mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
752 lines
23 KiB
Python
752 lines
23 KiB
Python
# -*- coding: utf-8 -*-
|
|
import numpy as np
|
|
import cv2
|
|
import torch
|
|
|
|
from functools import partial
|
|
import random
|
|
from scipy import ndimage
|
|
import scipy
|
|
import scipy.stats as ss
|
|
from scipy.interpolate import interp2d
|
|
from scipy.linalg import orth
|
|
import albumentations
|
|
|
|
import ldm.modules.image_degradation.utils_image as util
|
|
|
|
"""
|
|
# --------------------------------------------
|
|
# Super-Resolution
|
|
# --------------------------------------------
|
|
#
|
|
# Kai Zhang (cskaizhang@gmail.com)
|
|
# https://github.com/cszn
|
|
# From 2019/03--2021/08
|
|
# --------------------------------------------
|
|
"""
|
|
|
|
|
|
def modcrop_np(img, sf):
|
|
"""
|
|
Args:
|
|
img: numpy image, WxH or WxHxC
|
|
sf: scale factor
|
|
Return:
|
|
cropped image
|
|
"""
|
|
w, h = img.shape[:2]
|
|
im = np.copy(img)
|
|
return im[: w - w % sf, : h - h % sf, ...]
|
|
|
|
|
|
"""
|
|
# --------------------------------------------
|
|
# anisotropic Gaussian kernels
|
|
# --------------------------------------------
|
|
"""
|
|
|
|
|
|
def analytic_kernel(k):
|
|
"""Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
|
|
k_size = k.shape[0]
|
|
# Calculate the big kernels size
|
|
big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
|
|
# Loop over the small kernel to fill the big one
|
|
for r in range(k_size):
|
|
for c in range(k_size):
|
|
big_k[2 * r : 2 * r + k_size, 2 * c : 2 * c + k_size] += (
|
|
k[r, c] * k
|
|
)
|
|
# Crop the edges of the big kernel to ignore very small values and increase run time of SR
|
|
crop = k_size // 2
|
|
cropped_big_k = big_k[crop:-crop, crop:-crop]
|
|
# Normalize to 1
|
|
return cropped_big_k / cropped_big_k.sum()
|
|
|
|
|
|
def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
|
|
"""generate an anisotropic Gaussian kernel
|
|
Args:
|
|
ksize : e.g., 15, kernel size
|
|
theta : [0, pi], rotation angle range
|
|
l1 : [0.1,50], scaling of eigenvalues
|
|
l2 : [0.1,l1], scaling of eigenvalues
|
|
If l1 = l2, will get an isotropic Gaussian kernel.
|
|
Returns:
|
|
k : kernel
|
|
"""
|
|
|
|
v = np.dot(
|
|
np.array(
|
|
[[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
|
|
),
|
|
np.array([1.0, 0.0]),
|
|
)
|
|
V = np.array([[v[0], v[1]], [v[1], -v[0]]])
|
|
D = np.array([[l1, 0], [0, l2]])
|
|
Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
|
|
k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
|
|
|
|
return k
|
|
|
|
|
|
def gm_blur_kernel(mean, cov, size=15):
|
|
center = size / 2.0 + 0.5
|
|
k = np.zeros([size, size])
|
|
for y in range(size):
|
|
for x in range(size):
|
|
cy = y - center + 1
|
|
cx = x - center + 1
|
|
k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
|
|
|
|
k = k / np.sum(k)
|
|
return k
|
|
|
|
|
|
def shift_pixel(x, sf, upper_left=True):
|
|
"""shift pixel for super-resolution with different scale factors
|
|
Args:
|
|
x: WxHxC or WxH
|
|
sf: scale factor
|
|
upper_left: shift direction
|
|
"""
|
|
h, w = x.shape[:2]
|
|
shift = (sf - 1) * 0.5
|
|
xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
|
|
if upper_left:
|
|
x1 = xv + shift
|
|
y1 = yv + shift
|
|
else:
|
|
x1 = xv - shift
|
|
y1 = yv - shift
|
|
|
|
x1 = np.clip(x1, 0, w - 1)
|
|
y1 = np.clip(y1, 0, h - 1)
|
|
|
|
if x.ndim == 2:
|
|
x = interp2d(xv, yv, x)(x1, y1)
|
|
if x.ndim == 3:
|
|
for i in range(x.shape[-1]):
|
|
x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
