Optimize numpy point cloud creation script

Question

I am writing a simple script in numpy which takes a 640 x 480 depth image (a 2D numpy array of bytes), and converts it into a num_points x 3 numpy array of points, given a pinhole camera model. The math is fairly simple, and I've gotten the script to work -- but its extremely slow. Apparently, it takes 2 seconds for each frame. I've written a similar script in C++ before and gotten ~100ms per frame. I'm wondering what optimizations I can make to my python script. Can any of this be vectorized? Could I benefit from parallelization?

def create_point_cloud(self, depth_image):
    shape = depth_image.shape;
    rows = shape[0];
    cols = shape[1];

    points = np.zeros((rows * cols, 3), np.float32);

    bytes_to_units = (1.0 / 256.0);

    # Linear iterator for convenience
    i = 0
    # For each pixel in the image...
    for r in xrange(0, rows):
        for c in xrange(0, cols):
            # Get the depth in bytes
            depth = depth_image[r, c, 0];

            # If the depth is 0x0 or 0xFF, its invalid.
            # By convention it should be replaced by a NaN depth.
            if(depth > 0 and depth < 255):
                # The true depth of the pixel in units
                z = depth * bytes_to_units;

                # Get the x, y, z coordinates in units of the pixel
                # using the intrinsics of the camera (cx, cy, fx, fy)
                points[i, 0] = (c - self.cx) / self.fx * z;
                points[i, 1] = (r - self.cy) / self.fy * z;
                points[i, 2] = z
            else:
                # Invalid points have a NaN depth
                points[i, 2] = np.nan;
            i = i + 1
    return points

plonser · Accepted Answer · 2015-02-02 18:34:35Z

1

I can't check it because I don't have your data but the following code should do the job

def create_point_cloud_vectorized(self,depth_image):
    im_shape = depth_image.shape

    # get the depth
    d = depth_image[:,:,0]

    # replace the invalid data with np.nan
    depth = np.where( (d > 0) & (d < 255), d /256., np.nan)

    # get x and y data in a vectorized way
    row = (np.arange(im_shape[0])[:,None] - self.cx) / self.fx * depth
    col = (np.arange(im_shape[1])[None,:] - self.cy) / self.fy * depth

    # combine x,y,depth and bring it into the correct shape
    return array((row,col,depth)).reshape(3,-1).swapaxes(0,1)

answered Feb 2, 2015 at 18:34

plonser

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1 Comment

mklingen Over a year ago

Thank you. I will try this out. For the record I decided to just do this on the GPU, and it has become a moot point.

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