Pose Estimation
π Overview
Pose estimation detects 33 body keypoints in real time (head, shoulders, elbows, wrists, hips, knees, ankles, and so on) and draws the skeleton on the frame.
It uses the MediaPipe Pose model with OpenCV for video capture, and cvzone's PoseModule wraps the whole pipeline. It is the shared foundation for projects such as an AI fitness trainer, motion capture, and body-controlled interaction.
π§° What you need
- Computer + camera
- Python 3.7+ environment
- pip install: opencv-python, mediapipe, cvzone
- Optional: enough space to frame your whole body or half body
π§ Step by step
Install the dependencies
pip install opencv-python mediapipe cvzone
Check that the camera driver works
Open the camera
Read frames with VideoCapture and keep the image stable
A simple background with the person centred in frame works best
Initialise the pose detector
Instantiate the detector with PoseModule
If confidence is too low, static mode improves stability at a higher compute cost
Draw the skeleton
findPose() does the detection and skeleton drawing
Parameters control whether keypoints and connecting lines are shown
Extract coordinates and angles
lmList provides the 33 keypoint coordinates
Compute joint angles from pairs of bone vectors to prepare for movement analysis
Apply it to fitness or interaction
Use angle thresholds to judge whether a movement is correct (a squat angle, for instance)
This is the core prerequisite module of the AI fitness trainer project
π‘ Tips
- Standing too far or too close reduces keypoint accuracy; keep your full body in frame
- A side view gives more accurate elbow and knee angles; a front view suffers from self-occlusion
- When chasing frame rate, draw only the keypoints and skip the connecting lines to cut the drawing cost