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