Add utility to draw bounding boxes

docs/source/utils.rst

@@ -7,3 +7,4 @@ torchvision.utils
 
 .. autofunction:: save_image
 
+.. autofunction:: draw_bounding_boxes

test/common_utils.py

@@ -9,6 +9,7 @@
 import torch
 import errno
 import __main__
+import random
 
 from numbers import Number
 from torch._six import string_classes
@@ -30,6 +31,12 @@ def get_tmp_dir(src=None, **kwargs):
         shutil.rmtree(tmp_dir)
 
 
+def set_rng_seed(seed):
+    torch.manual_seed(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+
+
 ACCEPT = os.getenv('EXPECTTEST_ACCEPT')
 TEST_WITH_SLOW = os.getenv('PYTORCH_TEST_WITH_SLOW', '0') == '1'
 # TEST_WITH_SLOW = True  # TODO: Delete this line once there is a PYTORCH_TEST_WITH_SLOW aware CI job

test/test_models.py

@@ -1,23 +1,15 @@
-from common_utils import TestCase, map_nested_tensor_object, freeze_rng_state
+from common_utils import TestCase, map_nested_tensor_object, freeze_rng_state, set_rng_seed
 from collections import OrderedDict
 from itertools import product
 import functools
 import operator
 import torch
 import torch.nn as nn
-import numpy as np
 from torchvision import models
 import unittest
-import random
 import warnings
 
 
-def set_rng_seed(seed):
-    torch.manual_seed(seed)
-    random.seed(seed)
-    np.random.seed(seed)
-
-
 def get_available_classification_models():
     # TODO add a registration mechanism to torchvision.models
     return [k for k, v in models.__dict__.items() if callable(v) and k[0].lower() == k[0] and k[0] != "_"]

test/test_utils.py

@@ -6,6 +6,7 @@
 import unittest
 from io import BytesIO
 import torchvision.transforms.functional as F
+from torchvision.io.image import read_image
 from PIL import Image
 
 
@@ -79,6 +80,21 @@ def test_save_image_single_pixel_file_object(self):
             self.assertTrue(torch.equal(F.to_tensor(img_orig), F.to_tensor(img_bytes)),
                             'Pixel Image not stored in file object')
 
+    def test_draw_boxes(self):
+        img = torch.full((3, 100, 100), 255, dtype=torch.uint8)
+        boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0],
+                             [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+        labels = ["a", "b", "c", "d"]
+        colors = ["green", "#FF00FF", (0, 255, 0), "red"]
+        result = utils.draw_bounding_boxes(img, boxes, labels=labels, colors=colors)
+
+        path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_util.png")
+        if not os.path.exists(path):
+            Image.fromarray(result.permute(1, 2, 0).numpy()).save(path)
+
+        expected = read_image(path)
+        self.assertTrue(torch.equal(result, expected))
+
 
 if __name__ == '__main__':
     unittest.main()

torchvision/utils.py

@@ -1,8 +1,13 @@
 from typing import Union, Optional, List, Tuple, Text, BinaryIO
-import io
 import pathlib
 import torch
 import math
+import numpy as np
+from PIL import Image, ImageDraw
+from PIL import ImageFont
+
+__all__ = ["make_grid", "save_image", "draw_bounding_boxes"]
+
 irange = range
 
 
@@ -121,10 +126,64 @@ def save_image(
             If a file object was used instead of a filename, this parameter should always be used.
         **kwargs: Other arguments are documented in ``make_grid``.
     """
-    from PIL import Image
     grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
                      normalize=normalize, range=range, scale_each=scale_each)
     # 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,
+    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.
+
+    Args:
+        image (Tensor): Tensor of shape (C x H x W)
+        bboxes (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]`.
+        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()
+
+    draw = ImageDraw.Draw(img_to_draw)
+
+    for i, bbox in enumerate(img_boxes):
+        color = None if colors is None else colors[i]
+        draw.rectangle(bbox, width=width, outline=color)
+
+        if labels is not None:
+            txt_font = ImageFont.load_default() if font is None else ImageFont.truetype(font=font, size=font_size)
+            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)