|
|
|
|
return x
|
|
|
|
|
|
def blur(x, k):
|
|
"""
|
|
x: image, NxcxHxW
|
|
k: kernel, Nx1xhxw
|
|
"""
|
|
n, c = x.shape[:2]
|
|
p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
|
|
x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
|
|
k = k.repeat(1, c, 1, 1)
|
|
k = k.view(-1, 1, k.shape[2], k.shape[3])
|
|
x = x.view(1, -1, x.shape[2], x.shape[3])
|
|
x = torch.nn.functional.conv2d(
|
|
x, k, bias=None, stride=1, padding=0, groups=n * c
|
|
)
|
|
x = x.view(n, c, x.shape[2], x.shape[3])
|
|
|
|
return x
|
|
|
|
|
|
def gen_kernel(
|
|
k_size=np.array([15, 15]),
|
|
scale_factor=np.array([4, 4]),
|
|
min_var=0.6,
|
|
max_var=10.0,
|
|
noise_level=0,
|
|
):
|
|
""" "
|
|
# modified version of https://github.com/assafshocher/BlindSR_dataset_generator
|
|
# Kai Zhang
|
|
# min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
|
|
# max_var = 2.5 * sf
|
|
"""
|
|
# Set random eigen-vals (lambdas) and angle (theta) for COV matrix
|
|
lambda_1 = min_var + np.random.rand() * (max_var - min_var)
|
|
lambda_2 = min_var + np.random.rand() * (max_var - min_var)
|
|
theta = np.random.rand() * np.pi # random theta
|
|
noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
|
|
|
|
# Set COV matrix using Lambdas and Theta
|
|
LAMBDA = np.diag([lambda_1, lambda_2])
|
|
Q = np.array(
|
|
[[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]
|
|
)
|
|
SIGMA = Q @ LAMBDA @ Q.T
|
|
INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
|
|
|
|
# Set expectation position (shifting kernel for aligned image)
|
|
MU = k_size // 2 - 0.5 * (
|
|
scale_factor - 1
|
|
) # - 0.5 * (scale_factor - k_size % 2)
|
|
MU = MU[None, None, :, None]
|
|
|
|
# Create meshgrid for Gaussian
|
|
[X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
|
|
Z = np.stack([X, Y], 2)[:, :, :, None]
|
|
|
|
# Calcualte Gaussian for every pixel of the kernel
|
|
ZZ = Z - MU
|
|
ZZ_t = ZZ.transpose(0, 1, 3, 2)
|
|
raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
|
|
|
|
# shift the kernel so it will be centered
|
|
# raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
|
|
|
|
# Normalize the kernel and return
|
|
# kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
|
|
kernel = raw_kernel / np.sum(raw_kernel)
|
|
return kernel
|
|
|
|
|
|
def fspecial_gaussian(hsize, sigma):
|
|
hsize = [hsize, hsize]
|
|
siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
|
|
std = sigma
|
|
[x, y] = np.meshgrid(
|
|
np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1)
|
|
)
|
|
arg = -(x * x + y * y) / (2 * std * std)
|
|
h = np.exp(arg)
|
|
h[h < scipy.finfo(float).eps * h.max()] = 0
|
|
sumh = h.sum()
|
|
if sumh != 0:
|
|
h = h / sumh
|
|
return h
|
|
|
|
|
|
def fspecial_laplacian(alpha):
|
|
alpha = max([0, min([alpha, 1])])
|
|
h1 = alpha / (alpha + 1)
|
|
h2 = (1 - alpha) / (alpha + 1)
|
|
h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
|
|
h = np.array(h)
|
|
return h
|
|
|
|
|
|
def fspecial(filter_type, *args, **kwargs):
|
|
"""
|
|
python code from:
|
|
https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
|
|
"""
|
|
if filter_type == 'gaussian':
|
|
return fspecial_gaussian(*args, **kwargs)
|
|
if filter_type == 'laplacian':
|
|
return fspecial_laplacian(*args, **kwargs)
|
|
|
|
|
|
"""
|
|
# --------------------------------------------
|
|
# degradation models
|
|
# --------------------------------------------
|
|
"""
|
|
|
|
|
|
def bicubic_degradation(x, sf=3):
|
|
"""
|
|
Args:
|
|
x: HxWxC image, [0, 1]
|
|
sf: down-scale factor
|
|
Return:
|
