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__all__ = ['rec'] |
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import os, sys |
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__dir__ = os.path.dirname(os.path.abspath(__file__)) |
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sys.path.insert(0, os.path.abspath(os.path.join(__dir__, 'PaddleOCR_ali1k_det_rec_300epoch_standalone'))) |
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os.environ["FLAGS_allocator_strategy"] = 'auto_growth' |
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import cv2 |
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import copy |
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import numpy as np |
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import json |
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import time |
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import logging |
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from PIL import Image |
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import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.utility as utility |
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import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_rec as predict_rec |
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import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_det as predict_det |
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import PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.predict_cls as predict_cls |
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from PaddleOCR_ali1k_det_rec_300epoch_standalone.ppocr.utils.utility import get_image_file_list, check_and_read |
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from PaddleOCR_ali1k_det_rec_300epoch_standalone.ppocr.utils.logging import get_logger |
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from PaddleOCR_ali1k_det_rec_300epoch_standalone.tools.infer.utility import draw_ocr_box_txt, get_rotate_crop_image |
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logger = get_logger() |
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class TextSystem(object): |
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def __init__(self, args): |
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if not args.show_log: |
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logger.setLevel(logging.INFO) |
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self.text_detector = predict_det.TextDetector(args) |
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self.text_recognizer = predict_rec.TextRecognizer(args) |
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self.use_angle_cls = args.use_angle_cls |
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self.drop_score = args.drop_score |
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if self.use_angle_cls: |
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self.text_classifier = predict_cls.TextClassifier(args) |
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self.args = args |
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self.crop_image_res_index = 0 |
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def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res): |
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os.makedirs(output_dir, exist_ok=True) |
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bbox_num = len(img_crop_list) |
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for bno in range(bbox_num): |
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cv2.imwrite( |
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os.path.join(output_dir, |
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f"mg_crop_{bno+self.crop_image_res_index}.jpg"), |
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img_crop_list[bno]) |
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logger.debug(f"{bno}, {rec_res[bno]}") |
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self.crop_image_res_index += bbox_num |
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def __call__(self, img, cls=True): |
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time_dict = {'det': 0, 'rec': 0, 'csl': 0, 'all': 0} |
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start = time.time() |
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ori_im = img.copy() |
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dt_boxes, elapse = self.text_detector(img) |
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time_dict['det'] = elapse |
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logger.debug("dt_boxes num : {}, elapse : {}".format( |
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len(dt_boxes), elapse)) |
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if dt_boxes is None: |
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return None, None |
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img_crop_list = [] |
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dt_boxes = sorted_boxes(dt_boxes) |
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for bno in range(len(dt_boxes)): |
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tmp_box = copy.deepcopy(dt_boxes[bno]) |
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img_crop = get_rotate_crop_image(ori_im, tmp_box) |
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img_crop_list.append(img_crop) |
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if self.use_angle_cls and cls: |
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img_crop_list, angle_list, elapse = self.text_classifier( |
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img_crop_list) |
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time_dict['cls'] = elapse |
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logger.debug("cls num : {}, elapse : {}".format( |
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len(img_crop_list), elapse)) |
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rec_res, elapse = self.text_recognizer(img_crop_list) |
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time_dict['rec'] = elapse |
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logger.debug("rec_res num : {}, elapse : {}".format( |
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len(rec_res), elapse)) |
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if self.args.save_crop_res: |
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self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list, |
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rec_res) |
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filter_boxes, filter_rec_res = [], [] |
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for box, rec_result in zip(dt_boxes, rec_res): |
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text, score = rec_result |
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if score >= self.drop_score: |
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filter_boxes.append(box) |
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filter_rec_res.append(rec_result) |
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end = time.time() |
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time_dict['all'] = end - start |
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return filter_boxes, filter_rec_res, time_dict |
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def sorted_boxes(dt_boxes): |
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""" |
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Sort text boxes in order from top to bottom, left to right |
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args: |
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dt_boxes(array):detected text boxes with shape [4, 2] |
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return: |
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sorted boxes(array) with shape [4, 2] |
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""" |
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num_boxes = dt_boxes.shape[0] |
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0])) |
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_boxes = list(sorted_boxes) |
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for i in range(num_boxes - 1): |
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for j in range(i, 0, -1): |
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if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \ |
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(_boxes[j + 1][0][0] < _boxes[j][0][0]): |
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tmp = _boxes[j] |
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_boxes[j] = _boxes[j + 1] |
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_boxes[j + 1] = tmp |
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else: |
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break |
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return _boxes |
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def main(args): |
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image_file_list = get_image_file_list(args.image_dir) |
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image_file_list = image_file_list[args.process_id::args.total_process_num] |
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text_sys = TextSystem(args) |
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is_visualize = True |
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font_path = args.vis_font_path |
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drop_score = args.drop_score |
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draw_img_save_dir = args.draw_img_save_dir |
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os.makedirs(draw_img_save_dir, exist_ok=True) |
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save_results = [] |
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logger.info( |
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"In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', " |
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"if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320" |
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) |
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if args.warmup: |
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img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8) |
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for i in range(10): |
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res = text_sys(img) |
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total_time = 0 |
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cpu_mem, gpu_mem, gpu_util = 0, 0, 0 |
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_st = time.time() |
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count = 0 |
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for idx, image_file in enumerate(image_file_list): |
