def _compute_targets(ex_rois, gt_rois):
""" compute bbox targets for an image """
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois):
""" compute bbox targets for an image """
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(config.TRAIN.BBOX_MEANS))
/ np.array(config.TRAIN.BBOX_STDS))
return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if config.TRAIN.BBOX_NORMALIZATION_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(config.TRAIN.BBOX_MEANS)) /
np.array(config.TRAIN.BBOX_STDS))
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32,
copy=False)
