Pose Estimation

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Using Python to demonstrate Ultralytics: Pose Estimation effects on images, videos, and real-time detection.

1. Model Introduction

Pose estimation is a task that involves identifying the location of specific points (often called keypoints) in an image. Keypoints can represent parts of an object, such as joints, landmarks, or other notable features. The position of keypoints is typically represented by a set of 2D [x, y] or 3D [x, y, visible] coordinates.

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

In the default pose model of YOLO11, there are 17 keypoints, 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 images under the ultralytics project (not ultralytics built-in images).

Enter the code folder:

cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code:

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python3 03.pose_image.py

Result Preview

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

Example 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. Pose Estimation: Video

Use yolo11n-pose.pt to predict videos under the ultralytics project (not ultralytics built-in videos).

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

Result Preview

YOLO recognition output video location: /home/pi/ultralytics/ultralytics/output/

Example 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. Pose Estimation: Real-time Detection

4.1. USB Camera

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

Enter the code folder:

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cd /home/pi/ultralytics/ultralytics/yahboom_demo

Run the code. Click on the preview window and press the q key to terminate the program!

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python3 03.pose_camera_usb.py

Result Preview

YOLO recognition output video location: /home/pi/ultralytics/ultralytics/output/

Example 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 'mp4
 Instance Segmentation
Image Classification