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# How to Compute The Area of a Set of Bounding Boxes in PyTorch?

• Last Updated : 10 Oct, 2022

In this article, we are going to see how to compute the area of a set of bounding boxes in PyTorch. We can compute the area of a set of bounding boxes by using the box_area() method of torchvision.io module.

## box_area() method

This method accepts bounding boxes as an input and returns the area of the given bounding boxes. The input bounding boxes must be torch Tensors with [N,4] size, where N represents the number of bounding boxes for which the area will be computed. The bounding boxes are expected to be in the format (x_min, y_min, x_max, y_max), where 0 ≤ x_min < x_max, and 0 ≤ y_min < y_max. Before computing the area of a bounding box we use unsqueeze to make this bounding box tensor into a 2D tensor.

Syntax: torchvision.ops.box_area(boxes)

Parameter:

• boxes: This method accepts bounding boxes as input.

Return: This method return area for each box.

## Stepwise Implementation

Step 1: Import the required libraries.

## Python

 `import` `torch``import` `torchvision``from` `torchvision.io ``import` `read_image``from` `torchvision.utils ``import` `draw_bounding_boxes``from` `torchvision.ops ``import` `box_area`

## Python

 `img ``=` `read_image(``'img.png'``)`

Step 3: define a bounding box and convert this box into a torch tensor.

## Python

 `b_box ``=` `[``80``, ``70``, ``500``, ``200``]``b_box ``=` `torch.tensor(b_box, dtype``=``torch.``int``)`

Step 4: unsqueeze the given bounding box to make it a 2D tensor. Execute this step only if we want to compute the area of a single bounding box else skip this step.

## Python

 `b_box ``=` `b_box.unsqueeze(``0``)`

Step 5: Compute the above defined bounding box area and store this computed area in a variable for further use.

## Python

 `area ``=` `box_area(b_box)`

Step 6: set this computed area on the label.

## Python

 `label ``=` `[f``"b_box area = {area.item()}"``]`

Step 7: Draw a bounding box on the image and put the above-defined label on box.

## Python

 `img ``=` `draw_bounding_boxes(img, b_box, labels``=``label,``                          ``width``=``4``, colors``=``(``255``, ``0``, ``0``))`

Step 8: transform this image into a PIL image

## Python

 `img ``=` `torchvision.transforms.ToPILImage()(img)`

Step 9: Display the output image.

## Python

 `img.show()`

The below image is used for demonstration: Example 1:

in this example, we are computing the area of a single bounding box and set this computed area as a label.

## Python

 `# Import the required libraries``import` `torch``import` `torchvision``from` `torchvision.io ``import` `read_image``from` `torchvision.utils ``import` `draw_bounding_boxes``from` `torchvision.ops ``import` `box_area` `# read input image from your computer``img ``=` `read_image(``'img.png'``)` `# bounding box are xmin, ymin, xmax, ymax``b_box ``=` `[``80``, ``70``, ``500``, ``200``]` `# convert the bounding box to torch tensor``b_box ``=` `torch.tensor(b_box, dtype``=``torch.``int``)` `# unsqueeze the given bounding box to make``# it 2D tensor``b_box ``=` `b_box.unsqueeze(``0``)` `# Compute the bounding box area``area ``=` `box_area(b_box)` `# set this computed area on label``label ``=` `[f``"b_box area = {area.item()}"``]` `# draw the above define bounding box on image``# Set the above define label on image``img ``=` `draw_bounding_boxes(img, b_box, labels``=``label,``                          ``width``=``4``, colors``=``(``255``, ``0``, ``0``))`  `# transform this image to PIL image``img ``=` `torchvision.transforms.ToPILImage()(img)` `# display result``img.show()`

Output: Example 2:

in this example, we are computing the area of multiple bounding boxes and set this computed area as a label for each box.

## Python

 `# Import the required libraries``import` `torch``from` `PIL ``import` `Image``import` `torchvision``from` `torchvision.io ``import` `read_image``from` `torchvision.utils ``import` `draw_bounding_boxes``from` `torchvision.ops ``import` `box_area` `# read input image from your computer``img ``=` `read_image(``'img.png'``)` `# create boxes``b_box1 ``=` `[``80``, ``70``, ``500``, ``200``]``b_box2 ``=` `[``80``, ``230``, ``500``, ``300``]``b_box3 ``=` `[``580``, ``70``, ``720``, ``300``]``b_box ``=` `[b_box1, b_box2,b_box3]` `# convert the bounding box to torch tensor``b_box ``=` `torch.tensor(b_box, dtype``=``torch.``int``)` `# Compute the bounding box area``area ``=` `box_area(b_box)` `# set this computed area on label``labels ``=` `[f``"b_box area ={n}"` `for` `n ``in` `area]` `# draw the above define bounding boxes on image``img``=``draw_bounding_boxes(img, b_box, labels ``=` `labels, width``=``4``,``                        ``colors``=``[``"orange"``, ``"white"``,``"red"``])` `# transform this image to PIL image``img ``=` `torchvision.transforms.ToPILImage()(img)` `# display result``img.show()`

Output: My Personal Notes arrow_drop_up