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(__file__)
  sys.path.append(__dir__)
  sys.path.append(os.path.join(__dir__, '../..'))
  
  import tools.infer.utility as utility
  from ppocr.utils.utility import initial_logger
  logger = initial_logger()
  import cv2
  import tools.infer.predict_det as predict_det
  import tools.infer.predict_rec as predict_rec
  import copy
  import numpy as np
  import math
  import time
  from ppocr.utils.utility import get_image_file_list
  from PIL import Image
  from tools.infer.utility import draw_ocr
  from tools.infer.utility import draw_ocr_box_txt
  
  
  class TextSystem(object):
      def __init__(self, args):
          self.text_detector = predict_det.TextDetector(args)
          self.text_recognizer = predict_rec.TextRecognizer(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(np.linalg.norm(points[0] - points[1]))
          img_crop_height = int(np.linalg.norm(points[0] - points[3]))
          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_crop,
              M, (img_crop_width, img_crop_height),
              borderMode=cv2.BORDER_REPLICATE)
          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)
          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)
          rec_res, elapse = self.text_recognizer(img_crop_list)
          # self.print_draw_crop_rec_res(img_crop_list, rec_res)
          return dt_boxes, 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
  
  
  if __name__ == "__main__":
      args = utility.parse_args()
      image_file_list = get_image_file_list(args.image_dir)
      text_sys = TextSystem(args)
      is_visualize = True
      for image_file in image_file_list:
          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
          print("Predict time of %s: %.3fs" % (image_file, elapse))
          dt_num = len(dt_boxes)
          dt_boxes_final = []
          for dno in range(dt_num):
              text, score = rec_res[dno]
              if score >= 0.5:
                  text_str = "%s, %.3f" % (text, score)
                  print(text_str)
                  dt_boxes_final.append(dt_boxes[dno])
  
          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(
                  image, boxes, txts, scores, draw_txt=True, drop_score=0.5)
              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))))
  