Yolo Pose Estimation and Skeleton: Difference between revisions
From wikiluntti
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plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)) | plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB)) | ||
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The results vector is <code>tensor</code>, which includes <code>results.keypoints.xy</code> and <code>results.keypoints.conf</code> data. | |||
=== Pose to skeleton === | === Pose to skeleton === | ||
Revision as of 21:11, 5 October 2025
Introduction
Make a pose estimator and use it to make a moving skeleton.
Use Yolo from Ultralytics.
- Python 3.7+
- Yolo v11
- A CUDA-enabled GPU (optional but recommended for faster inference).
pip install ultralytics opencv-python numpy
Yolo
There are 17 keypoints. YOLOv11’s pose model outputs:
- (x, y) coordinates for each keypoint and
- confidence scores indicating the model’s certainty in each keypoint’s position.
Image detection
-
AN image before and after the code.
from ultralytics import YOLO
import matplotlib.pyplot as plt
import cv2
from PIL import Image
model = YOLO("yolo11n-pose.pt") # n, s, m, l, x versions available
results = model.predict(source="sample_image.jpg")
plt.figure(figsize=(10, 10))
plt.title('YOLOv11 Pose Results')
plt.axis('off')
plt.imshow(cv2.cvtColor(results[0].plot(), cv2.COLOR_BGR2RGB))
The results vector is tensor, which includes results.keypoints.xy and results.keypoints.conf data.