|
bicubicly downsampled LR image
|
|
"""
|
|
x = util.imresize_np(x, scale=1 / sf)
|
|
return x
|
|
|
|
|
|
def srmd_degradation(x, k, sf=3):
|
|
"""blur + bicubic downsampling
|
|
Args:
|
|
x: HxWxC image, [0, 1]
|
|
k: hxw, double
|
|
sf: down-scale factor
|
|
Return:
|
|
downsampled LR image
|
|
Reference:
|
|
@inproceedings{zhang2018learning,
|
|
title={Learning a single convolutional super-resolution network for multiple degradations},
|
|
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
|
|
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
|
|
pages={3262--3271},
|
|
year={2018}
|
|
}
|
|
"""
|
|
x = ndimage.filters.convolve(
|
|
x, np.expand_dims(k, axis=2), mode='wrap'
|
|
) # 'nearest' | 'mirror'
|
|
x = bicubic_degradation(x, sf=sf)
|
|
return x
|
|
|
|
|
|
def dpsr_degradation(x, k, sf=3):
|
|
"""bicubic downsampling + blur
|
|
Args:
|
|
x: HxWxC image, [0, 1]
|
|
k: hxw, double
|
|
sf: down-scale factor
|
|
Return:
|
|
downsampled LR image
|
|
Reference:
|
|
@inproceedings{zhang2019deep,
|
|
title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
|
|
author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
|
|
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
|
|
pages={1671--1681},
|
|
year={2019}
|
|
}
|
|
"""
|
|
x = bicubic_degradation(x, sf=sf)
|
|
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
|
|
return x
|
|
|
|
|
|
def classical_degradation(x, k, sf=3):
|
|
"""blur + downsampling
|
|
Args:
|
|
x: HxWxC image, [0, 1]/[0, 255]
|
|
k: hxw, double
|
|
sf: down-scale factor
|
|
Return:
|
|
downsampled LR image
|
|
"""
|
|
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
|
|
# x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
|
|
st = 0
|
|
return x[st::sf, st::sf, ...]
|
|
|
|
|
|
def add_sharpening(img, weight=0.5, radius=50, threshold=10):
|
|
"""USM sharpening. borrowed from real-ESRGAN
|
|
Input image: I; Blurry image: B.
|
|
1. K = I + weight * (I - B)
|
|
2. Mask = 1 if abs(I - B) > threshold, else: 0
|
|
3. Blur mask:
|
|
4. Out = Mask * K + (1 - Mask) * I
|
|
Args:
|
|
img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
|
|
weight (float): Sharp weight. Default: 1.
|
|
radius (float): Kernel size of Gaussian blur. Default: 50.
|
|
threshold (int):
|
|
"""
|
|
if radius % 2 == 0:
|
|
radius += 1
|
|
blur = cv2.GaussianBlur(img, (radius, radius), 0)
|
|
residual = img - blur
|
|
mask = np.abs(residual) * 255 > threshold
|
|
mask = mask.astype('float32')
|
|
soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
|
|
|
|
K = img + weight * residual
|
|
K = np.clip(K, 0, 1)
|
|
return soft_mask * K + (1 - soft_mask) * img
|
|
|
|
|
|
def add_blur(img, sf=4):
|
|
wd2 = 4.0 + sf
|
|
wd = 2.0 + 0.2 * sf
|
|
|
|
wd2 = wd2 / 4
|
|
wd = wd / 4
|
|
|
|
if random.random() < 0.5:
|
|
l1 = wd2 * random.random()
|
|
l2 = wd2 * random.random()
|
|
k = anisotropic_Gaussian(
|
|
ksize=random.randint(2, 11) + 3,
|
|
theta=random.random() * np.pi,
|
|
l1=l1,
|
|
l2=l2,
|
|
)
|
|
else:
|
|
k = fspecial(
|
|
'gaussian', random.randint(2, 4) + 3, wd * random.random()
|
|
)
|
|
img = ndimage.filters.convolve(
|
|
img, np.expand_dims(k, axis=2), mode='mirror'
|
|
)
|
|
|
|
return img
|
|
|
|
|
|
def add_resize(img, sf=4):
|
|
rnum = np.random.rand()
|
|
if rnum > 0.8: # up
|
|
sf1 = random.uniform(1, 2)
|
|
elif rnum < 0.7: # down
|
|
sf1 = random.uniform(0.5 / sf, 1)
|
|
else:
|
|
sf1 = 1.0
|
|
img = cv2.resize(
|
|
img,
|
|
(int(sf1 * img.shape[1]), int(sf1 * img.shape[0])),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
