def compute_targets(proposals, gt_rois, iou_threshold, normalize_means,
normalize_stds):
"""Compute bounding-box regression targets for an image."""
if len(gt_rois) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((proposals.shape[0], 6), dtype=np.float32)
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(proposals, dtype=np.float),
np.ascontiguousarray(gt_rois, dtype=np.float))
# Indices of examples for which we try to make predictions
ex_inds = np.where(
ex_gt_overlaps >= iou_threshold)[0] # cfg.TRAIN.BBOX_THRESH
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment_inds = ex_gt_overlaps.argmax(axis=1)
gt_assignment_rois = gt_rois[gt_assignment_inds, :]
regression_targets = bbox_transform(proposals[ex_inds],
gt_assignment_rois[ex_inds])
# Optionally normalize targets by a precomputed mean and stdev
if normalize_means is not None:
regression_targets = (regression_targets -
normalize_means) / normalize_stds
targets = np.zeros((proposals.shape[0], 6), dtype=np.float32)
targets[ex_inds, :4] = regression_targets
targets[ex_inds, 4] = gt_rois[gt_assignment_inds[ex_inds], 4]
targets[ex_inds, 5] = 1 # bbiw
return targets
def compute_targets(proposals, gt_rois, iou_threshold, normalize_means, normalize_stds):
"""Compute bounding-box regression targets for an image."""
if len(gt_rois) == 0:
# Bail if the image has no ground-truth ROIs
return np.zeros((proposals.shape[0], 6), dtype=np.float32)
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(
np.ascontiguousarray(proposals, dtype=np.float),
np.ascontiguousarray(gt_rois, dtype=np.float))
# Indices of examples for which we try to make predictions
ex_inds = np.where(ex_gt_overlaps >= iou_threshold)[0] # cfg.TRAIN.BBOX_THRESH
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment_inds = ex_gt_overlaps.argmax(axis=1)
gt_assignment_rois = gt_rois[gt_assignment_inds, :]
regression_targets = bbox_transform(proposals[ex_inds], gt_assignment_rois[ex_inds])
# Optionally normalize targets by a precomputed mean and stdev
if normalize_means is not None:
regression_targets = (regression_targets - normalize_means) / normalize_stds
targets = np.zeros((proposals.shape[0], 6), dtype=np.float32)
targets[ex_inds, :4] = regression_targets
targets[ex_inds, 4] = gt_rois[gt_assignment_inds[ex_inds], 4]
targets[ex_inds, 5] = 1 # bbiw
return targets
def _compute_targets(ex_rois, gt_rois):
"""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] == 5
return bbox_transform(ex_rois, gt_rois[:, :4]).astype(np.float32, copy=False)
def _compute_targets(ex_rois, gt_rois):
"""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] == 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)
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 cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
/ np.array(cfg.TRAIN.BBOX_NORMALIZE_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 cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS)) /
np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32,
copy=False)
def _compute_targets(self, 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 self._normalize_targets:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(self._normalize_means))
/ np.array(self._normalize_stds))
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32, copy=False)
def _compute_targets(self, 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 self._normalize_targets:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(self._normalize_means))
/ np.array(self._normalize_stds))
return np.hstack((labels[:, np.newaxis], targets)).astype(np.float32, copy=False)