.. include:: /index.rst :start-after: start_hello_message :end-before: end_hello_message .. _mp_pose_squat: 8. Squat Counter ========================================== ------------------------------------------------------------ 1. Overview ------------------------------------------------------------ In the previous chapter, we implemented basic human pose estimation. This chapter builds on that foundation to implement a simple **Squat Counter** using MediaPipe Pose. This is a practical example of combining: - Pose detection - Action recognition - Real-time counting It can be used in smart fitness systems, home workout assistants, or motion analysis applications. .. image:: img/mp_pose_s2.png :alt: Squat Count Example :align: center ------------------------------------------------------------ 2. How It Works ------------------------------------------------------------ The squat counter is implemented using the following logic: 1. Use MediaPipe Pose to detect 33 body keypoints. 2. Select key joints (Shoulder, Hip, Ankle). 3. Use the normalized y-coordinates to estimate hip height. 4. Define upper and lower thresholds (e.g., 0.55 and 0.45). 5. Use a simple state machine to detect the transition: "standing → squatting → standing". 6. Increase the counter when a full squat cycle is completed. 7. Display the squat count and current hip value on the screen. .. note:: - This example does not use joint angle calculation. - It relies on normalized coordinates to reduce computation. - The method is lightweight and suitable for Raspberry Pi. ------------------------ 3. Run the Code ------------------------ .. important:: Before you start, make sure: * The pan-tilt is assembled * You can access the Raspberry Pi desktop * The code package is installed * Fusion HAT+ is installed and configured * OpenCV is installed For detailed instructions, see :ref:`opencv_install`. #. Open the terminal and enter the following command: .. code-block:: bash sudo python3 ~/ai-lab-kit/mediapipe/mp_pose_squat.py #. After running the program, a window titled "Show Video" opens and displays the live camera feed. .. raw:: html

When a person stands in front of the camera: - MediaPipe Pose detects 33 body landmarks in real time. - A full-body skeleton is drawn on the screen. - The system continuously calculates the relative hip position (HipRel). As you perform squats: - When you move down and your hip passes the lower threshold (DOWN_TH), the system marks that you are in the "bottom" position. - When you stand back up and the hip passes the upper threshold (UP_TH), the squat counter increases by 1. The screen displays: - ``Squats: N`` — the total number of completed squats. - ``HipRel: value`` — the current normalized hip position used for detection. The counter only increases after a full movement cycle (stand → squat → stand), preventing duplicate counting. Press ``q`` to exit the program. The camera stops and the OpenCV window closes automatically. ----------------------------- 4. Complete Code ----------------------------- Here is the complete squat counter implementation: .. code-block:: python from picamera2 import Picamera2, Preview import cv2 import mediapipe.python.solutions.pose as mp_pose import mediapipe.python.solutions.drawing_utils as drawing import mediapipe.python.solutions.drawing_styles as drawing_styles # Initialize the Pose model pose = mp_pose.Pose( static_image_mode=False, model_complexity=1, enable_segmentation=True, ) # ---- Count and threshold ---- squat_count = 0 in_bottom = False DOWN_TH = 0.55 # Hip relative position > 0.55 is considered "full squat" UP_TH = 0.45 # Hip relative position < 0.45 is considered "stand up" # Open the camera picam2 = Picamera2() config = picam2.create_preview_configuration( main={"size": (640, 480), "format": "XRGB8888"} , ) picam2.configure(config) picam2.start() print("Streaming... press 'q' to quit") while True: frame_bgra = picam2.capture_array() # XRGB8888 to BGRA frame_bgr = cv2.cvtColor(frame_bgra, cv2.COLOR_BGRA2BGR) # Convert the frame from BGR to RGB (required by MediaPipe) frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB) # Process the frame for pose detection and tracking results = pose.process(frame_rgb) # Convert the frame back from RGB to BGR (required by OpenCV) frame = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR) # If pose is detected, draw landmarks and connections on the frame if results.pose_landmarks: drawing.draw_landmarks( frame, results.pose_landmarks, mp_pose.POSE_CONNECTIONS, landmark_drawing_spec=drawing_styles.get_default_pose_landmarks_style(), ) # Count