Example scripts for the detection of lanes using the ultra fast lane detection v2 model in ONNX/TensorRT.

Example scripts for the detection of objects using the YOLOv5/YOLOv5-lite/YOLOv8 model in ONNX/TensorRT.

1. Requirements

2. Examples

3. Demo

4. License

• OpenCV, Scikit-learn, onnxruntime, pycuda and pytorch.

• Install :

  The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:

  pip install -r requirements.txt
  

• Comvert Onnx to TenserRT model :

  python convertOnnxToTensorRT.py
  

• Video inference :

  • Setting Config :

    Note : can support onnx/tensorRT format model. But it needs to match the same model type.

  lane_config = {
   "model_path": "./TrafficLaneDetector/models/culane_res18.trt",
   "model_type" : LaneModelType.UFLDV2_CULANE
  }
  
  object_config = {
   "model_path": './ObjectDetector/models/yolov8l-coco.trt',
   "model_type" : ObjectModelType.YOLOV8,
   "classes_path" : './ObjectDetector/models/coco_label.txt',
   "box_score" : 0.4,
   "box_nms_iou" : 0.45
  }

  Target	Model Type	Describe
  Lanes	LaneModelType.UFLD_TUSIMPLE	Support Tusimple data with ResNet18 backbone.
  Lanes	LaneModelType.UFLD_CULANE	Support CULane data with ResNet18 backbone.
  Lanes	LaneModelType.UFLDV2_TUSIMPLE	Support Tusimple data with ResNet18/34 backbone.
  Lanes	LaneModelType.UFLDV2_CULANE	Support CULane data with ResNet18/34 backbone.
  Object	ObjectModelType.YOLOV5	Support yolov5n/s/m/l/x model.
  Object	ObjectModelType.YOLOV5_LITE	Support yolov5lite-e/s/c/g model.
  Object	ObjectModelType.YOLOV8	Support yolov8n/s/m/l/x model.

  • Run :

  python demo.py
  

• Demo Youtube Video

• Display Switch

  

GPLv3 License key requirements :

• Disclose Source
• License and Copyright Notice
• Same License
• State Changes