import numpy as np def bbox_transform(ex_rois, gt_rois): """ computes the distance from ground-truth boxes to the given boxes, normed by their size :param ex_rois: n * 4 numpy array, given boxes :param gt_rois: n * 4 numpy array, ground-truth boxes :return: deltas: n * 4 numpy array, ground-truth boxes """ ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0 ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0 ex_ctr_x = ex_rois[:, 0] + 0.5 * ex_widths ex_ctr_y = ex_rois[:, 1] + 0.5 * ex_heights assert np.min(ex_widths) > 0.1 and np.min(ex_heights) > 0.1, \ 'Invalid boxes found: {} {}'.format(ex_rois[np.argmin(ex_widths), :], ex_rois[np.argmin(ex_heights), :]) gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0 gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0 gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights # warnings.catch_warnings() # warnings.filterwarnings('error') targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights targets_dw = np.log(gt_widths / ex_widths) targets_dh = np.log(gt_heights / ex_heights) targets = np.vstack( (targets_dx, targets_dy, targets_dw, targets_dh)).transpose() return targets def bbox_transform_inv(boxes, deltas): boxes = boxes.astype(deltas.dtype, copy=False) widths = boxes[:, 2] - boxes[:, 0] + 1.0 heights = boxes[:, 3] - boxes[:, 1] + 1.0 ctr_x = boxes[:, 0] + 0.5 * widths ctr_y = boxes[:, 1] + 0.5 * heights dx = deltas[:, 0::4] dy = deltas[:, 1::4] dw = deltas[:, 2::4] dh = deltas[:, 3::4] pred_ctr_x = ctr_x[:, np.newaxis] pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis] pred_w = widths[:, np.newaxis] pred_h = np.exp(dh) * heights[:, np.newaxis] pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype) # x1 pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w # y1 pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h # x2 pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w # y2 pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h return pred_boxes def clip_boxes(boxes, im_shape): """ Clip boxes to image boundaries. """ # x1 >= 0 boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0) # y1 >= 0 boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0) # x2 < im_shape[1] boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0) # y2 < im_shape[0] boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0) return boxes