#!/usr/bin/env python3 # Copyright (c) the JPEG XL Project Authors. All rights reserved. # # Use of this source code is governed by a BSD-style # license that can be found in the LICENSE file. """Demo for upscaling. Given an upscaling factor which can be 2, 4 or 8 we demo upscaling an image by that factor. usage: upscaler_demo.py [-h] [--upscaling_factor N] input_filename output_filename Upscaling of an image by a factor of 2, 4 or 8. positional arguments: input_filename of the PNG image to be upscaled. output_filename where the upscaled image is written as PNG. optional arguments: -h, --help show this help message and exit --upscaling_factor N where N must be 2, 4 (default) or 8. """ from PIL import Image import argparse import numpy as np def convolution(pixels, kernel): """ Returns the convolution of `pixels` with `kernel`. Uses padding such that the shape of the returned convoluted array is the same as the shape of `pixels`, scaled by the upscaling_factor implied by the `kernel`. Args: pixels: A [heigth, width]- or [height, width, num_channels]-array representing an image. kernel: A [upscaling_factor, upscaling_factor, kernel_size, kernel_size]-array used for the convolution. Returns: A [upscaling_factor*heigth, upscaling_factor*width]- or [upscaling_factor*height, upscaling_factor*width, num_channels]-array representing the convoluted upscaled image. """ upscaling_factor, _, kernel_size, _ = kernel.shape output_shape = list(pixels.shape) output_shape[0] *= upscaling_factor output_shape[1] *= upscaling_factor shaped_pixels = pixels.reshape(pixels.shape[:2] + (-1,)) pad_width = kernel_size//2 padded_pixels = np.pad( shaped_pixels, 2*[2*[pad_width]] + [[0, 0]], mode='edge') x, y, _ = shaped_pixels.shape convoluted = np.block([[np.einsum('rc...,RCrc->...RC', padded_pixels[i - pad_width: i + pad_width + 1, j - pad_width: j + pad_width + 1], kernel) for j in range(pad_width, pad_width + y)] for i in range(pad_width, pad_width + x)]) return np.moveaxis(convoluted, 0, -1).reshape(output_shape) def main(): parser = argparse.ArgumentParser( description="Upscaling of an image by a factor of 2, 4 or 8.") parser.add_argument( "--upscaling_factor", type=int, help="where N must be 2, 4 (default) or 8.", nargs=1, default=[4], metavar='N') parser.add_argument( "input_filename", type=str, help="of the PNG image to be upscaled." ) parser.add_argument( "output_filename", type=str, help="where the upscaled image is written as PNG." ) args = parser.parse_args() upscaling_factor = args.upscaling_factor[0] kernel_size = 5 if upscaling_factor not in (2, 4, 8): raise ValueError("upscaling_factor must be 2, 4 or 8.") kernel = np.array(_get_scaling_kernels()[upscaling_factor]) assert kernel.shape == ( upscaling_factor, upscaling_factor, kernel_size, kernel_size) orig_raw = Image.open(args.input_filename) orig = orig_raw.convert('RGB') if orig_raw.mode == 'P' else orig_raw upscaled_float = convolution(np.array(orig), kernel) upscaled = Image.fromarray( np.rint(np.clip(upscaled_float, 0, 255)).astype(np.uint8), orig.mode) upscaled.save(args.output_filename) def _get_scaling_kernels(): return {2: [[[[-0.017162003089909145, -0.0345230259724203, -0.04022174342753632, -0.029210135410064335, -0.006246448474415789], [-0.0345230259724203, 0.14111091126932612, 0.28896754962953114, 0.0027871809188615613, -0.016102674925096382], [-0.04022174342753632, 0.28896754962953114, 0.5666155013385713, 0.037776067445408776, -0.01986694439461126], [-0.029210135410064335, 0.0027871809188615535, 0.03777606744540877, -0.031447310821961526, -0.011850679991269755], [-0.006246448474415788, -0.01610267492509638, -0.019866944394611258, -0.011850679991269755, -0.0021353894928012747]], [[-0.006246448474415787, -0.029210135410064328, -0.040221743427536316, -0.034523025972420296, -0.01716200308990914], [-0.01610267492509638, 0.0027871809188615582, 0.2889675496295311, 0.1411109112693261, -0.034523025972420296], [-0.019866944394611254, 0.037776067445408755, 0.5666155013385712, 0.2889675496295311, -0.04022174342753631], [-0.011850679991269751, -0.03144731082196152, 0.037776067445408755, 