Introducing aipose 1/X: A Python Library for Pose Estimation

If you're working with computer vision or machine learning projects, you may have come across the need for pose estimation. This is the process of detecting and tracking the position and orientation of objects in an image or video. Fortunately, there are many open-source libraries available to help with this task, and today I want to introduce you to aipose.

One of the primary advantages of using aipose is its ease of use. With just a few lines of code, you can load an image or video and use the library to detect and track human bodies. aipose provides pre-trained models for detecting body keypoints and estimating their poses, so you don't have to spend time training your own modelsor researching projects without documentation or one file with a lot of hundreds on code lines.

With just a few lines of code, you can load an image or video and use the library to detect and track human bodies.

Here's an example of how to use aipose to detect and track a person in an image:

import cv2
import numpy as np
from numpy import ndarray
from typing import List

from aipose.models.yolov7.domain import YoloV7Pose, YoloV7PoseKeypoints
from aipose.plot import plot


# Load image
image: ndarray = cv2.imread("person.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Create pose estimator
model = YoloV7Pose()

# Detect and track body
prediction = model(image)

# Draw keypoints and skeleton
plot(image, np.array([value.raw_keypoint for value in prediction]))

In this example, we first load an image using the OpenCV library. Then, we create a PoseEstimator object from aipose and use it to detect and track the body in the image. Finally, we draw the detected keypoints and skeleton onto the image and display it.

aipose also provides functions for working with videos and webcams, as well as for saving the detected poses to a CSV file for further analysis.

Overall, aipose is a powerful and easy-to-use library for pose estimation in Python. Whether you're working on a computer vision or machine learning project, or just want to experiment with pose estimation, I highly recommend giving aipose a try.

To get started, you can install aipose using pip:

pip install aipose

You can also find the source code and documentation on GitHub:

https://github.com/Tlaloc-Es/aipose

I hope this introduction to aipose has been helpful. If you have any questions or feedback, feel free to leave a comment below!

Notebook with the example: https://github.com/Tlaloc-Es/aipose/blob/master/notebooks/plot_keypoints.ipynb