674 lines
28 KiB
Python
674 lines
28 KiB
Python
# 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.insert(0, 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
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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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elif self.rec_algorithm == "VisionLAN":
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postprocess_params = {
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'name': 'VLLabelDecode',
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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 == 'ViTSTR':
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postprocess_params = {
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'name': 'ViTSTRLabelDecode',
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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 == 'ABINet':
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postprocess_params = {
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'name': 'ABINetLabelDecode',
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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 == "SPIN":
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postprocess_params = {
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'name': 'SPINLabelDecode',
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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 == "RobustScanner":
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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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"rm_symbol": True
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}
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elif self.rec_algorithm == 'RFL':
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postprocess_params = {
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'name': 'RFLLabelDecode',
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"character_dict_path": None,
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"use_space_char": args.use_space_char
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}
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elif self.rec_algorithm == "PREN":
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postprocess_params = {'name': 'PRENLabelDecode'}
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elif self.rec_algorithm == "CAN":
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self.inverse = args.rec_image_inverse
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postprocess_params = {
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'name': 'CANLabelDecode',
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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.postprocess_params = 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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self.return_word_box = args.return_word_box
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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' or self.rec_algorithm == 'ViTSTR':
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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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if self.rec_algorithm == 'ViTSTR':
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img = image_pil.resize([imgW, imgH], Image.BICUBIC)
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else:
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img = image_pil.resize([imgW, imgH], 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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if self.rec_algorithm == 'ViTSTR':
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norm_img = norm_img.astype(np.float32) / 255.
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else:
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norm_img = norm_img.astype(np.float32) / 128. - 1.
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return norm_img
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elif self.rec_algorithm == 'RFL':
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_CUBIC)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image / 255
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resized_image = resized_image[np.newaxis, :]
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resized_image -= 0.5
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resized_image /= 0.5
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return resized_image
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assert imgC == img.shape[2]
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imgW = int((imgH * 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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if self.rec_algorithm == 'RARE':
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if resized_w > self.rec_image_shape[2]:
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resized_w = self.rec_image_shape[2]
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imgW = self.rec_image_shape[2]
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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_vl(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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img = img[:, :, ::-1] # bgr2rgb
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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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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return resized_image
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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 resize_norm_img_spin(self, img):
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# return padding_im
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img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC)
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img = np.array(img, np.float32)
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img = np.expand_dims(img, -1)
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img = img.transpose((2, 0, 1))
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mean = [127.5]
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std = [127.5]
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mean = np.array(mean, dtype=np.float32)
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std = np.array(std, dtype=np.float32)
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mean = np.float32(mean.reshape(1, -1))
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stdinv = 1 / np.float32(std.reshape(1, -1))
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img -= mean
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img *= stdinv
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return img
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def resize_norm_img_svtr(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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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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return resized_image
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def resize_norm_img_abinet(self, img, image_shape):
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imgC, imgH, imgW = image_shape
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR)
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resized_image = resized_image.astype('float32')
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resized_image = resized_image / 255.
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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resized_image = (
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resized_image - mean[None, None, ...]) / std[None, None, ...]
