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 numpy as np
  import math
  import time
  import traceback
  import paddle
  
  import tools.infer.utility as utility
  from ppocr.postprocess import build_post_process
  from ppocr.utils.logging import get_logger
  from ppocr.utils.utility import get_image_file_list, check_and_read_gif
  from PIL import ImageFont, ImageDraw, Image
  
  logger = get_logger()
  
  
  class TextRecognizer(object):
      def __init__(self, args):
          self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
          self.character_type = args.rec_char_type
          self.rec_batch_num = args.rec_batch_num
          self.rec_algorithm = args.rec_algorithm
          self.max_text_length = args.max_text_length
          postprocess_params = {
              # 'name': 'CTCLabelDecode',
              'name': 'HpocrCTCLabelDecode',
              "character_type": args.rec_char_type,
              "character_dict_path": args.rec_char_dict_path,
              "use_space_char": args.use_space_char
          }
          if self.rec_algorithm == "SRN":
              postprocess_params = {
                  'name': 'SRNLabelDecode',
                  "character_type": args.rec_char_type,
                  "character_dict_path": args.rec_char_dict_path,
                  "use_space_char": args.use_space_char
              }
          elif self.rec_algorithm == "RARE":
              postprocess_params = {
                  'name': 'AttnLabelDecode',
                  "character_type": args.rec_char_type,
                  "character_dict_path": args.rec_char_dict_path,
                  "use_space_char": args.use_space_char
              }
          self.postprocess_op = build_post_process(postprocess_params)
          self.predictor, self.input_tensor, self.output_tensors = \
              utility.create_predictor(args, 'rec', logger)
  
      def resize_norm_img(self, img, max_wh_ratio):
          imgC, imgH, imgW = self.rec_image_shape
          assert imgC == img.shape[2]
          if self.character_type == "ch":
              imgW = int((32 * max_wh_ratio))
          h, w = img.shape[:2]
          ratio = w / float(h)
          if math.ceil(imgH * ratio) > imgW:
              resized_w = imgW
          else:
              resized_w = int(math.ceil(imgH * ratio))
          resized_image = cv2.resize(img, (resized_w, imgH))
          resized_image = resized_image.astype('float32')
          resized_image = resized_image.transpose((2, 0, 1)) / 255
          resized_image -= 0.5
          resized_image /= 0.5
          padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
          padding_im[:, :, 0:resized_w] = resized_image
          return padding_im
  
      def resize_norm_img_srn(self, img, image_shape):
          imgC, imgH, imgW = image_shape
  
          img_black = np.zeros((imgH, imgW))
          im_hei = img.shape[0]
          im_wid = img.shape[1]
  
          if im_wid <= im_hei * 1:
              img_new = cv2.resize(img, (imgH * 1, imgH))
          elif im_wid <= im_hei * 2:
              img_new = cv2.resize(img, (imgH * 2, imgH))
          elif im_wid <= im_hei * 3:
              img_new = cv2.resize(img, (imgH * 3, imgH))
          else:
              img_new = cv2.resize(img, (imgW, imgH))
  
          img_np = np.asarray(img_new)
          img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
          img_black[:, 0:img_np.shape[1]] = img_np
          img_black = img_black[:, :, np.newaxis]
  
          row, col, c = img_black.shape
          c = 1
  
          return np.reshape(img_black, (c, row, col)).astype(np.float32)
  
      def srn_other_inputs(self, image_shape, num_heads, max_text_length):
  
          imgC, imgH, imgW = image_shape
          feature_dim = int((imgH / 8) * (imgW / 8))
  
          encoder_word_pos = np.array(range(0, feature_dim)).reshape(
              (feature_dim, 1)).astype('int64')
          gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
              (max_text_length, 1)).astype('int64')
  
          gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
          gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
              [-1, 1, max_text_length, max_text_length])
          gsrm_slf_attn_bias1 = np.tile(
              gsrm_slf_attn_bias1,
              [1, num_heads, 1, 1]).astype('float32') * [-1e9]
  
          gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
              [-1, 1, max_text_length, max_text_length])
          gsrm_slf_attn_bias2 = np.tile(
              gsrm_slf_attn_bias2,
              [1, num_heads, 1, 1]).astype('float32') * [-1e9]
  
          encoder_word_pos = encoder_word_pos[np.newaxis, :]
          gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
  
          return [
              encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
              gsrm_slf_attn_bias2
          ]
  
      def process_image_srn(self, img, image_shape, num_heads, max_text_length):
          norm_img = self.resize_norm_img_srn(img, image_shape)
          norm_img = norm_img[np.newaxis, :]
  
          [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
              self.srn_other_inputs(image_shape, num_heads, max_text_length)
  
          gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
          gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
          encoder_word_pos = encoder_word_pos.astype(np.int64)
          gsrm_word_pos = gsrm_word_pos.astype(np.int64)
  
          return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
                  gsrm_slf_attn_bias2)
  
      def __call__(self, img_list):
          img_num = len(img_list)
          # Calculate the aspect ratio of all text bars
          width_list = []
          for img in img_list:
              width_list.append(img.shape[1] / float(img.shape[0]))
          # Sorting can speed up the recognition process
          indices = np.argsort(np.array(width_list))
  
          # rec_res = []
          rec_res = [['', 0.0]] * img_num
          batch_num = self.rec_batch_num
          elapse = 0
          for beg_img_no in range(0, img_num, batch_num):
              end_img_no = min(img_num, beg_img_no + batch_num)
              norm_img_batch = []
              max_wh_ratio = 0
              for ino in range(beg_img_no, end_img_no):
                  # h, w = img_list[ino].shape[0:2]
                  h, w = img_list[indices[ino]].shape[0:2]
                  wh_ratio = w * 1.0 / h
                  max_wh_ratio = max(max_wh_ratio, wh_ratio)
              for ino in range(beg_img_no, end_img_no):
                  if self.rec_algorithm != "SRN":
                      norm_img = self.resize_norm_img(img_list[indices[ino]],
                                                      max_wh_ratio)
                      norm_img = norm_img[np.newaxis, :]
                      norm_img_batch.append(norm_img)
                  else:
                      norm_img = self.process_image_srn(img_list[indices[ino]],
                                                        self.rec_image_shape, 8,
                                                        self.max_text_length)
                      encoder_word_pos_list = []
                      gsrm_word_pos_list = []
                      gsrm_slf_attn_bias1_list = []
                      gsrm_slf_attn_bias2_list = []
                      encoder_word_pos_list.append(norm_img[1])
                      gsrm_word_pos_list.append(norm_img[2])
                      gsrm_slf_attn_bias1_list.append(norm_img[3])
                      gsrm_slf_attn_bias2_list.append(norm_img[4])
                      norm_img_batch.append(norm_img[0])
              norm_img_batch = np.concatenate(norm_img_batch)
              norm_img_batch = norm_img_batch.copy()
  
              if self.rec_algorithm == "SRN":
                  starttime = time.time()
                  encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
                  gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
                  gsrm_slf_attn_bias1_list = np.concatenate(
                      gsrm_slf_attn_bias1_list)
                  gsrm_slf_attn_bias2_list = np.concatenate(
                      gsrm_slf_attn_bias2_list)
  
                  inputs = [
                      norm_img_batch,
                      encoder_word_pos_list,
                      gsrm_word_pos_list,
                      gsrm_slf_attn_bias1_list,
                      gsrm_slf_attn_bias2_list,
                  ]
                  input_names = self.predictor.get_input_names()
                  for i in range(len(input_names)):
                      input_tensor = self.predictor.get_input_handle(input_names[
                                                                         i])
                      input_tensor.copy_from_cpu(inputs[i])
                  self.predictor.run()
                  outputs = []
                  for output_tensor in self.output_tensors:
                      output = output_tensor.copy_to_cpu()
                      outputs.append(output)
                  preds = {"predict": outputs[2]}
              else:
                  starttime = time.time()
                  self.input_tensor.copy_from_cpu(norm_img_batch)
                  self.predictor.run()
  
