upscaler_demo.py (54125B)
1 #!/usr/bin/env python3 2 3 # Copyright (c) the JPEG XL Project Authors. All rights reserved. 4 # 5 # Use of this source code is governed by a BSD-style 6 # license that can be found in the LICENSE file. 7 8 """Demo for upscaling. 9 10 Given an upscaling factor which can be 2, 4 or 8 we demo upscaling an image by 11 that factor. 12 13 usage: upscaler_demo.py [-h] [--upscaling_factor N] input_filename output_filename 14 15 Upscaling of an image by a factor of 2, 4 or 8. 16 17 positional arguments: 18 input_filename of the PNG image to be upscaled. 19 output_filename where the upscaled image is written as PNG. 20 21 optional arguments: 22 -h, --help show this help message and exit 23 --upscaling_factor N where N must be 2, 4 (default) or 8. 24 """ 25 from PIL import Image 26 27 import argparse 28 import numpy as np 29 30 31 def convolution(pixels, kernel): 32 """ 33 Returns the convolution of `pixels` with `kernel`. 34 35 Uses padding such that the shape of the returned convoluted array is the 36 same as the shape of `pixels`, scaled by the upscaling_factor implied by the 37 `kernel`. 38 39 Args: 40 pixels: A [height, width]- or [height, width, num_channels]-array 41 representing an image. 42 43 kernel: A [upscaling_factor, upscaling_factor, kernel_size, 44 kernel_size]-array used for the convolution. 45 46 Returns: 47 A [upscaling_factor*height, upscaling_factor*width]- or 48 [upscaling_factor*height, upscaling_factor*width, num_channels]-array representing the 49 convoluted upscaled image. 50 """ 51 upscaling_factor, _, kernel_size, _ = kernel.shape 52 output_shape = list(pixels.shape) 53 output_shape[0] *= upscaling_factor 54 output_shape[1] *= upscaling_factor 55 shaped_pixels = pixels.reshape(pixels.shape[:2] + (-1,)) 56 pad_width = kernel_size//2 57 padded_pixels = np.pad( 58 shaped_pixels, 2*[2*[pad_width]] + [[0, 0]], mode='edge') 59 x, y, _ = shaped_pixels.shape 60 convoluted = np.block([[np.einsum('rc...,RCrc->...RC', 61 padded_pixels[i - pad_width: i + pad_width + 1, 62 j - pad_width: j + pad_width + 1], 63 kernel) 64 for j in range(pad_width, pad_width + y)] 65 for i in range(pad_width, pad_width + x)]) 66 return np.moveaxis(convoluted, 0, -1).reshape(output_shape) 67 68 69 def main(): 70 parser = argparse.ArgumentParser( 71 description="Upscaling of an image by a factor of 2, 4 or 8.") 72 parser.add_argument( 73 "--upscaling_factor", 74 type=int, 75 help="where N must be 2, 4 (default) or 8.", 76 nargs=1, 77 default=[4], 78 metavar='N') 79 80 parser.add_argument( 81 "input_filename", 82 type=str, 83 help="of the PNG image to be upscaled." 84 ) 85 parser.add_argument( 86 "output_filename", 87 type=str, 88 help="where the upscaled image is written as PNG." 89 ) 90 91 args = parser.parse_args() 92 upscaling_factor = args.upscaling_factor[0] 93 kernel_size = 5 94 if upscaling_factor not in (2, 4, 8): 95 raise ValueError("upscaling_factor must be 2, 4 or 8.") 96 kernel = np.array(_get_scaling_kernels()[upscaling_factor]) 97 assert kernel.shape == ( 98 upscaling_factor, upscaling_factor, kernel_size, kernel_size) 99 orig_raw = Image.open(args.input_filename) 100 orig = orig_raw.convert('RGB') if orig_raw.mode == 'P' else orig_raw 101 upscaled_float = convolution(np.array(orig), kernel) 102 103 upscaled = Image.fromarray( 104 np.rint(np.clip(upscaled_float, 0, 255)).astype(np.uint8), orig.mode) 105 upscaled.save(args.output_filename) 106 107 108 def _get_scaling_kernels(): 109 return {2: [[[[-0.017162003089909145, -0.0345230259724203, -0.04022174342753632, 110 -0.029210135410064335, -0.006246448474415789], [-0.0345230259724203, 111 0.14111091126932612, 0.28896754962953114, 0.0027871809188615613, 112 -0.016102674925096382], [-0.04022174342753632, 0.28896754962953114, 113 0.5666155013385713, 0.037776067445408776, -0.01986694439461126], 114 [-0.029210135410064335, 0.0027871809188615535, 0.03777606744540877, 115 -0.031447310821961526, -0.011850679991269755], [-0.006246448474415788, 116 -0.01610267492509638, -0.019866944394611258, -0.011850679991269755, 117 -0.0021353894928012747]], [[-0.006246448474415787, -0.029210135410064328, 118 -0.040221743427536316, -0.034523025972420296, -0.01716200308990914], 119 [-0.01610267492509638, 0.0027871809188615582, 0.2889675496295311, 120 0.1411109112693261, -0.034523025972420296], [-0.019866944394611254, 121 0.037776067445408755, 0.5666155013385712, 0.2889675496295311, 122 -0.04022174342753631], [-0.011850679991269751, -0.03144731082196152, 123 0.037776067445408755, 0.00278718091886156, -0.029210135410064324], 124 [-0.0021353894928012743, -0.011850679991269751, -0.01986694439461125, 125 -0.016102674925096375, -0.006246448474415786]]], [[[-0.006246448474415788, 126 -0.01610267492509638, -0.019866944394611258, -0.011850679991269755, 127 -0.0021353894928012747], [-0.02921013541006433, 0.002787180918861557, 128 0.03777606744540876, -0.031447310821961526, -0.011850679991269755], 129 [-0.04022174342753632, 0.28896754962953114, 0.5666155013385712, 130 0.037776067445408776, -0.019866944394611258], [-0.0345230259724203, 131 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