Hands-on: Deploy YOLO on a Webcam Using ONNX/TensorRT

Deploying a YOLO model on a webcam using ONNX and TensorRT enables real-time object detection with low latency and high efficiency, particularly on NVIDIA GPUs and edge devices like the Jetson Nano or Xavier.

What You Will Learn

• Convert YOLO models to the ONNX format.
• Utilize ONNX Runtime or TensorRT for efficient inference.
• Capture and process video feeds from a webcam.
• Perform real-time object detection on live video.

Prerequisites

• Python: Version 3.8 or higher.
• NVIDIA GPU: With CUDA Toolkit installed.
• Required Libraries:
  • opencv-python
  • onnxruntime (or tensorrt and its Python bindings)
  • numpy
• Pre-trained YOLO Model: A pre-trained YOLOv5 model is recommended, but YOLOv8 or a custom-trained model can also be used.

Step 1: Export YOLO Model to ONNX

This step involves converting your YOLO model (e.g., YOLOv5) into the ONNX format, which is a portable format for deep learning models.

For YOLOv5:

1. Clone the YOLOv5 repository:

   git clone https://github.com/ultralytics/yolov5
   cd yolov5

2. Install dependencies:

   pip install -r requirements.txt

3. Export the model to ONNX:

   python export.py --weights yolov5s.pt --include onnx

This command will generate a yolov5s.onnx file in the yolov5 directory. You can replace yolov5s.pt with the path to your desired YOLO model weights.

Step 2: Load ONNX Model and Set Up Inference

Once the model is in ONNX format, you can load it using ONNX Runtime or TensorRT for inference.

Using ONNX Runtime (Python)

ONNX Runtime is a high-performance inference engine for ONNX models that is cross-platform and easy to deploy.

import onnxruntime as ort
import numpy as np
import cv2

# Load the ONNX model
session = ort.InferenceSession("yolov5s.onnx")

# Get input and output names from the model
input_name = session.get_inputs()[0].name
output_names = [output.name for output in session.get_outputs()]

# Preprocessing function for webcam frames
def preprocess(image):
    """
    Resizes the image, converts BGR to RGB, transposes HWC to CHW,
    and normalizes pixel values to [0, 1].
    """
    # Resize to the model's expected input size (e.g., 640x640 for YOLOv5)
    img = cv2.resize(image, (640, 640))

    # Convert BGR to RGB and change from HWC to CHW format
    img = img[:, :, ::-1].transpose(2, 0, 1)

    # Add a batch dimension and normalize to [0, 1]
    img = np.expand_dims(img, axis=0).astype(np.float32) / 255.0
    return img

Explanation of preprocess:

• cv2.resize(image, (640, 640)): Resizes the input image to the dimensions expected by the YOLO model. YOLOv5 typically uses 640x640.
• img[:, :, ::-1]: Converts the image from BGR color format (default in OpenCV) to RGB format, which is often required by deep learning models.
• .transpose(2, 0, 1): Changes the image format from Height-Width-Channels (HWC) to Channels-Height-Width (CHW). This is a common format for neural network inputs.
• np.expand_dims(img, axis=0): Adds a batch dimension to the image, making it a 4D tensor (Batch, Channels, Height, Width).
• .astype(np.float32) / 255.0: Converts the pixel values to float32 and normalizes them to the range [0, 1], which is standard for many neural network inputs.

Step 3: Capture Webcam Feed and Perform Inference

This step outlines how to capture frames from your webcam, preprocess them, and run inference using the loaded ONNX model.

# Initialize webcam capture
cap = cv2.VideoCapture(0) # 0 is typically the default webcam

while True:
    # Read a frame from the webcam
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame")
        break

    # Preprocess the frame for the model
    input_tensor = preprocess(frame)

    # Run inference
    # The output will be a list of numpy arrays corresponding to the model's outputs
    outputs = session.run(output_names, {input_name: input_tensor})

    # --- TODO: Postprocessing ---
    # The 'outputs' variable contains the raw predictions.
    # You need to postprocess these outputs to extract bounding boxes,
    # class labels, and confidence scores. This typically involves:
    # 1. Filtering detections based on a confidence threshold.
    # 2. Applying Non-Maximum Suppression (NMS) to remove overlapping boxes.
    # 3. Mapping class IDs to human-readable names.
    # YOLO's postprocessing logic can be found in yolov5/utils/general.py.
    # For example, `apply_classifier`, `scale_coords`, `non_max_suppression`.

