Please check the last section of this README for commentary on the changes made to the starter repository and the points of the rubric addressed.
This is Capstone project for C++ Udacity NanoDegree by using C++ and OpenCV to implement lane detector for self-driving cars.
- cmake >= 3.7
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- OpenCV >= 4.0
- All installation instructions can be found here
- Or you can just install it using vcpkg package manager using the following command: vcpkg install opencv4:x64-linux
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
- On Windows using MSVC or clang intgrated with visual studio 2019.
- Code was implemented and tested on Windows machine using Visual Studio 2019 Community and MSVC Release / Debug and X64-clang-Release. Also it's working on WSL GCC-8.4
- Clone this repo.
- Make a build directory in the top level directory:
mkdir build && cd build - Compile:
cmake .. && make - Copy: project_video.mp4 file to the same location of executable file (build/)
- Run it:
./laneDetector.
.
├── CMakeLists.txt
├── CMakeSettings.json
├── GIF.gif
├── LICENSE
├── README.md
├── include
│ ├── LaneCalculator.h
│ ├── LaneDetector.h
│ ├── LanePlotter.h
│ └── LanePredictor.h
├── project_video.mp4
└── src
├── LaneCalculator.cpp
├── LaneDetector.cpp
├── LanePlotter.cpp
├── LanePredictor.cpp
└── main.cpp
| Criteria | Description |
|---|---|
| The project demonstrates an understanding of C++ functions and control structures. | All code is seperated into functions and methods |
| The project reads data from a file and process the data, or the program writes data to a file. | Using OpenCv's VideoCapture class I capture frames from project_video.mp4 file to process it into the program then show the result frames as video to user. |
| Criteria | Description |
|---|---|
| The project uses Object Oriented Programming techniques. | Most of code is located at LaneDetector, LanePredictor, LaneCalculator and LanePlotter classes |
| Classes use appropriate access specifiers for class members. | LaneDetector which is the main class owns unique_ptr to LanePredictor, LaneCalculator and LanePlotter objects which are all private objects |
| Class constructors utilize member initialization lists. | Done by default for most class which hold private/public data members. |
| Classes encapsulate behavior. | LaneDetector class own private unique_ptrs to utils classes |
| Classes follow an appropriate inheritance hierarchy. | LaneDetector is composed of LaneCalculator, LanePlotter and LanePredictor classes |
| Criteria | Description |
|---|---|
| The project uses scope / Resource Acquisition Is Initialization (RAII) where appropriate. | All containers used in the project are RAII-based as its from STL, and using unique_ptr in LaneDetector class data members |
| The project uses smart pointers instead of raw pointers. | I used smart pointers specially unique_ptrs in LaneDetector class |
| The project makes use of references in function declarations. | All objects that are non-trivally copyable are based by reference such as vectors of lines std::vector<Line>. |
| The project follows the Rule of 5. | Implemented for all needfull classes such as LanePredictor. |
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