idf/utils/noise.py

import numpy as np
import cv2

import random
from scipy import ndimage
import scipy
import scipy.stats as ss
from scipy.interpolate import interp2d
from scipy.linalg import orth

# https://github.com/haoyuc/MaskedDenoising/blob/9cd4c62a7a82178d86f197e11f2d0ba3ab1fbd5a/utils/utils_mask.py#L379

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 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.]))
    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 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)


def add_blur(img, sf=4):
    wd2 = 4.0 + sf
    wd = 2.0 + 0.2*sf
    if random.random() < 0.5:
        l1 = wd2*random.random()
        l2 = wd2*random.random()
        k = anisotropic_Gaussian(ksize=2*random.randint(2,11)+3, theta=random.random()*np.pi, l1=l1, l2=l2)
    else:
        k = fspecial('gaussian', 2*random.randint(2,11)+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_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.
        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.
    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.
        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 single2uint(img):
    return np.uint8((img.clip(0, 1)*255.).round())

def uint2single(img):
    return np.float32(img/255.)

def add_JPEG_noise(img):
    quality_factor = random.randint(30, 95)
    img = cv2.cvtColor(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(uint2single(img), cv2.COLOR_BGR2RGB)
    return img
    
def add_correlated_Gaussian_noise(img, noise_level1=2, noise_level2=25, filter_size=3, generator=None):
    if generator is None:
        rng = np.random.default_rng()
    else:
        rng = generator

    if noise_level1 == noise_level2:
        noise_level = noise_level1
    else:
        noise_level = rng.integers(noise_level1, noise_level2, size=1)

    n = rng.normal(0.0, noise_level / 255.0, img.shape).astype(np.float32)
    n = ndimage.uniform_filter(n, size=filter_size)
    result = np.clip(img + n, 0.0, 1.0)

    return result

def add_Gaussian_noise(img, noise_level1=2, noise_level2=25, generator=None, channel_wise=False):
    if generator is None:
        rng = np.random.default_rng()
    else:
        rng = generator

    C, H, W = img.shape
    if channel_wise:
        if noise_level1 == noise_level2:
            noise_level = noise_level1
        else:
            noise_level = rng.integers(noise_level1, noise_level2, size=3)
        n = np.concatenate([rng.normal(0.0, noise_level[i] / 255.0, (1, H, W)).astype(np.float32) for i, _ in enumerate(range(C))], axis=0)
    else:
        if noise_level1 == noise_level2:
            noise_level = noise_level1
        else:
            noise_level = rng.integers(noise_level1, noise_level2, size=1)
    
        n = rng.normal(0.0, noise_level / 255.0, (C, H, W)).astype(np.float32)
    result = np.clip(img + n, 0.0, 1.0)

    return result, noise_level