213 lines
8.4 KiB
Python
213 lines
8.4 KiB
Python
|
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"]
|
||
|
|
||
|
|
||
|
@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 :attr:`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``.
|
||
|
|
||
|
Example:
|
||
|
See this notebook `here <https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91>`_
|
||
|
|
||
|
"""
|
||
|
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[List[Union[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 filled, Resulting Tensor should be saved as PNG image.
|
||
|
|
||
|
Args:
|
||
|
image (Tensor): Tensor of shape (C x H x W)
|
||
|
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 (List[Union[str, Tuple[int, int, int]]]): List containing the colors of 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.
|
||
|
"""
|
||
|
|
||
|
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")
|
||
|
|
||
|
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
|
||
|
else:
|
||
|
color = colors[i]
|
||
|
|
||
|
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:
|
||
|
draw.text((bbox[0], bbox[1]), labels[i], fill=color, font=txt_font)
|
||
|
|
||
|
return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1)
|