本文整理汇总了Python中maskrcnn_benchmark.structures.keypoint.PersonKeypoints.CONNECTIONS属性的典型用法代码示例。如果您正苦于以下问题：Python PersonKeypoints.CONNECTIONS属性的具体用法？Python PersonKeypoints.CONNECTIONS怎么用？Python PersonKeypoints.CONNECTIONS使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类maskrcnn_benchmark.structures.keypoint.PersonKeypoints的用法示例。
在下文中一共展示了PersonKeypoints.CONNECTIONS属性的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_keypoints
# 需要导入模块: from maskrcnn_benchmark.structures.keypoint import PersonKeypoints [as 别名]
# 或者: from maskrcnn_benchmark.structures.keypoint.PersonKeypoints import CONNECTIONS [as 别名]
def vis_keypoints(img, kps, kp_thresh=2, alpha=0.7):
"""Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob).
"""
dataset_keypoints = PersonKeypoints.NAMES
kp_lines = PersonKeypoints.CONNECTIONS
# Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv.
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(kp_lines) + 2)]
colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors]
# Perform the drawing on a copy of the image, to allow for blending.
kp_mask = np.copy(img)
# Draw mid shoulder / mid hip first for better visualization.
mid_shoulder = (
kps[:2, dataset_keypoints.index('right_shoulder')] +
kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0
sc_mid_shoulder = np.minimum(
kps[2, dataset_keypoints.index('right_shoulder')],
kps[2, dataset_keypoints.index('left_shoulder')])
mid_hip = (
kps[:2, dataset_keypoints.index('right_hip')] +
kps[:2, dataset_keypoints.index('left_hip')]) / 2.0
sc_mid_hip = np.minimum(
kps[2, dataset_keypoints.index('right_hip')],
kps[2, dataset_keypoints.index('left_hip')])
nose_idx = dataset_keypoints.index('nose')
if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh:
cv2.line(
kp_mask, tuple(mid_shoulder), tuple(kps[:2, nose_idx]),
color=colors[len(kp_lines)], thickness=2, lineType=cv2.LINE_AA)
if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh:
cv2.line(
kp_mask, tuple(mid_shoulder), tuple(mid_hip),
color=colors[len(kp_lines) + 1], thickness=2, lineType=cv2.LINE_AA)
# Draw the keypoints.
for l in range(len(kp_lines)):
i1 = kp_lines[l][0]
i2 = kp_lines[l][1]
p1 = kps[0, i1], kps[1, i1]
p2 = kps[0, i2], kps[1, i2]
if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh:
cv2.line(
kp_mask, p1, p2,
color=colors[l], thickness=2, lineType=cv2.LINE_AA)
if kps[2, i1] > kp_thresh:
cv2.circle(
kp_mask, p1,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
if kps[2, i2] > kp_thresh:
cv2.circle(
kp_mask, p2,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
# Blend the keypoints.
return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0) 示例2: vis_keypoints
# 需要导入模块: from maskrcnn_benchmark.structures.keypoint import PersonKeypoints [as 别名]
# 或者: from maskrcnn_benchmark.structures.keypoint.PersonKeypoints import CONNECTIONS [as 别名]
def vis_keypoints(img, kps, kp_thresh=2, alpha=0.7):
"""Visualizes keypoints (adapted from vis_one_image).
kps has shape (4, #keypoints) where 4 rows are (x, y, logit, prob).
"""
dataset_keypoints = PersonKeypoints.NAMES
kp_lines = PersonKeypoints.CONNECTIONS
# Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv.
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(kp_lines) + 2)]
colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors]
# Perform the drawing on a copy of the image, to allow for blending.
kp_mask = np.copy(img)
# Draw mid shoulder / mid hip first for better visualization.
mid_shoulder = (
kps[:2, dataset_keypoints.index('right_shoulder')]
+ kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0
sc_mid_shoulder = np.minimum(
kps[2, dataset_keypoints.index('right_shoulder')],
kps[2, dataset_keypoints.index('left_shoulder')])
mid_hip = (
kps[:2, dataset_keypoints.index('right_hip')]
+ kps[:2, dataset_keypoints.index('left_hip')]) / 2.0
sc_mid_hip = np.minimum(
kps[2, dataset_keypoints.index('right_hip')],
kps[2, dataset_keypoints.index('left_hip')])
nose_idx = dataset_keypoints.index('nose')
if sc_mid_shoulder > kp_thresh and kps[2, nose_idx] > kp_thresh:
cv2.line(
kp_mask, tuple(mid_shoulder), tuple(kps[:2, nose_idx]),
color=colors[len(kp_lines)], thickness=2, lineType=cv2.LINE_AA)
if sc_mid_shoulder > kp_thresh and sc_mid_hip > kp_thresh:
cv2.line(
kp_mask, tuple(mid_shoulder), tuple(mid_hip),
color=colors[len(kp_lines) + 1], thickness=2, lineType=cv2.LINE_AA)
# Draw the keypoints.
for l in range(len(kp_lines)):
i1 = kp_lines[l][0]
i2 = kp_lines[l][1]
p1 = kps[0, i1], kps[1, i1]
p2 = kps[0, i2], kps[1, i2]
if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh:
cv2.line(
kp_mask, p1, p2,
color=colors[l], thickness=2, lineType=cv2.LINE_AA)
if kps[2, i1] > kp_thresh:
cv2.circle(
kp_mask, p1,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
if kps[2, i2] > kp_thresh:
cv2.circle(
kp_mask, p2,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
# Blend the keypoints.
return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0)