o ?߱i{ @sdZddlZddlmZddlZddlZddlmZddl m Z ddl m Z ddgZ dd ejejBd eeBd efd dZ   dd ejejBd eeBdededed df ddZdded eejejfd eeefd dfddZdS)zoimage visualization utilities. based on https://gitlab.com/qsh.zh/jam/-/blob/master/jamviz/img.py MIT License N)Union) rearrange)Image) make_gridshow_batch_imgsave_batch_imgimg_datashapereturncCst|tr ||}}n.t|tr1d|vrt|t|}}n|d}t|dt|d}}ntd|dt|tjr_|jddvsHJt|d|| |}| ddd}n(t|t j r|jddvrzt|d||d |d }n t|d||d |d }|||fS) aE Reshapes a batch of images for visualization, organizing them into a grid format. Args: img_data (torch.Tensor | np.ndarray): The image data to be reshaped, can be either a PyTorch tensor or a NumPy array. shape (int | str, optional): Defines the layout of the grid. If an integer is provided, it specifies both the number of rows and columns. If a string is provided in the format 'nrowxncol', it parses to individual row and column numbers. Defaults to 7. Returns: tuple: A tuple containing: img (np.ndarray | torch.Tensor): The image data arranged in grid format. nrow (int): Number of rows in the grid. ncol (int): Number of columns in the grid. Raises: RuntimeError: If the shape parameter is neither an int nor a string, or if it's a string that doesn't contain 'x'. Example: >>> tensor_images = torch.rand(64, 3, 28, 28) # Example tensor of 64 images >>> img_grid, rows, cols = _reshape_viz_batch_img(tensor_images, '8x8') >>> img_grid.shape (224, 224, 3) xrzshape z not support)r Nz(b t) c h w -> (b h) (t w) c)bz(b t) h w c -> (b h) (t w) c) isinstanceintstrsplit RuntimeErrortorchTensorr rdetachcpupermutenpndarrayr)r r nrowncolZgrid_imgimgr W/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/imaginaire/visualize/img.py_reshape_viz_batch_img$s"       r"rFTgridis_n1p1 auto_n1p1c Csddlm}|r|dd}n*|r9t|tjr&|dkr%|dd}nt|tjr9t|dkr9|dd}t ||\}}}|j ||||fd| d| |dS)a Displays a batch of images using matplotlib after arranging them into a specified grid layout. Args: img_data (torch.Tensor | np.ndarray): The image data to be displayed. shape (int | str, optional): The grid shape to organize the images. Defaults to 7. grid (int, optional): Scaling factor for each image in the grid, affecting the overall size of the displayed figure. Defaults to 3. is_n1p1 (bool, optional): Whether to normalize the images from [-1, 1] to [0, 1] for visualization. Defaults to False. auto_n1p1 (bool, optional): If true, automatically adjusts images from [-1, 1] to [0, 1] based on minimum pixel value detection. Defaults to True. Returns: None: This function does not return anything but displays the image grid using matplotlib. Example: >>> tensor_images = torch.rand(64, 3, 28, 28) # Example tensor of 64 images >>> show_batch_img(tensor_images, '8x8') rNr rg)figsizeoff) matplotlib.pyplotpyplotrrrminitemrrr"figureaxisimshow) r r r#r$r%pltrrrr r r!rQs      fpathcCszt||\}}}t|tjrt|}|dddd dtj  }t |}tjtj|dd||dS)a Saves a batch of images to a file after arranging them into a grid format. Handles both PyTorch tensors and NumPy arrays as input. Args: fpath (str): File path where the image will be saved. img_data (Union[torch.Tensor, np.ndarray]): The image data to be saved. Can be a PyTorch tensor or a NumPy array. shape (Union[int, str], optional): The grid shape to organize the images. Can be an integer specifying equal number of rows and columns, or a string specifying 'nrowxncol'. Defaults to 7. Returns: None: This function does not return anything but saves the image to the specified file path. Raises: RuntimeError: If the input shape is neither an integer nor a string, or it does not include 'x' when provided as a string. Example: >>> tensor_images = torch.rand(64, 3, 28, 28) # Example tensor of 64 images >>> save_batch_img('path/to/save/image.png', tensor_images, '8x8') # This saves the image grid to 'path/to/save/image.png' g?rrT)exist_okN)r"rrrr from_numpymuladd_clamp_touint8numpyr fromarrayosmakedirspathdirnamesave)r0r r r_ndarrimr r r!r{s  & )r)rrFT)__doc__r;typingrr9rreinopsrPILrZtorchvision.utilsr__all__rrrrtupler"boolrrr r r r!s<    $/  4*