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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| import sys | |
| from PIL import Image | |
| __dir__ = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.append(__dir__) | |
| sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..'))) | |
| os.environ["FLAGS_allocator_strategy"] = 'auto_growth' | |
| import cv2 | |
| import numpy as np | |
| import math | |
| import time | |
| import traceback | |
| import paddle | |
| import tools.infer.utility as utility | |
| from ppocr.postprocess import build_post_process | |
| from ppocr.utils.logging import get_logger | |
| from ppocr.utils.utility import get_image_file_list, check_and_read | |
| logger = get_logger() | |
| class TextRecognizer(object): | |
| def __init__(self, args): | |
| self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")] | |
| self.rec_batch_num = args.rec_batch_num | |
| self.rec_algorithm = args.rec_algorithm | |
| postprocess_params = { | |
| 'name': 'CTCLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| if self.rec_algorithm == "SRN": | |
| postprocess_params = { | |
| 'name': 'SRNLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "RARE": | |
| postprocess_params = { | |
| 'name': 'AttnLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == 'NRTR': | |
| postprocess_params = { | |
| 'name': 'NRTRLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "SAR": | |
| postprocess_params = { | |
| 'name': 'SARLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "VisionLAN": | |
| postprocess_params = { | |
| 'name': 'VLLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == 'ViTSTR': | |
| postprocess_params = { | |
| 'name': 'ViTSTRLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == 'ABINet': | |
| postprocess_params = { | |
| 'name': 'ABINetLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "SPIN": | |
| postprocess_params = { | |
| 'name': 'SPINLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "RobustScanner": | |
| postprocess_params = { | |
| 'name': 'SARLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char, | |
| "rm_symbol": True | |
| } | |
| elif self.rec_algorithm == 'RFL': | |
| postprocess_params = { | |
| 'name': 'RFLLabelDecode', | |
| "character_dict_path": None, | |
| "use_space_char": args.use_space_char | |
| } | |
| elif self.rec_algorithm == "SATRN": | |
| postprocess_params = { | |
| 'name': 'SATRNLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char, | |
| "rm_symbol": True | |
| } | |
| elif self.rec_algorithm == "PREN": | |
| postprocess_params = {'name': 'PRENLabelDecode'} | |
| elif self.rec_algorithm == "CAN": | |
| self.inverse = args.rec_image_inverse | |
| postprocess_params = { | |
| 'name': 'CANLabelDecode', | |
| "character_dict_path": args.rec_char_dict_path, | |
| "use_space_char": args.use_space_char | |
| } | |
| self.postprocess_op = build_post_process(postprocess_params) | |
| self.predictor, self.input_tensor, self.output_tensors, self.config = \ | |
| utility.create_predictor(args, 'rec', logger) | |
| self.benchmark = args.benchmark | |
| self.use_onnx = args.use_onnx | |
| if args.benchmark: | |
| import auto_log | |
| pid = os.getpid() | |
| gpu_id = utility.get_infer_gpuid() | |
| self.autolog = auto_log.AutoLogger( | |
| model_name="rec", | |
| model_precision=args.precision, | |
| batch_size=args.rec_batch_num, | |
| data_shape="dynamic", | |
| save_path=None, #args.save_log_path, | |
| inference_config=self.config, | |
| pids=pid, | |
| process_name=None, | |
| gpu_ids=gpu_id if args.use_gpu else None, | |
