87 lines
3.3 KiB
Python
87 lines
3.3 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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from pp_onnx.cls_postprocess import ClsPostProcess
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from pp_onnx.predict_base import PredictBase
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class TextClassifier(PredictBase):
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def __init__(self, args):
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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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self.postprocess_op = ClsPostProcess(label_list=args.label_list)
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# 初始化模型
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self.cls_onnx_session = self.get_onnx_session(args.cls_model_dir, args.use_gpu)
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self.cls_input_name = self.get_input_name(self.cls_onnx_session)
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self.cls_output_name = self.get_output_name(self.cls_onnx_session)
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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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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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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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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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input_feed = self.get_input_feed(self.cls_input_name, norm_img_batch)
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outputs = self.cls_onnx_session.run(self.cls_output_name, input_feed=input_feed)
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prob_out = outputs[0]
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cls_result = self.postprocess_op(prob_out)
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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
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