本文整理汇总了Python中detectron.utils.boxes.flip_boxes方法的典型用法代码示例。如果您正苦于以下问题：Python boxes.flip_boxes方法的具体用法？Python boxes.flip_boxes怎么用？Python boxes.flip_boxes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detectron.utils.boxes的用法示例。
在下文中一共展示了boxes.flip_boxes方法的3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: im_detect_bbox_hflip
# 需要导入模块: from detectron.utils import boxes [as 别名]
# 或者: from detectron.utils.boxes import flip_boxes [as 别名]
def im_detect_bbox_hflip(
model, im, target_scale, target_max_size, box_proposals=None
):
"""Performs bbox detection on the horizontally flipped image.
Function signature is the same as for im_detect_bbox.
"""
# Compute predictions on the flipped image
im_hf = im[:, ::-1, :]
im_width = im.shape[1]
if not cfg.MODEL.FASTER_RCNN:
box_proposals_hf = box_utils.flip_boxes(box_proposals, im_width)
else:
box_proposals_hf = None
scores_hf, boxes_hf, im_scale = im_detect_bbox(
model, im_hf, target_scale, target_max_size, boxes=box_proposals_hf
)
# Invert the detections computed on the flipped image
boxes_inv = box_utils.flip_boxes(boxes_hf, im_width)
return scores_hf, boxes_inv, im_scale 示例2: im_detect_mask_hflip
# 需要导入模块: from detectron.utils import boxes [as 别名]
# 或者: from detectron.utils.boxes import flip_boxes [as 别名]
def im_detect_mask_hflip(model, im, target_scale, target_max_size, boxes):
"""Performs mask detection on the horizontally flipped image.
Function signature is the same as for im_detect_mask_aug.
"""
# Compute the masks for the flipped image
im_hf = im[:, ::-1, :]
boxes_hf = box_utils.flip_boxes(boxes, im.shape[1])
im_scale = im_conv_body_only(model, im_hf, target_scale, target_max_size)
masks_hf = im_detect_mask(model, im_scale, boxes_hf)
# Invert the predicted soft masks
masks_inv = masks_hf[:, :, :, ::-1]
return masks_inv 示例3: im_detect_keypoints_hflip
# 需要导入模块: from detectron.utils import boxes [as 别名]
# 或者: from detectron.utils.boxes import flip_boxes [as 别名]
def im_detect_keypoints_hflip(model, im, target_scale, target_max_size, boxes):
"""Computes keypoint predictions on the horizontally flipped image.
Function signature is the same as for im_detect_keypoints_aug.
"""
# Compute keypoints for the flipped image
im_hf = im[:, ::-1, :]
boxes_hf = box_utils.flip_boxes(boxes, im.shape[1])
im_scale = im_conv_body_only(model, im_hf, target_scale, target_max_size)
heatmaps_hf = im_detect_keypoints(model, im_scale, boxes_hf)
# Invert the predicted keypoints
heatmaps_inv = keypoint_utils.flip_heatmaps(heatmaps_hf)
return heatmaps_inv