squat without using hip angle lms = results.pose_landmarks.landmark # left 11-23-27 (shoulder, hip, ankle) # right 12-24-28 (shoulder, hip, ankle) idx_sets = [(11,23,27), (12,24,28)] hip_rel_list = [] for sh, hp, an in idx_sets: try: y_sh, y_hp, y_an = lms[sh].y, lms[hp].y, lms[an].y base = abs(y_an - y_sh) # Distance between shoulder and ankle if base > 1e-6: hip_rel = (y_hp - y_sh) / base # Position of hip relative to shoulder, 0.5 means hip is in the middle, 0 means hip is at the top, 1 means hip is at the bottom hip_rel_list.append(hip_rel) except IndexError: pass if hip_rel_list: hip_rel = min(hip_rel_list) # Choose the smaller one, which is more stable # State machine: # from low -> mark "in_bottom"; # from back to high -> count +1 if not in_bottom and hip_rel >= DOWN_TH: in_bottom = True elif in_bottom and hip_rel <= UP_TH: squat_count += 1 in_bottom = False # Display cv2.putText(frame, f"Squats: {squat_count}", (20, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3, cv2.LINE_AA) cv2.putText(frame, f"HipRel: {hip_rel:.2f}", (20, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2, cv2.LINE_AA) # Display the frame with annotations cv2.imshow("Show Video", frame) # Exit the loop if 'q' key is pressed if cv2.waitKey(1) & 0xff == ord('q'): break # Release the camera picam2.stop_preview() picam2.stop() cv2.destroyAllWindows() After executing the script, the system will: - Detect the human skeleton; - Calculate the relative hip position; - Count +1 when a complete cycle from "squat down" to "stand up" is finished; - Display **Squats: N** and the current HipRel value on the screen in real-time. ----------------------------------------------- 5. Coordinate and State Design ----------------------------------------------- We use the following 6 keypoints (3 on each side): .. list-table:: :header-rows: 1 * - Keypoint - Index - Description * - Shoulder - 11 (Left) / 12 (Right) - Upper reference * - Hip - 23 (Left) / 24 (Right) - Core for calculating squat position * - Ankle - 27 (Left) / 28 (Right) - Lower reference .. image:: img/mp_pose_s1.png :alt: MediaPipe Pose Keypoints :align: center **Hip Relative** value calculation formula: .. math:: hip\_rel = \frac{hip_y - shoulder_y}{ankle_y - shoulder_y} - Larger hip_rel means closer to the ground (i.e., squatting down). - Smaller hip_rel means standing upright. We define two thresholds: - **DOWN_TH = 0.55**: Considered entering the bottom of the squat - **UP_TH = 0.45**: Considered returning to standing Use a simple state machine for reliable counting: .. code-block:: python if hip_rel >= DOWN_TH: in_bottom = True if in_bottom and hip_rel <= UP_TH: squat_count += 1 in_bottom = False ---------------------------------------------------- 6. Parameter Tuning and Optimization ---------------------------------------------------- .. list-table:: :header-rows: 1 * - Parameter - Description - Adjustment Suggestion * - DOWN_TH - Squat action threshold - Higher value requires deeper squat to count * - UP_TH - Stand up action threshold - Lower value requires standing more upright * - model_complexity - Pose model complexity - Use 1 for faster speed * - Resolution - Affects frame rate and accuracy - Recommended 640×480 .. tip:: For people of different heights, adaptive thresholds or personalized calibration can be used for more accurate counting. --------------------------------------------------------- 5. Troubleshooting --------------------------------------------------------- - Inaccurate counting If the squat count is not accurate, the threshold values may not match your body position or camera angle. Try printing ``hip_rel`` in real time and adjust ``DOWN_TH`` and ``UP_TH`` accordingly. Also make sure your squat form is consistent and clearly visible. - Person not detected If the body is not detected, improve lighting conditions and avoid complex backgrounds. Make sure you are standing fully inside the frame and facing the camera directly. - High latency If the video response is slow, reduce ``model_complexity`` to 1 and lower the camera resolution (for example, 640×480 or 320×240). Close unnecessary background programs to improve performance. ----------------------------- 6. Summary ----------------------------- - Implemented a **real-time squat counter** using Pose keypoints + state machine; - No complex angle calculations required, high operational efficiency; - Suitable for Raspberry Pi or other edge device applications; - Future extensions possible: - Push-up/Sit-up detection - Data recording and visualization - Automatic rhythm guidance and training feedback