优化 numpy 点云创建脚本

Question

我正在用 numpy 编写一个简单的脚本，它获取 640 x 480 深度图像（二维 numpy 字节数组），并在给定针孔的情况下将其转换为 num_points x 3 numpy 点数组相机型号。数学相当简单，我已经让脚本运行了——但它非常慢。显然，每帧需要 2 秒。我以前用 C++ 编写过一个类似的脚本，每帧大约 100 毫秒。我想知道我可以对 python 脚本进行哪些优化。这些都可以矢量化吗？我可以从并行化中受益吗？

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

Answer 1

我无法检查它，因为我没有你的数据，但下面的代码应该可以完成这项工作

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)

优化 numpy 点云创建脚本

Optimize numpy point cloud creation script

python

optimization

numpy

computer-vision

point-clouds