338 lines
14 KiB
Python
Executable File
338 lines
14 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(os.path.abspath(__file__))
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sys.path.append(__dir__)
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
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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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import paddle.fluid as fluid
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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, check_and_read_gif
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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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if args.use_pdserving is False:
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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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self.use_zero_copy_run = args.use_zero_copy_run
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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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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self.text_len = args.max_text_length
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char_ops_params = {
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"character_type": args.rec_char_type,
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char,
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"max_text_length": args.max_text_length
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}
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if self.rec_algorithm in ["CRNN", "Rosetta", "STAR-Net"]:
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char_ops_params['loss_type'] = 'ctc'
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self.loss_type = 'ctc'
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elif self.rec_algorithm == "RARE":
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char_ops_params['loss_type'] = 'attention'
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self.loss_type = 'attention'
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elif self.rec_algorithm == "SRN":
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char_ops_params['loss_type'] = 'srn'
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self.loss_type = 'srn'
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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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assert imgC == img.shape[2]
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wh_ratio = max(max_wh_ratio, imgW * 1.0 / imgH)
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if self.character_type == "ch":
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imgW = int((32 * wh_ratio))
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h, w = img.shape[:2]
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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 resize_norm_img_srn(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img_black = np.zeros((imgH, imgW))
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im_hei = img.shape[0]
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im_wid = img.shape[1]
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if im_wid <= im_hei * 1:
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img_new = cv2.resize(img, (imgH * 1, imgH))
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elif im_wid <= im_hei * 2:
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img_new = cv2.resize(img, (imgH * 2, imgH))
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elif im_wid <= im_hei * 3:
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img_new = cv2.resize(img, (imgH * 3, imgH))
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else:
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img_new = cv2.resize(img, (imgW, imgH))
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img_np = np.asarray(img_new)
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img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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img_black[:, 0:img_np.shape[1]] = img_np
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img_black = img_black[:, :, np.newaxis]
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row, col, c = img_black.shape
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c = 1
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return np.reshape(img_black, (c, row, col)).astype(np.float32)
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def srn_other_inputs(self, image_shape, num_heads, max_text_length,
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char_num):
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imgC, imgH, imgW = image_shape
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feature_dim = int((imgH / 8) * (imgW / 8))
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encoder_word_pos = np.array(range(0, feature_dim)).reshape(
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(feature_dim, 1)).astype('int64')
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gsrm_word_pos = np.array(range(0, max_text_length)).reshape(
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(max_text_length, 1)).astype('int64')
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gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
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gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias1 = np.tile(
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gsrm_slf_attn_bias1,
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[1, num_heads, 1, 1]).astype('float32') * [-1e9]
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gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape(
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[-1, 1, max_text_length, max_text_length])
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gsrm_slf_attn_bias2 = np.tile(
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gsrm_slf_attn_bias2,
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[1, num_heads, 1, 1]).astype('float32') * [-1e9]
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encoder_word_pos = encoder_word_pos[np.newaxis, :]
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gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
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return [
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encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2
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]
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def process_image_srn(self,
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img,
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image_shape,
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num_heads,
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max_text_length,
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char_ops=None):
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norm_img = self.resize_norm_img_srn(img, image_shape)
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norm_img = norm_img[np.newaxis, :]
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char_num = char_ops.get_char_num()
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[encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
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self.srn_other_inputs(image_shape, num_heads, max_text_length, char_num)
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gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32)
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gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32)
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return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
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gsrm_slf_attn_bias2)
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def __call__(self, img_list):
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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#rec_res = []
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rec_res = [['', 0.0]] * img_num
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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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h, w = img_list[indices[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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if self.loss_type != "srn":
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norm_img = self.resize_norm_img(img_list[indices[ino]],
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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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else:
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norm_img = self.process_image_srn(img_list[indices[ino]],
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self.rec_image_shape, 8,
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25, self.char_ops)
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encoder_word_pos_list = []
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gsrm_word_pos_list = []
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gsrm_slf_attn_bias1_list = []
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gsrm_slf_attn_bias2_list = []
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encoder_word_pos_list.append(norm_img[1])
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gsrm_word_pos_list.append(norm_img[2])
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gsrm_slf_attn_bias1_list.append(norm_img[3])
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gsrm_slf_attn_bias2_list.append(norm_img[4])
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norm_img_batch.append(norm_img[0])
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norm_img_batch = np.concatenate(norm_img_batch, axis=0)
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norm_img_batch = norm_img_batch.copy()
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if self.loss_type == "srn":
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starttime = time.time()
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encoder_word_pos_list = np.concatenate(encoder_word_pos_list)
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gsrm_word_pos_list = np.concatenate(gsrm_word_pos_list)
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gsrm_slf_attn_bias1_list = np.concatenate(
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gsrm_slf_attn_bias1_list)
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gsrm_slf_attn_bias2_list = np.concatenate(
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gsrm_slf_attn_bias2_list)
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starttime = time.time()
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norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
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encoder_word_pos_list = fluid.core.PaddleTensor(
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encoder_word_pos_list)
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gsrm_word_pos_list = fluid.core.PaddleTensor(gsrm_word_pos_list)
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gsrm_slf_attn_bias1_list = fluid.core.PaddleTensor(
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gsrm_slf_attn_bias1_list)
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gsrm_slf_attn_bias2_list = fluid.core.PaddleTensor(
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gsrm_slf_attn_bias2_list)
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inputs = [
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norm_img_batch, encoder_word_pos_list,
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gsrm_slf_attn_bias1_list, gsrm_slf_attn_bias2_list,
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gsrm_word_pos_list
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]
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self.predictor.run(inputs)
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else:
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starttime = time.time()
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if self.use_zero_copy_run:
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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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else:
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norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
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self.predictor.run([norm_img_batch])
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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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if len(valid_ind) == 0:
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continue
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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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rec_res[indices[beg_img_no + rno]] = [preds_text, score]
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elif self.loss_type == 'srn':
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rec_idx_batch = self.output_tensors[0].copy_to_cpu()
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probs = self.output_tensors[1].copy_to_cpu()
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char_num = self.char_ops.get_char_num()
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preds = rec_idx_batch.reshape(-1)
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elapse = time.time() - starttime
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predict_time += elapse
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total_preds = preds.copy()
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for ino in range(int(len(rec_idx_batch) / self.text_len)):
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preds = total_preds[ino * self.text_len:(ino + 1) *
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self.text_len]
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ind = np.argmax(probs, axis=1)
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valid_ind = np.where(preds != int(char_num - 1))[0]
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if len(valid_ind) == 0:
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continue
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score = np.mean(probs[valid_ind, ind[valid_ind]])
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preds = preds[:valid_ind[-1] + 1]
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preds_text = self.char_ops.decode(preds)
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rec_res[indices[beg_img_no + ino]] = [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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rec_res[indices[beg_img_no + rno]] = [preds_text, score]
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return rec_res, predict_time
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def main(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, flag = check_and_read_gif(image_file)
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if not flag:
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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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if __name__ == "__main__":
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main(utility.parse_args())
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