Posture Detection

This lesson provides a deep dive into pose estimation, highlighting key body landmarks and explaining how to interpret them to assess posture.

Posture Detection

The ability to detect and assess posture has a multitude of applications ranging from health tech to gaming. While many libraries offer solutions for facial and hand detection, for full-body posture detection, a combination of tools like CVZone’s PoseModule and the mediapipe library often provides more comprehensive results. Specifically, CVZone’s PoseModule leverages the capabilities of mediapipe for pose estimation.

1. Install mediapipe:

   To harness the capabilities of mediapipe, you’ll first need to install it. Run the following command in your terminal or command prompt:

   pip3 install mediapipe
   

2. Capture video and detect posture:

   from cvzone.PoseModule import PoseDetector
   import cv2
   
   # Initialize the webcam to the default camera (index 0)
   cap = cv2.VideoCapture(0)
   
   # Initialize the PoseDetector class. Here, we're using default parameters. For a deep dive into what each parameter signifies, consider checking the documentation.
   detector = PoseDetector(staticMode=False,
                           modelComplexity=1,
                           smoothLandmarks=True,
                           enableSegmentation=False,
                           smoothSegmentation=True,
                           detectionCon=0.5,
                           trackCon=0.5)
   
   # Loop to continuously get frames from the webcam
   while True:
       # Capture each frame from the webcam
       success, img = cap.read()
   
       # Detect human pose in the frame
       img = detector.findPose(img)
   
       # Extract body landmarks and possibly a bounding box 
       # Set draw=True to visualize landmarks and bounding box on the image
       lmList, bboxInfo = detector.findPosition(img, draw=True, bboxWithHands=False)
   
       # If body landmarks are detected
       if lmList:
           # Extract the center of the bounding box around the detected pose
           center = bboxInfo["center"]
   
           # Visualize the center of the bounding box
           cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)
   
           # Calculate the distance between landmarks 11 and 15 and visualize it
           length, img, info = detector.findDistance(lmList[11][0:2],
                                                     lmList[15][0:2],
                                                     img=img,
                                                     color=(255, 0, 0),
                                                     scale=10)
   
           # Calculate and visualize the angle formed by landmarks 11, 13, and 15
           # This can be used as an illustrative example of how posture might be inferred from body landmarks.
           angle, img = detector.findAngle(lmList[11][0:2],
                                           lmList[13][0:2],
                                           lmList[15][0:2],
                                           img=img,
                                           color=(0, 0, 255),
                                           scale=10)
   
           # Check if the calculated angle is close to a reference angle of 50 degrees (with a leeway of 10 degrees)
           isCloseAngle50 = detector.angleCheck(myAngle=angle,
                                                targetAngle=50,
                                                offset=10)
   
           # Print the result of the angle comparison
           print(isCloseAngle50)
   
       # Display the processed frame
       cv2.imshow("Image", img)
   
       # Introduce a brief pause of 1 millisecond between frames
       cv2.waitKey(1)
   

Deepening Your Understanding:

• Visualizing Landmarks: To get a clearer grasp of what’s being detected, consider drawing lines connecting the landmarks. This can help visualize the skeletal structure detected by the PoseModule.

• Analyzing Posture: By computing angles between specific landmarks (e.g., the angle between the hip, knee, and ankle), you can discern certain postures like slouching or leaning.

• Real-time Feedback: Innovate by developing a system that alerts users in real-time if they adopt an incorrect posture.

• Integration with IoT: Envision a future where smart chairs adjust automatically based on a user’s posture or devices that offer gentle reminders to adjust one’s seating position. The possibilities are vast!

This lesson lays the foundation for posture detection and assessment. The real charm in computer vision emerges when you amalgamate techniques and integrate systems to tackle real-world challenges.

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