def point_cloud_to_image()

in utils/pc_util.py [0:0]

• 26 lines of code
• 8 McCabe index (conditional complexity)

def point_cloud_to_image(points, imgsize, radius=1.0, num_sample=128):
    """ input is Nx3 points
        output is imgsize*imgsize*num_sample*3
        assumes points are in range [-radius, radius]
        samples num_sample points in each pixel, if there are less than
        num_sample points, replicate the points
        Added on Feb 19
    """
    img = np.zeros((imgsize, imgsize, num_sample, 3))
    pixel = 2*radius/float(imgsize)
    locations = (points[:,0:2] + radius)/pixel # Nx2
    locations = locations.astype(int)
    loc2pc = {}
    for n in range(points.shape[0]):
        loc = tuple(locations[n,:])
        if loc not in loc2pc:
            loc2pc[loc] = []
        loc2pc[loc].append(points[n,:])
    for i in range(imgsize):
        for j in range(imgsize):
            if (i,j) not in loc2pc:
                img[i,j,:,:] = np.zeros((num_sample,3))
            else:
                pc = loc2pc[(i,j)]
                pc = np.vstack(pc)
                if pc.shape[0]>num_sample:
                    pc = random_sampling(pc, num_sample, False)
                elif pc.shape[0]<num_sample:
                    pc = np.lib.pad(pc, ((0,num_sample-pc.shape[0]),(0,0)), 'edge')
                pc_center = (np.array([i,j])+0.5)*pixel - radius
                pc[:,0:2] = (pc[:,0:2] - pc_center)/pixel
                img[i,j,:,:] = pc
    return img