本文整理汇总了Python中bbox_transform.bbox_overlaps方法的典型用法代码示例。如果您正苦于以下问题：Python bbox_transform.bbox_overlaps方法的具体用法？Python bbox_transform.bbox_overlaps怎么用？Python bbox_transform.bbox_overlaps使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类bbox_transform的用法示例。
在下文中一共展示了bbox_transform.bbox_overlaps方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compute_bbox_regression_targets
# 需要导入模块: import bbox_transform [as 别名]
# 或者: from bbox_transform import bbox_overlaps [as 别名]
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
# 需要导入模块: import bbox_transform [as 别名]
# 或者: from bbox_transform import bbox_overlaps [as 别名]
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
# 需要导入模块: import bbox_transform [as 别名]
# 或者: from bbox_transform import bbox_overlaps [as 别名]
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4: compute_bbox_regression_targets
# 需要导入模块: import bbox_transform [as 别名]
# 或者: from bbox_transform import bbox_overlaps [as 别名]
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):
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 >= 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5: compute_bbox_mask_targets_and_label
# 需要导入模块: import bbox_transform [as 别名]
# 或者: from bbox_transform import bbox_overlaps [as 别名]
def compute_bbox_mask_targets_and_label(rois, overlaps, labels, seg, flipped):
"""
given rois, overlaps, gt labels, seg, compute bounding box mask 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 >= 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, :]
mask_targets, mask_label = compute_mask_and_label(ex_rois, labels[ex_inds], seg, flipped)
return mask_targets, mask_label, ex_inds