Python OpenPose

OpenPose is an open-source library that allows for real-time multi-person keypoint detection and pose estimation. It is based on deep learning and computer vision techniques and can be used for various applications such as human-computer interaction, sports analysis, and augmented reality.

In this article, we will explore the basics of OpenPose and how to use it in Python. We will cover the installation process, keypoint detection, and pose estimation. Let's get started!

Installation

To use OpenPose in Python, we first need to install the necessary dependencies. OpenPose requires OpenCV, Caffe, and a few other libraries. Here are the installation steps:

  1. Install OpenCV: pip install opencv-python

  2. Install Caffe: Follow the instructions on the official Caffe website to install it on your system.

  3. Clone the OpenPose repository: `git clone

  4. Build OpenPose: Navigate to the cloned repository and follow the build instructions to compile the library.

Once the installation is complete, we can start using OpenPose in Python.

Keypoint Detection

OpenPose can detect keypoints on a person's body, such as the locations of the wrists, elbows, and knees. This information can be used for various applications like gesture recognition or body tracking.

To detect keypoints using OpenPose in Python, we first need to import the necessary libraries:

import cv2
import numpy as np
import openpose

Next, we need to load the pre-trained OpenPose model:

net = cv2.dnn.readNetFromCaffe('path/to/openpose.prototxt', 'path/to/openpose.caffemodel')

Once the model is loaded, we can use it to detect keypoints on an input image:

image = cv2.imread('path/to/image.jpg')
blob = cv2.dnn.blobFromImage(image, 1.0, (368, 368), (127.5, 127.5, 127.5), swapRB=True, crop=False)
net.setInput(blob)
output = net.forward()

The output variable will contain the detected keypoints. We can then visualize them on the image:

for i in range(0, output.shape[0]):
    confidenceMap = output[i, :, :, 0]
    keypoints = []
    for j in range(0, output.shape[1]):
        probMap = confidenceMap[j, :, :]
        minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
        x = (image.shape[1] * point[0]) / output.shape[3]
        y = (image.shape[0] * point[1]) / output.shape[2]
        keypoints.append((int(x), int(y)))
        cv2.circle(image, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)

Now we can display the image with keypoints:

cv2.imshow('Keypoints', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Pose Estimation

Apart from keypoint detection, OpenPose can also estimate the pose of a person, which includes the position and orientation of various body parts.

To perform pose estimation using OpenPose in Python, we can use a similar approach as before:

net = cv2.dnn.readNetFromCaffe('path/to/openpose_pose.prototxt', 'path/to/openpose_pose.caffemodel')
output = net.forward()

The output variable will contain the estimated poses. We can visualize them on the image in a similar way as before:

for i in range(0, output.shape[0]):
    probMap = output[i, :, :, 0]
    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
    x = (image.shape[1] * point[0]) / output.shape[3]
    y = (image.shape[0] * point[1]) / output.shape[2]
    cv2.circle(image, (int(x), int(y)), 8, (0, 255, 255), thickness=-1, lineType=cv2.FILLED)

Now we can display the image with pose estimation:

cv2.imshow('Pose Estimation', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Conclusion

In this article, we have explored the basics of using OpenPose in Python. We have learned how to detect keypoints and estimate poses using the OpenPose library. OpenPose is a powerful tool that can be used for various computer vision applications. With its real-time capabilities and accurate pose estimation, it opens up new possibilities in fields like sports analysis, human-computer interaction, and augmented reality.

Remember to refer to the official OpenPose documentation and examples for more advanced usage and customization options. Keep exploring and experimenting with OpenPose to unlock its full potential in your projects!

Gantt Chart

gantt
    dateFormat  YYYY-MM-DD
    title Example Gantt Chart
    section Phase 1
    Task 1           :a