本文整理汇总了Python中detectron.utils.boxes.soft_nms方法的典型用法代码示例。如果您正苦于以下问题：Python boxes.soft_nms方法的具体用法？Python boxes.soft_nms怎么用？Python boxes.soft_nms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.soft_nms方法的1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: box_results_with_nms_and_limit
# 需要导入模块: from detectron.utils import boxes [as 别名]
# 或者: from detectron.utils.boxes import soft_nms [as 别名]
def box_results_with_nms_and_limit(scores, boxes):
"""Returns bounding-box detection results by thresholding on scores and
applying non-maximum suppression (NMS).
`boxes` has shape (#detections, 4 * #classes), where each row represents
a list of predicted bounding boxes for each of the object classes in the
dataset (including the background class). The detections in each row
originate from the same object proposal.
`scores` has shape (#detection, #classes), where each row represents a list
of object detection confidence scores for each of the object classes in the
dataset (including the background class). `scores[i, j]`` corresponds to the
box at `boxes[i, j * 4:(j + 1) * 4]`.
"""
num_classes = cfg.MODEL.NUM_CLASSES
cls_boxes = [[] for _ in range(num_classes)]
# Apply threshold on detection probabilities and apply NMS
# Skip j = 0, because it's the background class
for j in range(1, num_classes):
inds = np.where(scores[:, j] > cfg.TEST.SCORE_THRESH)[0]
scores_j = scores[inds, j]
boxes_j = boxes[inds, j * 4:(j + 1) * 4]
dets_j = np.hstack((boxes_j, scores_j[:, np.newaxis])).astype(
np.float32, copy=False
)
if cfg.TEST.SOFT_NMS.ENABLED:
nms_dets, _ = box_utils.soft_nms(
dets_j,
sigma=cfg.TEST.SOFT_NMS.SIGMA,
overlap_thresh=cfg.TEST.NMS,
score_thresh=0.0001,
method=cfg.TEST.SOFT_NMS.METHOD
)
else:
keep = box_utils.nms(dets_j, cfg.TEST.NMS)
nms_dets = dets_j[keep, :]
# Refine the post-NMS boxes using bounding-box voting
if cfg.TEST.BBOX_VOTE.ENABLED:
nms_dets = box_utils.box_voting(
nms_dets,
dets_j,
cfg.TEST.BBOX_VOTE.VOTE_TH,
scoring_method=cfg.TEST.BBOX_VOTE.SCORING_METHOD
)
cls_boxes[j] = nms_dets
# Limit to max_per_image detections **over all classes**
if cfg.TEST.DETECTIONS_PER_IM > 0:
image_scores = np.hstack(
[cls_boxes[j][:, -1] for j in range(1, num_classes)]
)
if len(image_scores) > cfg.TEST.DETECTIONS_PER_IM:
image_thresh = np.sort(image_scores)[-cfg.TEST.DETECTIONS_PER_IM]
for j in range(1, num_classes):
keep = np.where(cls_boxes[j][:, -1] >= image_thresh)[0]
cls_boxes[j] = cls_boxes[j][keep, :]
im_results = np.vstack([cls_boxes[j] for j in range(1, num_classes)])
boxes = im_results[:, :-1]
scores = im_results[:, -1]
return scores, boxes, cls_boxes