Pil Mean Of All Non-transparent/black Pixels In Rgba Image

I want to achieve the same effect as in: cv::mean for non black pixel However I am using PIL and converting a PIL image to cv image and back is too much overhead. I have tried usin

Solution 1:

It might not be the cleanest solution, but I got it to work.

defmean(rgb, a):
    """
    Supply with an RGB PIL Image and Alpha Channel PIL Image.
    Calculates the mean over all non-fully-transparent pixels in rgb.
    """

    a_arr = np.array(a)       # Convert Alpha values Image to array.
    img_arr = np.array(rgb)   # Convert Image RGB values to array.
    mask = (a_arr > 0)        # Create mask from all non-transparent pixels
    stuff = img_arr[mask]     # Array containing all pixels that aren't transparent

    rows = len(stuff)         # Get the row size.if rows < 1:              # If all pixels are transparent:return (0, 0, 0)      # The mean is simply black
    cols = len(stuff[0])      # Else, continue with the size of cols

    data = np.zeros([cols, rows, 3], dtype = np.uint8) # Create an array to contain the pixels
    data[:] = stuff           # Put the pixels with at least a > 0 into the created array.

    c_img = Image.fromarray(data, 'RGB') # Convert back to RGB PIL Imagereturn ImageStat.Stat(c_img).mean # Calculate the mean over all pixels

Performance-wise, it was enough for my case.

About 3.44 seconds to convert about a thousand 16x16 image files. The process was:

Taking the mean then saving a Image.new('RGB', (16, 16), mean).