Python boxes.xywh_to_xyxy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 xywh_to_xyxy [as 别名]
def _add_gt_annotations(self, entry):
        """Add ground truth annotation metadata to an roidb entry."""
        ann_ids = self.COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)
        objs = self.COCO.loadAnns(ann_ids)
        # Sanitize bboxes -- some are invalid
        valid_objs = []
        valid_segms = []
        width = entry['width']
        height = entry['height']
        for obj in objs:
            # crowd regions are RLE encoded and stored as dicts
            if obj['area'] < cfg.TRAIN.GT_MIN_AREA:
                continue
            if 'ignore' in obj and obj['ignore'] == 1:
                continue
            # Convert form (x1, y1, w, h) to (x1, y1, x2, y2)
            x1, y1, x2, y2 = box_utils.xywh_to_xyxy(obj['bbox'])
            x1, y1, x2, y2 = box_utils.clip_xyxy_to_image(
                x1, y1, x2, y2, height, width
            )
            # Require non-zero seg area and more than 1x1 box size
            if obj['area'] > 0 and x2 > x1 and y2 > y1:
                obj['clean_bbox'] = [x1, y1, x2, y2]
                valid_objs.append(obj)
        num_valid_objs = len(valid_objs)

        gt_classes = np.zeros((num_valid_objs), dtype=entry['gt_classes'].dtype)
        for ix, obj in enumerate(valid_objs):
            cls = self.json_category_id_to_contiguous_id[obj['category_id']]
            gt_classes[ix] = cls

        for cls in gt_classes:
            entry['gt_classes'][0, cls] = 1 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import xywh_to_xyxy [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)) 

# 需要导入模块: from utils import boxes [as 别名]
# 或者: from utils.boxes import xywh_to_xyxy [as 别名]
def save_im_masks(im, M, id, dir):
    from utils.boxes import xywh_to_xyxy
    import os
    try:
        os.mkdir(os.path.join('vis', dir))
    except:
        pass
    M[M > 0] = 1
    aug_rles = mask_util.encode(np.asarray(M, order='F'))
    boxes = xywh_to_xyxy(np.asarray(mask_util.toBbox(aug_rles)))
    boxes = np.append(boxes, np.ones((len(boxes), 2)), 1)

    from utils.vis import vis_one_image
    vis_one_image(im, str(id), os.path.join('vis', dir), boxes, segms=aug_rles, keypoints=None, thresh=0.9,box_alpha=0.8,  show_class=False,)