127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import numpy as np
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from pp_onnx.imaug import transform, create_operators
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from pp_onnx.db_postprocess import DBPostProcess
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from pp_onnx.predict_base import PredictBase
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class TextDetector(PredictBase):
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def __init__(self, args):
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self.args = args
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self.det_algorithm = args.det_algorithm
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pre_process_list = [{
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'DetResizeForTest': {
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'limit_side_len': args.det_limit_side_len,
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'limit_type': args.det_limit_type,
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}
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}, {
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'NormalizeImage': {
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'std': [0.229, 0.224, 0.225],
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'mean': [0.485, 0.456, 0.406],
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'scale': '1./255.',
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'order': 'hwc'
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}
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}, {
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'ToCHWImage': None
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}, {
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'KeepKeys': {
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'keep_keys': ['image', 'shape']
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}
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}]
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postprocess_params = {}
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postprocess_params['name'] = 'DBPostProcess'
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postprocess_params["thresh"] = args.det_db_thresh
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postprocess_params["box_thresh"] = args.det_db_box_thresh
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postprocess_params["max_candidates"] = 1000
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postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
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postprocess_params["use_dilation"] = args.use_dilation
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postprocess_params["score_mode"] = args.det_db_score_mode
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postprocess_params["box_type"] = args.det_box_type
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# 实例化预处理操作类
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self.preprocess_op = create_operators(pre_process_list)
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# self.postprocess_op = build_post_process(postprocess_params)
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# 实例化后处理操作类
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self.postprocess_op = DBPostProcess(**postprocess_params)
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# 初始化模型
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self.det_onnx_session = self.get_onnx_session(args.det_model_dir, args.use_gpu)
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self.det_input_name = self.get_input_name(self.det_onnx_session)
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self.det_output_name = self.get_output_name(self.det_onnx_session)
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def order_points_clockwise(self, pts):
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rect = np.zeros((4, 2), dtype="float32")
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s = pts.sum(axis=1)
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rect[0] = pts[np.argmin(s)]
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rect[2] = pts[np.argmax(s)]
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tmp = np.delete(pts, (np.argmin(s), np.argmax(s)), axis=0)
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diff = np.diff(np.array(tmp), axis=1)
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rect[1] = tmp[np.argmin(diff)]
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rect[3] = tmp[np.argmax(diff)]
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return rect
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def clip_det_res(self, points, img_height, img_width):
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for pno in range(points.shape[0]):
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points[pno, 0] = int(min(max(points[pno, 0], 0), img_width - 1))
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points[pno, 1] = int(min(max(points[pno, 1], 0), img_height - 1))
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return points
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def filter_tag_det_res(self, dt_boxes, image_shape):
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img_height, img_width = image_shape[0:2]
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dt_boxes_new = []
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for box in dt_boxes:
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if type(box) is list:
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box = np.array(box)
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box = self.order_points_clockwise(box)
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box = self.clip_det_res(box, img_height, img_width)
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rect_width = int(np.linalg.norm(box[0] - box[1]))
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rect_height = int(np.linalg.norm(box[0] - box[3]))
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if rect_width <= 3 or rect_height <= 3:
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continue
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dt_boxes_new.append(box)
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dt_boxes = np.array(dt_boxes_new)
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return dt_boxes
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def filter_tag_det_res_only_clip(self, dt_boxes, image_shape):
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img_height, img_width = image_shape[0:2]
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dt_boxes_new = []
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for box in dt_boxes:
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if type(box) is list:
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box = np.array(box)
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box = self.clip_det_res(box, img_height, img_width)
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dt_boxes_new.append(box)
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dt_boxes = np.array(dt_boxes_new)
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return dt_boxes
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def __call__(self, img):
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ori_im = img.copy()
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data = {'image': img}
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data = transform(data, self.preprocess_op)
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img, shape_list = data
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if img is None:
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return None, 0
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img = np.expand_dims(img, axis=0)
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shape_list = np.expand_dims(shape_list, axis=0)
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img = img.copy()
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input_feed = self.get_input_feed(self.det_input_name, img)
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outputs = self.det_onnx_session.run(self.det_output_name, input_feed=input_feed)
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preds = {}
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preds['maps'] = outputs[0]
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post_result = self.postprocess_op(preds, shape_list)
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dt_boxes = post_result[0]['points']
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if self.args.det_box_type == 'poly':
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dt_boxes = self.filter_tag_det_res_only_clip(dt_boxes, ori_im.shape)
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else:
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dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)
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return dt_boxes
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