Source code for torchvision.utils

from typing import Union, Optional, List, Tuple, Text, BinaryIO
import pathlib
import torch
import math
import warnings
import numpy as np
from PIL import Image, ImageDraw, ImageFont, ImageColor

__all__ = ["make_grid", "save_image", "draw_bounding_boxes", "draw_segmentation_masks"]


@torch.no_grad()
def make_grid(
    tensor: Union[torch.Tensor, List[torch.Tensor]],
    nrow: int = 8,
    padding: int = 2,
    normalize: bool = False,
    value_range: Optional[Tuple[int, int]] = None,
    scale_each: bool = False,
    pad_value: int = 0,
    **kwargs
) -> torch.Tensor:
    """
    Make a grid of images.

    Args:
        tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
            or a list of images all of the same size.
        nrow (int, optional): Number of images displayed in each row of the grid.
            The final grid size is ``(B / nrow, nrow)``. Default: ``8``.
        padding (int, optional): amount of padding. Default: ``2``.
        normalize (bool, optional): If True, shift the image to the range (0, 1),
            by the min and max values specified by ``value_range``. Default: ``False``.
        value_range (tuple, optional): tuple (min, max) where min and max are numbers,
            then these numbers are used to normalize the image. By default, min and max
            are computed from the tensor.
        scale_each (bool, optional): If ``True``, scale each image in the batch of
            images separately rather than the (min, max) over all images. Default: ``False``.
        pad_value (float, optional): Value for the padded pixels. Default: ``0``.

    Returns:
        grid (Tensor): the tensor containing grid of images.
    """
    if not (torch.is_tensor(tensor) or
            (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))):
        raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}')

    if "range" in kwargs.keys():
        warning = "range will be deprecated, please use value_range instead."
        warnings.warn(warning)
        value_range = kwargs["range"]

    # if list of tensors, convert to a 4D mini-batch Tensor
    if isinstance(tensor, list):
        tensor = torch.stack(tensor, dim=0)

    if tensor.dim() == 2:  # single image H x W
        tensor = tensor.unsqueeze(0)
    if tensor.dim() == 3:  # single image
        if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
            tensor = torch.cat((tensor, tensor, tensor), 0)
        tensor = tensor.unsqueeze(0)

    if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
        tensor = torch.cat((tensor, tensor, tensor), 1)

    if normalize is True:
        tensor = tensor.clone()  # avoid modifying tensor in-place
        if value_range is not None:
            assert isinstance(value_range, tuple), \
                "value_range has to be a tuple (min, max) if specified. min and max are numbers"

        def norm_ip(img, low, high):
            img.clamp_(min=low, max=high)
            img.sub_(low).div_(max(high - low, 1e-5))

        def norm_range(t, value_range):
            if value_range is not None:
                norm_ip(t, value_range[0], value_range[1])
            else:
                norm_ip(t, float(t.min()), float(t.max()))

        if scale_each is True:
            for t in tensor:  # loop over mini-batch dimension
                norm_range(t, value_range)
        else:
            norm_range(tensor, value_range)

    if tensor.size(0) == 1:
        return tensor.squeeze(0)

    # make the mini-batch of images into a grid
    nmaps = tensor.size(0)
    xmaps = min(nrow, nmaps)
    ymaps = int(math.ceil(float(nmaps) / xmaps))
    height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
    num_channels = tensor.size(1)
    grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value)
    k = 0
    for y in range(ymaps):
        for x in range(xmaps):
            if k >= nmaps:
                break
            # Tensor.copy_() is a valid method but seems to be missing from the stubs
            # https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_
            grid.narrow(1, y * height + padding, height - padding).narrow(  # type: ignore[attr-defined]
                2, x * width + padding, width - padding
            ).copy_(tensor[k])
            k = k + 1
    return grid


@torch.no_grad()
def save_image(
    tensor: Union[torch.Tensor, List[torch.Tensor]],
    fp: Union[Text, pathlib.Path, BinaryIO],
    format: Optional[str] = None,
    **kwargs
) -> None:
    """
    Save a given Tensor into an image file.

    Args:
        tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
            saves the tensor as a grid of images by calling ``make_grid``.
        fp (string or file object): A filename or a file object
        format(Optional):  If omitted, the format to use is determined from the filename extension.
            If a file object was used instead of a filename, this parameter should always be used.
        **kwargs: Other arguments are documented in ``make_grid``.
    """

    grid = make_grid(tensor, **kwargs)
    # Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
    ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
    im = Image.fromarray(ndarr)
    im.save(fp, format=format)


@torch.no_grad()
def draw_bounding_boxes(
    image: torch.Tensor,
    boxes: torch.Tensor,
    labels: Optional[List[str]] = None,
    colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None,
    fill: Optional[bool] = False,
    width: int = 1,
    font: Optional[str] = None,
    font_size: int = 10
) -> torch.Tensor:

    """
    Draws bounding boxes on given image.
    The values of the input image should be uint8 between 0 and 255.
    If fill is True, Resulting Tensor should be saved as PNG image.

