Pose Estimation

Use Python to demonstrate the effect of Ultralytics: Pose Estimation in image, video, and real-time detection.

1. Model Introduction

Pose estimation is a task that involves identifying the location of specific points (usually called key points) in an image. Key points can represent parts of an object, such as joints, landmarks, or other significant features. The location of key points is usually represented by a set of two-dimensional [x, y] or 3D [x, y, visible] coordinates

The output of the pose estimation model is a set of points representing the key points of objects in the image, and usually also includes a confidence score for each point. Pose estimation is a good choice when you need to identify specific parts of objects in the scene and their positional relationship with each other.

In the default pose model of YOLO11, there are 17 key points, each representing a different part of the human body. Here is the mapping of each index to the corresponding body joint:

1. Nose
2. Left eye
3. Right eye
4. Left ear
5. Right ear
6. Left shoulder
7. Right shoulder
8. Left elbow
9. Right elbow
10. Left wrist
11. Right wrist
12. Left hip
13. Right hip
14. Left knee
15. Right knee
16. Left ankle
17. Right ankle

2. Pose estimation: image

Use yolo11n-pose.pt to predict the images under the ultralytics project (not the images that come with ultralytics).

Enter the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 03.pose_image.py

Effect preview

Yolo recognition output image location: /home/pi/ultralytics/ultralytics/output/

Sample code:

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from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-pose.pt")
​
# Run batched inference on a list of images
results = model("/home/pi/ultralytics/ultralytics/assets/people.jpg")  # return a list of Results objects
​
# Process results list
for result in results:
    # boxes = result.boxes  # Boxes object for bounding box outputs
    # masks = result.masks  # Masks object for segmentation masks outputs
    keypoints = result.keypoints  # Keypoints object for pose outputs
    # probs = result.probs  # Probs object for classification outputs
    # obb = result.obb  # Oriented boxes object for OBB outputs
    result.show()  # display to screen
    result.save(filename="/home/pi/ultralytics/ultralytics/output/people_output.jpg")  # save to disk

3. Posture estimation: video

Use yolo11n-pose.pt to predict the video under the ultralytics project (not the video that comes with ultralytics).

Enter the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 03.pose_video.py

Effect preview

Video location of yolo recognition output: /home/pi/ultralytics/ultralytics/output/

Sample code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-pose.pt")
​
# Open the video file
video_path = "/home/pi/ultralytics/ultralytics/videos/people_animals.mp4"
cap = cv2.VideoCapture(video_path)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/pi/ultralytics/ultralytics/output/03.people_animals_output.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
# Release the video capture and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()

4. Posture estimation: real-time detection

4.1. USB camera

Use yolo11n-pose.pt to predict the USB camera screen.

Enter the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code: Click the preview screen and press the q key to terminate the program!

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python3 03.pose_camera_usb.py

Effect preview

Yolo recognizes the output video location: /home/pi/ultralytics/ultralytics/output/

Sample code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-pose.pt")
​
# Open the cammera
cap = cv2.VideoCapture(0)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/pi/ultralytics/ultralytics/output/03.pose_camera_usb.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
# Release the video capture and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()

4.2, CSI camera

Use yolo11n-pose.pt to predict the CSI camera screen.

Enter the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code: Click the preview screen, press the q key to terminate the program!

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python3 03.pose_camera_csi.py

Effect preview

Yolo recognizes the output video location: /home/pi/ultralytics/ultralytics/output/

Sample code:

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import cv2
from picamera2 import Picamera2
​
from ultralytics import YOLO
​
# Initialize the Picamera2
picam2 = Picamera2()
picam2.preview_configuration.main.size = (640, 480)
picam2.preview_configuration.main.format = "RGB888"
picam2.preview_configuration.align()
picam2.configure("preview")
picam2.start()
​
# Load the YOLO11 model
model = YOLO("/home/pi/ultralytics/ultralytics/yolo11n-pose.pt")
​
# Set up video output
output_path = "/home/pi/ultralytics/ultralytics/output/03.pose_camera_csi.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(output_path, fourcc, 30, (640, 480))
​
while True:
    # Capture frame-by-frame
    frame = picam2.capture_array()
​
    # Run YOLO11 inference on the frame
    results = model(frame)
​
    # Visualize the results on the frame
    annotated_frame = results[0].plot()
​
​
    # Write the frame to the video file
    out.write(annotated_frame)
​
    # Display the resulting frame
    cv2.imshow("Camera", annotated_frame)
​
    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) == ord("q"):
        break
​
# Release resources and close windows
picam2.close()
out.release()
cv2.destroyAllWindows()

References

https://docs.ultralytics.com/tasks/pose/