本文整理匯總了Python中bbox_transform.bbox_transform方法的典型用法代碼示例。如果您正苦於以下問題：Python bbox_transform.bbox_transform方法的具體用法？Python bbox_transform.bbox_transform怎麽用？Python bbox_transform.bbox_transform使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類bbox_transform的用法示例。
在下文中一共展示了bbox_transform.bbox_transform方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: compute_bbox_regression_targets
# 需要導入模塊: import bbox_transform [as 別名]
# 或者: from bbox_transform import bbox_transform [as 別名]
def compute_bbox_regression_targets(rois, overlaps, labels, cfg):
"""
given rois, overlaps, gt labels, compute bounding box regression targets
:param rois: roidb[i]['boxes'] k * 4
:param overlaps: roidb[i]['max_overlaps'] k * 1
:param labels: roidb[i]['max_classes'] k * 1
:return: targets[i][class, dx, dy, dw, dh] k * 5
"""
# Ensure ROIs are floats
rois = rois.astype(np.float, copy=False)
# Sanity check
if len(rois) != len(overlaps):
print 'bbox regression: this should not happen'
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
print 'something wrong : zero ground truth rois'
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_REGRESSION_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(rois[ex_inds, :], rois[gt_inds, :])
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets 示例2: compute_bbox_regression_targets
# 需要導入模塊: import bbox_transform [as 別名]
# 或者: from bbox_transform import bbox_transform [as 別名]
def compute_bbox_regression_targets(rois, overlaps, labels):
"""
given rois, overlaps, gt labels, compute bounding box regression targets
:param rois: roidb[i]['boxes'] k * 4
:param overlaps: roidb[i]['max_overlaps'] k * 1
:param labels: roidb[i]['max_classes'] k * 1
:return: targets[i][class, dx, dy, dw, dh] k * 5
"""
# Ensure ROIs are floats
rois = rois.astype(np.float, copy=False)
# Sanity check
if len(rois) != len(overlaps):
logger.warning('bbox regression: len(rois) != len(overlaps)')
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
logger.warning('bbox regression: len(gt_inds) == 0')
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= config.TRAIN.BBOX_REGRESSION_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(rois[ex_inds, :], rois[gt_inds, :])
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets 示例3: compute_bbox_regression_targets
# 需要導入模塊: import bbox_transform [as 別名]
# 或者: from bbox_transform import bbox_transform [as 別名]
def compute_bbox_regression_targets(rois, overlaps, labels, cfg):
"""
given rois, overlaps, gt labels, compute bounding box regression targets
:param rois: roidb[i]['boxes'] k * 4
:param overlaps: roidb[i]['max_overlaps'] k * 1
:param labels: roidb[i]['max_classes'] k * 1
:return: targets[i][class, dx, dy, dw, dh] k * 5
"""
# Ensure ROIs are floats
rois = rois.astype(np.float, copy=False)
# Sanity check
if len(rois) != len(overlaps):
print 'bbox regression: this should not happen'
# Indices of ground-truth ROIs
gt_inds = np.where(overlaps == 1)[0]
if len(gt_inds) == 0:
print 'something wrong : zero ground truth rois'
# Indices of examples for which we try to make predictions
ex_inds = np.where(overlaps >= cfg.TRAIN.BBOX_REGRESSION_THRESH)[0]
# Get IoU overlap between each ex ROI and gt ROI
ex_gt_overlaps = bbox_overlaps(rois[ex_inds, :], rois[gt_inds, :])
# Find which gt ROI each ex ROI has max overlap with:
# this will be the ex ROI's gt target
gt_assignment = ex_gt_overlaps.argmax(axis=1)
gt_rois = rois[gt_inds[gt_assignment], :]
ex_rois = rois[ex_inds, :]
targets = np.zeros((rois.shape[0], 5), dtype=np.float32)
targets[ex_inds, 0] = labels[ex_inds]
targets[ex_inds, 1:] = bbox_transform(ex_rois, gt_rois)
return targets