bbox_regressor.py 文件源码

def predict_bbox_regressor(model, feat, ex_boxes):
  if ex_boxes.size == 0:
    return np.array([]).reshape(-1, 4)

  # predict regression targets
  Y = np.dot(feat, model.Beta[:-1]) + model.Beta[-1]

  # invert transformation
  Y = dot(Y, model.T_inv)

  # read out prediction
  dst_size = Y[:, 2:]
  dst_ctr = Y[:, 2:]

  src_size = ex_boxes[:, 2:]
  src_ctr = ex_boxes[:, :2] + 0.5 * src_size

  pred_size = np.exp(dst_size) * src_size
  pred_ctr = dst_ctr * src_ctr + src_ctr

  pred = np.c_[pred_ctr - 0.5 * pred_size, pred_size]

  return pred