本文整理汇总了Python中detectron.utils.colormap.colormap方法的典型用法代码示例。如果您正苦于以下问题：Python colormap.colormap方法的具体用法？Python colormap.colormap怎么用？Python colormap.colormap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.colormap的用法示例。
在下文中一共展示了colormap.colormap方法的2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: vis_one_image_opencv
# 需要导入模块: from detectron.utils import colormap [as 别名]
# 或者: from detectron.utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
show_box=False, dataset=None, show_class=False):
"""Constructs a numpy array with the detections visualized."""
if isinstance(boxes, list):
boxes, segms, keypoints, classes = convert_from_cls_format(
boxes, segms, keypoints)
if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
return im
if segms is not None and len(segms) > 0:
masks = mask_util.decode(segms)
color_list = colormap()
mask_color_id = 0
# Display in largest to smallest order to reduce occlusion
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
sorted_inds = np.argsort(-areas)
for i in sorted_inds:
bbox = boxes[i, :4]
score = boxes[i, -1]
if score < thresh:
continue
# show box (off by default)
if show_box:
im = vis_bbox(
im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))
# show class (off by default)
if show_class:
class_str = get_class_string(classes[i], score, dataset)
im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)
# show mask
if segms is not None and len(segms) > i:
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
im = vis_mask(im, masks[..., i], color_mask)
# show keypoints
if keypoints is not None and len(keypoints) > i:
im = vis_keypoints(im, keypoints[i], kp_thresh)
return im 示例2: vis_one_image_opencv
# 需要导入模块: from detectron.utils import colormap [as 别名]
# 或者: from detectron.utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
show_box=False, dataset=None, show_class=False,
alpha=0.4, show_border=True, border_thick=1, bbox_thick=1, font_scale=0.35):
"""Constructs a numpy array with the detections visualized."""
if isinstance(boxes, list):
boxes, segms, keypoints, classes = convert_from_cls_format(
boxes, segms, keypoints)
if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
return im
if segms is not None and len(segms) > 0:
masks = mask_util.decode(segms)
color_list = colormap()
mask_color_id = 0
# Display in largest to smallest order to reduce occlusion
areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
sorted_inds = np.argsort(-areas)
for i in sorted_inds:
bbox = boxes[i, :4]
score = boxes[i, -1]
if score < thresh:
continue
# show box (off by default)
if show_box:
im = vis_bbox(
im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]), thick=bbox_thick)
# show class (off by default)
if show_class:
class_str = get_class_string(classes[i], score, dataset)
im = vis_class(im, (bbox[0], bbox[1] - 2), class_str, font_scale=font_scale)
# show mask
if segms is not None and len(segms) > i:
color_mask = color_list[mask_color_id % len(color_list), 0:3]
mask_color_id += 1
im = vis_mask(im, masks[..., i], color_mask, alpha=alpha,
show_border=show_border, border_thick=border_thick)
# show keypoints
if keypoints is not None and len(keypoints) > i:
im = vis_keypoints(im, keypoints[i], kp_thresh)
return im