Abstract:

This video provides a comprehensive, step-by-step guide to developing an HRNet pose detection application on the Snapdragon X Elite platform, leveraging the Snapdragon Hexagon NPU and ONNX Runtime QNN. The tutorial covers model acquisition from Qualcomm AI Hub, crucial environment setup and verification for NPU access, detailed exploration of the HRNet model's expected inputs and outputs, and the implementation of essential pre-processing and post-processing helper functions. The culmination is a real-time pose detection application that processes live camera feeds, with a demonstration of a bonus yoga pose detection feature, highlighting the platform's capabilities for real-world AI applications.

Summarizing the HRNet Pose Detection App Development:

• 0:08 Introduction to HRNet Pose Detection: Derek Johnson from Qualcomm introduces the development of an HRNet pose detection app utilizing Snapdragon X Elite, the Snapdragon Hexagon NPU, and ONNX Runtime QNN for efficient on-device AI.
• 0:39 Qualcomm AI Hub & Model Acquisition: The process begins by downloading the HRNet pose quantized model from Qualcomm AI Hub, specifically chosen for the Snapdragon Hexagon NPU. It's recommended to review the associated research paper for deeper understanding.
• 1:38 ONNX Runtime & Environment Setup: ONNX (Open Neural Network Exchange) Runtime QNN is highlighted as the key to direct NPU access. The importance of a specific Python environment (ARM64, not emulated AMD64) is stressed for Hexagon NPU utilization, with external blog resources suggested for detailed setup.
• 3:45 Essential Imports & Dependencies: Key libraries are imported, including OpenCV (CV2), NumPy, and onnxruntime-qnn, which is the gateway to Hexagon. The pip install onnxruntime-qnn command is noted for automatically bundling necessary drivers.
• 4:55 Path Management & Driver Location: The tutorial explains how to locate the onnxruntime site packages, which contain the critical drivers (e.g., QnnHtp.dll) needed for NPU communication, simplifying previous manual SDK installations.
• 7:33 Setting Up the Inference Session: An ort.InferenceSession is created, pointing to the downloaded HRNet model and configuring QN_ExecutionProvider to leverage the Hexagon NPU. Troubleshooting tips are provided for ensuring the NPU provider is correctly detected.
• 11:02 Understanding Model Inputs and Outputs:
  • Input: The HRNet model expects images in a 1x3x256x192 (Batch Size, Channels, Height, Width) float32 format.
  • Output (Heatmaps): The model outputs 1x17x64x48 float32 heatmaps, where 17 corresponds to different human keypoints (e.g., eyes, ears, kneecaps) as defined by the Coco dataset.
• 13:50 Helper Function 1: Image Transformation (transform_numpy_opencv): This function preprocesses camera images by resizing them to the model's expected dimensions (256x192), normalizing pixel values (0-1 range), and transposing the image format from Height-Width-Channel to Channel-Height-Width. This "shape shifting" is crucial for model compatibility.
• 18:16 Helper Function 2: Keypoint Processing (keypoint_processor_numpy): This function post-processes the model's heatmap outputs. It extracts the most probable coordinate (max value) for each of the 17 keypoints from their respective heatmaps and scales these coordinates back up to the original image dimensions.
• 23:07 Real-time Inference Loop & Visualization:
  • 24:09 Camera Capture: Initializes the system's webcam (cv2.VideoCapture(0)) for continuous frame reading.
  • 26:17 Frame Processing: Each captured frame is passed through the image transformation helper, then its dimensions are expanded to include the batch size (1x3x256x192) as required by the model.
  • 32:01 Hexagon NPU Inference: The preprocessed frame is then passed to session.run() for real-time inference on the Hexagon NPU.
  • 33:07 Output Post-Processing: The inference output (heatmaps) undergoes post-processing to extract keypoint coordinates, which are then drawn as red circles on the original scaled image using OpenCV.
  • 36:34 Display: The resulting image with detected keypoints is resized back to the camera's native resolution (640x480) and displayed, allowing users to see live pose detection.
• 37:08 Real-World Application & Conclusion: The video concludes with a recap of the entire development process and showcases a "bonus" yoga pose detection app built on the HRNet solution. This demonstrates the potential for various real-world applications, such as dance sequence detection or physical therapy assistance, leveraging the Snapdragon X Elite and Hexagon NPU.