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What is the effect of max_val * (depth - depth_min) / (depth_max - depth_min) in the below function?

def write_depth(path, depth, bits=2):
    """Write depth map to pfm and png file.

    Args:
        path (str): filepath without extension
        depth (array): depth
    """
    write_pfm(path + ".pfm", depth.astype(np.float32))

    depth_min = depth.min()
    depth_max = depth.max()

    max_val = (2**(8*bits))-1

    if depth_max - depth_min > np.finfo("float").eps:
        out = max_val * (depth - depth_min) / (depth_max - depth_min)
    else:
        out = np.zeros(depth.shape, dtype=depth.type)

    if bits == 1:
        cv2.imwrite(path + ".png", out.astype("uint8"))
    elif bits == 2:
        cv2.imwrite(path + ".png", out.astype("uint16"))

    return

My question is not about syntax but about the reason or the effect of that calculation?

  1. What effect on the array does subtracting the depth_min have? Other than changing the mean and median. Why do this step?
  2. What effect on the array does multiplying by max_val have?
  3. What effect on the array does dividing by the range have?
  4. Why do this instead of just converting each floating point to an integer?

I have seen the output of this function, which is a depth map where lighter gray pixels denote close objects and darker pixels denote far away objects.

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    $\begingroup$ it is a normalisation factor, to take values in the range [depth_min, depth_max] and project them to values in the range [0, max_value] uniformly $\endgroup$
    – Nikos M.
    Jan 27 '21 at 10:00

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