# 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, logger=None): if logger is None: logger = get_logger() 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, "max_text_length": args.max_text_length, } 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 in ["CPPD", "CPPDPadding"]: postprocess_params = { "name": "CPPDLabelDecode", "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, } elif self.rec_algorithm == "LaTeXOCR": postprocess_params = { "name": "LaTeXOCRDecode", "rec_char_dict_path": args.rec_char_dict_path, } elif self.rec_algorithm == "ParseQ": postprocess_params = { "name": "ParseQLabelDecode", "character_dict_path": args.rec_char_dict_path, "use_space_char": args.use_space_char, } self.postprocess_op = build_post_process(postprocess_params) self.postprocess_params = 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, # not used if logger is not None 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, ) self.return_word_box = args.return_word_box 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.Resampling.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) / 255.0 else: norm_img = norm_img.astype(np.float32) / 128.0 - 1.0 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_cppd_padding( self, img, image_shape, padding=True, interpolation=cv2.INTER_LINEAR ): imgC, imgH, imgW = image_shape h = img.shape[0] w = img.shape[1] if not padding: resized_image = cv2.resize(img, (imgW, imgH), interpolation=interpolation) resized_w = imgW else: ratio = w / float(h) if math.ceil(imgH * ratio) > imgW: resized_w = imgW else: resized_w = int(math.ceil(imgH * ratio)) resized_image = cv2.resize(img, (resized_w, imgH)) resized_image = resized_image.astype("float32") 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 padding_im = np.zeros((imgC, imgH, imgW), dtype=np.float32) padding_im[:, :, 0:resized_w] = resized_image return padding_im 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 / 255.0 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 pad_(self, img, divable=32): threshold = 128 data = np.array(img.convert("LA")) if data[..., -1].var() == 0: data = (data[..., 0]).astype(np.uint8) else: data = (255 - data[..., -1]).astype(np.uint8) data = (data - data.min()) / (data.max() - data.min()) * 255 if data.mean() > threshold: # To invert the text to white gray = 255 * (data < threshold).astype(np.uint8) else: gray = 255 * (data > threshold).astype(np.uint8) data = 255 - data coords = cv2.findNonZero(gray) # Find all non-zero points (text) a, b, w, h = cv2.boundingRect(coords) # Find minimum spanning bounding box rect = data[b : b + h, a : a + w] im = Image.fromarray(rect).convert("L") dims = [] for x in [w, h]: div, mod = divmod(x, divable) dims.append(divable * (div + (1 if mod > 0 else 0))) padded = Image.new("L", dims, 255) padded.paste(im, (0, 0, im.size[0], im.size[1])) return padded def minmax_size_( self, img, max_dimensions, min_dimensions, ): if max_dimensions is not None: ratios = [a / b for a, b in zip(img.size, max_dimensions)] if any([r > 1 for r in ratios]): size = np.array(img.size) // max(ratios) img = img.resize(tuple(size.astype(int)), Image.BILINEAR) if min_dimensions is not None: # hypothesis: there is a dim in img smaller than min_dimensions, and return a proper dim >= min_dimensions padded_size = [ max(img_dim, min_dim) for img_dim, min_dim in zip(img.size, min_dimensions) ] if padded_size != list(img.size): # assert hypothesis padded_im = Image.new("L", padded_size, 255) padded_im.paste(img, img.getbbox()) img = padded_im return img def norm_img_latexocr(self, img): # CAN only predict gray scale image shape = (1, 1, 3) mean = [0.7931, 0.7931, 0.7931] std = [0.1738, 0.1738, 0.1738] scale = np.float32(1.0 / 255.0) min_dimensions = [32, 32] max_dimensions = [672, 192] mean = np.array(mean).reshape(shape).astype("float32") std = np.array(std).reshape(shape).astype("float32") im_h, im_w = img.shape[:2] if ( min_dimensions[0] <= im_w <= max_dimensions[0] and min_dimensions[1] <= im_h <= max_dimensions[1] ): pass else: img = Image.fromarray(np.uint8(img)) img = self.minmax_size_(self.pad_(img), max_dimensions, min_dimensions) img = np.array(img) im_h, im_w = img.shape[:2] img = np.dstack([img, img, img]) img = (img.astype("float32") * scale - mean) / std img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) divide_h = math.ceil(im_h / 16) * 16 divide_w = math.ceil(im_w / 16) * 16 img = np.pad( img, ((0, divide_h - im_h), (0, divide_w - im_w)), constant_values=(1, 1) ) img = img[:, :, np.newaxis].transpose(2, 0, 1) 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 wh_ratio_list = [] 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) wh_ratio_list.append(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", "ParseQ", "CPPD"]: 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 ["CPPDPadding"]: norm_img = self.resize_norm_img_cppd_padding( 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) elif self.rec_algorithm == "LaTeXOCR": norm_img = self.norm_img_latexocr(img_list[indices[ino]]) norm_img = norm_img[np.newaxis, :] norm_img_batch.append(norm_img) 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).T, ] 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 elif self.rec_algorithm == "LaTeXOCR": inputs = [norm_img_batch] 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, ) elif self.postprocess_params["name"] == "LaTeXOCRDecode": preds = [p.reshape([-1]) for p in preds] rec_result = self.postprocess_op(preds) 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) valid_image_file_list = [] img_list = [] # logger log_file = args.save_log_path if os.path.isdir(args.save_log_path) or ( not os.path.exists(args.save_log_path) and args.save_log_path.endswith("/") ): log_file = os.path.join(log_file, "benchmark_recognition.log") logger = get_logger(log_file=log_file) # create text recognizer text_recognizer = TextRecognizer(args) 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())