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resized_image = resized_image.transpose((2, 0, 1))
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resized_image = resized_image.astype('float32')
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return resized_image
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def norm_img_can(self, img, image_shape):
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img = cv2.cvtColor(
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img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image
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if self.inverse:
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img = 255 - img
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if self.rec_image_shape[0] == 1:
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h, w = img.shape
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_, imgH, imgW = self.rec_image_shape
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if h < imgH or w < imgW:
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padding_h = max(imgH - h, 0)
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padding_w = max(imgW - w, 0)
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img_padded = np.pad(img, ((0, padding_h), (0, padding_w)),
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'constant',
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constant_values=(255))
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img = img_padded
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img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w
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img = img.astype('float32')
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return img
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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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if self.rec_algorithm == "SRN":
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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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if self.rec_algorithm == "SAR":
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valid_ratios = []
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imgC, imgH, imgW = self.rec_image_shape[:3]
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max_wh_ratio = imgW / imgH
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wh_ratio_list = []
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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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wh_ratio_list.append(wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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if 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.append(valid_ratio)
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norm_img_batch.append(norm_img)
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elif self.rec_algorithm == "SRN":
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norm_img = self.process_image_srn(
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img_list[indices[ino]], self.rec_image_shape, 8, 25)
|
|
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])
|
|
elif self.rec_algorithm == "SVTR":
|
|
norm_img = self.resize_norm_img_svtr(img_list[indices[ino]],
|
|
self.rec_image_shape)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
norm_img_batch.append(norm_img)
|
|
elif self.rec_algorithm in ["VisionLAN", "PREN"]:
|
|
norm_img = self.resize_norm_img_vl(img_list[indices[ino]],
|
|
self.rec_image_shape)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
norm_img_batch.append(norm_img)
|
|
elif self.rec_algorithm == 'SPIN':
|
|
norm_img = self.resize_norm_img_spin(img_list[indices[ino]])
|
|
norm_img = norm_img[np.newaxis, :]
|
|
norm_img_batch.append(norm_img)
|
|
elif self.rec_algorithm == "ABINet":
|
|
norm_img = self.resize_norm_img_abinet(
|
|
img_list[indices[ino]], self.rec_image_shape)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
norm_img_batch.append(norm_img)
|
|
elif self.rec_algorithm == "RobustScanner":
|
|
norm_img, _, _, valid_ratio = self.resize_norm_img_sar(
|
|
img_list[indices[ino]],
|
|
self.rec_image_shape,
|
|
width_downsample_ratio=0.25)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
valid_ratio = np.expand_dims(valid_ratio, axis=0)
|
|
valid_ratios = []
|
|
valid_ratios.append(valid_ratio)
|
|
norm_img_batch.append(norm_img)
|
|
word_positions_list = []
|
|
word_positions = np.array(range(0, 40)).astype('int64')
|
|
word_positions = np.expand_dims(word_positions, axis=0)
|
|
word_positions_list.append(word_positions)
|
|
elif self.rec_algorithm == "CAN":
|
|
norm_img = self.norm_img_can(img_list[indices[ino]],
|
|
max_wh_ratio)
|
|
norm_img = norm_img[np.newaxis, :]
|
|
norm_img_batch.append(norm_img)
|
|
