def autocontrast()

in infrastructure/pillow-layer/python/PIL/ImageOps.py [0:0]

• 56 lines of code
• 22 McCabe index (conditional complexity)

def autocontrast(image, cutoff=0, ignore=None, mask=None):
    """
    Maximize (normalize) image contrast. This function calculates a
    histogram of the input image (or mask region), removes ``cutoff`` percent of the
    lightest and darkest pixels from the histogram, and remaps the image
    so that the darkest pixel becomes black (0), and the lightest
    becomes white (255).

    :param image: The image to process.
    :param cutoff: The percent to cut off from the histogram on the low and
                   high ends. Either a tuple of (low, high), or a single
                   number for both.
    :param ignore: The background pixel value (use None for no background).
    :param mask: Histogram used in contrast operation is computed using pixels
                 within the mask. If no mask is given the entire image is used
                 for histogram computation.
    :return: An image.
    """
    histogram = image.histogram(mask)
    lut = []
    for layer in range(0, len(histogram), 256):
        h = histogram[layer : layer + 256]
        if ignore is not None:
            # get rid of outliers
            try:
                h[ignore] = 0
            except TypeError:
                # assume sequence
                for ix in ignore:
                    h[ix] = 0
        if cutoff:
            # cut off pixels from both ends of the histogram
            if not isinstance(cutoff, tuple):
                cutoff = (cutoff, cutoff)
            # get number of pixels
            n = 0
            for ix in range(256):
                n = n + h[ix]
            # remove cutoff% pixels from the low end
            cut = n * cutoff[0] // 100
            for lo in range(256):
                if cut > h[lo]:
                    cut = cut - h[lo]
                    h[lo] = 0
                else:
                    h[lo] -= cut
                    cut = 0
                if cut <= 0:
                    break
            # remove cutoff% samples from the high end
            cut = n * cutoff[1] // 100
            for hi in range(255, -1, -1):
                if cut > h[hi]:
                    cut = cut - h[hi]
                    h[hi] = 0
                else:
                    h[hi] -= cut
                    cut = 0
                if cut <= 0:
                    break
        # find lowest/highest samples after preprocessing
        for lo in range(256):
            if h[lo]:
                break
        for hi in range(255, -1, -1):
            if h[hi]:
                break
        if hi <= lo:
            # don't bother
            lut.extend(list(range(256)))
        else:
            scale = 255.0 / (hi - lo)
            offset = -lo * scale
            for ix in range(256):
                ix = int(ix * scale + offset)
                if ix < 0:
                    ix = 0
                elif ix > 255:
                    ix = 255
                lut.append(ix)
    return _lut(image, lut)