Incorrect conversion from Raw RGB Depth image to gray-CodePudding

I am working with a simulation in Python equipped with a depth sensor. The visualization it's done in C . The sensor gives me the following image that I need to convert to gray.

For the conversion, I have the next formula:

normalized = (R   G * 256   B * 256 * 256) / (256 * 256 * 256 - 1)
in_meters = 1000 * normalized

For converting the image to gray in C , I've written the following code:

cv::Mat ConvertRawToDepth(cv::Mat raw_image)
{
    // raw_image.type() => CV_8UC3

    // Extend raw image to 2 bytes per pixel
    cv::Mat raw_extended = cv::Mat::Mat(raw_image.rows, raw_image.cols, CV_16UC3, raw_image.data);

    // Split into channels
    std::vector<cv::Mat> raw_ch(3);
    cv::split(raw_image, raw_ch); // B, G, R

    // Create and calculate 1 channel gray image of depth based on the formula
    cv::Mat depth_gray = cv::Mat::zeros(raw_ch[0].rows, raw_ch[0].cols, CV_32FC1);
    depth_gray = 1000.0 * (raw_ch[2]   raw_ch[1] * 256   raw_ch[0] * 65536) / (16777215.0);

    // Create final BGR image
    cv::Mat depth_3d;
    cv::cvtColor(depth_gray, depth_3d, cv::COLOR_GRAY2BGR);

    return depth_3d;
}

Achieving the next result:

If I do the conversion in python, I can simply write:

def convert_raw_to_depth(raw_image):
    raw_image = raw_image[:, :, :3]
    raw_image = raw_image.astype(np.float32)
    
    # Apply (R   G * 256   B * 256 * 256) / (256 * 256 * 256 - 1).
    depth = np.dot(raw_image, [65536.0, 256.0, 1.0])
    depth /= 16777215.0  # (256.0 * 256.0 * 256.0 - 1.0)
    depth *= 1000
    
    return depth

Achieving the next result:

It's clear that in python it's done better, but the formula is the same, the image is the same, then why is it a difference and how can I rewrite the code in C to give me similar results as in Python?

CodePudding user response：

It looks like you are dealing with np.float32 array in Python while CV_8UC3 array in C .

Try converting to CV_32FC3 before calculation.

    // Convert to float and split into channels
    cv::Mat raw_image_float;
    raw_image.convertTo(raw_image_float, CV_32FC3);
    std::vector<cv::Mat> raw_ch(3);
    cv::split(raw_image_float, raw_ch); // B, G, R