text: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  import sys
  
  __dir__ = os.path.dirname(os.path.abspath(__file__))
  sys.path.append(__dir__)
  sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
  
  os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
  
  import cv2
  import copy
  import numpy as np
  import time
  from PIL import Image
  import tools.infer.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 ppocr.utils.utility import get_image_file_list, check_and_read_gif
  from ppocr.utils.logging import get_logger
  from tools.infer.utility import draw_ocr_box_txt
  
  logger = get_logger()
  
  
  class TextSystem(object):
      def __init__(self, args):
          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)
  
      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])
              logger.info(bno, rec_res[bno])
  
      def __call__(self, img):
          ori_im = img.copy()
          dt_boxes, elapse = self.text_detector(img)
          logger.info("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)
              logger.info("cls num  : {}, elapse : {}".format(
                  len(img_crop_list), elapse))
          # import pdb;pdb.set_trace()
          rec_res, elapse = self.text_recognizer(img_crop_list)
          logger.info("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
  
  import json
  def to_json(dt_boxes, texts, scores, img_fname, json_fname):
      json_file = {}
      json_file["filename"] = img_fname
      boxes = []
      # import pdb;pdb.set_trace()
      for box, text, score in zip(dt_boxes, texts, scores):
          xl, yl, xr, yr = box[:, 0].min(), box[:, 1].min(), box[:, 0].max(), box[:, 1].max() 
          boxes.append({
              "score": float(score),
              "bbox": [int(xl), int(yl), int(xr), int(yr)],
              "text": text
          })
  
      json_file["boxes"] = boxes
      with open(json_fname, 'w', encoding='utf-8') as file:
          json.dump(json_file, file, ensure_ascii=False, indent=1)
  
  def main(args):
      text_sys = TextSystem(args)
      is_visualize = True
      font_path = args.vis_font_path
      drop_score = args.drop_score
  
      img_root = args.image_dir
  
      for root, dir, files in os.walk(img_root):
          # json_root = root.replace('jw-0908_trainval', 'jw-0908_trainval_ppocr')
          # os.makedirs(json_root, exist_ok=True)
          draw_img_save = root.replace('jw-0908_trainval', 'jw-0908_trainval_ppocr_vis')
          os.makedirs(draw_img_save, exist_ok=True)
          for file in files:
              if file.endswith('.json'):
                  continue
              image_file = os.path.join(root, file)
              img, flag = check_and_read_gif(image_file)
              if not flag:
                  img = cv2.imread(image_file)
              if img is None:
                  logger.info("error in loading image:{}".format(image_file))
                  continue
              starttime = time.time()
              dt_boxes, rec_res = text_sys(img)
              elapse = time.time() - starttime
              logger.info("Predict time of %s: %.3fs" % (image_file, elapse))
  
              for text, score in rec_res:
                  logger.info("{}, {:.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))]
  
                  to_json(boxes, txts, scores, os.path.basename(image_file), os.path.join(root, os.path.basename(image_file).replace('.jpg', '.json')))
  
                  draw_img = draw_ocr_box_txt(
                      image,
                      boxes,
                      txts,
                      scores,
                      drop_score=drop_score,
                      font_path=font_path)
                  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])
                  logger.info("The visualized image saved in {}".format(
                      os.path.join(draw_img_save, os.path.basename(image_file))))
  
  
  if __name__ == "__main__":
      main(utility.parse_args())
  