本文整理汇总了Python中eulerangles.euler2mat方法的典型用法代码示例。如果您正苦于以下问题：Python eulerangles.euler2mat方法的具体用法？Python eulerangles.euler2mat怎么用？Python eulerangles.euler2mat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eulerangles的用法示例。
在下文中一共展示了eulerangles.euler2mat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: draw_point_cloud
# 需要导入模块: import eulerangles [as 别名]
# 或者: from eulerangles import euler2mat [as 别名]
def draw_point_cloud(input_points, canvasSize=500, space=200, diameter=25,
xrot=0, yrot=0, zrot=0, switch_xyz=[0,1,2], normalize=True):
""" Render point cloud to image with alpha channel.
Input:
points: Nx3 numpy array (+y is up direction)
Output:
gray image as numpy array of size canvasSizexcanvasSize
"""
image = np.zeros((canvasSize, canvasSize))
if input_points is None or input_points.shape[0] == 0:
return image
points = input_points[:, switch_xyz]
M = euler2mat(zrot, yrot, xrot)
points = (np.dot(M, points.transpose())).transpose()
# Normalize the point cloud
# We normalize scale to fit points in a unit sphere
if normalize:
centroid = np.mean(points, axis=0)
points -= centroid
furthest_distance = np.max(np.sqrt(np.sum(abs(points)**2,axis=-1)))
points /= furthest_distance
# Pre-compute the Gaussian disk
radius = (diameter-1)/2.0
disk = np.zeros((diameter, diameter))
for i in range(diameter):
for j in range(diameter):
if (i - radius) * (i-radius) + (j-radius) * (j-radius) <= radius * radius:
disk[i, j] = np.exp((-(i-radius)**2 - (j-radius)**2)/(radius**2))
mask = np.argwhere(disk > 0)
dx = mask[:, 0]
dy = mask[:, 1]
dv = disk[disk > 0]
# Order points by z-buffer
zorder = np.argsort(points[:, 2])
points = points[zorder, :]
points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2])))
max_depth = np.max(points[:, 2])
for i in range(points.shape[0]):
j = points.shape[0] - i - 1
x = points[j, 0]
y = points[j, 1]
xc = canvasSize/2 + (x*space)
yc = canvasSize/2 + (y*space)
xc = int(np.round(xc))
yc = int(np.round(yc))
px = dx + xc
py = dy + yc
image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3
image = image / np.max(image)
return image 示例2: draw_point_cloud
# 需要导入模块: import eulerangles [as 别名]
# 或者: from eulerangles import euler2mat [as 别名]
def draw_point_cloud(input_points, canvasSize=500, space=200, diameter=25,
xrot=0, yrot=0, zrot=0, switch_xyz=[0, 1, 2], normalize=True):
""" Render point cloud to image with alpha channel.
Input:
points: Nx3 numpy array (+y is up direction)
Output:
gray image as numpy array of size canvasSizexcanvasSize
"""
image = np.zeros((canvasSize, canvasSize))
if input_points is None or input_points.shape[0] == 0:
return image
points = input_points[:, switch_xyz]
M = euler2mat(zrot, yrot, xrot)
points = (np.dot(M, points.transpose())).transpose()
# Normalize the point cloud
# We normalize scale to fit points in a unit sphere
if normalize:
centroid = np.mean(points, axis=0)
points -= centroid
furthest_distance = np.max(np.sqrt(np.sum(abs(points) ** 2, axis=-1)))
points /= furthest_distance
# Pre-compute the Gaussian disk
radius = (diameter - 1) / 2.0
disk = np.zeros((diameter, diameter))
for i in range(diameter):
for j in range(diameter):
if (i - radius) * (i - radius) + (j - radius) * (j - radius) <= radius * radius:
disk[i, j] = np.exp((-(i - radius) ** 2 - (j - radius) ** 2) / (radius ** 2))
mask = np.argwhere(disk > 0)
dx = mask[:, 0]
dy = mask[:, 1]
dv = disk[disk > 0]
# Order points by z-buffer
zorder = np.argsort(points[:, 2])
points = points[zorder, :]
points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2])))
max_depth = np.max(points[:, 2])
for i in range(points.shape[0]):
j = points.shape[0] - i - 1
x = points[j, 0]
y = points[j, 1]
xc = canvasSize / 2 + (x * space)
yc = canvasSize / 2 + (y * space)
xc = int(np.round(xc))
yc = int(np.round(yc))
px = dx + xc
py = dy + yc
image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3
image = image / np.max(image)
return image