import cv2
import math
import numpy as np
import random
import torch
from scipy import special
from scipy.stats import multivariate_normal
from torchvision.transforms.functional_tensor import rgb_to_grayscale

# -------------------------------------------------------------------- #
# --------------------------- blur kernels --------------------------- #
# -------------------------------------------------------------------- #


# --------------------------- util functions --------------------------- #
def sigma_matrix2(sig_x, sig_y, theta):
    """Calculate the rotated sigma matrix (two dimensional matrix).

    Args:
        sig_x (float):
        sig_y (float):
        theta (float): Radian measurement.

    Returns:
        ndarray: Rotated sigma matrix.
    """
    d_matrix = np.array([[sig_x**2, 0], [0, sig_y**2]])
    u_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]])
    return np.dot(u_matrix, np.dot(d_matrix, u_matrix.T))


def mesh_grid(kernel_size):
    """Generate the mesh grid, centering at zero.

    Args:
        kernel_size (int):

    Returns:
        xy (ndarray): with the shape (kernel_size, kernel_size, 2)
        xx (ndarray): with the shape (kernel_size, kernel_size)
        yy (ndarray): with the shape (kernel_size, kernel_size)
    """
    ax = np.arange(-kernel_size // 2 + 1., kernel_size // 2 + 1.)
    xx, yy = np.meshgrid(ax, ax)
    xy = np.hstack((xx.reshape((kernel_size * kernel_size, 1)), yy.reshape(kernel_size * kernel_size,
                                                                           1))).reshape(kernel_size, kernel_size, 2)
    return xy, xx, yy


def pdf2(sigma_matrix, grid):
    """Calculate PDF of the bivariate Gaussian distribution.

    Args:
        sigma_matrix (ndarray): with the shape (2, 2)
        grid (ndarray): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size.

    Returns:
        kernel (ndarrray): un-normalized kernel.
    """
    inverse_sigma = np.linalg.inv(sigma_matrix)
    kernel = np.exp(-0.5 * np.sum(np.dot(grid, inverse_sigma) * grid, 2))
    return kernel


def cdf2(d_matrix, grid):
    """Calculate the CDF of the standard bivariate Gaussian distribution.
        Used in skewed Gaussian distribution.

    Args:
        d_matrix (ndarrasy): skew matrix.
        grid (ndarray): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size.

    Returns:
        cdf (ndarray): skewed cdf.
    """
    rv = multivariate_normal([0, 0], [[1, 0], [0, 1]])
    grid = np.dot(grid, d_matrix)
    cdf = rv.cdf(grid)
    return cdf


def bivariate_Gaussian(kernel_size, sig_x, sig_y, theta, grid=None, isotropic=True):
    """Generate a bivariate isotropic or anisotropic Gaussian kernel.

    In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.

    Args:
        kernel_size (int):
        sig_x (float):
        sig_y (float):
        theta (float): Radian measurement.
        grid (ndarray, optional): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size. Default: None
        isotropic (bool):

    Returns:
        kernel (ndarray): normalized kernel.
    """
    if grid is None:
        grid, _, _ = mesh_grid(kernel_size)
    if isotropic:
        sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
    else:
        sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
    kernel = pdf2(sigma_matrix, grid)
    kernel = kernel / np.sum(kernel)
    return kernel


def bivariate_generalized_Gaussian(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True):
    """Generate a bivariate generalized Gaussian kernel.

    ``Paper: Parameter Estimation For Multivariate Generalized Gaussian Distributions``

    In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.

    Args:
        kernel_size (int):
        sig_x (float):
        sig_y (float):
        theta (float): Radian measurement.
        beta (float): shape parameter, beta = 1 is the normal distribution.
        grid (ndarray, optional): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size. Default: None

    Returns:
        kernel (ndarray): normalized kernel.
    """
    if grid is None:
        grid, _, _ = mesh_grid(kernel_size)
    if isotropic:
        sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
    else:
        sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
    inverse_sigma = np.linalg.inv(sigma_matrix)
    kernel = np.exp(-0.5 * np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta))
    kernel = kernel / np.sum(kernel)
    return kernel


def bivariate_plateau(kernel_size, sig_x, sig_y, theta, beta, grid=None, isotropic=True):
    """Generate a plateau-like anisotropic kernel.