norm_image_mask = np.ones(norm_img.shape, dtype='float32')
|
|
word_label = np.ones([1, 36], dtype='int64')
|
|
norm_img_mask_batch = []
|
|
word_label_list = []
|
|
norm_img_mask_batch.append(norm_image_mask)
|
|
word_label_list.append(word_label)
|
|
else:
|
|
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)
|
|
norm_img_batch = np.concatenate(norm_img_batch)
|
|
norm_img_batch = norm_img_batch.copy()
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
|
|
if self.rec_algorithm == "SRN":
|
|
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,
|
|
]
|
|
if self.use_onnx:
|
|
input_dict = {}
|
|
input_dict[self.input_tensor.name] = norm_img_batch
|
|
outputs = self.predictor.run(self.output_tensors,
|
|
input_dict)
|
|
preds = {"predict": outputs[2]}
|
|
else:
|
|
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)
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
preds = {"predict": outputs[2]}
|
|
elif self.rec_algorithm == "SAR":
|
|
valid_ratios = np.concatenate(valid_ratios)
|
|
inputs = [
|
|
norm_img_batch,
|
|
np.array(
|
|
[valid_ratios], dtype=np.float32),
|
|
]
|
|
if self.use_onnx:
|
|
input_dict = {}
|
|
input_dict[self.input_tensor.name] = norm_img_batch
|
|
outputs = self.predictor.run(self.output_tensors,
|
|
input_dict)
|
|
preds = outputs[0]
|
|
else:
|
|
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)
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
preds = outputs[0]
|
|
elif self.rec_algorithm == "RobustScanner":
|
|
valid_ratios = np.concatenate(valid_ratios)
|
|
word_positions_list = np.concatenate(word_positions_list)
|
|
inputs = [norm_img_batch, valid_ratios, word_positions_list]
|
|
|
|
if self.use_onnx:
|
|
input_dict = {}
|
|
input_dict[self.input_tensor.name] = norm_img_batch
|
|
outputs = self.predictor.run(self.output_tensors,
|
|
input_dict)
|
|
preds = outputs[0]
|
|
else:
|
|
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)
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
preds = outputs[0]
|
|
elif self.rec_algorithm == "CAN":
|
|
norm_img_mask_batch = np.concatenate(norm_img_mask_batch)
|
|
word_label_list = np.concatenate(word_label_list)
|
|
inputs = [norm_img_batch, norm_img_mask_batch, word_label_list]
|
|
if self.use_onnx:
|
|
input_dict = {}
|
|
input_dict[self.input_tensor.name] = norm_img_batch
|
|
outputs = self.predictor.run(self.output_tensors,
|
|
input_dict)
|
|
preds = outputs
|
|
else:
|
|
input_names = self.predictor.get_input_names()
|
|
input_tensor = []
|
|
for i in range(len(input_names)):
|
|
input_tensor_i = self.predictor.get_input_handle(
|
|
input_names[i])
|
|
input_tensor_i.copy_from_cpu(inputs[i])
|
|
input_tensor.append(input_tensor_i)
|
|
self.input_tensor = input_tensor
|
|
self.predictor.run()
|
|
outputs = []
|
|
for output_tensor in self.output_tensors:
|
|
output = output_tensor.copy_to_cpu()
|
|
outputs.append(output)
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
preds = outputs
|
|
else:
|
|
if self.use_onnx:
|
|
input_dict = {}
|
|
input_dict[self.input_tensor.name] = norm_img_batch
|
|
outputs = self.predictor.run(self.output_tensors,
|
|
input_dict)
|
|
preds = outputs[0]
|
|
else:
|
|
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)
|
|
if self.benchmark:
|
|
self.autolog.times.stamp()
|
|
if len(outputs) != 1:
|
|
preds = outputs
|
|
else:
|
|
preds = outputs[0]
|
|
if self.postprocess_params['name'] == 'CTCLabelDecode':
|
|
rec_result = self.postprocess_op(preds, return_word_box=self.return_word_box, wh_ratio_list=wh_ratio_list, max_wh_ratio=max_wh_ratio)
|
|
else:
|
|
rec_result = self.postprocess_op(preds)
|
|
for rno in range(len(rec_result)):
|
|
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
|
|
if self.benchmark:
|
|
self.autolog.times.end(stamp=True)
|
|
return rec_res, time.time() - st
|
|
|
|
|
|
def main(args):
|
|
image_file_list = get_image_file_list(args.image_dir)
|
|
text_recognizer = TextRecognizer(args)
|
|
valid_image_file_list = []
|
|
img_list = []
|
|
|
|
logger.info(
|
|
"In PP-OCRv3, rec_image_shape parameter defaults to '3, 48, 320', "
|
|
"if you are using recognition model with PP-OCRv2 or an older version, please set --rec_image_shape='3,32,320"
|
|
)
|
|
# warmup 2 times
|
|
if args.warmup:
|
|
img = np.random.uniform(0, 255, [48, 320, 3]).astype(np.uint8)
|
|
for i in range(2):
|
|
res = text_recognizer([img] * int(args.rec_batch_num))
|
|
|
|
for image_file in image_file_list:
|
|
img, flag, _ = check_and_read(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)
|
|
try:
|
|
rec_res, _ = text_recognizer(img_list)
|
|
|
|
except Exception as E:
|
|
logger.info(traceback.format_exc())
|
|
logger.info(E)
|
|
exit()
|
|
for ino in range(len(img_list)):
|
|
logger.info("Predicts of {}:{}".format(valid_image_file_list[ino],
|
|
rec_res[ino]))
|
|
if args.benchmark:
|
|
text_recognizer.autolog.report()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(utility.parse_args())
|