Spaces:
Running
on
T4
Running
on
T4
File size: 5,997 Bytes
561c629 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
# -*- coding: utf-8 -*-
import argparse
import cv2
import torch
import numpy as np
import os, shutil, time
import sys, random
from multiprocessing import Pool
from os import path as osp
from tqdm import tqdm
from math import log10, sqrt
import torch.nn.functional as F
root_path = os.path.abspath('.')
sys.path.append(root_path)
from degradation.ESR.degradations_functionality import *
from degradation.ESR.diffjpeg import *
from degradation.ESR.utils import filter2D
from degradation.image_compression.jpeg import JPEG
from degradation.image_compression.webp import WEBP
from degradation.image_compression.heif import HEIF
from degradation.image_compression.avif import AVIF
from opt import opt
def PSNR(original, compressed):
mse = np.mean((original - compressed) ** 2)
if(mse == 0): # MSE is zero means no noise is present in the signal .
# Therefore PSNR have no importance.
return 100
max_pixel = 255.0
psnr = 20 * log10(max_pixel / sqrt(mse))
return psnr
def downsample_1st(out, opt):
# Resize with different mode
updown_type = random.choices(['up', 'down', 'keep'], opt['resize_prob'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, opt['resize_range'][1])
elif updown_type == 'down':
scale = np.random.uniform(opt['resize_range'][0], 1)
else:
scale = 1
mode = random.choice(opt['resize_options'])
out = F.interpolate(out, scale_factor=scale, mode=mode)
return out
def downsample_2nd(out, opt, ori_h, ori_w):
# Second Resize for 4x scaling
if opt['scale'] == 4:
updown_type = random.choices(['up', 'down', 'keep'], opt['resize_prob2'])[0]
if updown_type == 'up':
scale = np.random.uniform(1, opt['resize_range2'][1])
elif updown_type == 'down':
scale = np.random.uniform(opt['resize_range2'][0], 1)
else:
scale = 1
mode = random.choice(opt['resize_options'])
# Resize这边改回来原来的版本，不用连续的resize了
# out = F.interpolate(out, scale_factor=scale, mode=mode)
out = F.interpolate(
out, size=(int(ori_h / opt['scale'] * scale), int(ori_w / opt['scale'] * scale)), mode=mode
)
return out
def common_degradation(out, opt, kernels, process_id, verbose = False):
jpeger = DiffJPEG(differentiable=False).cuda()
kernel1, kernel2 = kernels
downsample_1st_position = random.choices([0, 1, 2])[0]
if opt['scale'] == 4:
# Only do the second downsample at 4x scale
downsample_2nd_position = random.choices([0, 1, 2])[0]
else:
# print("We don't use the second resize")
downsample_2nd_position = -1
####---------------------------- Frist Degradation ----------------------------------####
batch_size, _, ori_h, ori_w = out.size()
if downsample_1st_position == 0:
out = downsample_1st(out, opt)
# Bluring kernel
out = filter2D(out, kernel1)
if verbose: print(f"(1st) blur noise")
if downsample_1st_position == 1:
out = downsample_1st(out, opt)
# Noise effect (gaussian / poisson)
gray_noise_prob = opt['gray_noise_prob']
if np.random.uniform() < opt['gaussian_noise_prob']:
# Gaussian noise
out = random_add_gaussian_noise_pt(
out, sigma_range=opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
name = "gaussian_noise"
else:
# Poisson noise
out = random_add_poisson_noise_pt(
out,
scale_range=opt['poisson_scale_range'],
gray_prob=gray_noise_prob,
clip=True,
rounds=False)
name = "poisson_noise"
if verbose: print("(1st) " + str(name))
if downsample_1st_position == 2:
out = downsample_1st(out, opt)
# Choose an image compression codec (All degradation batch use the same codec)
image_codec = random.choices(opt['compression_codec1'], opt['compression_codec_prob1'])[0] # All lower case
if image_codec == "jpeg":
out = JPEG.compress_tensor(out)
elif image_codec == "webp":
try:
out = WEBP.compress_tensor(out, idx=process_id)
except Exception:
print("There is exception again in webp!")
out = WEBP.compress_tensor(out, idx=process_id)
elif image_codec == "heif":
out = HEIF.compress_tensor(out, idx=process_id)
elif image_codec == "avif":
out = AVIF.compress_tensor(out, idx=process_id)
else:
raise NotImplementedError("We don't have such image compression designed!")
# ##########################################################################################
# ####---------------------------- Second Degradation ----------------------------------####
if downsample_2nd_position == 0:
out = downsample_2nd(out, opt, ori_h, ori_w)
# Add blur 2nd time
if np.random.uniform() < opt['second_blur_prob']:
# 这个bluring不是必定触发的
if verbose: print("(2nd) blur noise")
out = filter2D(out, kernel2)
if downsample_2nd_position == 1:
out = downsample_2nd(out, opt, ori_h, ori_w)
# Add noise 2nd time
gray_noise_prob = opt['gray_noise_prob2']
if np.random.uniform() < opt['gaussian_noise_prob2']:
# gaussian noise
if verbose: print("(2nd) gaussian noise")
out = random_add_gaussian_noise_pt(
out, sigma_range=opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
name = "gaussian_noise"
else:
# poisson noise
if verbose: print("(2nd) poisson noise")
out = random_add_poisson_noise_pt(
out, scale_range=opt['poisson_scale_range2'], gray_prob=gray_noise_prob, clip=True, rounds=False)
name = "poisson_noise"
if downsample_2nd_position == 2:
out = downsample_2nd(out, opt, ori_h, ori_w)
return out |