摘要
之前已经创建过利用点云数据创建360全景图像的功能，但是其主要依赖于matplotlib创建图像。现在创造一个纯numpy方法，这样作为预处理步骤可以更快速便捷。
以下为函数功能：
# ==============================================================================
# SCALE_TO_255
# ==============================================================================
def scale_to_255(a, min, max, dtype=np.uint8):
""" Scales an array of values from specified min, max range to 0-255
Optionally specify the data type of the output (default is uint8)
"""
return (((a - min) / float(max - min)) * 255).astype(dtype)
# ==============================================================================
# POINT_CLOUD_TO_PANORAMA
# ==============================================================================
def point_cloud_to_panorama(points,
v_res=0.42,
h_res = 0.35,
v_fov = (-24.9, 2.0),
d_range = (0,100),
y_fudge=3
):
""" Takes point cloud data as input and creates a 360 degree panoramic
image, returned as a numpy array.
Args:
points: (np array)
The numpy array containing the point cloud. .
The shape should be at least Nx3 (allowing for more columns)
- Where N is the number of points, and
- each point is specified by at least 3 values (x, y, z)
v_res: (float)
vertical angular resolution in degrees. This will influence the
height of the output image.
h_res: (float)
horizontal angular resolution in degrees. This will influence
the width of the output image.
v_fov: (tuple of two floats)
Field of view in degrees (-min_negative_angle, max_positive_angle)
d_range: (tuple of two floats) (default = (0,100))
Used for clipping distance values to be within a min and max range.
y_fudge: (float)
A hacky fudge factor to use if the theoretical calculations of
vertical image height do not match the actual data.
Returns:
A numpy array representing a 360 degree panoramic image of the point
cloud.
"""
# Projecting to 2D
x_points = points[:, 0]
y_points = points[:, 1]
z_points = points[:, 2]
r_points = points[:, 3]
d_points = np.sqrt(x_points ** 2 + y_points ** 2) # map distance relative to origin
#d_points = np.sqrt(x_points**2 + y_points**2 + z_points**2) # abs distance
# We use map distance, because otherwise it would not project onto a cylinder,
# instead, it would map onto a segment of slice of a sphere.
# RESOLUTION AND FIELD OF VIEW SETTINGS
v_fov_total = -v_fov[0] + v_fov[1]
# CONVERT TO RADIANS
v_res_rad = v_res * (np.pi / 180)
h_res_rad = h_res * (np.pi / 180)
# MAPPING TO CYLINDER
x_img = np.arctan2(y_points, x_points) / h_res_rad
y_img = -(np.arctan2(z_points, d_points) / v_res_rad)
# THEORETICAL MAX HEIGHT FOR IMAGE
d_plane = (v_fov_total/v_res) / (v_fov_total* (np.pi / 180))
h_below = d_plane * np.tan(-v_fov[0]* (np.pi / 180))
h_above = d_plane * np.tan(v_fov[1] * (np.pi / 180))
y_max = int(np.ceil(h_below+h_above + y_fudge))
# SHIFT COORDINATES TO MAKE 0,0 THE MINIMUM
x_min = -360.0 / h_res / 2
x_img = np.trunc(-x_img - x_min).astype(np.int32)
x_max = int(np.ceil(360.0 / h_res))
y_min = -((v_fov[1] / v_res) + y_fudge)
y_img = np.trunc(y_img - y_min).astype(np.int32)
# CLIP DISTANCES
d_points = np.clip(d_points, a_min=d_range[0], a_max=d_range[1])
# CONVERT TO IMAGE ARRAY
img = np.zeros([y_max + 1, x_max + 1], dtype=np.uint8)
img[y_img, x_img] = scale_to_255(d_points, min=d_range[0], max=d_range[1])
return img以下为利用Velodyne 64E的使用样例：
im = point_cloud_to_panorama(points,
v_res=0.42,
h_res=0.35,
v_fov=(-24.9, 2.0),
y_fudge=3,
d_range=(0,100))以下为使用Kitti数据集中的一帧输出的样例：
以下为使用光谱色彩映射渲染的同一帧图像：
但有一点需要注意的是，在使用matplotlib的时候，效果并不是很好。图像并不是很流畅，存在空值的水平条纹，作者认为这应该和点位置到像素位置的离散化方式有关。