ccm-esp32-vision-node

Embedded camera node for ESP32-S3 with a modular C++ architecture for real-time computer vision. This project demonstrates professional embedded CV engineering: camera bring-up, modular pipelines, and efficient processing on low-power microcontrollers.

Work-in-progress: This repository is part of the CCMCode Embedded Vision toolkit and serves as a portfolio-quality example of modern ESP-IDF C++ development.

---

🚀 Features (Current & Planned)

✅ Implemented

• ESP-IDF C++ project with multi-component architecture (camera_node, cv_pipeline, stream_server, utils, drivers)
• Fully compiling project with ESP32-S3 target under WSL Ubuntu
• C++ CameraNode component using the esp32-camera managed component
• Real-time FPS measurement with esp_timer

🛠 In Progress

• C++ computer vision pipeline (cv_pipeline)
• Frame timing and lightweight profiling utilities
• Clean, extensible class-based architecture for embedded CV

📌 Planned

• Color blob detector (HSV thresholding)
• ROI extraction + simple pre-processing steps
• MJPEG or raw frame Wi-Fi streaming server
• UART debug dashboard (FPS, memory, timings)
• Benchmarks across frame sizes & pixel formats
• Example: simple color-object tracker

---

🧱 Project Architecture

For a detailed component diagram and explanation, see docs/architecture.md.

ccm-esp32-vision-node/
│
├── components/
│   ├── camera_node/       # Camera bring-up abstraction
│   ├── cv_pipeline/       # Modular image processing pipeline (WIP)
│   ├── stream_server/     # Wi-Fi streaming endpoint (planned)
│   ├── drivers/           # Camera/sensor-specific helpers
│   └── utils/             # Logging, timers, profiling
│
├── firmware/
│   ├── main/              # App entry point (main.cpp)
│   └── CMakeLists.txt     # ESP-IDF project definition
│
├── docs/                  # Architecture & benchmarks (planned)
└── examples/              # Sample demos (planned)

---

🧪 Tested / Target Environment

This project is being developed and tested with:

• Chip: ESP32-S3
• SDK: ESP-IDF (Linux / WSL)
• Host environment: Windows 10 + WSL Ubuntu
• Tooling: VS Code, idf.py, CMake

Planned hardware targets include:

• ESP32-S3 dev boards with PSRAM
• Camera modules based on OV2640 / OV5640

---

🖥️ Getting Started (ESP-IDF)

1. Install ESP-IDF

Follow the official install instructions: https://docs.espressif.com/projects/esp-idf/en/latest/esp32s3/get-started/

2. Clone the repository

git clone https://github.com/CahillMeyer/ccm-esp32-vision-node.git
cd ccm-esp32-vision-node/firmware

3. Configure the target

idf.py set-target esp32s3
idf.py menuconfig

4. Build & flash

idf.py build
idf.py flash monitor

---

📊 Status

This project successfully:

• builds under WSL Ubuntu
• uses the esp32-camera managed component
• compiles a fully working C++ main loop
• initializes and reads from the camera
• produces frame timing (FPS) logs

Next steps will add actual image-processing functionality.

---

🗺️ Roadmap

• Add CvPipeline grayscale + threshold stage
• Add frame buffer pre-processing (ROI, downsample)
• Add MJPEG Wi-Fi streaming server
• Add architecture diagrams
• Add host-side unit tests for pipeline components
• Add example demos (color tracking, region extraction)

---

📄 License

MIT License

---

👤 Author

Cahill Meyer
Embedded Vision Engineer
GitHub: https://github.com/CahillMeyer