| time_keys=[ | |
| 'preprocess_time', 'inference_time', 'postprocess_time' | |
| ], | |
| warmup=0, | |
| logger=logger) | |
| def resize_norm_img(self, img, max_wh_ratio): | |
| imgC, imgH, imgW = self.rec_image_shape | |
| if self.rec_algorithm == 'NRTR' or self.rec_algorithm == 'ViTSTR': | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # return padding_im | |
| image_pil = Image.fromarray(np.uint8(img)) | |
| if self.rec_algorithm == 'ViTSTR': | |
| img = image_pil.resize([imgW, imgH], Image.BICUBIC) | |
| else: | |
| img = image_pil.resize([imgW, imgH], Image.LANCZOS) | |
| img = np.array(img) | |
| norm_img = np.expand_dims(img, -1) | |
| norm_img = norm_img.transpose((2, 0, 1)) | |
| if self.rec_algorithm == 'ViTSTR': | |
| norm_img = norm_img.astype(np.float32) / 255. | |
| else: | |
| norm_img = norm_img.astype(np.float32) / 128. - 1. | |
| return norm_img | |
| elif self.rec_algorithm == 'RFL': | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| resized_image = cv2.resize( | |
| img, (imgW, imgH), interpolation=cv2.INTER_CUBIC) | |
| resized_image = resized_image.astype('float32') | |
| resized_image = resized_image / 255 | |
| resized_image = resized_image[np.newaxis, :] | |
| resized_image -= 0.5 | |
| resized_image /= 0.5 | |
| return resized_image | |
| assert imgC == img.shape[2] | |
| imgW = int((imgH * max_wh_ratio)) | |
| if self.use_onnx: | |
| w = self.input_tensor.shape[3:][0] | |
| if isinstance(w, str): | |
| pass | |
| elif w is not None and w > 0: | |
| imgW = w | |
| h, w = img.shape[:2] | |
| ratio = w / float(h) | |
| if math.ceil(imgH * ratio) > imgW: | |
| resized_w = imgW | |
| else: | |
| resized_w = int(math.ceil(imgH * ratio)) | |
| if self.rec_algorithm == 'RARE': | |
| if resized_w > self.rec_image_shape[2]: | |
| resized_w = self.rec_image_shape[2] | |
| imgW = self.rec_image_shape[2] | |
| resized_image = cv2.resize(img, (resized_w, imgH)) | |
| resized_image = resized_image.astype('float32') | |
| resized_image = resized_image.transpose((2, 0, 1)) / 255 | |
| resized_image -= 0.5 | |
| resized_image /= 0.5 | |
| padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) | |
| padding_im[:, :, 0:resized_w] = resized_image | |
| return padding_im | |
| def resize_norm_img_vl(self, img, image_shape): | |
| imgC, imgH, imgW = image_shape | |
| img = img[:, :, ::-1] # bgr2rgb | |
| resized_image = cv2.resize( | |
| img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) | |
| resized_image = resized_image.astype('float32') | |
| resized_image = resized_image.transpose((2, 0, 1)) / 255 | |
| return resized_image | |
| def resize_norm_img_srn(self, img, image_shape): | |
| imgC, imgH, imgW = image_shape | |
| img_black = np.zeros((imgH, imgW)) | |
| im_hei = img.shape[0] | |
| im_wid = img.shape[1] | |
| if im_wid <= im_hei * 1: | |
| img_new = cv2.resize(img, (imgH * 1, imgH)) | |
| elif im_wid <= im_hei * 2: | |
| img_new = cv2.resize(img, (imgH * 2, imgH)) | |
| elif im_wid <= im_hei * 3: | |
| img_new = cv2.resize(img, (imgH * 3, imgH)) | |
| else: | |
| img_new = cv2.resize(img, (imgW, imgH)) | |
| img_np = np.asarray(img_new) | |
| img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY) | |
| img_black[:, 0:img_np.shape[1]] = img_np | |
| img_black = img_black[:, :, np.newaxis] | |
| row, col, c = img_black.shape | |
| c = 1 | |
| return np.reshape(img_black, (c, row, col)).astype(np.float32) | |
| def srn_other_inputs(self, image_shape, num_heads, max_text_length): | |
| imgC, imgH, imgW = image_shape | |
| feature_dim = int((imgH / 8) * (imgW / 8)) | |
