162 lines
6.6 KiB
Python
Executable File
162 lines
6.6 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, '../..'))
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import tools.infer.utility as utility
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from ppocr.utils.utility import initial_logger
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logger = initial_logger()
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from ppocr.utils.utility import get_image_file_list
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import cv2
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import copy
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import numpy as np
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import math
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import time
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from ppocr.utils.character import CharacterOps
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class TextRecognizer(object):
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def __init__(self, args):
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self.predictor, self.input_tensor, self.output_tensors =\
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utility.create_predictor(args, mode="rec")
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image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.rec_image_shape = image_shape
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self.character_type = args.rec_char_type
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self.rec_batch_num = args.rec_batch_num
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self.rec_algorithm = args.rec_algorithm
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char_ops_params = {}
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char_ops_params["character_type"] = args.rec_char_type
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char_ops_params["character_dict_path"] = args.rec_char_dict_path
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if self.rec_algorithm != "RARE":
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char_ops_params['loss_type'] = 'ctc'
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self.loss_type = 'ctc'
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else:
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char_ops_params['loss_type'] = 'attention'
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self.loss_type = 'attention'
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self.char_ops = CharacterOps(char_ops_params)
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def resize_norm_img(self, img, max_wh_ratio):
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imgC, imgH, imgW = self.rec_image_shape
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if self.character_type == "ch":
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imgW = int(32 * max_wh_ratio)
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h = img.shape[0]
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w = img.shape[1]
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32)
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padding_im[:, :, 0:resized_w] = resized_image
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return padding_im
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def __call__(self, img_list):
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img_num = len(img_list)
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rec_res = []
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batch_num = self.rec_batch_num
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predict_time = 0
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[ino].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[ino], max_wh_ratio)
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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starttime = time.time()
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self.input_tensor.copy_from_cpu(norm_img_batch)
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self.predictor.zero_copy_run()
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if self.loss_type == "ctc":
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rec_idx_batch = self.output_tensors[0].copy_to_cpu()
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rec_idx_lod = self.output_tensors[0].lod()[0]
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predict_batch = self.output_tensors[1].copy_to_cpu()
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predict_lod = self.output_tensors[1].lod()[0]
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elapse = time.time() - starttime
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predict_time += elapse
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for rno in range(len(rec_idx_lod) - 1):
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beg = rec_idx_lod[rno]
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end = rec_idx_lod[rno + 1]
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rec_idx_tmp = rec_idx_batch[beg:end, 0]
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preds_text = self.char_ops.decode(rec_idx_tmp)
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beg = predict_lod[rno]
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end = predict_lod[rno + 1]
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probs = predict_batch[beg:end, :]
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ind = np.argmax(probs, axis=1)
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blank = probs.shape[1]
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valid_ind = np.where(ind != (blank - 1))[0]
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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rec_res.append([preds_text, score])
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else:
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rec_idx_batch = self.output_tensors[0].copy_to_cpu()
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predict_batch = self.output_tensors[1].copy_to_cpu()
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elapse = time.time() - starttime
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predict_time += elapse
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for rno in range(len(rec_idx_batch)):
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end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
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if len(end_pos) <= 1:
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preds = rec_idx_batch[rno, 1:]
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score = np.mean(predict_batch[rno, 1:])
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else:
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preds = rec_idx_batch[rno, 1:end_pos[1]]
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score = np.mean(predict_batch[rno, 1:end_pos[1]])
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preds_text = self.char_ops.decode(preds)
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rec_res.append([preds_text, score])
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return rec_res, predict_time
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if __name__ == "__main__":
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args = utility.parse_args()
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image_file_list = get_image_file_list(args.image_dir)
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text_recognizer = TextRecognizer(args)
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valid_image_file_list = []
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img_list = []
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for image_file in image_file_list:
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img = cv2.imread(image_file)
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if img is None:
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logger.info("error in loading image:{}".format(image_file))
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continue
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valid_image_file_list.append(image_file)
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img_list.append(img)
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try:
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rec_res, predict_time = text_recognizer(img_list)
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except Exception as e:
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print(e)
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logger.info(
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"ERROR!!!! \n"
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"Please read the FAQ：https://github.com/PaddlePaddle/PaddleOCR#faq \n"
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"If your model has tps module: "
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"TPS does not support variable shape.\n"
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"Please set --rec_image_shape='3,32,100' and --rec_char_type='en' ")
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exit()
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for ino in range(len(img_list)):
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print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
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print("Total predict time for %d images:%.3f" %
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(len(img_list), predict_time))
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