utils.py

import math
import pathlib
import warnings
from typing import Union, Optional, List, Tuple, BinaryIO

import numpy as np
import torch
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[str, 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 (color or list of colors, optional): List containing the colors
            of the boxes or single color for all boxes. The color can be represented as
            PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
        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[Union[List[Union[str, Tuple[int, int, int]]], 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 (color or list of colors, optional): List containing the colors
            of the masks or single color for all masks. The color can be represented as
            PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
            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)
        colors_.append(torch.tensor(color, dtype=out_dtype))

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


def _log_api_usage_once(obj: object) -> None:
    torch._C._log_api_usage_once(f"{obj.__module__}.{obj.__class__.__name__}")