    # Placeholder for displaying the frame with detections
    # For now, we'll just display the original frame
    cv2.imshow("YOLO ONNX Inference", frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the webcam and destroy all OpenCV windows
cap.release()
cv2.destroyAllWindows()

Postprocessing Guidance:

The raw output from the YOLO model is a tensor containing detection information. To visualize these detections, you need to implement a postprocessing pipeline. This typically includes:

• Decoding Predictions: Interpreting the output tensor to get bounding box coordinates (x, y, width, height), objectness scores, and class probabilities.
• Confidence Thresholding: Filtering out detections with low confidence scores.
• Non-Maximum Suppression (NMS): Eliminating redundant bounding boxes that detect the same object.
• Class Mapping: Assigning human-readable class names to the detected objects.

The YOLOv5 repository provides utility functions in utils/general.py that can be adapted for this purpose.

Step 4: TensorRT Deployment (Optional for Speed)

For maximum performance on NVIDIA GPUs, you can convert your ONNX model to a TensorRT engine. TensorRT is NVIDIA's SDK for high-performance deep learning inference.

Convert ONNX to TensorRT Engine (CLI)

You can use the trtexec tool, which is part of the TensorRT installation, for this conversion.

# Navigate to your TensorRT installation directory or ensure trtexec is in your PATH
# Example command:
trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.trt --fp16

• --onnx: Specifies the input ONNX model.
• --saveEngine: Specifies the output file for the TensorRT engine.
• --fp16: Enables FP16 precision, which can significantly speed up inference on GPUs that support it while maintaining good accuracy.

Inference Using TensorRT (Python)

Integrating TensorRT directly into Python for inference is more involved than ONNX Runtime. It typically requires:

• TensorRT Python Bindings: These need to be built or installed.
• CUDA Memory Management: Explicitly managing memory on the GPU.
• Engine Building and Execution: Loading the .trt engine, creating an execution context, and performing kernel launches.

Libraries like pycuda can assist with CUDA memory management. For simpler integration, you might consider using a TensorRT wrapper or a framework that abstracts these details. Many examples for TensorRT inference are available in the official NVIDIA TensorRT samples repository.

Key Benefits of ONNX/TensorRT Inference

Optimization Tips

• Dynamic ONNX Export: Use the --dynamic flag during ONNX export if you need to handle variable input sizes without recompiling the model.
• Quantization: For edge deployments (like Jetson), consider applying post-training quantization (e.g., INT8) to reduce model size and improve inference speed, potentially with a slight accuracy trade-off.
• Batch Size: For real-time webcam applications where latency is critical, using a batch size of 1 is usually optimal.
• CUDA Streams: Leverage CUDA stream handling for more efficient data transfers between the host (CPU) and the device (GPU), minimizing I/O bottlenecks.

Conclusion

Deploying YOLO models using ONNX or TensorRT is a powerful way to transform deep learning models into high-performance, real-time applications. Whether you're developing a smart camera, an autonomous drone, or an AI-powered kiosk, this setup provides a production-ready and highly scalable solution for object detection.

Keywords

• YOLO ONNX deployment
• TensorRT YOLO inference
• real-time object detection webcam
• YOLOv5 ONNX tutorial
• TensorRT GPU acceleration
• ONNX Runtime inference
• YOLO TensorRT Jetson Nano
• ONNX model webcam Python
• YOLOv8 real-time detection
• low latency deep learning inference

Interview Questions

• What is ONNX and how does it help in deploying deep learning models? ONNX (Open Neural Network Exchange) is an open format designed to represent deep learning models. It acts as an intermediate representation, allowing models trained in one framework (e.g., PyTorch, TensorFlow) to be deployed using a different inference engine (e.g., ONNX Runtime, TensorRT). This interoperability simplifies deployment across various hardware and software platforms.

• How do you export a YOLOv5 model to ONNX format? You export a YOLOv5 model to ONNX using the export.py script provided in the YOLOv5 repository. The command typically looks like python export.py --weights <your_model>.pt --include onnx.

