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.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
  
  os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
  
  import cv2
  import numpy as np
  import time
  import sys
  
  import tools.infer.utility as utility
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read_gif
  from ppocr.data import create_operators, transform
  from ppocr.postprocess import build_post_process
  import json
  logger = get_logger()
  
  
  class TextDetector(object):
      def __init__(self, args):
          self.args = args
          self.det_algorithm = args.det_algorithm
          self.use_onnx = args.use_onnx
          pre_process_list = [{
              'DetResizeForTest': {
                  'limit_side_len': args.det_limit_side_len,
                  'limit_type': args.det_limit_type,
              }
          }, {
              'NormalizeImage': {
                  'std': [0.229, 0.224, 0.225],
                  'mean': [0.485, 0.456, 0.406],
                  'scale': '1./255.',
                  'order': 'hwc'
              }
          }, {
              'ToCHWImage': None
          }, {
              'KeepKeys': {
                  'keep_keys': ['image', 'shape']
              }
          }]
          postprocess_params = {}
          if self.det_algorithm == "DB":
              postprocess_params['name'] = 'DBPostProcess'
              postprocess_params["thresh"] = args.det_db_thresh
              postprocess_params["box_thresh"] = args.det_db_box_thresh
              postprocess_params["max_candidates"] = 1000
              postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
              postprocess_params["use_dilation"] = args.use_dilation
              postprocess_params["score_mode"] = args.det_db_score_mode
          elif self.det_algorithm == "EAST":
              postprocess_params['name'] = 'EASTPostProcess'
              postprocess_params["score_thresh"] = args.det_east_score_thresh
              postprocess_params["cover_thresh"] = args.det_east_cover_thresh
              postprocess_params["nms_thresh"] = args.det_east_nms_thresh
          elif self.det_algorithm == "SAST":
              pre_process_list[0] = {
                  'DetResizeForTest': {
                      'resize_long': args.det_limit_side_len
                  }
              }
              postprocess_params['name'] = 'SASTPostProcess'
              postprocess_params["score_thresh"] = args.det_sast_score_thresh
              postprocess_params["nms_thresh"] = args.det_sast_nms_thresh
              self.det_sast_polygon = args.det_sast_polygon
              if self.det_sast_polygon:
                  postprocess_params["sample_pts_num"] = 6
                  postprocess_params["expand_scale"] = 1.2
                  postprocess_params["shrink_ratio_of_width"] = 0.2
              else:
                  postprocess_params["sample_pts_num"] = 2
                  postprocess_params["expand_scale"] = 1.0
                  postprocess_params["shrink_ratio_of_width"] = 0.3
          elif self.det_algorithm == "PSE":
              postprocess_params['name'] = 'PSEPostProcess'
              postprocess_params["thresh"] = args.det_pse_thresh
              postprocess_params["box_thresh"] = args.det_pse_box_thresh
              postprocess_params["min_area"] = args.det_pse_min_area
              postprocess_params["box_type"] = args.det_pse_box_type
              postprocess_params["scale"] = args.det_pse_scale
              self.det_pse_box_type = args.det_pse_box_type
          else:
              logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
              sys.exit(0)
  
          self.preprocess_op = create_operators(pre_process_list)
          self.postprocess_op = build_post_process(postprocess_params)
          self.predictor, self.input_tensor, self.output_tensors, self.config = utility.create_predictor(
              args, 'det', logger)
  
          if self.use_onnx:
              img_h, img_w = self.input_tensor.shape[2:]
              if img_h is not None and img_w is not None and img_h > 0 and img_w > 0:
                  pre_process_list[0] = {
                      'DetResizeForTest': {
                          'image_shape': [img_h, img_w]
                      }
                  }
          self.preprocess_op = create_operators(pre_process_list)
  
          if args.benchmark:
              import auto_log
              pid = os.getpid()
              gpu_id = utility.get_infer_gpuid()
              self.autolog = auto_log.AutoLogger(
                  model_name="det",
                  model_precision=args.precision,
                  batch_size=1,
                  data_shape="dynamic",
                  save_path=None,
                  inference_config=self.config,
                  pids=pid,
                  process_name=None,
                  gpu_ids=gpu_id if args.use_gpu else None,
                  time_keys=[
                      'preprocess_time', 'inference_time', 'postprocess_time'
                  ],
                  warmup=2,
                  logger=logger)
  
      def order_points_clockwise(self, pts):
          """
          reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
          # sort the points based on their x-coordinates
          """
          xSorted = pts[np.argsort(pts[:, 0]), :]
  
          # grab the left-most and right-most points from the sorted
          # x-roodinate points
          leftMost = xSorted[:2, :]
          rightMost = xSorted[2:, :]
  