|
|
return img
|
|
|
|
|
|
# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
|
|
# noise_level = random.randint(noise_level1, noise_level2)
|
|
# rnum = np.random.rand()
|
|
# if rnum > 0.6: # add color Gaussian noise
|
|
# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
|
|
# elif rnum < 0.4: # add grayscale Gaussian noise
|
|
# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
|
|
# else: # add noise
|
|
# L = noise_level2 / 255.
|
|
# D = np.diag(np.random.rand(3))
|
|
# U = orth(np.random.rand(3, 3))
|
|
# conv = np.dot(np.dot(np.transpose(U), D), U)
|
|
# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
|
|
# img = np.clip(img, 0.0, 1.0)
|
|
# return img
|
|
|
|
|
|
def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
|
|
noise_level = random.randint(noise_level1, noise_level2)
|
|
rnum = np.random.rand()
|
|
if rnum > 0.6: # add color Gaussian noise
|
|
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
|
|
np.float32
|
|
)
|
|
elif rnum < 0.4: # add grayscale Gaussian noise
|
|
img = img + np.random.normal(
|
|
0, noise_level / 255.0, (*img.shape[:2], 1)
|
|
).astype(np.float32)
|
|
else: # add noise
|
|
L = noise_level2 / 255.0
|
|
D = np.diag(np.random.rand(3))
|
|
U = orth(np.random.rand(3, 3))
|
|
conv = np.dot(np.dot(np.transpose(U), D), U)
|
|
img = img + np.random.multivariate_normal(
|
|
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
|
|
).astype(np.float32)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
return img
|
|
|
|
|
|
def add_speckle_noise(img, noise_level1=2, noise_level2=25):
|
|
noise_level = random.randint(noise_level1, noise_level2)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
rnum = random.random()
|
|
if rnum > 0.6:
|
|
img += img * np.random.normal(
|
|
0, noise_level / 255.0, img.shape
|
|
).astype(np.float32)
|
|
elif rnum < 0.4:
|
|
img += img * np.random.normal(
|
|
0, noise_level / 255.0, (*img.shape[:2], 1)
|
|
).astype(np.float32)
|
|
else:
|
|
L = noise_level2 / 255.0
|
|
D = np.diag(np.random.rand(3))
|
|
U = orth(np.random.rand(3, 3))
|
|
conv = np.dot(np.dot(np.transpose(U), D), U)
|
|
img += img * np.random.multivariate_normal(
|
|
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
|
|
).astype(np.float32)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
return img
|
|
|
|
|
|
def add_Poisson_noise(img):
|
|
img = np.clip((img * 255.0).round(), 0, 255) / 255.0
|
|
vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
|
|
if random.random() < 0.5:
|
|
img = np.random.poisson(img * vals).astype(np.float32) / vals
|
|
else:
|
|
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
|
|
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
|
|
noise_gray = (
|
|
np.random.poisson(img_gray * vals).astype(np.float32) / vals
|
|
- img_gray
|
|
)
|
|
img += noise_gray[:, :, np.newaxis]
|
|
img = np.clip(img, 0.0, 1.0)
|
|
return img
|
|
|
|
|
|
def add_JPEG_noise(img):
|
|
quality_factor = random.randint(80, 95)
|
|
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
|
|
result, encimg = cv2.imencode(
|
|
'.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
|
|
)
|
|
img = cv2.imdecode(encimg, 1)
|
|
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
|
|
return img
|
|
|
|
|
|
def random_crop(lq, hq, sf=4, lq_patchsize=64):
|
|
h, w = lq.shape[:2]
|
|
rnd_h = random.randint(0, h - lq_patchsize)
|
|
rnd_w = random.randint(0, w - lq_patchsize)
|
|
lq = lq[rnd_h : rnd_h + lq_patchsize, rnd_w : rnd_w + lq_patchsize, :]
|
|
|
|
rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
|
|
hq = hq[
|
|
rnd_h_H : rnd_h_H + lq_patchsize * sf,
|
|
rnd_w_H : rnd_w_H + lq_patchsize * sf,
|
|
:,
|
|
]
|
|
return lq, hq
|
|
|
|
|
|
def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
|
|
"""
|
|
This is the degradation model of BSRGAN from the paper
|
|
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
|
|
----------
|
|
img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
|
|
sf: scale factor
|
|
isp_model: camera ISP model
|
|
Returns
|
|
-------
|
|
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
|
|
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
|
|
"""
|
|
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
|
|
sf_ori = sf
|
|
|
|
h1, w1 = img.shape[:2]
|
|
img = img.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
|
|
h, w = img.shape[:2]
|
|
|
|
if h < lq_patchsize * sf or w < lq_patchsize * sf:
|
|
raise ValueError(f'img size ({h1}X{w1}) is too small!')
