mirror of
https://github.com/PaddlePaddle/PaddleOCR.git
synced 2025-11-04 11:49:14 +00:00
416 lines
17 KiB
Python
Executable File
416 lines
17 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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from PIL import Image
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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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os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
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import cv2
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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 traceback
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import paddle
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import tools.infer.utility as utility
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from ppocr.postprocess import build_post_process
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from ppocr.utils.logging import get_logger
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from ppocr.utils.utility import get_image_file_list, check_and_read_gif
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logger = get_logger()
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class TextRecognizer(object):
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def __init__(self, args):
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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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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postprocess_params = {
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'name': 'CTCLabelDecode',
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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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}
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if self.rec_algorithm == "SRN":
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postprocess_params = {
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'name': 'SRNLabelDecode',
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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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}
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elif self.rec_algorithm == "RARE":
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postprocess_params = {
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'name': 'AttnLabelDecode',
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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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}
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elif self.rec_algorithm == 'NRTR':
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postprocess_params = {
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'name': 'NRTRLabelDecode',
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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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}
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elif self.rec_algorithm == "SAR":
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postprocess_params = {
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'name': 'SARLabelDecode',
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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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}
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self.postprocess_op = build_post_process(postprocess_params)
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self.predictor, self.input_tensor, self.output_tensors, self.config = \
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utility.create_predictor(args, 'rec', logger)
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self.benchmark = args.benchmark
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self.use_onnx = args.use_onnx
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if args.benchmark:
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import auto_log
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pid = os.getpid()
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gpu_id = utility.get_infer_gpuid()
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self.autolog = auto_log.AutoLogger(
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model_name="rec",
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model_precision=args.precision,
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batch_size=args.rec_batch_num,
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data_shape="dynamic",
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save_path=None, #args.save_log_path,
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inference_config=self.config,
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pids=pid,
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process_name=None,
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gpu_ids=gpu_id if args.use_gpu else None,
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time_keys=[
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'preprocess_time', 'inference_time', 'postprocess_time'
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],
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warmup=0,
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logger=logger)
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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.rec_algorithm == 'NRTR':
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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image_pil = Image.fromarray(np.uint8(img))
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img = image_pil.resize([100, 32], Image.ANTIALIAS)
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img = np.array(img)
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norm_img = np.expand_dims(img, -1)
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norm_img = norm_img.transpose((2, 0, 1))
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return norm_img.astype(np.float32) / 128. - 1.
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assert imgC == img.shape[2]
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imgW = int((32 * max_wh_ratio))
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if self.use_onnx:
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w = self.input_tensor.shape[3:][0]
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if w is not None and w > 0:
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imgW = w
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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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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, img, image_shape, num_heads, max_text_length):
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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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[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)
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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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encoder_word_pos = encoder_word_pos.astype(np.int64)
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gsrm_word_pos = gsrm_word_pos.astype(np.int64)
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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 resize_norm_img_sar(self, img, image_shape,
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width_downsample_ratio=0.25):
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imgC, imgH, imgW_min, imgW_max = image_shape
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h = img.shape[0]
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w = img.shape[1]
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valid_ratio = 1.0
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# make sure new_width is an integral multiple of width_divisor.
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width_divisor = int(1 / width_downsample_ratio)
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# resize
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ratio = w / float(h)
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resize_w = math.ceil(imgH * ratio)
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if resize_w % width_divisor != 0:
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resize_w = round(resize_w / width_divisor) * width_divisor
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if imgW_min is not None:
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resize_w = max(imgW_min, resize_w)
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if imgW_max is not None:
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valid_ratio = min(1.0, 1.0 * resize_w / imgW_max)
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resize_w = min(imgW_max, resize_w)
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resized_image = cv2.resize(img, (resize_w, imgH))
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resized_image = resized_image.astype('float32')
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# norm
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if image_shape[0] == 1:
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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else:
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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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resize_shape = resized_image.shape
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padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32)
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padding_im[:, :, 0:resize_w] = resized_image
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pad_shape = padding_im.shape
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return padding_im, resize_shape, pad_shape, valid_ratio
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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 = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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st = time.time()
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if self.benchmark:
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self.autolog.times.start()
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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[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.rec_algorithm != "SRN" and self.rec_algorithm != "SAR":
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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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elif self.rec_algorithm == "SAR":
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norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
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img_list[indices[ino]], self.rec_image_shape)
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norm_img = norm_img[np.newaxis, :]
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valid_ratio = np.expand_dims(valid_ratio, axis=0)
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valid_ratios = []
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valid_ratios.append(valid_ratio)
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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(
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img_list[indices[ino]], self.rec_image_shape, 8, 25)
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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)
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norm_img_batch = norm_img_batch.copy()
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if self.benchmark:
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self.autolog.times.stamp()
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if self.rec_algorithm == "SRN":
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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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inputs = [
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norm_img_batch,
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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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]
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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outputs = self.predictor.run(self.output_tensors,
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input_dict)
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preds = {"predict": outputs[2]}
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else:
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(
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input_names[i])
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input_tensor.copy_from_cpu(inputs[i])
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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if self.benchmark:
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self.autolog.times.stamp()
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preds = {"predict": outputs[2]}
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elif self.rec_algorithm == "SAR":
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valid_ratios = np.concatenate(valid_ratios)
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inputs = [
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norm_img_batch,
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valid_ratios,
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]
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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outputs = self.predictor.run(self.output_tensors,
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input_dict)
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preds = outputs[0]
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else:
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input_names = self.predictor.get_input_names()
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for i in range(len(input_names)):
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input_tensor = self.predictor.get_input_handle(
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input_names[i])
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input_tensor.copy_from_cpu(inputs[i])
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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if self.benchmark:
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self.autolog.times.stamp()
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preds = outputs[0]
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else:
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if self.use_onnx:
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input_dict = {}
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input_dict[self.input_tensor.name] = norm_img_batch
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outputs = self.predictor.run(self.output_tensors,
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input_dict)
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preds = outputs[0]
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else:
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self.input_tensor.copy_from_cpu(norm_img_batch)
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self.predictor.run()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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if self.benchmark:
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self.autolog.times.stamp()
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if len(outputs) != 1:
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preds = outputs
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else:
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preds = outputs[0]
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rec_result = self.postprocess_op(preds)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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if self.benchmark:
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self.autolog.times.end(stamp=True)
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return rec_res, time.time() - st
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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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# warmup 2 times
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if args.warmup:
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img = np.random.uniform(0, 255, [32, 320, 3]).astype(np.uint8)
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for i in range(2):
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res = text_recognizer([img] * int(args.rec_batch_num))
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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, _ = text_recognizer(img_list)
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except Exception as E:
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logger.info(traceback.format_exc())
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logger.info(E)
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exit()
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for ino in range(len(img_list)):
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logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
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rec_res[ino]))
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if args.benchmark:
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text_recognizer.autolog.report()
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if __name__ == "__main__":
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main(utility.parse_args())
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