          # now, sort the left-most coordinates according to their
          # y-coordinates so we can grab the top-left and bottom-left
          # points, respectively
          leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
          (tl, bl) = leftMost
  
          rightMost = rightMost[np.argsort(rightMost[:, 1]), :]
          (tr, br) = rightMost
  
          rect = np.array([tl, tr, br, bl], dtype="float32")
          return rect
  
      def clip_det_res(self, points, img_height, img_width):
          for pno in range(points.shape[0]):
              points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
              points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
          return points
  
      def filter_tag_det_res(self, dt_boxes, image_shape):
          img_height, img_width = image_shape[0:2]
          dt_boxes_new = []
          for box in dt_boxes:
              box = self.order_points_clockwise(box)
              box = self.clip_det_res(box, img_height, img_width)
              rect_width = int(np.linalg.norm(box[0] - box[1]))
              rect_height = int(np.linalg.norm(box[0] - box[3]))
              if rect_width <= 3 or rect_height <= 3:
                  continue
              dt_boxes_new.append(box)
          dt_boxes = np.array(dt_boxes_new)
          return dt_boxes
  
      def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
          img_height, img_width = image_shape[0:2]
          dt_boxes_new = []
          for box in dt_boxes:
              box = self.clip_det_res(box, img_height, img_width)
              dt_boxes_new.append(box)
          dt_boxes = np.array(dt_boxes_new)
          return dt_boxes
  
      def __call__(self, img):
          ori_im = img.copy()
          data = {'image': img}
  
          st = time.time()
  
          if self.args.benchmark:
              self.autolog.times.start()
  
          data = transform(data, self.preprocess_op)
          img, shape_list = data
          if img is None:
              return None, 0
          img = np.expand_dims(img, axis=0)
          shape_list = np.expand_dims(shape_list, axis=0)
          img = img.copy()
  
          if self.args.benchmark:
              self.autolog.times.stamp()
          if self.use_onnx:
              input_dict = {}
              input_dict[self.input_tensor.name] = img
              outputs = self.predictor.run(self.output_tensors, input_dict)
          else:
              self.input_tensor.copy_from_cpu(img)
              self.predictor.run()
              outputs = []
              for output_tensor in self.output_tensors:
                  output = output_tensor.copy_to_cpu()
                  outputs.append(output)
              if self.args.benchmark:
                  self.autolog.times.stamp()
  
          preds = {}
          if self.det_algorithm == "EAST":
              preds['f_geo'] = outputs[0]
              preds['f_score'] = outputs[1]
          elif self.det_algorithm == 'SAST':
              preds['f_border'] = outputs[0]
              preds['f_score'] = outputs[1]
              preds['f_tco'] = outputs[2]
              preds['f_tvo'] = outputs[3]
          elif self.det_algorithm in ['DB', 'PSE']:
              preds['maps'] = outputs[0]
          else:
              raise NotImplementedError
  
          #self.predictor.try_shrink_memory()
          post_result = self.postprocess_op(preds, shape_list)
          dt_boxes = post_result[0]['points']
          if (self.det_algorithm == "SAST" and
                  self.det_sast_polygon) or (self.det_algorithm == "PSE" and
                                             self.det_pse_box_type == 'poly'):
              dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
          else:
              dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
  
          if self.args.benchmark:
              self.autolog.times.end(stamp=True)
          et = time.time()
          return dt_boxes, et - st
  
  
  if __name__ == "__main__":
      args = utility.parse_args()
      image_file_list = get_image_file_list(args.image_dir)
      text_detector = TextDetector(args)
      count = 0
      total_time = 0
      draw_img_save = "./inference_results"
  
      if args.warmup:
          img = np.random.uniform(0, 255, [640, 640, 3]).astype(np.uint8)
          for i in range(2):
              res = text_detector(img)
  
      if not os.path.exists(draw_img_save):
          os.makedirs(draw_img_save)
      save_results = []
      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:
              logger.info("error in loading image:{}".format(image_file))
              continue
          st = time.time()
          dt_boxes, _ = text_detector(img)
          elapse = time.time() - st
          if count > 0:
              total_time += elapse
          count += 1
          save_pred = os.path.basename(image_file) + "\t" + str(
              json.dumps(np.array(dt_boxes).astype(np.int32).tolist())) + "\n"
          save_results.append(save_pred)
          logger.info(save_pred)
          logger.info("The predict time of {}: {}".format(image_file, elapse))
          src_im = utility.draw_text_det_res(dt_boxes, image_file)
          img_name_pure = os.path.split(image_file)[-1]
          img_path = os.path.join(draw_img_save,
                                  "det_res_{}".format(img_name_pure))
          cv2.imwrite(img_path, src_im)
          logger.info("The visualized image saved in {}".format(img_path))
  
      with open(os.path.join(draw_img_save, "det_results.txt"), 'w') as f:
          f.writelines(save_results)
          f.close()
      if args.benchmark:
          text_detector.autolog.report()
  