|
|
|
|
hq = img.copy()
|
|
|
|
if sf == 4 and random.random() < scale2_prob: # downsample1
|
|
if np.random.rand() < 0.5:
|
|
img = cv2.resize(
|
|
img,
|
|
(int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
else:
|
|
img = util.imresize_np(img, 1 / 2, True)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
sf = 2
|
|
|
|
shuffle_order = random.sample(range(7), 7)
|
|
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
|
|
if idx1 > idx2: # keep downsample3 last
|
|
shuffle_order[idx1], shuffle_order[idx2] = (
|
|
shuffle_order[idx2],
|
|
shuffle_order[idx1],
|
|
)
|
|
|
|
for i in shuffle_order:
|
|
|
|
if i == 0:
|
|
img = add_blur(img, sf=sf)
|
|
|
|
elif i == 1:
|
|
img = add_blur(img, sf=sf)
|
|
|
|
elif i == 2:
|
|
a, b = img.shape[1], img.shape[0]
|
|
# downsample2
|
|
if random.random() < 0.75:
|
|
sf1 = random.uniform(1, 2 * sf)
|
|
img = cv2.resize(
|
|
img,
|
|
(int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
else:
|
|
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
|
|
k_shifted = shift_pixel(k, sf)
|
|
k_shifted = (
|
|
k_shifted / k_shifted.sum()
|
|
) # blur with shifted kernel
|
|
img = ndimage.filters.convolve(
|
|
img, np.expand_dims(k_shifted, axis=2), mode='mirror'
|
|
)
|
|
img = img[0::sf, 0::sf, ...] # nearest downsampling
|
|
img = np.clip(img, 0.0, 1.0)
|
|
|
|
elif i == 3:
|
|
# downsample3
|
|
img = cv2.resize(
|
|
img,
|
|
(int(1 / sf * a), int(1 / sf * b)),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
img = np.clip(img, 0.0, 1.0)
|
|
|
|
elif i == 4:
|
|
# add Gaussian noise
|
|
img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
|
|
|
|
elif i == 5:
|
|
# add JPEG noise
|
|
if random.random() < jpeg_prob:
|
|
img = add_JPEG_noise(img)
|
|
|
|
elif i == 6:
|
|
# add processed camera sensor noise
|
|
if random.random() < isp_prob and isp_model is not None:
|
|
with torch.no_grad():
|
|
img, hq = isp_model.forward(img.copy(), hq)
|
|
|
|
# add final JPEG compression noise
|
|
img = add_JPEG_noise(img)