| encoder_word_pos = np.array(range(0, feature_dim)).reshape( | |
| (feature_dim, 1)).astype('int64') | |
| gsrm_word_pos = np.array(range(0, max_text_length)).reshape( | |
| (max_text_length, 1)).astype('int64') | |
| gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) | |
| gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape( | |
| [-1, 1, max_text_length, max_text_length]) | |
| gsrm_slf_attn_bias1 = np.tile( | |
| gsrm_slf_attn_bias1, | |
| [1, num_heads, 1, 1]).astype('float32') * [-1e9] | |
| gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape( | |
| [-1, 1, max_text_length, max_text_length]) | |
| gsrm_slf_attn_bias2 = np.tile( | |
| gsrm_slf_attn_bias2, | |
| [1, num_heads, 1, 1]).astype('float32') * [-1e9] | |
| encoder_word_pos = encoder_word_pos[np.newaxis, :] | |
| gsrm_word_pos = gsrm_word_pos[np.newaxis, :] | |
| return [ | |
| encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, | |
| gsrm_slf_attn_bias2 | |
| ] | |
| def process_image_srn(self, img, image_shape, num_heads, max_text_length): | |
| norm_img = self.resize_norm_img_srn(img, image_shape) | |
| norm_img = norm_img[np.newaxis, :] | |
| [encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \ | |
| self.srn_other_inputs(image_shape, num_heads, max_text_length) | |
| gsrm_slf_attn_bias1 = gsrm_slf_attn_bias1.astype(np.float32) | |
| gsrm_slf_attn_bias2 = gsrm_slf_attn_bias2.astype(np.float32) | |
| encoder_word_pos = encoder_word_pos.astype(np.int64) | |
| gsrm_word_pos = gsrm_word_pos.astype(np.int64) | |
| return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, | |
| gsrm_slf_attn_bias2) | |
| def resize_norm_img_sar(self, img, image_shape, | |
| width_downsample_ratio=0.25): | |
| imgC, imgH, imgW_min, imgW_max = image_shape | |
| h = img.shape[0] | |
| w = img.shape[1] | |
| valid_ratio = 1.0 | |
| # make sure new_width is an integral multiple of width_divisor. | |
| width_divisor = int(1 / width_downsample_ratio) | |
| # resize | |
| ratio = w / float(h) | |
| resize_w = math.ceil(imgH * ratio) | |
| if resize_w % width_divisor != 0: | |
| resize_w = round(resize_w / width_divisor) * width_divisor | |
| if imgW_min is not None: | |
| resize_w = max(imgW_min, resize_w) | |
| if imgW_max is not None: | |
| valid_ratio = min(1.0, 1.0 * resize_w / imgW_max) | |
| resize_w = min(imgW_max, resize_w) | |
| resized_image = cv2.resize(img, (resize_w, imgH)) | |
| resized_image = resized_image.astype('float32') | |
| # norm | |
| if image_shape[0] == 1: | |
| resized_image = resized_image / 255 | |
| resized_image = resized_image[np.newaxis, :] | |
| else: | |
| resized_image = resized_image.transpose((2, 0, 1)) / 255 | |
| resized_image -= 0.5 | |
| resized_image /= 0.5 | |
| resize_shape = resized_image.shape | |
| padding_im = -1.0 * np.ones((imgC, imgH, imgW_max), dtype=np.float32) | |
| padding_im[:, :, 0:resize_w] = resized_image | |
| pad_shape = padding_im.shape | |
| return padding_im, resize_shape, pad_shape, valid_ratio | |
| def resize_norm_img_spin(self, img): | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| # return padding_im | |
| img = cv2.resize(img, tuple([100, 32]), cv2.INTER_CUBIC) | |
| img = np.array(img, np.float32) | |
| img = np.expand_dims(img, -1) | |
| img = img.transpose((2, 0, 1)) | |
| mean = [127.5] | |
| std = [127.5] | |
| mean = np.array(mean, dtype=np.float32) | |
| std = np.array(std, dtype=np.float32) | |
| mean = np.float32(mean.reshape(1, -1)) | |
| stdinv = 1 / np.float32(std.reshape(1, -1)) | |
| img -= mean | |
| img *= stdinv | |
| return img | |
| def resize_norm_img_svtr(self, img, image_shape): | |