0.00278718091886156, -0.029210135410064324], [-0.0021353894928012743, -0.011850679991269751, -0.01986694439461125, -0.016102674925096375, -0.006246448474415786]]], [[[-0.006246448474415788, -0.01610267492509638, -0.019866944394611258, -0.011850679991269755, -0.0021353894928012747], [-0.02921013541006433, 0.002787180918861557, 0.03777606744540876, -0.031447310821961526, -0.011850679991269755], [-0.04022174342753632, 0.28896754962953114, 0.5666155013385712, 0.037776067445408776, -0.019866944394611258], [-0.0345230259724203, 0.14111091126932612, 0.28896754962953114, 0.0027871809188615595, -0.016102674925096382], [-0.017162003089909145, -0.03452302597242031, -0.04022174342753633, -0.029210135410064335, -0.006246448474415789]], [[-0.0021353894928012747, -0.011850679991269755, -0.019866944394611258, -0.01610267492509638, -0.006246448474415788], [-0.011850679991269755, -0.031447310821961526, 0.03777606744540876, 0.002787180918861564, -0.02921013541006433], [-0.019866944394611258, 0.037776067445408776, 0.5666155013385712, 0.28896754962953114, -0.040221743427536316], [-0.016102674925096382, 0.002787180918861556, 0.28896754962953114, 0.14111091126932615, -0.0345230259724203], [-0.006246448474415789, -0.029210135410064335, -0.04022174342753633, -0.0345230259724203, -0.017162003089909145]]]], 4: [[[[-0.024190672183733018, -0.03491987403959535, -0.036933511116288356, -0.03094284535390427, -0.005297851729507614], [-0.03491987403959535, 0.23651958284942343, 0.3339294481745815, -0.010735433431237009, -0.013131808617501706], [-0.036933511116288356, 0.3339294481745815, 0.4691419769580017, -0.0020927007975838127, -0.014845888917802386], [-0.030942845353904277, -0.010735433431237024, -0.0020927007975838035, -0.035516824721615874, -0.007548300818273063], [-0.005297851729507614, -0.013131808617501708, -0.014845888917802386, -0.007548300818273063, -0.0009165296078520004]], [[-0.01663431734052121, -0.03556862997573282, -0.0388890539890255, -0.035168498619353575, -0.009894687488916538], [-0.03556694367519552, 0.13048175192612746, 0.40103024797994685, 0.03951149796198834, -0.02077584470399766], [-0.04064806042030105, 0.18942529580147974, 0.5627989220290085, 0.06674400125646836, -0.023354943007463536], [-0.0226791877794674, -0.023635779153108244, 0.0031580414703133823, -0.03399097960642573, -0.013595188211470589], [-0.003354666868160516, -0.011632944561351362, -0.016102939237729652, -0.00974087766582541, -0.0019162161212866041]], [[-0.009894687488916542, -0.03516849861935358, -0.03888905398902551, -0.035568629975732825, -0.016634317340521215], [-0.020775844703997664, 0.03951149796198835, 0.4010302479799469, 0.13048175192612743, -0.03556694367519553], [-0.02335494300746354, 0.06674400125646836, 0.5627989220290086, 0.18942529580147976, -0.04064806042030106], [-0.01359518821147059, -0.033990979606425734, 0.003158041470313383, -0.02363577915310824, -0.022679187779467407], [-0.0019162161212866043, -0.00974087766582541, -0.016102939237729656, -0.011632944561351364, -0.0033546668681605166]], [[-0.005297851729507613, -0.030942845353904264, -0.036933511116288356, -0.034919874039595344, -0.024190672183733015], [-0.013131808617501703, -0.010735433431237012, 0.33392944817458137, 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[-0.0038444335414517827, -0.016679994683747115, -0.02534993547253667, -0.018973492447769916, -0.006459118117528575]], [[-0.0033546668681605166, -0.022679187779467407, -0.04064806042030106, -0.03556694367519554, -0.016634317340521218], [-0.011632944561351364, -0.023635779153108254, 0.18942529580147976, 0.13048175192612743, -0.035568629975732825], [-0.016102939237729656, 0.0031580414703133762, 0.5627989220290086, 0.40103024797994696, -0.038889053989025486], [-0.009740877665825409, -0.033990979606425734, 0.06674400125646834, 0.03951149796198835, -0.035168498619353575], [-0.0019162161212866041, -0.013595188211470589, -0.02335494300746354, -0.02077584470399766, -0.00989468748891654]]], [[[-0.009894687488916542, -0.020775844703997664, -0.023354943007463547, -0.01359518821147059, -0.0019162161212866046], [-0.03516849861935359, 0.03951149796198835, 0.06674400125646836, -0.03399097960642574, -0.009740877665825412], [-0.03888905398902551, 0.401030247979947, 0.5627989220290087, 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