|
|
|
|
# random crop
|
|
img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
|
|
|
|
return img, hq
|
|
|
|
|
|
# todo no isp_model?
|
|
def degradation_bsrgan_variant(image, sf=4, isp_model=None):
|
|
"""
|
|
This is the degradation model of BSRGAN from the paper
|
|
"Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
|
|
----------
|
|
sf: scale factor
|
|
isp_model: camera ISP model
|
|
Returns
|
|
-------
|
|
img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
|
|
hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
|
|
"""
|
|
image = util.uint2single(image)
|
|
isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
|
|
sf_ori = sf
|
|
|
|
h1, w1 = image.shape[:2]
|
|
image = image.copy()[: w1 - w1 % sf, : h1 - h1 % sf, ...] # mod crop
|
|
h, w = image.shape[:2]
|
|
|
|
hq = image.copy()
|
|
|
|
if sf == 4 and random.random() < scale2_prob: # downsample1
|
|
if np.random.rand() < 0.5:
|
|
image = cv2.resize(
|
|
image,
|
|
(int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
else:
|
|
image = util.imresize_np(image, 1 / 2, True)
|
|
image = np.clip(image, 0.0, 1.0)
|
|
sf = 2
|
|
|
|
shuffle_order = random.sample(range(7), 7)
|
|
idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
|
|
if idx1 > idx2: # keep downsample3 last
|
|
shuffle_order[idx1], shuffle_order[idx2] = (
|
|
shuffle_order[idx2],
|
|
shuffle_order[idx1],
|
|
)
|
|
|
|
for i in shuffle_order:
|
|
|
|
if i == 0:
|
|
image = add_blur(image, sf=sf)
|
|
|
|
# elif i == 1:
|
|
# image = add_blur(image, sf=sf)
|
|
|
|
if i == 0:
|
|
pass
|
|
|
|
elif i == 2:
|
|
a, b = image.shape[1], image.shape[0]
|
|
# downsample2
|
|
if random.random() < 0.8:
|
|
sf1 = random.uniform(1, 2 * sf)
|
|
image = cv2.resize(
|
|
image,
|
|
(
|
|
int(1 / sf1 * image.shape[1]),
|
|
int(1 / sf1 * image.shape[0]),
|
|
),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
else:
|
|
k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
|
|
k_shifted = shift_pixel(k, sf)
|
|
k_shifted = (
|
|
k_shifted / k_shifted.sum()
|
|
) # blur with shifted kernel
|
|
image = ndimage.filters.convolve(
|
|
image, np.expand_dims(k_shifted, axis=2), mode='mirror'
|
|
)
|
|
image = image[0::sf, 0::sf, ...] # nearest downsampling
|
|
|
|
image = np.clip(image, 0.0, 1.0)
|
|
|
|
elif i == 3:
|
|
# downsample3
|
|
image = cv2.resize(
|
|
image,
|
|
(int(1 / sf * a), int(1 / sf * b)),
|
|
interpolation=random.choice([1, 2, 3]),
|
|
)
|
|
image = np.clip(image, 0.0, 1.0)
|
|
|
|
elif i == 4:
|
|
# add Gaussian noise
|
|
image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
|
|
|
|
elif i == 5:
|
|
# add JPEG noise
|
|
if random.random() < jpeg_prob:
|
|
image = add_JPEG_noise(image)
|
|
#
|
|
# elif i == 6:
|
|
# # add processed camera sensor noise
|
|
# if random.random() < isp_prob and isp_model is not None:
|
|
# with torch.no_grad():
|
|
# img, hq = isp_model.forward(img.copy(), hq)
|
|
|
|
# add final JPEG compression noise
|
|
image = add_JPEG_noise(image)
|
|
image = util.single2uint(image)
|
|
example = {'image': image}
|
|
return example
|
|
|
|
|
|
if __name__ == '__main__':
|
|
print('hey')
|
|
img = util.imread_uint('utils/test.png', 3)
|
|
img = img[:448, :448]
|
|
h = img.shape[0] // 4
|
|
print('resizing to', h)
|
|
sf = 4
|
|
deg_fn = partial(degradation_bsrgan_variant, sf=sf)
|
|
for i in range(20):
|
|
print(i)
|
|
img_hq = img
|
|
img_lq = deg_fn(img)['image']
|
|
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
|
|
print(img_lq)
|
|
img_lq_bicubic = albumentations.SmallestMaxSize(
|
|
max_size=h, interpolation=cv2.INTER_CUBIC
|
|
)(image=img_hq)['image']
|
|
print(img_lq.shape)
|
|
print('bicubic', img_lq_bicubic.shape)
|
|
print(img_hq.shape)
|
|
lq_nearest = cv2.resize(
|
|
util.single2uint(img_lq),
|
|
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
|
|
interpolation=0,
|
|
)
|
|
lq_bicubic_nearest = cv2.resize(
|
|
util.single2uint(img_lq_bicubic),
|
|
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
|
|
interpolation=0,
|
|
)
|
|
img_concat = np.concatenate(
|
|
[lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
|
|
)
|
|
util.imsave(img_concat, str(i) + '.png')
|