| imgC, imgH, imgW = image_shape | |
| resized_image = cv2.resize( | |
| img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) | |
| resized_image = resized_image.astype('float32') | |
| resized_image = resized_image.transpose((2, 0, 1)) / 255 | |
| resized_image -= 0.5 | |
| resized_image /= 0.5 | |
| return resized_image | |
| def resize_norm_img_abinet(self, img, image_shape): | |
| imgC, imgH, imgW = image_shape | |
| resized_image = cv2.resize( | |
| img, (imgW, imgH), interpolation=cv2.INTER_LINEAR) | |
| resized_image = resized_image.astype('float32') | |
| resized_image = resized_image / 255. | |
| mean = np.array([0.485, 0.456, 0.406]) | |
| std = np.array([0.229, 0.224, 0.225]) | |
| resized_image = ( | |
| resized_image - mean[None, None, ...]) / std[None, None, ...] | |
| resized_image = resized_image.transpose((2, 0, 1)) | |
| resized_image = resized_image.astype('float32') | |
| return resized_image | |
| def norm_img_can(self, img, image_shape): | |
| img = cv2.cvtColor( | |
| img, cv2.COLOR_BGR2GRAY) # CAN only predict gray scale image | |
| if self.inverse: | |
| img = 255 - img | |
| if self.rec_image_shape[0] == 1: | |
| h, w = img.shape | |
| _, imgH, imgW = self.rec_image_shape | |
| if h < imgH or w < imgW: | |
| padding_h = max(imgH - h, 0) | |
| padding_w = max(imgW - w, 0) | |
| img_padded = np.pad(img, ((0, padding_h), (0, padding_w)), | |
| 'constant', | |
| constant_values=(255)) | |
| img = img_padded | |
| img = np.expand_dims(img, 0) / 255.0 # h,w,c -> c,h,w | |
| img = img.astype('float32') | |
| return img | |
| def __call__(self, img_list): | |
| img_num = len(img_list) | |
| # Calculate the aspect ratio of all text bars | |
| width_list = [] | |
| for img in img_list: | |
| width_list.append(img.shape[1] / float(img.shape[0])) | |
| # Sorting can speed up the recognition process | |
| indices = np.argsort(np.array(width_list)) | |
| rec_res = [['', 0.0]] * img_num | |
| batch_num = self.rec_batch_num | |
| st = time.time() | |
| if self.benchmark: | |
| self.autolog.times.start() | |
| for beg_img_no in range(0, img_num, batch_num): | |
| end_img_no = min(img_num, beg_img_no + batch_num) | |
| norm_img_batch = [] | |
| if self.rec_algorithm == "SRN": | |
| encoder_word_pos_list = [] | |
| gsrm_word_pos_list = [] | |
| gsrm_slf_attn_bias1_list = [] | |
| gsrm_slf_attn_bias2_list = [] | |
| if self.rec_algorithm == "SAR": | |
| valid_ratios = [] | |
| imgC, imgH, imgW = self.rec_image_shape[:3] | |
| max_wh_ratio = imgW / imgH | |
| # max_wh_ratio = 0 | |
| for ino in range(beg_img_no, end_img_no): | |
| h, w = img_list[indices[ino]].shape[0:2] | |
| wh_ratio = w * 1.0 / h | |
| max_wh_ratio = max(max_wh_ratio, wh_ratio) | |
| for ino in range(beg_img_no, end_img_no): | |
| if self.rec_algorithm == "SAR": | |
| norm_img, _, _, valid_ratio = self.resize_norm_img_sar( | |
| img_list[indices[ino]], self.rec_image_shape) | |
| norm_img = norm_img[np.newaxis, :] | |
| valid_ratio = np.expand_dims(valid_ratio, axis=0) | |
| valid_ratios.append(valid_ratio) | |
| norm_img_batch.append(norm_img) | |
| elif self.rec_algorithm == "SRN": | |
| norm_img = self.process_image_srn( | |
| 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 in ["SVTR", "SATRN"]: | |
| 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] | |
| self.predictor.try_shrink_memory() | |
| rec_result = self.postprocess_op(preds) | |
| for rno in range(len(rec_result)): | |
| rec_res[indices[beg_img_no + rno]] = rec_result[rno] | |
| 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()) | |