100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
import os
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import cv2
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import copy
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import pp_onnx.predict_det as predict_det
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import pp_onnx.predict_cls as predict_cls
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import pp_onnx.predict_rec as predict_rec
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from pp_onnx.utils import get_rotate_crop_image, get_minarea_rect_crop
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class TextSystem(object):
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def __init__(self, args):
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self.text_detector = predict_det.TextDetector(args)
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self.text_recognizer = predict_rec.TextRecognizer(args)
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self.use_angle_cls = args.use_angle_cls
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self.drop_score = args.drop_score
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if self.use_angle_cls:
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self.text_classifier = predict_cls.TextClassifier(args)
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self.args = args
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self.crop_image_res_index = 0
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def draw_crop_rec_res(self, output_dir, img_crop_list, rec_res):
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os.makedirs(output_dir, exist_ok=True)
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bbox_num = len(img_crop_list)
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for bno in range(bbox_num):
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cv2.imwrite(
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os.path.join(output_dir,
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f"mg_crop_{bno+self.crop_image_res_index}.jpg"),
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img_crop_list[bno])
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self.crop_image_res_index += bbox_num
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def __call__(self, img, cls=True):
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ori_im = img.copy()
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# 文字检测
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dt_boxes = self.text_detector(img)
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if dt_boxes is None:
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return None, None
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img_crop_list = []
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dt_boxes = sorted_boxes(dt_boxes)
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# 图片裁剪
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for bno in range(len(dt_boxes)):
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tmp_box = copy.deepcopy(dt_boxes[bno])
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if self.args.det_box_type == "quad":
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img_crop = get_rotate_crop_image(ori_im, tmp_box)
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else:
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img_crop = get_minarea_rect_crop(ori_im, tmp_box)
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img_crop_list.append(img_crop)
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# 方向分类
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if self.use_angle_cls and cls:
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img_crop_list, angle_list = self.text_classifier(img_crop_list)
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# 图像识别
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rec_res = self.text_recognizer(img_crop_list)
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if self.args.save_crop_res:
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self.draw_crop_rec_res(self.args.crop_res_save_dir, img_crop_list,rec_res)
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filter_boxes, filter_rec_res = [], []
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for box, rec_result in zip(dt_boxes, rec_res):
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text, score = rec_result
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if score >= self.drop_score:
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filter_boxes.append(box)
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filter_rec_res.append(rec_result)
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# import IPython
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# IPython.embed(header='L-70')
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return filter_boxes, filter_rec_res
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def sorted_boxes(dt_boxes):
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"""
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Sort text boxes in order from top to bottom, left to right
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args:
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dt_boxes(array):detected text boxes with shape [4, 2]
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return:
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sorted boxes(array) with shape [4, 2]
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"""
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num_boxes = dt_boxes.shape[0]
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sorted_boxes = sorted(dt_boxes, key=lambda x: (x[0][1], x[0][0]))
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_boxes = list(sorted_boxes)
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for i in range(num_boxes - 1):
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for j in range(i, -1, -1):
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if abs(_boxes[j + 1][0][1] - _boxes[j][0][1]) < 10 and \
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(_boxes[j + 1][0][0] < _boxes[j][0][0]):
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tmp = _boxes[j]
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_boxes[j] = _boxes[j + 1]
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_boxes[j + 1] = tmp
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else:
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break
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return _boxes
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