    1 / (1+x^(beta))


    In the isotropic mode, only `sig_x` is used. `sig_y` and `theta` is ignored.

    Args:
        kernel_size (int):
        sig_x (float):
        sig_y (float):
        theta (float): Radian measurement.
        beta (float): shape parameter, beta = 1 is the normal distribution.
        grid (ndarray, optional): generated by :func:`mesh_grid`,
            with the shape (K, K, 2), K is the kernel size. Default: None

    Returns:
        kernel (ndarray): normalized kernel.
    """
    if grid is None:
        grid, _, _ = mesh_grid(kernel_size)
    if isotropic:
        sigma_matrix = np.array([[sig_x**2, 0], [0, sig_x**2]])
    else:
        sigma_matrix = sigma_matrix2(sig_x, sig_y, theta)
    inverse_sigma = np.linalg.inv(sigma_matrix)
    kernel = np.reciprocal(np.power(np.sum(np.dot(grid, inverse_sigma) * grid, 2), beta) + 1)
    kernel = kernel / np.sum(kernel)
    return kernel


def random_bivariate_Gaussian(kernel_size,
                              sigma_x_range,
                              sigma_y_range,
                              rotation_range,
                              noise_range=None,
                              isotropic=True):
    """Randomly generate bivariate isotropic or anisotropic Gaussian kernels.

    In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored.

    Args:
        kernel_size (int):
        sigma_x_range (tuple): [0.6, 5]
        sigma_y_range (tuple): [0.6, 5]
        rotation range (tuple): [-math.pi, math.pi]
        noise_range(tuple, optional): multiplicative kernel noise,
            [0.75, 1.25]. Default: None

    Returns:
        kernel (ndarray):
    """
    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
    assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.'
    sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])
    if isotropic is False:
        assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.'
        assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.'
        sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])
        rotation = np.random.uniform(rotation_range[0], rotation_range[1])
    else:
        sigma_y = sigma_x
        rotation = 0

    kernel = bivariate_Gaussian(kernel_size, sigma_x, sigma_y, rotation, isotropic=isotropic)

    # add multiplicative noise
    if noise_range is not None:
        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
        kernel = kernel * noise
    kernel = kernel / np.sum(kernel)
    return kernel


def random_bivariate_generalized_Gaussian(kernel_size,
                                          sigma_x_range,
                                          sigma_y_range,
                                          rotation_range,
                                          beta_range,
                                          noise_range=None,
                                          isotropic=True):
    """Randomly generate bivariate generalized Gaussian kernels.

    In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored.

    Args:
        kernel_size (int):
        sigma_x_range (tuple): [0.6, 5]
        sigma_y_range (tuple): [0.6, 5]
        rotation range (tuple): [-math.pi, math.pi]
        beta_range (tuple): [0.5, 8]
        noise_range(tuple, optional): multiplicative kernel noise,
            [0.75, 1.25]. Default: None

    Returns:
        kernel (ndarray):
    """
    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
    assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.'
    sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])
    if isotropic is False:
        assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.'
        assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.'
        sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])
        rotation = np.random.uniform(rotation_range[0], rotation_range[1])
    else:
        sigma_y = sigma_x
        rotation = 0

    # assume beta_range[0] < 1 < beta_range[1]
    if np.random.uniform() < 0.5:
        beta = np.random.uniform(beta_range[0], 1)
    else:
        beta = np.random.uniform(1, beta_range[1])

    kernel = bivariate_generalized_Gaussian(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic)

    # add multiplicative noise
    if noise_range is not None:
        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
        kernel = kernel * noise
    kernel = kernel / np.sum(kernel)
    return kernel


def random_bivariate_plateau(kernel_size,
                             sigma_x_range,
                             sigma_y_range,
                             rotation_range,
                             beta_range,
                             noise_range=None,
                             isotropic=True):
    """Randomly generate bivariate plateau kernels.

