438 lines
19 KiB
Python
438 lines
19 KiB
Python
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from PIL import Image
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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 torch
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from tqdm import tqdm
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from ...pytorchocr.base_ocr_v20 import BaseOCRV20
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from . import pytorchocr_utility as utility
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from ...pytorchocr.postprocess import build_post_process
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from ...pytorchocr.modeling.backbones.rec_hgnet import ConvBNAct
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class TextRecognizer(BaseOCRV20):
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def __init__(self, args, **kwargs):
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self.device = args.device
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self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
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self.character_type = args.rec_char_type
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self.rec_batch_num = args.rec_batch_num
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self.rec_algorithm = args.rec_algorithm
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self.max_text_length = args.max_text_length
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postprocess_params = {
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'name': 'CTCLabelDecode',
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"character_type": args.rec_char_type,
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char
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}
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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_type": args.rec_char_type,
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char
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}
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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_type": args.rec_char_type,
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"character_dict_path": args.rec_char_dict_path,
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"use_space_char": args.use_space_char
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}
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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 == '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 == "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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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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self.postprocess_op = build_post_process(postprocess_params)
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self.limited_max_width = args.limited_max_width
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self.limited_min_width = args.limited_min_width
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self.weights_path = args.rec_model_path
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self.yaml_path = args.rec_yaml_path
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network_config = utility.get_arch_config(self.weights_path)
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weights = self.read_pytorch_weights(self.weights_path)
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self.out_channels = self.get_out_channels(weights)
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if self.rec_algorithm == 'NRTR':
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self.out_channels = list(weights.values())[-1].numpy().shape[0]
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elif self.rec_algorithm == 'SAR':
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self.out_channels = list(weights.values())[-3].numpy().shape[0]
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kwargs['out_channels'] = self.out_channels
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super(TextRecognizer, self).__init__(network_config, **kwargs)
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self.load_state_dict(weights)
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self.net.eval()
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self.net.to(self.device)
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for module in self.net.modules():
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if isinstance(module, ConvBNAct):
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if module.use_act:
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torch.quantization.fuse_modules(module, ['conv', 'bn', 'act'], inplace=True)
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else:
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torch.quantization.fuse_modules(module, ['conv', 'bn'], inplace=True)
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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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max_wh_ratio = max(max_wh_ratio, imgW / imgH)
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imgW = int(imgH * max_wh_ratio)
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imgW = max(min(imgW, self.limited_max_width), self.limited_min_width)
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h, w = img.shape[:2]
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ratio = w / float(h)
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ratio_imgH = max(math.ceil(imgH * ratio), self.limited_min_width)
