AI Lecture Term Project – 4th Grade, 2nd Term
This project demonstrates real-time monocular depth estimation using a pre-trained MiDaS (v2.1 Large) model in ONNX format. Built with C++ and OpenCV’s DNN module, it captures live webcam feed and visualizes depth maps in real time.
The project follows this directory structure:
RealTime-Depth-Camera/
├── .vscode/ # Editor configs for VSCode (optional)
├── build/ # Compiled output binary (auto-generated)
│ └── depth_estimate # Executable built from depth_estimate.cpp
├── docs/ # For storing additional docs or reports (optional)
├── src/ # Source code and model files
│ ├── depth_estimate.cpp # Main source file for real-time depth estimation
│ └── models/ # Contains pre-trained ONNX models for depth estimation
│ ├── midas_v21_384.onnx # MiDaS v2.1 large model (384x384 resolution)
│ └── midas_v21_small_256.onnx # MiDaS v2.1 small model (256x256 resolution)
│
├── .gitignore # Git ignore rules
├── CMakeFileList.txt # CMake-related configuration (if used)
├── launch.sh # Build and run script for compiling and executing
└── README.md # Project documentation (this file)
- C++11 or later
- OpenCV 4.x (built with DNN module)
- Webcam (for live video input)
- MiDaS ONNX model (included in
src/models)
You can build and run the project using the provided shell script:
chmod +x launch.sh
./launch.shThis script will:
- Create the build/ directory if it doesn't exist
- Compile depth_estimate.cpp
- Run the resulting executable
- ❗️Note: Ensure the model file midas_v21_384.onnx is located at src/models/.
MiDaS (Monocular Depth Estimation) is a deep learning model developed by Intel Labs that estimates depth from a single RGB image. This project uses the v2.1 Large model in ONNX format.
- Displays the original webcam frame
- Displays the corresponding real-time depth map
- Shows the current FPS in the depth window
- Press q to exit the application.
- For macOS or systems without GPU support, the model runs on CPU by default.
- You can modify the backend to use CUDA if supported:
net.setPreferableBackend(DNN_BACKEND_CUDA);
net.setPreferableTarget(DNN_TARGET_CUDA);
This project is for academic use only. Model usage is subject to its original MiDaS license.