    In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored.

    Args:
        kernel_size (int):
        sigma_x_range (tuple): [0.6, 5]
        sigma_y_range (tuple): [0.6, 5]
        rotation range (tuple): [-math.pi/2, math.pi/2]
        beta_range (tuple): [1, 4]
        noise_range(tuple, optional): multiplicative kernel noise,
            [0.75, 1.25]. Default: None

    Returns:
        kernel (ndarray):
    """
    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
    assert sigma_x_range[0] < sigma_x_range[1], 'Wrong sigma_x_range.'
    sigma_x = np.random.uniform(sigma_x_range[0], sigma_x_range[1])
    if isotropic is False:
        assert sigma_y_range[0] < sigma_y_range[1], 'Wrong sigma_y_range.'
        assert rotation_range[0] < rotation_range[1], 'Wrong rotation_range.'
        sigma_y = np.random.uniform(sigma_y_range[0], sigma_y_range[1])
        rotation = np.random.uniform(rotation_range[0], rotation_range[1])
    else:
        sigma_y = sigma_x
        rotation = 0

    # TODO: this may be not proper
    if np.random.uniform() < 0.5:
        beta = np.random.uniform(beta_range[0], 1)
    else:
        beta = np.random.uniform(1, beta_range[1])

    kernel = bivariate_plateau(kernel_size, sigma_x, sigma_y, rotation, beta, isotropic=isotropic)
    # add multiplicative noise
    if noise_range is not None:
        assert noise_range[0] < noise_range[1], 'Wrong noise range.'
        noise = np.random.uniform(noise_range[0], noise_range[1], size=kernel.shape)
        kernel = kernel * noise
    kernel = kernel / np.sum(kernel)

    return kernel


def random_mixed_kernels(kernel_list,
                         kernel_prob,
                         kernel_size=21,
                         sigma_x_range=(0.6, 5),
                         sigma_y_range=(0.6, 5),
                         rotation_range=(-math.pi, math.pi),
                         betag_range=(0.5, 8),
                         betap_range=(0.5, 8),
                         noise_range=None):
    """Randomly generate mixed kernels.

    Args:
        kernel_list (tuple): a list name of kernel types,
            support ['iso', 'aniso', 'skew', 'generalized', 'plateau_iso',
            'plateau_aniso']
        kernel_prob (tuple): corresponding kernel probability for each
            kernel type
        kernel_size (int):
        sigma_x_range (tuple): [0.6, 5]
        sigma_y_range (tuple): [0.6, 5]
        rotation range (tuple): [-math.pi, math.pi]
        beta_range (tuple): [0.5, 8]
        noise_range(tuple, optional): multiplicative kernel noise,
            [0.75, 1.25]. Default: None

    Returns:
        kernel (ndarray):
    """
    kernel_type = random.choices(kernel_list, kernel_prob)[0]
    if kernel_type == 'iso':
        kernel = random_bivariate_Gaussian(
            kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=True)
    elif kernel_type == 'aniso':
        kernel = random_bivariate_Gaussian(
            kernel_size, sigma_x_range, sigma_y_range, rotation_range, noise_range=noise_range, isotropic=False)
    elif kernel_type == 'generalized_iso':
        kernel = random_bivariate_generalized_Gaussian(
            kernel_size,
            sigma_x_range,
            sigma_y_range,
            rotation_range,
            betag_range,
            noise_range=noise_range,
            isotropic=True)
    elif kernel_type == 'generalized_aniso':
        kernel = random_bivariate_generalized_Gaussian(
            kernel_size,
            sigma_x_range,
            sigma_y_range,
            rotation_range,
            betag_range,
            noise_range=noise_range,
            isotropic=False)
    elif kernel_type == 'plateau_iso':
        kernel = random_bivariate_plateau(
            kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=True)
    elif kernel_type == 'plateau_aniso':
        kernel = random_bivariate_plateau(
            kernel_size, sigma_x_range, sigma_y_range, rotation_range, betap_range, noise_range=None, isotropic=False)
    return kernel


np.seterr(divide='ignore', invalid='ignore')


def circular_lowpass_kernel(cutoff, kernel_size, pad_to=0):
    """2D sinc filter


