(dt_boxes[bno]) img_crop = get_rotate_crop_image(ori_im, tmp_box) img_crop_list.append(img_crop) if self.use_angle_cls and cls: img_crop_list, angle_list, elapse = self.text_classifier( img_crop_list) time_dict['cls'] = elapse logger.debug("cls num : {}, elapse : {}".format( len(img_crop_list), elapse)) rec_res, elapse = self.text_recognizer(img_crop_list) time_dict['rec'] = elapse logger.debug("rec_res num : {}, elapse : {}".format( len(rec_res), elapse)) if self.args.save_crop_res: self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list, rec_res) filter_boxes, filter_rec_res = [], [] for box, rec_result in zip(dt_boxes, rec_res): text, score = rec_result if score >= self.drop_score: filter_boxes.append(box) filter_rec_res.append(rec_result) end = time.time() time_dict['all'] = end - start return filter_boxes, filter_rec_res, time_dict def sorted_boxes(dt_boxes): """ Sort text boxes in order from top to bottom, left to right args: dt_boxes(array):detected text boxes with shape [4, 2] return: sorted boxes(array) with shape [4, 2] """ num_boxes = dt_boxes.shape[0] sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) _boxes = list(sorted_boxes) for i in range(num_boxes - 1): for j in range(i, 0, -1): if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \ (_boxes[j + 1][0][0] < _boxes[j][0][0]): tmp = _boxes[j] _boxes[j] = _boxes[j + 1] _boxes[j + 1] = tmp else: break return _boxes def main(args): image_file_list = get_image_file_list(args.image_dir) image_file_list = image_file_list[args.process_id::args.total_process_num] text_sys = TextSystem(args) is_visualize = True font_path = args.vis_font_path drop_score = args.drop_score draw_img_save_dir = args.draw_img_save_dir os.makedirs(draw_img_save_dir, exist_ok=True) save_results = [] logger.info( "In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', " "if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320" ) # warm up 10 times if args.warmup: img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) for i in range(10): res = text_sys(img) total_time = 0 cpu_mem, gpu_mem, gpu_util = 0, 0, 0 _st = time.time() count = 0 for idx, image_file in enumerate(image_file_list): img, flag, _ = check_and_read(image_file) if not flag: img = cv2.imread(image_file) if img is None: logger.debug("error in loading image:{}".format(image_file)) continue starttime = time.time() dt_boxes, rec_res, time_dict = text_sys(img) elapse = time.time() - starttime total_time += elapse logger.debug( str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) for text, score in rec_res: logger.debug("{}, {:.3f}".format(text, score)) res = [{ "transcription": rec_res[idx][0], "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), } for idx in range(len(dt_boxes))] save_pred = os.path.basename(image_file) + "\t" + json.dumps( res, ensure_ascii=False) + "\n" save_results.append(save_pred) if is_visualize: image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) boxes = dt_boxes txts = [rec_res[i][0] for i in range(len(rec_res))] scores = [rec_res[i][1] for i in range(len(rec_res))] draw_img = draw_ocr_box_txt( image, boxes, txts, scores, drop_score=drop_score, font_path=font_path) if flag: image_file = image_file[:-3] + "png" cv2.imwrite( os.path.join(draw_img_save_dir, os.path.basename(image_file)), draw_img[:, :, ::-1]) logger.debug("The visualized image saved in {}".format( os.path.join(draw_img_save_dir, os.path.basename(image_file)))) logger.info("The predict total time is {}".format(time.time() - _st)) if args.benchmark: text_sys.text_detector.autolog.report() text_sys.text_recognizer.autolog.report() with open( os.path.join(draw_img_save_dir, "system_results.txt"), 'w', encoding='utf-8') as f: f.writelines(save_results) class AttributeDict(dict): def __getattr__(self, attr): return self[attr] def __setattr__(self, attr, value): self[attr] = value # sysargv = ['--image_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg', '--det_algorithm', 'DB', '--det_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_det_infer', '--det_limit_side_len', '1024', '--det_db_unclip_ratio', '3.5', '--rec_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_rec_infer', '--rec_char_dict_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ppocr_keys.txt', '--use_gpu', 'False', '--enable_mkldnn', 'True', '--vis_font_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf'] # ch sysargv = ['--image_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg', '--det_algorithm', 'DB', '--det_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_det_infer', '--det_limit_side_len', '1024', '--det_db_unclip_ratio', '3.5', '--rec_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/japan_PP-OCRv3_rec_infer', '--rec_char_dict_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/japan_dict.txt', '--use_gpu', 'False', '--enable_mkldnn', 'True', '--vis_font_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf'] # jp args = utility.parse_args(sysargv) text_sys = TextSystem(args) def rec(img: str | cv2.typing.MatLike): if isinstance(img, (str)): img = cv2.imread(img) dt_boxes, rec_res, time_dict = text_sys(img) res = [{ "transcription": rec_res[idx][0], "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), } for idx in range(len(dt_boxes))] pil_image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) boxes = dt_boxes txts = [rec_res[i][0] for i in range(len(rec_res))] scores = [rec_res[i][1] for i in range(len(rec_res))] return txts, boxes, scores, pil_image def showBox(txts, boxes, scores, pil_image): draw_img = draw_ocr_box_txt( pil_image, boxes, txts, scores, drop_score=0.5, font_path='PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf' ) cv2.imshow("result", draw_img) cv2.waitKey(0) if __name__ == "__main__": # text_detector = predict_det.TextDetector( AttributeDict({"det_algorithm": "DB"}) ) #text_recognizer = predict_rec.TextRecognizer(args) # img = cv2.imread(image_file) # starttime = time.time() # dt_boxes, rec_res, time_dict = text_sys(img) # elapse = time.time() - starttime """ python3 tools/infer/predict_system.py \ --image_dir="train_data/det/test/25.jpg" \ --det_algorithm="DB" \ --det_model_dir="output/det_model" \ --det_limit_side_len=960 \ --det_db_unclip_ratio=3.5 \ --rec_model_dir="output/rec_model/Student" \ --rec_char_dict_path="train_data/keys.txt" \ --use_gpu False \ --enable_mkldnn=True """ # import sys # sys.argv.append( '--image_dir' ) # # sys.argv.append( 'train_data/det/test/12.jpg' ) # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg' ) # sys.argv.append( '--det_algorithm' ) # sys.argv.append( 'DB' ) # sys.argv.append( '--det_model_dir' ) # # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch/output/det_model' ) # 自已训练的 # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_det_infer' ) # 官方的 # sys.argv.append( '--det_limit_side_len' ) # # sys.argv.append( '960' ) # 自已的 # sys.argv.append( '1024' ) # 官方的 # sys.argv.append( '--det_db_unclip_ratio' ) # sys.argv.append( '3.5' ) # sys.argv.append( '--rec_model_dir' ) # # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch/output/rec_model/Student' ) # 自已的 # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_rec_infer' ) # 官方的 # sys.argv.append( '--rec_char_dict_path' ) # # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch/train_data/keys.txt' ) # 自已的 # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ppocr_keys.txt' ) # 官方的词表 # sys.argv.append( '--use_gpu' ) # sys.argv.append( 'False' ) # sys.argv.append( '--enable_mkldnn' ) # sys.argv.append( 'True' ) # sys.argv.append( '--vis_font_path' ) # sys.argv.append( 'PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf' ) sysargv = ['--image_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg', '--det_algorithm', 'DB', '--det_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_det_infer', '--det_limit_side_len', '1024', '--det_db_unclip_ratio', '3.5', '--rec_model_dir', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ch_PP-OCRv3_rec_infer', '--rec_char_dict_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/official_models/ppocr_keys.txt', '--use_gpu', 'False', '--enable_mkldnn', 'True', '--vis_font_path', 'PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf'] # args = utility.parse_args() args = utility.parse_args(sysargv) text_sys = TextSystem(args) img = cv2.imread('PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg') #img = cv2.imread('images/ch.png') dt_boxes, rec_res, time_dict = text_sys(img) res = [{ "transcription": rec_res[idx][0], "points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), } for idx in range(len(dt_boxes))] image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) boxes = dt_boxes txts = [rec_res[i][0] for i in range(len(rec_res))] scores = [rec_res[i][1] for i in range(len(rec_res))] font_path = args.vis_font_path drop_score = args.drop_score draw_img = draw_ocr_box_txt( image, boxes, txts, scores, drop_score=drop_score, font_path=font_path) # 缩放图片, 统一 800 宽 height, width, colorNum = img.shape newWidth = 800 if width > newWidth: rate = newWidth / width newHeight = int(rate * height) dim = (newWidth, newHeight) img_des = cv2.resize(draw_img, dim, interpolation=cv2.INTER_LINEAR) #img.resize (new OpenCvSharp.Size(0, 0), rate, rate, InterpolationFlags.Linear); else: img_des = draw_img.copy() cv2.imshow("result", draw_img) cv2.waitKey(0) main(args) # pip install paddlepaddle "paddleocr==2.7.0.0" -i https://mirror.baidu.com/pypi/simple # apt install python3.10-dev # pip install paddlepaddle "paddleocr==2.7.5" -i https://mirror.baidu.com/pypi/simple # from paddleocr import PaddleOCR, draw_ocr # # `ch`, `en`, `fr`, `german`, `korean`, `japan` # ocr = PaddleOCR(use_angle_cls=True, lang="ch") # need to run only once to download and load model into memory # img_path = './images/ch.png' # result = ocr.ocr(img_path, cls=True) # for idx in range(len(result)): # res = result[idx] # for line in res: # print(line)