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resized_w = min(imgW, int(ratio_imgH))
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resized_image = cv2.resize(img, (resized_w, imgH)) /127.5 - 1
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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.transpose((2, 0, 1))
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return padding_im
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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_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 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, tqdm_enable=False, tqdm_desc="OCR-rec Predict"):
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img_num = len(img_list)
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# Calculate the aspect ratio of all text bars
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width_list = []
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for img in img_list:
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width_list.append(img.shape[1] / float(img.shape[0]))
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# Sorting can speed up the recognition process
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indices = np.argsort(np.array(width_list))
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# rec_res = []
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rec_res = [['', 0.0]] * img_num
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batch_num = self.rec_batch_num
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elapse = 0
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# for beg_img_no in range(0, img_num, batch_num):
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with tqdm(total=img_num, desc=tqdm_desc, disable=not tqdm_enable) as pbar:
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index = 0
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for beg_img_no in range(0, img_num, batch_num):
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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
|
|||
|
|
max_wh_ratio = width_list[indices[end_img_no - 1]]
|
|||
|
|
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 = []
|
|||
|
|
valid_ratios.append(valid_ratio)
|
|||
|
|
norm_img_batch.append(norm_img)
|
|||
|
|
|
|||
|
|
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 == "SRN":
|
|||
|
|
norm_img = self.process_image_srn(img_list[indices[ino]],
|
|||
|
|
self.rec_image_shape, 8,
|
|||
|
|
self.max_text_length)
|
|||
|
|
encoder_word_pos_list = []
|
|||
|
|
gsrm_word_pos_list = []
|
|||
|
|
gsrm_slf_attn_bias1_list = []
|
|||
|
|
gsrm_slf_attn_bias2_list = []
|
|||
|
|
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 == "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.rec_algorithm == "SRN":
|
|||
|
|
starttime = time.time()
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
inp = torch.from_numpy(norm_img_batch)
|
|||
|
|
encoder_word_pos_inp = torch.from_numpy(encoder_word_pos_list)
|
|||
|
|
gsrm_word_pos_inp = torch.from_numpy(gsrm_word_pos_list)
|
|||
|
|
gsrm_slf_attn_bias1_inp = torch.from_numpy(gsrm_slf_attn_bias1_list)
|
|||
|
|
gsrm_slf_attn_bias2_inp = torch.from_numpy(gsrm_slf_attn_bias2_list)
|
|||
|
|
|
|||
|
|
inp = inp.to(self.device)
|
|||
|
|
encoder_word_pos_inp = encoder_word_pos_inp.to(self.device)
|
|||
|
|
gsrm_word_pos_inp = gsrm_word_pos_inp.to(self.device)
|
|||
|
|
gsrm_slf_attn_bias1_inp = gsrm_slf_attn_bias1_inp.to(self.device)
|
|||
|
|
gsrm_slf_attn_bias2_inp = gsrm_slf_attn_bias2_inp.to(self.device)
|
|||
|
|
|
|||
|
|
backbone_out = self.net.backbone(inp) # backbone_feat
|
|||
|
|
prob_out = self.net.head(backbone_out, [encoder_word_pos_inp, gsrm_word_pos_inp, gsrm_slf_attn_bias1_inp, gsrm_slf_attn_bias2_inp])
|
|||
|
|
# preds = {"predict": prob_out[2]}
|
|||
|
|
preds = {"predict": prob_out["predict"]}
|
|||
|
|
|
|||
|
|
elif self.rec_algorithm == "SAR":
|
|||
|
|
starttime = time.time()
|
|||
|
|
# valid_ratios = np.concatenate(valid_ratios)
|
|||
|
|
# inputs = [
|
|||
|
|
# norm_img_batch,
|
|||
|
|
# valid_ratios,
|
|||
|
|
# ]
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
inp = torch.from_numpy(norm_img_batch)
|
|||
|
|
inp = inp.to(self.device)
|
|||
|
|
preds = self.net(inp)
|
|||
|
|
|
|||
|
|
elif self.rec_algorithm == "CAN":
|
|||
|
|
starttime = time.time()
|
|||
|
|
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]
|
|||
|
|
|
|||
|
|
inp = [torch.from_numpy(e_i) for e_i in inputs]
|
|||
|
|
inp = [e_i.to(self.device) for e_i in inp]
|
|||
|
|
with torch.no_grad():
|
|||
|
|
outputs = self.net(inp)
|
|||
|
|
outputs = [v.cpu().numpy() for k, v in enumerate(outputs)]
|
|||
|
|
|
|||
|
|
preds = outputs
|
|||
|
|
|
|||
|
|
else:
|
|||
|
|
starttime = time.time()
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
inp = torch.from_numpy(norm_img_batch)
|
|||
|
|
inp = inp.to(self.device)
|
|||
|
|
preds = self.net(inp)
|
|||
|
|
|
|||
|
|
with torch.no_grad():
|
|||
|
|
rec_result = self.postprocess_op(preds)
|
|||
|
|
|
|||
|
|
for rno in range(len(rec_result)):
|
|||
|
|
rec_res[indices[beg_img_no + rno]] = rec_result[rno]
|
|||
|
|
elapse += time.time() - starttime
|
|||
|
|
|
|||
|
|
# 更新进度条，每次增加batch_size，但要注意最后一个batch可能不足batch_size
|
|||
|
|
current_batch_size = min(batch_num, img_num - index * batch_num)
|
|||
|
|
index += 1
|
|||
|
|
pbar.update(current_batch_size)
|
|||
|
|
|
|||
|
|
# Fix NaN values in recognition results
|
|||
|
|
for i in range(len(rec_res)):
|
|||
|
|
text, score = rec_res[i]
|
|||
|
|
if isinstance(score, float) and math.isnan(score):
|
|||
|
|
rec_res[i] = (text, 0.0)
|
|||
|
|
|
|||
|
|
return rec_res, elapse
|