    Args:
        cutoff (float): cutoff frequency in radians (pi is max)
        kernel_size (int): horizontal and vertical size, must be odd.
        pad_to (int): pad kernel size to desired size, must be odd or zero.
    """
    assert kernel_size % 2 == 1, 'Kernel size must be an odd number.'
    kernel = np.fromfunction(
        lambda x, y: cutoff * special.j1(cutoff * np.sqrt(
            (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)) / (2 * np.pi * np.sqrt(
                (x - (kernel_size - 1) / 2)**2 + (y - (kernel_size - 1) / 2)**2)), [kernel_size, kernel_size])
    kernel[(kernel_size - 1) // 2, (kernel_size - 1) // 2] = cutoff**2 / (4 * np.pi)
    kernel = kernel / np.sum(kernel)
    if pad_to > kernel_size:
        pad_size = (pad_to - kernel_size) // 2
        kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size)))
    return kernel


# ------------------------------------------------------------- #
# --------------------------- noise --------------------------- #
# ------------------------------------------------------------- #

# ----------------------- Gaussian Noise ----------------------- #


def generate_gaussian_noise(img, sigma=10, gray_noise=False):
    """Generate Gaussian noise.

    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        sigma (float): Noise scale (measured in range 255). Default: 10.

    Returns:
        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
            float32.
    """
    if gray_noise:
        noise = np.float32(np.random.randn(*(img.shape[0:2]))) * sigma / 255.
        noise = np.expand_dims(noise, axis=2).repeat(3, axis=2)
    else:
        noise = np.float32(np.random.randn(*(img.shape))) * sigma / 255.
    return noise


def add_gaussian_noise(img, sigma=10, clip=True, rounds=False, gray_noise=False):
    """Add Gaussian noise.

    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        sigma (float): Noise scale (measured in range 255). Default: 10.

    Returns:
        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
            float32.
    """
    noise = generate_gaussian_noise(img, sigma, gray_noise)
    out = img + noise
    if clip and rounds:
        out = np.clip((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = np.clip(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


def generate_gaussian_noise_pt(img, sigma=10, gray_noise=0):
    """Add Gaussian noise (PyTorch version).

    Args:
        img (Tensor): Shape (b, c, h, w), range[0, 1], float32.
        scale (float | Tensor): Noise scale. Default: 1.0.

    Returns:
        (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1],
            float32.
    """
    b, _, h, w = img.size()
    if not isinstance(sigma, (float, int)):
        sigma = sigma.view(img.size(0), 1, 1, 1)
    if isinstance(gray_noise, (float, int)):
        cal_gray_noise = gray_noise > 0
    else:
        gray_noise = gray_noise.view(b, 1, 1, 1)
        cal_gray_noise = torch.sum(gray_noise) > 0

    if cal_gray_noise:
        noise_gray = torch.randn(*img.size()[2:4], dtype=img.dtype, device=img.device) * sigma / 255.
        noise_gray = noise_gray.view(b, 1, h, w)

    # always calculate color noise
    noise = torch.randn(*img.size(), dtype=img.dtype, device=img.device) * sigma / 255.

    if cal_gray_noise:
        noise = noise * (1 - gray_noise) + noise_gray * gray_noise
    return noise


def add_gaussian_noise_pt(img, sigma=10, gray_noise=0, clip=True, rounds=False):
    """Add Gaussian noise (PyTorch version).

    Args:
        img (Tensor): Shape (b, c, h, w), range[0, 1], float32.
        scale (float | Tensor): Noise scale. Default: 1.0.

