In this blog post, we will explore how to perform human pose estimation using PyTorch’s Keypoint R-CNN model and integrate it with ROS2 to visualize body joints and skeletons in RViz.
Table of Contents
- Introduction
- Model and Dataset
- Code Explanation
- Image Encoding and Decoding
- Inference and Visualization
- Conclusion
1. Introduction
Human pose estimation is a critical task in computer vision, involving the detection of key body joints in images or videos. This technology has applications in fields like robotics, healthcare, and sports analytics. In this guide, we will use the PyTorch framework and a pre-trained Keypoint R-CNN model to detect human poses and visualize the results in ROS2.
2. Model and Dataset
Model: Keypoint R-CNN
The Keypoint R-CNN model, part of the Detectron2 library developed by Facebook AI Research (FAIR), extends the Faster R-CNN object detection model to detect keypoints. Each detected person in an image has up to 17 keypoints, representing various body parts like shoulders, elbows, wrists, hips, knees, and ankles.
Dataset: COCO (Common Objects in Context)
The COCO dataset is a large-scale object detection, segmentation, and captioning dataset. It includes over 200,000 labeled images with annotations for keypoints of people, making it an ideal dataset for training pose estimation models.
3. Code Explanation
We will create a ROS2 package named opencv_tools
and a node that:
- Subscribes to an image topic.
- Uses a pre-trained Keypoint R-CNN model to estimate human poses.
- Publishes the image with keypoints and skeletons overlaid.
- Publishes MarkerArray messages for visualization in RViz.
Installing Dependencies
Ensure you have the necessary dependencies installed:
pip install torch torchvision opencv-python
Code Explanation
- Importing Necessary Libraries
These imports bring in the necessary libraries for working with ROS2 (rclpy
, Node
, Image
, Marker
, MarkerArray
), OpenCV (cv2
), and PyTorch (torch
, torchvision
). CvBridge
is used for converting between ROS images and OpenCV images.
2. Defining the PoseEstimationNode
Class
This defines a class PoseEstimationNode
which inherits from Node
. The constructor (__init__
) initializes the node with the name 'pose_estimation_node'
and logs an initialization message.
3. Setting Up Subscriptions and Publishers
self.create_subscription
: Subscribes to theinput_image
topic to receive images. Theimage_callback
method is called whenever a new image is received.self.create_publisher
: Creates publishers foroutput_image
andvisualization_marker_array
topics to publish processed images and marker arrays.
4. Initializing CvBridge and Model
self.bridge = CvBridge()
: Initializes CvBridge for converting between ROS and OpenCV images.self.device = torch.device("cpu")
: Specifies the device to run the model on (CPU in this case).self.model
: Loads the pre-trained Keypoint R-CNN model with COCO weights and moves it to the specified device. The model is set to evaluation mode usingself.model.eval()
.self.transforms
: Defines image transformations (conversion to tensor) to be applied before feeding the image to the model.
5. Image Callback Function
self.bridge.imgmsg_to_cv2
: Converts the ROS image message to an OpenCV image. If the image encoding is notbgr8
, it converts the encoding appropriately.- Error handling: Logs an error if the conversion fails.
6. Processing the Image for Pose Estimation
- The image is transformed to a tensor and passed through the model to get the outputs.
torch.no_grad()
: Disables gradient calculation for inference, which reduces memory usage and speeds up computations.
7. Handling Model Outputs
- Extracts keypoints and scores from the model output.
- Filters keypoints with confidence scores greater than 0.5.
- Initializes a
MarkerArray
to store the markers for visualization.
8. Drawing Keypoints and Skeleton
- For each keypoint with high confidence, a circle is drawn on the image and a
Marker
is created for visualization. - Skeleton pairs are defined to connect keypoints.
- For each valid pair, a line is drawn on the image and a
Marker
is created for visualization. - The
MarkerArray
is published to visualize keypoints and skeleton in RViz. - The processed image is converted back to a ROS message and published.
9. Main Function
- Initializes the ROS2 node, creates an instance of
PoseEstimationNode
, and keeps the node running. - Destroys the node and shuts down ROS2 when done.
4. Image Encoding and Decoding
In ROS2, images are published and subscribed to in the form of sensor_msgs/Image
messages. These messages need to be converted to OpenCV images for processing, and back to ROS messages for publishing. We use the CvBridge
library for these conversions.
Encoding: The input image is received as a ROS message and converted to an OpenCV image using CvBridge
.
Decoding: After processing, the modified image is converted back to a ROS message for publishing
5. Inference and Visualization
The inference involves passing the image through the Keypoint R-CNN model and extracting keypoints with high confidence scores. We then draw circles at these keypoints and lines connecting them to form a skeleton.
For visualization in RViz, we publish MarkerArray
messages with markers representing the keypoints and lines.
6. Conclusion
In this blog post, we’ve walked through the complete process of setting up human pose estimation using PyTorch and integrating it with ROS2 for visualization in RViz. The Keypoint R-CNN model provides robust performance on the COCO dataset, allowing for accurate detection and visualization of human poses.
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