Python Numpy Bbox

Python numpy bbox is a powerful library that provides support for calculating bounding boxes of arrays and images. This library is widely used in computer vision applications for object detection, segmentation, and tracking. The numpy bbox library provides tools to compute bounding boxes from points, polygons, and segmentation masks, and can be used for any kind of rectangular regions.

Installing numpy bbox

To install numpy bbox, you can use pip:

pip install numpy-bbox

Alternatively, you can clone the github repository:

git clone https://github.com/duckmatrix/numpy-bbox.git
cd numpy-bbox
python setup.py install

Usage

Once the library is installed, you can use it in your project by importing the numpy_bbox module:

import numpy_bbox as bbox

# Get the bounding box of 2D points
points = [[0, 0], [1, 1], [2, 2]]
bbox.from_points(points)

# Get the bounding box of 2D polygons
polygons = [[[0, 0], [1, 1], [2, 0]],
            [[1, 2], [2, 3], [3, 2]]]
bbox.from_polygons(polygons)

# Get the bounding box of 2D segmentation masks
masks = np.array([[0, 0, 0, 0, 1],
                  [0, 0, 0, 1, 1],
                  [0, 0, 0, 1, 1],
                  [0, 0, 1, 1, 1],
                  [1, 1, 1, 1, 1]])
bbox.from_mask(masks)

Conclusion

Python numpy bbox is an essential tool for any computer vision application that needs to calculate bounding boxes. It is easy to use and provides a wide range of functions to compute bounding boxes from various inputs. Whether you are tracking objects, segmenting images or detecting features, numpy bbox is a must-have library in your toolbox.

Happy Coding!