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img, flag, _ = check_and_read(image_file) |
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if not flag: |
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img = cv2.imread(image_file) |
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if img is None: |
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logger.debug("error in loading image:{}".format(image_file)) |
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continue |
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starttime = time.time() |
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dt_boxes, rec_res, time_dict = text_sys(img) |
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elapse = time.time() - starttime |
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total_time += elapse |
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logger.debug( |
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str(idx) + " Predict time of %s: %.3fs" % (image_file, elapse)) |
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for text, score in rec_res: |
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logger.debug("{}, {:.3f}".format(text, score)) |
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res = [{ |
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"transcription": rec_res[idx][0], |
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"points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), |
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} for idx in range(len(dt_boxes))] |
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save_pred = os.path.basename(image_file) + "\t" + json.dumps( |
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res, ensure_ascii=False) + "\n" |
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save_results.append(save_pred) |
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if is_visualize: |
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image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
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boxes = dt_boxes |
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txts = [rec_res[i][0] for i in range(len(rec_res))] |
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scores = [rec_res[i][1] for i in range(len(rec_res))] |
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draw_img = draw_ocr_box_txt( |
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image, |
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boxes, |
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txts, |
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scores, |
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drop_score=drop_score, |
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font_path=font_path) |
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if flag: |
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image_file = image_file[:-3] + "png" |
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cv2.imwrite( |
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os.path.join(draw_img_save_dir, os.path.basename(image_file)), |
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draw_img[:, :, ::-1]) |
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logger.debug("The visualized image saved in {}".format( |
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os.path.join(draw_img_save_dir, os.path.basename(image_file)))) |
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logger.info("The predict total time is {}".format(time.time() - _st)) |
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if args.benchmark: |
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text_sys.text_detector.autolog.report() |
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text_sys.text_recognizer.autolog.report() |
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with open( |
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os.path.join(draw_img_save_dir, "system_results.txt"), |
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'w', |
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encoding='utf-8') as f: |
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f.writelines(save_results) |
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class AttributeDict(dict): |
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def __getattr__(self, attr): |
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return self[attr] |
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def __setattr__(self, attr, value): |
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self[attr] = value |
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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'] |
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args = utility.parse_args(sysargv) |
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text_sys = TextSystem(args) |
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def rec(img: str | cv2.typing.MatLike): |
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if isinstance(img, (str)): |
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img = cv2.imread(img) |
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dt_boxes, rec_res, time_dict = text_sys(img) |
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res = [{ |
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"transcription": rec_res[idx][0], |
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"points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), |
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} for idx in range(len(dt_boxes))] |
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pil_image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
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boxes = dt_boxes |
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txts = [rec_res[i][0] for i in range(len(rec_res))] |
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scores = [rec_res[i][1] for i in range(len(rec_res))] |
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return txts, boxes, scores, pil_image |
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def showBox(txts, boxes, scores, pil_image): |
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draw_img = draw_ocr_box_txt( |
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pil_image, |
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boxes, |
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txts, |
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scores, |
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drop_score=0.5, |
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font_path='PaddleOCR_ali1k_det_rec_300epoch_standalone/fonts/simfang.ttf' |
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) |
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cv2.imshow("result", draw_img) |
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cv2.waitKey(0) |
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if __name__ == "__main__": |
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""" |
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python3 tools/infer/predict_system.py \ |
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--image_dir="train_data/det/test/25.jpg" \ |
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--det_algorithm="DB" \ |
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--det_model_dir="output/det_model" \ |
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--det_limit_side_len=960 \ |
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--det_db_unclip_ratio=3.5 \ |
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--rec_model_dir="output/rec_model/Student" \ |
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--rec_char_dict_path="train_data/keys.txt" \ |
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--use_gpu False \ |
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--enable_mkldnn=True |
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""" |
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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'] |
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args = utility.parse_args(sysargv) |
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text_sys = TextSystem(args) |
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img = cv2.imread('PaddleOCR_ali1k_det_rec_300epoch_standalone/train_data/det/train/3.jpg') |
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dt_boxes, rec_res, time_dict = text_sys(img) |
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res = [{ |
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"transcription": rec_res[idx][0], |
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"points": np.array(dt_boxes[idx]).astype(np.int32).tolist(), |
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} for idx in range(len(dt_boxes))] |
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image = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
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boxes = dt_boxes |
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txts = [rec_res[i][0] for i in range(len(rec_res))] |
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scores = [rec_res[i][1] for i in range(len(rec_res))] |
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font_path = args.vis_font_path |
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drop_score = args.drop_score |
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draw_img = draw_ocr_box_txt( |
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image, |
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boxes, |
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txts, |
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scores, |
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drop_score=drop_score, |
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font_path=font_path) |
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height, width, colorNum = img.shape |
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newWidth = 800 |
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if width > newWidth: |
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rate = newWidth / width |
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newHeight = int(rate * height) |
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dim = (newWidth, newHeight) |
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img_des = cv2.resize(draw_img, dim, interpolation=cv2.INTER_LINEAR) |
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else: |
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img_des = draw_img.copy() |
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cv2.imshow("result", draw_img) |
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cv2.waitKey(0) |
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main(args) |
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