    Args:
        image (Tensor): Tensor of shape (C x H x W) and dtype uint8.
        boxes (Tensor): Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note that
            the boxes are absolute coordinates with respect to the image. In other words: `0 <= xmin < xmax < W` and
            `0 <= ymin < ymax < H`.
        labels (List[str]): List containing the labels of bounding boxes.
        colors (Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]): List containing the colors
            or a single color for all of the bounding boxes. The colors can be represented as `str` or
            `Tuple[int, int, int]`.
        fill (bool): If `True` fills the bounding box with specified color.
        width (int): Width of bounding box.
        font (str): A filename containing a TrueType font. If the file is not found in this filename, the loader may
            also search in other directories, such as the `fonts/` directory on Windows or `/Library/Fonts/`,
            `/System/Library/Fonts/` and `~/Library/Fonts/` on macOS.
        font_size (int): The requested font size in points.

    Returns:
        img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted.
    """

    if not isinstance(image, torch.Tensor):
        raise TypeError(f"Tensor expected, got {type(image)}")
    elif image.dtype != torch.uint8:
        raise ValueError(f"Tensor uint8 expected, got {image.dtype}")
    elif image.dim() != 3:
        raise ValueError("Pass individual images, not batches")
    elif image.size(0) not in {1, 3}:
        raise ValueError("Only grayscale and RGB images are supported")

    if image.size(0) == 1:
        image = torch.tile(image, (3, 1, 1))

    ndarr = image.permute(1, 2, 0).numpy()
    img_to_draw = Image.fromarray(ndarr)

    img_boxes = boxes.to(torch.int64).tolist()

    if fill:
        draw = ImageDraw.Draw(img_to_draw, "RGBA")

    else:
        draw = ImageDraw.Draw(img_to_draw)

    txt_font = ImageFont.load_default() if font is None else ImageFont.truetype(font=font, size=font_size)

    for i, bbox in enumerate(img_boxes):
        if colors is None:
            color = None
        elif isinstance(colors, list):
            color = colors[i]
        else:
            color = colors

        if fill:
            if color is None:
                fill_color = (255, 255, 255, 100)
            elif isinstance(color, str):
                # This will automatically raise Error if rgb cannot be parsed.
                fill_color = ImageColor.getrgb(color) + (100,)
            elif isinstance(color, tuple):
                fill_color = color + (100,)
            draw.rectangle(bbox, width=width, outline=color, fill=fill_color)
        else:
            draw.rectangle(bbox, width=width, outline=color)

        if labels is not None:
            margin = width + 1
            draw.text((bbox[0] + margin, bbox[1] + margin), labels[i], fill=color, font=txt_font)

    return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)


@torch.no_grad()
def draw_segmentation_masks(
    image: torch.Tensor,
    masks: torch.Tensor,
    alpha: float = 0.8,
    colors: Optional[List[Union[str, Tuple[int, int, int]]]] = None,
) -> torch.Tensor:

    """
    Draws segmentation masks on given RGB image.
    The values of the input image should be uint8 between 0 and 255.

    Args:
        image (Tensor): Tensor of shape (3, H, W) and dtype uint8.
        masks (Tensor): Tensor of shape (num_masks, H, W) or (H, W) and dtype bool.
        alpha (float): Float number between 0 and 1 denoting the transparency of the masks.
            0 means full transparency, 1 means no transparency.
        colors (list or None): List containing the colors of the masks. The colors can
            be represented as PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
            When ``masks`` has a single entry of shape (H, W), you can pass a single color instead of a list
            with one element. By default, random colors are generated for each mask.

    Returns:
        img (Tensor[C, H, W]): Image Tensor, with segmentation masks drawn on top.
    """

    if not isinstance(image, torch.Tensor):
        raise TypeError(f"The image must be a tensor, got {type(image)}")
    elif image.dtype != torch.uint8:
        raise ValueError(f"The image dtype must be uint8, got {image.dtype}")
    elif image.dim() != 3:
        raise ValueError("Pass individual images, not batches")
    elif image.size()[0] != 3:
        raise ValueError("Pass an RGB image. Other Image formats are not supported")
    if masks.ndim == 2:
        masks = masks[None, :, :]
    if masks.ndim != 3:
        raise ValueError("masks must be of shape (H, W) or (batch_size, H, W)")
    if masks.dtype != torch.bool:
        raise ValueError(f"The masks must be of dtype bool. Got {masks.dtype}")
    if masks.shape[-2:] != image.shape[-2:]:
        raise ValueError("The image and the masks must have the same height and width")

    num_masks = masks.size()[0]
    if colors is not None and num_masks > len(colors):
        raise ValueError(f"There are more masks ({num_masks}) than colors ({len(colors)})")

    if colors is None:
        colors = _generate_color_palette(num_masks)

    if not isinstance(colors, list):
        colors = [colors]
    if not isinstance(colors[0], (tuple, str)):
        raise ValueError("colors must be a tuple or a string, or a list thereof")
    if isinstance(colors[0], tuple) and len(colors[0]) != 3:
        raise ValueError("It seems that you passed a tuple of colors instead of a list of colors")

    out_dtype = torch.uint8

    colors_ = []
    for color in colors:
        if isinstance(color, str):
            color = ImageColor.getrgb(color)
        color = torch.tensor(color, dtype=out_dtype)
        colors_.append(color)

    img_to_draw = image.detach().clone()
    # TODO: There might be a way to vectorize this
    for mask, color in zip(masks, colors_):
        img_to_draw[:, mask] = color[:, None]

    out = image * (1 - alpha) + img_to_draw * alpha
    return out.to(out_dtype)


def _generate_color_palette(num_masks):
    palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
    return [tuple((i * palette) % 255) for i in range(num_masks)]