    Returns:
        (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1],
            float32.
    """
    noise = generate_gaussian_noise_pt(img, sigma, gray_noise)
    out = img + noise
    if clip and rounds:
        out = torch.clamp((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = torch.clamp(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


# ----------------------- Random Gaussian Noise ----------------------- #
def random_generate_gaussian_noise(img, sigma_range=(0, 10), gray_prob=0):
    sigma = np.random.uniform(sigma_range[0], sigma_range[1])
    if np.random.uniform() < gray_prob:
        gray_noise = True
    else:
        gray_noise = False
    return generate_gaussian_noise(img, sigma, gray_noise)


def random_add_gaussian_noise(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False):
    noise = random_generate_gaussian_noise(img, sigma_range, gray_prob)
    out = img + noise
    if clip and rounds:
        out = np.clip((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = np.clip(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


def random_generate_gaussian_noise_pt(img, sigma_range=(0, 10), gray_prob=0):
    sigma = torch.rand(
        img.size(0), dtype=img.dtype, device=img.device) * (sigma_range[1] - sigma_range[0]) + sigma_range[0]
    gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device)
    gray_noise = (gray_noise < gray_prob).float()
    return generate_gaussian_noise_pt(img, sigma, gray_noise)


def random_add_gaussian_noise_pt(img, sigma_range=(0, 1.0), gray_prob=0, clip=True, rounds=False):
    noise = random_generate_gaussian_noise_pt(img, sigma_range, gray_prob)
    out = img + noise
    if clip and rounds:
        out = torch.clamp((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = torch.clamp(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


# ----------------------- Poisson (Shot) Noise ----------------------- #


def generate_poisson_noise(img, scale=1.0, gray_noise=False):
    """Generate poisson noise.


    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        scale (float): Noise scale. Default: 1.0.
        gray_noise (bool): Whether generate gray noise. Default: False.

    Returns:
        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
            float32.
    """
    if gray_noise:
        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # round and clip image for counting vals correctly
    img = np.clip((img * 255.0).round(), 0, 255) / 255.
    vals = len(np.unique(img))
    vals = 2**np.ceil(np.log2(vals))
    out = np.float32(np.random.poisson(img * vals) / float(vals))
    noise = out - img
    if gray_noise:
        noise = np.repeat(noise[:, :, np.newaxis], 3, axis=2)
    return noise * scale


def add_poisson_noise(img, scale=1.0, clip=True, rounds=False, gray_noise=False):
    """Add poisson noise.

    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        scale (float): Noise scale. Default: 1.0.
        gray_noise (bool): Whether generate gray noise. Default: False.

    Returns:
        (Numpy array): Returned noisy image, shape (h, w, c), range[0, 1],
            float32.
    """
    noise = generate_poisson_noise(img, scale, gray_noise)
    out = img + noise
    if clip and rounds:
        out = np.clip((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = np.clip(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


def generate_poisson_noise_pt(img, scale=1.0, gray_noise=0):
    """Generate a batch of poisson noise (PyTorch version)

    Args:
        img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32.
        scale (float | Tensor): Noise scale. Number or Tensor with shape (b).
            Default: 1.0.
        gray_noise (float | Tensor): 0-1 number or Tensor with shape (b).
            0 for False, 1 for True. Default: 0.

    Returns:
        (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1],
            float32.
    """
    b, _, h, w = img.size()
    if isinstance(gray_noise, (float, int)):
        cal_gray_noise = gray_noise > 0
    else:
        gray_noise = gray_noise.view(b, 1, 1, 1)
        cal_gray_noise = torch.sum(gray_noise) > 0
    if cal_gray_noise:
        img_gray = rgb_to_grayscale(img, num_output_channels=1)
        # round and clip image for counting vals correctly
        img_gray = torch.clamp((img_gray * 255.0).round(), 0, 255) / 255.
        # use for-loop to get the unique values for each sample
        vals_list = [len(torch.unique(img_gray[i, :, :, :])) for i in range(b)]
        vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list]
        vals = img_gray.new_tensor(vals_list).view(b, 1, 1, 1)
        out = torch.poisson(img_gray * vals) / vals
        noise_gray = out - img_gray
        noise_gray = noise_gray.expand(b, 3, h, w)

