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Repurposing masks into bounding boxes

The following example illustrates the operations available the torchvision.ops module for repurposing segmentation masks into object localization annotations for different tasks (e.g. transforming masks used by instance and panoptic segmentation methods into bounding boxes used by object detection methods).

# sphinx_gallery_thumbnail_path = "../../gallery/assets/repurposing_annotations_thumbnail.png"

import os
import numpy as np
import torch
import matplotlib.pyplot as plt

import torchvision.transforms.functional as F


ASSETS_DIRECTORY = "assets"

plt.rcParams["savefig.bbox"] = "tight"


def show(imgs):
    if not isinstance(imgs, list):
        imgs = [imgs]
    fix, axs = plt.subplots(ncols=len(imgs), squeeze=False)
    for i, img in enumerate(imgs):
        img = img.detach()
        img = F.to_pil_image(img)
        axs[0, i].imshow(np.asarray(img))
        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])

Masks

In tasks like instance and panoptic segmentation, masks are commonly defined, and are defined by this package, as a multi-dimensional array (e.g. a NumPy array or a PyTorch tensor) with the following shape:

(num_objects, height, width)

Where num_objects is the number of annotated objects in the image. Each (height, width) object corresponds to exactly one object. For example, if your input image has the dimensions 224 x 224 and has four annotated objects the shape of your masks annotation has the following shape:

(4, 224, 224).

A nice property of masks is that they can be easily repurposed to be used in methods to solve a variety of object localization tasks.

Converting Masks to Bounding Boxes

For example, the masks_to_boxes() operation can be used to transform masks into bounding boxes that can be used as input to detection models such as FasterRCNN and RetinaNet. We will take images and masks from the PenFudan Dataset.

from torchvision.io import read_image

img_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054.png")
mask_path = os.path.join(ASSETS_DIRECTORY, "FudanPed00054_mask.png")
img = read_image(img_path)
mask = read_image(mask_path)

Here the masks are represented as a PNG Image, with floating point values. Each pixel is encoded as different colors, with 0 being background. Notice that the spatial dimensions of image and mask match.

print(mask.size())
print(img.size())
print(mask)
torch.Size([1, 498, 533])
torch.Size([3, 498, 533])
tensor([[[0, 0, 0,  ..., 0, 0, 0],
         [0, 0, 0,  ..., 0, 0, 0],
         [0, 0, 0,  ..., 0, 0, 0],
         ...,
         [0, 0, 0,  ..., 0, 0, 0],
         [0, 0, 0,  ..., 0, 0, 0],
         [0, 0, 0,  ..., 0, 0, 0]]], dtype=torch.uint8)
# We get the unique colors, as these would be the object ids.
obj_ids = torch.unique(mask)

# first id is the background, so remove it.
obj_ids = obj_ids[1:]

# split the color-encoded mask into a set of boolean masks.
# Note that this snippet would work as well if the masks were float values instead of ints.
masks = mask == obj_ids[:, None, None]

Now the masks are a boolean tensor. The first dimension in this case 3 and denotes the number of instances: there are 3 people in the image. The other two dimensions are height and width, which are equal to the dimensions of the image. For each instance, the boolean tensors represent if the particular pixel belongs to the segmentation mask of the image.

print(masks.size())
print(masks)
torch.Size([3, 498, 533])
tensor([[[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]],

        [[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]],

        [[False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         ...,
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False],
         [False, False, False,  ..., False, False, False]]])

Let us visualize an image and plot its corresponding segmentation masks. We will use the draw_segmentation_masks() to draw the segmentation masks.

from torchvision.utils import draw_segmentation_masks

drawn_masks = []
for mask in masks:
    drawn_masks.append(draw_segmentation_masks(img, mask, alpha=0.8, colors="blue"))

show(drawn_masks)
plot repurposing annotations

To convert the boolean masks into bounding boxes. We will use the masks_to_boxes() from the torchvision.ops module It returns the boxes in (xmin, ymin, xmax, ymax) format.

from torchvision.ops import masks_to_boxes

boxes = masks_to_boxes(masks)
print(boxes.size())
print(boxes)
torch.Size([3, 4])
tensor([[ 96., 134., 181., 417.],
        [286., 113., 357., 331.],
        [363., 120., 436., 328.]])

As the shape denotes, there are 3 boxes and in (xmin, ymin, xmax, ymax) format. These can be visualized very easily with draw_bounding_boxes() utility provided in torchvision.utils.

from torchvision.utils import draw_bounding_boxes

drawn_boxes = draw_bounding_boxes(img, boxes, colors="red")
show(drawn_boxes)
plot repurposing annotations

These boxes can now directly be used by detection models in torchvision. Here is demo with a Faster R-CNN model loaded from fasterrcnn_resnet50_fpn()

from torchvision.models.detection import fasterrcnn_resnet50_fpn, FasterRCNN_ResNet50_FPN_Weights

weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
print(img.size())

tranforms = weights.transforms()
img = tranforms(img)
target = {}
target["boxes"] = boxes
target["labels"] = labels = torch.ones((masks.size(0),), dtype=torch.int64)
detection_outputs = model(img.unsqueeze(0), [target])
torch.Size([3, 498, 533])

Converting Segmentation Dataset to Detection Dataset

With this utility it becomes very simple to convert a segmentation dataset to a detection dataset. With this we can now use a segmentation dataset to train a detection model. One can similarly convert panoptic dataset to detection dataset. Here is an example where we re-purpose the dataset from the PenFudan Detection Tutorial.

class SegmentationToDetectionDataset(torch.utils.data.Dataset):
    def __init__(self, root, transforms):
        self.root = root
        self.transforms = transforms
        # load all image files, sorting them to
        # ensure that they are aligned
        self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages"))))
        self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks"))))

    def __getitem__(self, idx):
        # load images and masks
        img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
        mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])

        img = read_image(img_path)
        mask = read_image(mask_path)

        img = F.convert_image_dtype(img, dtype=torch.float)
        mask = F.convert_image_dtype(mask, dtype=torch.float)

        # We get the unique colors, as these would be the object ids.
        obj_ids = torch.unique(mask)

        # first id is the background, so remove it.
        obj_ids = obj_ids[1:]

        # split the color-encoded mask into a set of boolean masks.
        masks = mask == obj_ids[:, None, None]

        boxes = masks_to_boxes(masks)

        # there is only one class
        labels = torch.ones((masks.shape[0],), dtype=torch.int64)

        target = {}
        target["boxes"] = boxes
        target["labels"] = labels

        if self.transforms is not None:
            img, target = self.transforms(img, target)

        return img, target

Total running time of the script: ( 0 minutes 1.632 seconds)

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