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Human pose estimation with MediaPipe

If you're working on a machine vision application that involves any sort of interaction with people, chances are you'll need to perform pose estimation. Pose estimation is the process of identifying the locations of joints, limbs, and other key body parts in an image or video. It's super easy for humans to do, but it's non-trivial for a computer. Thankfully, this is one area where machine learning has proved invaluable. You could create your own ML model, but you'd need a lot of data, and there are open source libraries available to leverage existing models that do pose estimation very well. This is where MediaPipe comes in. MediaPipe is a framework created by Google for all sorts of machine learning use cases. That includes pose estimation, with MediaPipe's pose landmark detection. If this sounds like something that would be useful for you, just follow the steps below to get started.


1. Install the necessary dependencies


Ensure you have Python installed on your system, and then install the required packages. You can use pip, the Python package manager, to install the packages. Open a terminal and run the following commands:

pip install mediapipe
pip install opencv-python

2. Import the required modules


In your Python script, import the necessary modules for using MediaPipe and OpenCV.

import cv2
import mediapipe as mp

3. Initialize the MediaPipe Pose model


Create an instance of the MediaPipe Pose model.

mp_pose = mp.solutions.pose
pose = mp_pose.Pose()

4. Read the video stream


Open a video stream or use a webcam to capture frames.

cap = cv2.VideoCapture(0)  # Use 0 for webcam or provide the video file path
while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

5. Process each frame for pose estimation


Convert the frame to RGB format (MediaPipe requires RGB input), and pass it to the Pose model for estimation.

    # Convert the frame to RGB
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Perform pose estimation
    results = pose.process(frame_rgb)

6. Extract pose landmarks


Access the detected pose landmarks from the results and draw them on the frame.

    # Draw pose landmarks on the frame
    if results.pose_landmarks:
        mp_pose.draw_landmarks(
            frame,
            results.pose_landmarks,
            mp_pose.POSE_CONNECTIONS,
            mp_pose.DrawingSpec(color=(0, 0, 255), thickness=2, circle_radius=2),
            mp_pose.DrawingSpec(color=(0, 255, 0), thickness=2),
        )

7. Display the output


Show the annotated frame with the pose landmarks.

    # Display the output
    cv2.imshow('Pose Estimation', frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

8. Release resources


Once you're done, release the video capture and close any open windows.

cap.release()
cv2.destroyAllWindows()

That's it! You now have a basic implementation of pose estimation using MediaPipe in Python. You can customize the code to suit your specific requirements, such as saving the output or performing additional processing on the pose landmarks.


If you have any questions about pose estimation or need a professional software engineer to help with a project you're working on, schedule an intro call with CodeConda for free!

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