    # always calculate color noise
    # round and clip image for counting vals correctly
    img = torch.clamp((img * 255.0).round(), 0, 255) / 255.
    # use for-loop to get the unique values for each sample
    vals_list = [len(torch.unique(img[i, :, :, :])) for i in range(b)]
    vals_list = [2**np.ceil(np.log2(vals)) for vals in vals_list]
    vals = img.new_tensor(vals_list).view(b, 1, 1, 1)
    out = torch.poisson(img * vals) / vals
    noise = out - img
    if cal_gray_noise:
        noise = noise * (1 - gray_noise) + noise_gray * gray_noise
    if not isinstance(scale, (float, int)):
        scale = scale.view(b, 1, 1, 1)
    return noise * scale


def add_poisson_noise_pt(img, scale=1.0, clip=True, rounds=False, gray_noise=0):
    """Add poisson noise to a batch of images (PyTorch version).

    Args:
        img (Tensor): Input image, shape (b, c, h, w), range [0, 1], float32.
        scale (float | Tensor): Noise scale. Number or Tensor with shape (b).
            Default: 1.0.
        gray_noise (float | Tensor): 0-1 number or Tensor with shape (b).
            0 for False, 1 for True. Default: 0.

    Returns:
        (Tensor): Returned noisy image, shape (b, c, h, w), range[0, 1],
            float32.
    """
    noise = generate_poisson_noise_pt(img, scale, gray_noise)
    out = img + noise
    if clip and rounds:
        out = torch.clamp((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = torch.clamp(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


# ----------------------- Random Poisson (Shot) Noise ----------------------- #


def random_generate_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0):
    scale = np.random.uniform(scale_range[0], scale_range[1])
    if np.random.uniform() < gray_prob:
        gray_noise = True
    else:
        gray_noise = False
    return generate_poisson_noise(img, scale, gray_noise)


def random_add_poisson_noise(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False):
    noise = random_generate_poisson_noise(img, scale_range, gray_prob)
    out = img + noise
    if clip and rounds:
        out = np.clip((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = np.clip(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


def random_generate_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0):
    scale = torch.rand(
        img.size(0), dtype=img.dtype, device=img.device) * (scale_range[1] - scale_range[0]) + scale_range[0]
    gray_noise = torch.rand(img.size(0), dtype=img.dtype, device=img.device)
    gray_noise = (gray_noise < gray_prob).float()
    return generate_poisson_noise_pt(img, scale, gray_noise)


def random_add_poisson_noise_pt(img, scale_range=(0, 1.0), gray_prob=0, clip=True, rounds=False):
    noise = random_generate_poisson_noise_pt(img, scale_range, gray_prob)
    out = img + noise
    if clip and rounds:
        out = torch.clamp((out * 255.0).round(), 0, 255) / 255.
    elif clip:
        out = torch.clamp(out, 0, 1)
    elif rounds:
        out = (out * 255.0).round() / 255.
    return out


# ------------------------------------------------------------------------ #
# --------------------------- JPEG compression --------------------------- #
# ------------------------------------------------------------------------ #


def add_jpg_compression(img, quality=90):
    """Add JPG compression artifacts.

    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        quality (float): JPG compression quality. 0 for lowest quality, 100 for
            best quality. Default: 90.

    Returns:
        (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1],
            float32.
    """
    img = np.clip(img, 0, 1)
    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), quality]
    _, encimg = cv2.imencode('.jpg', img * 255., encode_param)
    img = np.float32(cv2.imdecode(encimg, 1)) / 255.
    return img


def random_add_jpg_compression(img, quality_range=(90, 100)):
    """Randomly add JPG compression artifacts.

    Args:
        img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32.
        quality_range (tuple[float] | list[float]): JPG compression quality
            range. 0 for lowest quality, 100 for best quality.
            Default: (90, 100).

    Returns:
        (Numpy array): Returned image after JPG, shape (h, w, c), range[0, 1],
            float32.
    """
    quality = np.random.uniform(quality_range[0], quality_range[1])
    return add_jpg_compression(img, int(quality))
