Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition telev

Get bare model, especially under wrapping with DistributedDataParallel or DataParallel.

Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or li

Reorder images to 'HWC' order. If the input_order is (h, w), return (h, w, 1); If the input_order is (c, h, w), return (h, w, c); If the

Save training states during training, which will be used for resuming. Args: epoch (int): Current epoch. curr

Set up schedulers.

Change to Y channel of YCbCr. Args: img (ndarray): Images with range [0, 255]. Returns: (ndarray): Images with range [0, 255

Build pretrained InceptionV3. Args: output_blocks (list[int]): Indices of blocks to return features of. Possible

Imagenet Superresolution Dataloader Performs following ops in order: 1. crops a crop of size s from image either as random o

Convert the type and range of the input image. It converts the input image to np.float32 type and range of [0, 1]. It is mainly used for pre-

Convert the type and range of the image according to dst_type. It converts the image to desired type and range. If `dst_type` is np.uint8, im

Calculate Gram matrix. Args: x (torch.Tensor): Tensor with shape of (n, c, h, w). Returns: torch.Tensor: Gra

Adaptive instance normalization. Adjust the reference features to have the similar color and illuminations as those in the degradate feature

Calculate metric from data and options. Args: opt (dict): Configuration. It must contain: type (str): Model type.

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])

Initialize weights for PixelShufflePack.

Take the mean over all non-batch dimensions.

source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the K

Randomly generate bivariate isotropic or anisotropic Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `

Randomly generate bivariate generalized Gaussian kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_rang

Randomly generate bivariate plateau kernels. In the isotropic mode, only `sigma_x_range` is used. `sigma_y_range` and `rotation_range` is ignored

Randomly generate mixed kernels. Args: kernel_list (tuple): a list name of kernel types, support ['iso', 'aniso', 'skew', 'ge

same as matlab rgb2ycbcr Input: im: uint8 [0,255] or float [0,1] only_y: only return Y channel

Convert a RGB image to YCbCr image. This function produces the same results as Matlab's `rgb2ycbcr` function. It implements the ITU-R BT.601

same as matlab rgb2ycbcr Input: im: float [0,1], N x 3 x H x W only_y: only return Y channel

Convert RGB images to YCbCr images (PyTorch version). It implements the ITU-R BT.601 conversion for standard-definition television. See more deta

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be

Apply wavelet decomposition, so that the content will have the same color as the style.

Initialize the best metric results dict for recording the best metric value and iteration.

Build dataloader. Args: dataset (torch.utils.data.Dataset): Dataset. dataset_opt (dict): Dataset options. It contains the followi

Build dataset from options. Args: dataset_opt (dict): Configuration for dataset. It must contain: name (str): Dataset name.

Calculate PSNR (Peak Signal-to-Noise Ratio) (PyTorch version). Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args:

Calculate SSIM (structural similarity) (PyTorch version). ``Paper: Image quality assessment: From error visibility to structural similarity``

Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

python code from: https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filte

Generate Gaussian noise. Args: img (Numpy array): Input image, shape (h, w, c), range [0, 1], float32. sigma (float): Noise scale

:param x: img of size (bs, c, h, w) :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])