This python application takes frames from a live video stream and perform object detection on GPUs. We use a pre-trained Single Shot Detection (SSD) model with Inception V2, apply TensorRT’s optimizations, generate a runtime for our GPU, and then perform inference on the video feed to get labels and bounding boxes. The application then annotates the original frames with these bounding boxes and class labels. The resulting video feed has bounding box predictions from our object detection network overlaid on it. The same approach can be extended to other tasks such as classification and segmentation.
A detailed explanation of this code sample can be found in the How to Apply Deep Learning for Common Applications webinar and also as a blog on the NVIDIA Medium Page.
./setup_environment.sh
python SSD_Model/detect_objects_webcam.py
Note: By changing the argument to the "p" flag, you can change which precision the model will run in. The options are FP32 (-p 32), FP16 (-p 16), and INT8 (-p 8)
Note: When running in INT8 precision, an extra step will be performed for calibration. But just like building an engine, it will only be performed the first time you run the model.