Python boxes.bbox_transform方法代码示例

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import bbox_transform [as 别名]
def get_det_boxes(self, boxes, scores, box_deltas, h_and_w):

        if cfg.TEST.BBOX_REG:
            if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG:
                # Remove predictions for bg class (compat with MSRA code)
                box_deltas = box_deltas[:, -4:]
            if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
                # (legacy) Optionally normalize targets by a precomputed mean and stdev
                box_deltas = box_deltas.view(-1, 4) * cfg.TRAIN.BBOX_NORMALIZE_STDS \
                             + cfg.TRAIN.BBOX_NORMALIZE_MEANS
            pred_boxes = box_utils.bbox_transform(boxes, box_deltas, cfg.MODEL.BBOX_REG_WEIGHTS)
            pred_boxes = box_utils.clip_tiled_boxes(pred_boxes, h_and_w)
            if cfg.MODEL.CLS_AGNOSTIC_BBOX_REG:
                pred_boxes = np.tile(pred_boxes, (1, scores.shape[1]))
        else:
            # Simply repeat the boxes, once for each class
            pred_boxes = np.tile(boxes, (1, scores.shape[1]))

        if cfg.DEDUP_BOXES > 0 and not cfg.MODEL.FASTER_RCNN:
            # Map scores and predictions back to the original set of boxes
            scores = scores[inv_index, :]
            pred_boxes = pred_boxes[inv_index, :]
            
        return scores, pred_boxes 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import bbox_transform [as 别名]
def test_bbox_transform_and_inverse(self):
        weights = (5, 5, 10, 10)
        src_boxes = random_boxes([10, 10, 20, 20], 1, 10)
        dst_boxes = random_boxes([10, 10, 20, 20], 1, 10)
        deltas = box_utils.bbox_transform_inv(
            src_boxes, dst_boxes, weights=weights
        )
        dst_boxes_reconstructed = box_utils.bbox_transform(
            src_boxes, deltas, weights=weights
        )
        np.testing.assert_array_almost_equal(
            dst_boxes, dst_boxes_reconstructed, decimal=5
        ) 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import bbox_transform [as 别名]
def test_bbox_dataset_to_prediction_roundtrip(self):
        """Simulate the process of reading a ground-truth box from a dataset,
        make predictions from proposals, convert the predictions back to the
        dataset format, and then use the COCO API to compute IoU overlap between
        the gt box and the predictions. These should have IoU of 1.
        """
        weights = (5, 5, 10, 10)
        # 1/ "read" a box from a dataset in the default (x1, y1, w, h) format
        gt_xywh_box = [10, 20, 100, 150]
        # 2/ convert it to our internal (x1, y1, x2, y2) format
        gt_xyxy_box = box_utils.xywh_to_xyxy(gt_xywh_box)
        # 3/ consider nearby proposal boxes
        prop_xyxy_boxes = random_boxes(gt_xyxy_box, 10, 10)
        # 4/ compute proposal-to-gt transformation deltas
        deltas = box_utils.bbox_transform_inv(
            prop_xyxy_boxes, np.array([gt_xyxy_box]), weights=weights
        )
        # 5/ use deltas to transform proposals to xyxy predicted box
        pred_xyxy_boxes = box_utils.bbox_transform(
            prop_xyxy_boxes, deltas, weights=weights
        )
        # 6/ convert xyxy predicted box to xywh predicted box
        pred_xywh_boxes = box_utils.xyxy_to_xywh(pred_xyxy_boxes)
        # 7/ use COCO API to compute IoU
        not_crowd = [int(False)] * pred_xywh_boxes.shape[0]
        ious = COCOmask.iou(pred_xywh_boxes, np.array([gt_xywh_box]), not_crowd)
        np.testing.assert_array_almost_equal(ious, np.ones(ious.shape))