Self-Driving-ish Computer Vision System

• This project generates images you've probably seen in autonomous driving demo
• Detection
  • Object Detection and Tracking
  • Lane Detection and Curve Fitting
  • Road Segmentation
  • Depth Estimation
• Transform using Projective Geometry and Pinhole Camera Model
  • Normal View -> Top View
  • Distance Calculation (image plane -> ground plane in world coordinate system)

Result

• YoutTube: https://youtu.be/GB4p_fjQZNE

Tested Environment

Computer

• Windows 10 (x64) + Visual Studio 2019
  • Intel Core i7-6700 @ 3.4GHz + NVIDIA GeForce GTX 1070
• Jetson Xavier NX. JetPack 4.6
  • You will get error if using JetPack 4.5 or before because of TensorRT error

Deep Learning Inference Framework

• TensorFlow Lite with XNNPACK delegate
  • CPU
  • Note: Running with CPU is very slow
• TensorRT
  • GPU

Usage

./main [input]
 - input:
    - use the default image file set in source code (main.cpp): blank
        - ./main
     - use video file: *.mp4, *.avi, *.webm
        - ./main test.mp4
     - use image file: *.jpg, *.png, *.bmp
        - ./main test.jpg
    - use camera: number (e.g. 0, 1, 2, ...)
        - ./main 0
    - use camera via gstreamer on Jetson: jetson
        - ./main jetson

• Mouse Drag: Change top view angle
• Keyboard (asdwzx) : Change top view position

How to build a project

0. Requirements

• OpenCV 4.x
• CMake
• TensorRT 8.0.x
  • If you get build error related to TensorRT, modify cmake settings for it in inference_helper/inference_helper/CMakeLists.txt

1. Download source code and pre-built libraries

• Download source code
  • If you use Windows, you can use Git Bash

  git clone https://github.com/iwatake2222/self-driving-ish_computer_vision_system.git
  cd self-driving-ish_computer_vision_system
  git submodule update --init
  sh inference_helper/third_party/download_prebuilt_libraries.sh

• Download models

  sh ./download_resource.sh

2-a. Build in Windows (Visual Studio)

• Configure and Generate a new project using cmake-gui for Visual Studio 2019 64-bit
  • Where is the source code : path-to-cloned-folder
  • Where to build the binaries : path-to-build (any)
• Open main.sln
• Set main project as a startup project, then build and run!
• Note:
  • You may need to modify cmake setting for TensorRT for your environment

2-b. Build in Linux (Jetson Xavier NX)

mkdir build && cd build
# cmake .. -DENABLE_TENSORRT=off
cmake .. -DENABLE_TENSORRT=on
make
./main

Note

cmake options

cmake .. -DENABLE_TENSORRT=off  # Use TensorFlow Lite (default)
cmake .. -DENABLE_TENSORRT=on   # Use TensorRT

cmake .. -DENABLE_SEGMENTATION=on    # Enable Road Segmentation function (default)
cmake .. -DENABLE_SEGMENTATION=off   # Disable Road Segmentation function

cmake .. -DENABLE_DEPTH=on    # Enable Depth Estimation function (default)
cmake .. -DENABLE_DEPTH=off   # Disable Depth Estimation function

Misc

• It will take very long time when you execute the app for the first time, due to model conversion
  • I took 80 minutes with RTX 3060ti
  • I took 10 - 20 minutes with GTX 1070

Software Design

Class Diagram

Data Flow Diagram

Model Information

Details

• Object Detection
  • YOLOX-Nano, 480x640
  • https://github.com/PINTO0309/PINTO_model_zoo/blob/main/132_YOLOX/download_nano_new.sh
  • https://github.com/PINTO0309/PINTO_model_zoo/blob/main/132_YOLOX/download_nano.sh
• Lane Detection
  • Ultra-Fast-Lane-Detection, 288x800
  • https://github.com/PINTO0309/PINTO_model_zoo/blob/main/140_Ultra-Fast-Lane-Detection/download_culane.sh
• Road Segmentation
  • road-segmentation-adas-0001, 512x896
  • https://github.com/PINTO0309/PINTO_model_zoo/blob/main/136_road-segmentation-adas-0001/download.sh
• Depth Estimation
  • LapDepth, 192x320
  • https://github.com/PINTO0309/PINTO_model_zoo/blob/main/148_LapDepth/download_ldrn_kitti_resnext101.sh
  • LapDepth, 256x512
  • 00_doc/pytorch_pkl_2_onnx_LapDepth.ipynb

Performance

• Input
  • Jetson Xavier NX: Camera
  • GTX 1070: mp4 video
• With TensorRT FP16
• "Total" includes image read, pre/post process, other image process, result image drawing, etc.

License

• Copyright 2021 iwatake2222
• Licensed under the Apache License, Version 2.0
  • LICENSE

Acknowledgements

I utilized the following OSS in this project. I appreciate your great works, thank you very much.

Code, Library

• TensorFlow
  • https://github.com/tensorflow/tensorflow
  • Copyright 2019 The TensorFlow Authors
  • Licensed under the Apache License, Version 2.0
  • Generated pre-built library
• TensorRT
  • https://github.com/nvidia/TensorRT
  • Copyright 2020 NVIDIA Corporation
  • Licensed under the Apache License, Version 2.0
  • Copied source code
• cvui
  • https://github.com/Dovyski/cvui
  • Copyright (c) 2016 Fernando Bevilacqua
  • Licensed under the MIT License (MIT)
  • Copied source code

Model

• PINTO_model_zoo
  • https://github.com/PINTO0309/PINTO_model_zoo
  • Copyright (c) 2019 Katsuya Hyodo
  • Licensed under the MIT License (MIT)
  • Copied converted model files
• YOLOX
  • https://github.com/Megvii-BaseDetection/YOLOX
  • Copyright (c) Megvii, Inc. and its affiliates. All Rights Reserved
  • Licensed under the Apache License, Version 2.0
• Ultra-Fast-Lane-Detection
  • https://github.com/cfzd/Ultra-Fast-Lane-Detection
  • Copyright (c) 2020 cfzd
  • Licensed under the MIT License (MIT)
• road-segmentation-adas-0001
  • https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/intel/road-segmentation-adas-0001
  • Copyright (c) 2021 Intel Corporation
  • Licensed under the Apache License, Version 2.0
• LapDepth-release
  • https://github.com/tjqansthd/LapDepth-release
  • Licensed under the GNU General Public License v3.0

Image Files

• OpenCV
  • https://github.com/opencv/opencv
  • Licensed under the Apache License, Version 2.0
• YoutTube
  • https://www.youtube.com/watch?v=tTuUjnISt9s
  • Licensed under the Creative Commons license
  • Copyright Dashcam Roadshow 2020