107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
import cv2
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import copy
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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 ...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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class TextClassifier(BaseOCRV20):
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def __init__(self, args, **kwargs):
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self.device = args.device
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self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
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self.cls_batch_num = args.cls_batch_num
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self.cls_thresh = args.cls_thresh
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postprocess_params = {
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'name': 'ClsPostProcess',
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"label_list": args.label_list,
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}
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self.postprocess_op = build_post_process(postprocess_params)
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self.weights_path = args.cls_model_path
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self.yaml_path = args.cls_yaml_path
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network_config = utility.get_arch_config(self.weights_path)
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super(TextClassifier, self).__init__(network_config, **kwargs)
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self.cls_image_shape = [int(v) for v in args.cls_image_shape.split(",")]
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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.load_pytorch_weights(self.weights_path)
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self.net.eval()
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self.net.to(self.device)
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def resize_norm_img(self, img):
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imgC, imgH, imgW = self.cls_image_shape
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h = img.shape[0]
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w = img.shape[1]
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ratio = w / float(h)
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imgW = max(min(imgW, self.limited_max_width), self.limited_min_width)
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ratio_imgH = math.ceil(imgH * ratio)
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ratio_imgH = max(ratio_imgH, self.limited_min_width)
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if ratio_imgH > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype('float32')
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if self.cls_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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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 __call__(self, img_list):
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img_list = copy.deepcopy(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 cls process
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indices = np.argsort(np.array(width_list))
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cls_res = [['', 0.0]] * img_num
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batch_num = self.cls_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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end_img_no = min(img_num, beg_img_no + batch_num)
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norm_img_batch = []
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max_wh_ratio = 0
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for ino in range(beg_img_no, end_img_no):
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h, w = img_list[indices[ino]].shape[0:2]
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wh_ratio = w * 1.0 / h
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max_wh_ratio = max(max_wh_ratio, wh_ratio)
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for ino in range(beg_img_no, end_img_no):
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norm_img = self.resize_norm_img(img_list[indices[ino]])
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norm_img = norm_img[np.newaxis, :]
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norm_img_batch.append(norm_img)
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norm_img_batch = np.concatenate(norm_img_batch)
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norm_img_batch = norm_img_batch.copy()
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starttime = time.time()
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with torch.no_grad():
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inp = torch.from_numpy(norm_img_batch)
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inp = inp.to(self.device)
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prob_out = self.net(inp)
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prob_out = prob_out.cpu().numpy()
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cls_result = self.postprocess_op(prob_out)
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elapse += time.time() - starttime
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for rno in range(len(cls_result)):
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label, score = cls_result[rno]
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cls_res[indices[beg_img_no + rno]] = [label, score]
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if '180' in label and score > self.cls_thresh:
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img_list[indices[beg_img_no + rno]] = cv2.rotate(
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img_list[indices[beg_img_no + rno]], 1)
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return img_list, cls_res, elapse
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