text: 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.
          Described in `Parameter Estimation For Multivariate Generalized
          Gaussian Distributions`_
          by Pascal et. al (2013).
  
      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.
  
      .. _Parameter Estimation For Multivariate Generalized Gaussian
      Distributions: https://arxiv.org/abs/1302.6498
      """
      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))
  
      Ref: https://stats.stackexchange.com/questions/203629/is-there-a-plateau-shaped-distribution
  
      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, ref: https://dsp.stackexchange.com/questions/58301/2-d-circularly-symmetric-low-pass-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.
  
      Ref: https://github.com/scikit-image/scikit-image/blob/main/skimage/util/noise.py#L37-L219
  
      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, quality)
  