[feature request] Image Histogram Transformation

It is often useful (especially in the field of astronomy) to transform the histogram of images. I would like to suggest an image histogram transformation function (under torchvision.transforms) that transforms the histogram of an image to match that of a template image as closely as possible. For instance, consider the following function:

def match_histogram(source, template):

    source   = np.asanyarray(source)
    template = np.asanyarray(template)
    oldshape = source.shape
    source   = source.ravel()
    template = template.ravel()

    # get the set of unique pixel values and their corresponding indices and
    # counts
    s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,
                                            return_counts=True)
    t_values, t_counts = np.unique(template, return_counts=True)

    # take the cumsum of the counts and normalize by the number of pixels to
    # get the empirical cumulative distribution functions for the source and
    # template images (maps pixel value --> quantile)
    s_quantiles  = np.cumsum(s_counts).astype(np.float32)
    s_quantiles /= s_quantiles[-1]
    t_quantiles  = np.cumsum(t_counts).astype(np.float32)
    t_quantiles /= t_quantiles[-1]

    # interpolate linearly to find the pixel values in the template image
    # that corresponds most closely to the quantiles in the source image
    interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)

    return interp_t_values[bin_idx].reshape(oldshape)

The function above is not optimal since it has to recalculate template image information. It is not discretized for float type images. It only performs for highly discretized images such as png (0-255 bins). It also performs poorly when the number of diverse pixels is too low which might be fixed by adding small noise.

Start at the torchvision.transforms entry point and review existing image transforms before assessing how a template image and reusable histogram information should be handled. Resolve the stated limitations for float images, low pixel diversity, and repeated template processing; done means a documented transformation that matches the source histogram to the template as closely as supported.