• What are the main differences between ONNX Runtime and TensorRT for inference?

  • ONNX Runtime: Cross-platform, easy to set up, good general-purpose performance. Supports CPU and GPU (with specific execution providers).
  • TensorRT: NVIDIA-specific, highly optimized for NVIDIA GPUs. Offers significant performance gains through techniques like layer fusion, kernel auto-tuning, and precision calibration (FP16, INT8). More complex to set up and requires an NVIDIA GPU.

• Describe the preprocessing steps necessary before feeding webcam frames into a YOLO ONNX model. Typical preprocessing steps include:

  1. Resizing: Resizing the image to the input dimensions expected by the YOLO model (e.g., 640x640).
  2. Color Conversion: Converting from BGR (OpenCV default) to RGB.
  3. Format Conversion: Transposing the image from HWC (Height, Width, Channels) to CHW (Channels, Height, Width).
  4. Normalization: Scaling pixel values to a specific range (e.g., [0, 1] or [-1, 1]) and converting to the appropriate data type (e.g., float32).
  5. Batching: Adding a batch dimension to the input tensor.

• How can TensorRT improve inference speed compared to ONNX Runtime? TensorRT achieves higher speeds by performing aggressive optimizations specific to NVIDIA hardware, such as:

  • Kernel Fusion: Combining multiple operations into a single GPU kernel to reduce kernel launch overhead.
  • Layer and Tensor Optimizations: Reordering and optimizing the execution of layers.
  • Precision Calibration: Utilizing lower precision formats like FP16 or INT8 to reduce computation and memory bandwidth requirements.
  • Platform-Specific Optimizations: Tailoring the execution plan to the specific GPU architecture.

• What is the role of postprocessing such as Non-Maximum Suppression (NMS) in YOLO detection? Postprocessing is crucial for converting the raw model outputs into meaningful object detections. NMS is a key part of this; it's an algorithm that helps to eliminate redundant bounding boxes that detect the same object. It works by selecting the box with the highest confidence score and suppressing other boxes that have a high overlap (IoU - Intersection over Union) with the selected box, ensuring that only one bounding box is predicted per object.

• How do you handle different input image sizes in ONNX models? There are several ways:

  1. Padding/Resizing: Resize all input images to a fixed size that the ONNX model expects (as shown in the preprocess function). This is the most common method.
  2. Dynamic Input Shapes: If the ONNX model was exported with dynamic input shapes enabled (e.g., --dynamic flag in YOLOv5 export), ONNX Runtime or TensorRT can handle varying input dimensions within the defined range.
  3. Model Modification: For more control, you might need to modify the model architecture itself to be more flexible with input sizes.

• What are some challenges in deploying deep learning models on NVIDIA Jetson devices?

  • Limited Resources: Jetson devices have less computational power and memory compared to desktop GPUs.
  • Power Consumption: Optimizing models for efficiency is critical to manage power draw.
  • Cross-Compilation: Sometimes models need to be compiled specifically for the Jetson's ARM architecture.
  • Software Environment: Managing CUDA, cuDNN, and TensorRT versions can be complex.
  • Quantization Accuracy: Achieving good accuracy with INT8 quantization might require careful calibration.

• Explain the workflow of converting an ONNX model to a TensorRT engine using trtexec. The trtexec workflow involves:

  1. Input: Providing the ONNX model file (.onnx) to trtexec.
  2. Configuration: Specifying desired optimizations, such as data precision (--fp16, --int8), input tensor shapes, and batch sizes.
  3. Engine Building: trtexec analyzes the ONNX graph, performs optimizations (layer fusion, kernel selection), and builds an optimized execution engine tailored to the target NVIDIA GPU.
  4. Output: Saving the optimized engine to a file (e.g., .trt). This engine can then be loaded by the TensorRT runtime for fast inference.

• How can you optimize YOLO models for edge deployment in terms of precision and batch size?

  • Precision: Use lower precision formats like FP16 or INT8. FP16 offers a good balance of speed and accuracy. INT8 provides the most significant speedups and memory savings but may require calibration to minimize accuracy loss.
  • Batch Size: For real-time applications on edge devices, a batch size of 1 is typically used to minimize latency. If processing multiple streams or performing tasks where latency is less critical, a larger batch size might improve throughput.