text: import os
  import sys
  import json
  
  __dir__ = os.path.dirname(os.path.abspath(__file__))
  sys.path.append(__dir__)
  sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
  
  import cv2
  import copy
  import numpy as np
  import time
  from PIL import Image
  import tools.infer.pytorchocr_utility as utility
  import tools.infer.predict_rec as predict_rec
  import tools.infer.predict_det as predict_det
  import tools.infer.predict_cls as predict_cls
  from pytorchocr.utils.utility import get_image_file_list, check_and_read_gif
  from tools.infer.pytorchocr_utility import draw_ocr_box_txt
  
  
  
  class TextSystem(object):
      def __init__(self, args, **kwargs):
          self.text_detector = predict_det.TextDetector(args, **kwargs)
          self.text_recognizer = predict_rec.TextRecognizer(args, **kwargs)
          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, **kwargs)
  
  
      def get_rotate_crop_image(self, img, points):
          '''
          img_height, img_width = img.shape[0:2]
          left = int(np.min(points[:, 0]))
          right = int(np.max(points[:, 0]))
          top = int(np.min(points[:, 1]))
          bottom = int(np.max(points[:, 1]))
          img_crop = img[top:bottom, left:right, :].copy()
          points[:, 0] = points[:, 0] - left
          points[:, 1] = points[:, 1] - top
          '''
          img_crop_width = int(
              max(
                  np.linalg.norm(points[0] - points[1]),
                  np.linalg.norm(points[2] - points[3])))
          img_crop_height = int(
              max(
                  np.linalg.norm(points[0] - points[3]),
                  np.linalg.norm(points[1] - points[2])))
          pts_std = np.float32([[0, 0], [img_crop_width, 0],
                                [img_crop_width, img_crop_height],
                                [0, img_crop_height]])
          M = cv2.getPerspectiveTransform(points, pts_std)
          dst_img = cv2.warpPerspective(
              img,
              M, (img_crop_width, img_crop_height),
              borderMode=cv2.BORDER_REPLICATE,
              flags=cv2.INTER_CUBIC)
          dst_img_height, dst_img_width = dst_img.shape[0:2]
          if dst_img_height * 1.0 / dst_img_width >= 1.5:
              dst_img = np.rot90(dst_img)
          return dst_img
  
      def print_draw_crop_rec_res(self, img_crop_list, rec_res):
          bbox_num = len(img_crop_list)
          for bno in range(bbox_num):
              cv2.imwrite("./output/img_crop_%d.jpg" % bno, img_crop_list[bno])
              print(bno, rec_res[bno])
  
      def __call__(self, img):
          ori_im = img.copy()
          dt_boxes, elapse = self.text_detector(img)
          print("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 = self.get_rotate_crop_image(ori_im, tmp_box)
              img_crop_list.append(img_crop)
          if self.use_angle_cls:
              img_crop_list, angle_list, elapse = self.text_classifier(
                  img_crop_list)
              print("cls num  : {}, elapse : {}".format(
                  len(img_crop_list), elapse))
  
          rec_res, elapse = self.text_recognizer(img_crop_list)
          print("rec_res num  : {}, elapse : {}".format(
              len(rec_res), elapse))
          # self.print_draw_crop_rec_res(img_crop_list, rec_res)
          filter_boxes, filter_rec_res = [], []
          for box, rec_reuslt in zip(dt_boxes, rec_res):
              text, score = rec_reuslt
              if score >= self.drop_score:
                  filter_boxes.append(box)
                  filter_rec_res.append(rec_reuslt)
          return filter_boxes, filter_rec_res
  
  
  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):
          if abs(_boxes[i + 1][0][1] - _boxes[i][0][1]) < 10 and \
                  (_boxes[i + 1][0][0] < _boxes[i][0][0]):
              tmp = _boxes[i]
              _boxes[i] = _boxes[i + 1]
              _boxes[i + 1] = tmp
      return _boxes
  
  
  def main(args):
      image_file_list = get_image_file_list(args.image_dir)
      text_sys = TextSystem(args)
      is_visualize = True
      font_path = args.vis_font_path
      drop_score = args.drop_score
      for image_file in image_file_list:
          img, flag = check_and_read_gif(image_file)
          if not flag:
              img = cv2.imread(image_file)
          if img is None:
              print("error in loading image:{}".format(image_file))
              continue
          starttime = time.time()
          dt_boxes, rec_res = text_sys(img)
          elapse = time.time() - starttime
          print("Predict time of %s: %.3fs" % (image_file, elapse))
  
          for text, score in rec_res:
              print("{}, {:.3f}".format(text, score))
  
          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)
              draw_img_save = "./inference_results/"
              if not os.path.exists(draw_img_save):
                  os.makedirs(draw_img_save)
              cv2.imwrite(
                  os.path.join(draw_img_save, os.path.basename(image_file)),
                  draw_img[:, :, ::-1])
              print("The visualized image saved in {}".format(
                  os.path.join(draw_img_save, os.path.basename(image_file))))
  
          if (args.output_json or args.output_json_path) is not None:
              json_dir = args.output_json_path or './inference_results/'
              if not os.path.exists(json_dir):
                  os.makedirs(json_dir)
              json_path = os.path.join(json_dir, os.path.basename(image_file) + '.json')
              # info
              boxes = [box.tolist() for box in dt_boxes]
              txts = [rec_res[i][0] for i in range(len(rec_res))]
              scores = [float(rec_res[i][1]) for i in range(len(rec_res))]
              # dictionary
              result = {}
              result['img_info'] = {}
              result['img_info']['filepath'] = image_file
              result['img_info']['width'] = img.shape[1]
              result['img_info']['height'] = img.shape[0]
              result['boxes'] = boxes
              result['texts'] = txts
              result['scores'] = scores
              # write
              with open(json_path, 'w+') as f:
                  json.dump(result, f)
  
  
  if __name__ == '__main__':
      main(utility.parse_args())