                  outputs = []
                  for output_tensor in self.output_tensors:
                      output = output_tensor.copy_to_cpu()
                      outputs.append(output)
                  preds = outputs[0]
              self.predictor.try_shrink_memory()
              rec_result = self.postprocess_op(preds)
              for rno in range(len(rec_result)):
                  rec_res[indices[beg_img_no + rno]] = rec_result[rno]
              elapse += time.time() - starttime
          return rec_res, elapse
  
  
  def main(args):
      image_file_list = get_image_file_list(args.image_dir)
      text_recognizer = TextRecognizer(args)
      total_run_time = 0.0
      total_images_num = 0
      valid_image_file_list = []
      img_list = []
      for idx, image_file in enumerate(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
          valid_image_file_list.append(image_file)
          img_list.append(img)
          if len(img_list) >= args.rec_batch_num or idx == len(
                  image_file_list) - 1:
              try:
                  rec_res, predict_time = text_recognizer(img_list)
                  total_run_time += predict_time
              except:
                  logger.info(traceback.format_exc())
                  logger.info(
                      "ERROR!!!! \n"
                      "Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
                      "If your model has tps module:  "
                      "TPS does not support variable shape.\n"
                      "Please set --rec_image_shape='3,32,100' and --rec_char_type='en' "
                  )
                  exit()
              for ino in range(len(img_list)):
                  logger.info("Predicts of {}:{}".format(valid_image_file_list[
                                                             ino], rec_res[ino]))
                  save_pre_img(args, valid_image_file_list[ino], rec_res[ino])
              total_images_num += len(valid_image_file_list)
              valid_image_file_list = []
              img_list = []
      logger.info("Total predict time for {} images, cost: {:.3f}".format(
          total_images_num, total_run_time))
  
  
  def save_pre_img(args, path, pre_text):
      font = ImageFont.truetype(args.vis_font_path, 25)
  
      img = cv2.imread(path)
  
      pre_img = img.copy()
      pre_img.fill(255)
      b, g, r, a = 255, 0, 0, 0
      img_pil = Image.fromarray(pre_img)
      # 创建画板
      draw = ImageDraw.Draw(img_pil)
      # 设置字体的颜色
      b, g, r, a = 0, 0, 0, 0
      # 在图片上绘制中文
      # print(pre_text)
      draw.text((2, 5), pre_text[0], font=font, fill=(0, 0, 255, 0))
      # 将图片转为numpy array的数据格式
      pre_img = np.array(img_pil)
      # res = cv2.vconcat([img, pretext])
  
      print(path)
      org_dir, name = path.rsplit('/', 1)
  
      pre_dir = args.rec_pre_save_dir
      pre_name_path = os.path.join(pre_dir, name.replace(".jpg", "_pre.jpg"))
      org_name_path = os.path.join(pre_dir, name)
  
      cv2.imwrite(pre_name_path, pre_img)
      for ii in pre_text[2]:
          cv2.line(img, (ii * 4, 0), (ii * 4, 32), (0, 0, 255), 1, 4)
      cv2.imwrite(org_name_path, img)
  
  
  if __name__ == "__main__":
      args = utility.parse_args()
      args.__dict__["image_dir"] = "../../img_test/"
      args.__dict__["rec_model_dir"] = "../../inference/rec/mv3_none_none_ctc/"
      args.__dict__["rec_image_shape"] = '3,32,280'
      args.__dict__["rec_char_type"] = 'ch'
      args.__dict__["rec_char_dict_path"] = "../../ppocr/utils/dict/char_std_v1.txt"
      args.__dict__["rec_batch_num"] = 1
      args.__dict__["use_gpu"] = False
      args.__dict__["rec_pre_save_dir"] = "../../img_test/pre"
      print(args)
  
      main(args)
  