VisionCLI is a C++ command-line tool for analyzing and processing images directly from the terminal. It uses OpenCV for object detection with YOLOv3, grayscale conversion, and image display functionality.

• Object detection using YOLOv3 neural network (detects 80 COCO object classes)
• Grayscale conversion with automatic saving of processed images
• Image display in OpenCV window (color or grayscale)
• CLI interface powered by CLI11
• Flexible YOLO model paths - use default or specify custom cfg/weights files
• Pre-configured YOLO models - all required files included in repository

• C++20 compiler (MSVC, GCC, or Clang)
• OpenCV >= 4.04.0
• CMake >= 3.10.10
• Windows (uses Windows API - windows.h)

Windows:

# Download OpenCV from https://opencv.org/releases/

# Extract to C:/opencv (or update CMakeLists.txt with your path)

Important: Update the OpenCV path in CMakeLists.txt line 6 to match your installation:

set(OpenCV_DIR "C:/opencv/build/x64/vc16/lib")

git clone https://github.com/Jedrzej-Klosowski/VisionCLI.git

cd VisionCLI

All YOLO model files are included - no additional downloads needed! ✅

mkdir build

cd build
cmake ..
cmake --build d .  --config Release

# Object detection with display
./VisionCLI --image ../assets/dog.jpg --run --show



# Convert to grayscale
./VisionCLI --image ../assets/dog.jpg --gray --show



# Use custom YOLO model
./VisionCLI --image photo.jpg --run --cfg path/to/custom.cfg --weights path/to/custom.weights



# Just display image
./VisionCLI --image photo.jpg --showw

Note: Using --run automatically enables --show

VisionCLI/
├── src/
│   └── main.cpp              # Main application source code
├── include/
│   ├── CLI11.hpp             # CLI argument parser library
│   ├── coco.names            # COCO dataset class names (80 objects)
│   ├── yolov3.cfg            # YOLOv3 neural network configuration
│   └── yolov3.weights        # YOLOv3 pre-trained weights (~248 MB)
├── assets/                   # Sample images for testing
├── CMakeLists.txt            # CMake build configuration
├── NOTES.md                  # Development notes and roadmap
├── README.md                 # This file
└── . gitignore                # Git ignore rules
gnore rules

VisionCLI can detect 80 different object classes from the COCO dataset:

Categories include:

• People & Animals:als: person, dog, cat, horse, bird, cow, elephant, bear, zebra, giraffe
• Vehicles: car, bicycle, motorcycle, bus, train, truck, boat, airplane
• Indoor Objects: chair, sofa, bed, dining table, toilet, TV, laptop, mouse, keyboard
• Food & Kitchen:hen: bottle, cup, fork, knife, spoon, bowl, banana, apple, sandwich, pizza, donut
• Sports: sports ball, kite, baseball bat, skateboard, surfboard, tennis racket
• And more...

Detection Parameters:

• Confidence threshold: 50%
• NMS threshold: 0.4 (Non-Maximum Suppression)
• Input size: 416×416 pixels
• Backend: OpenCV DNN (CPU)

When using --gray, creates a copy with suffix -szara-kopia:

Input:   assets/dog.jpg
Output: assets/dog-szara-kopia.jpg

Detected objects are shown with:

• Green bounding boxes
• Class name labels above boxes
• Displayed in OpenCV window

• C++20 (with C++17 filesystem support)
• OpenCV 4.x - Computer vision and image processing
• CMake - Cross-platform build system
• CLI11 - Modern command-line argument parsing
• YOLOv3 - Real-time object detection neural network

Requirements:

• Windows 10/11 (64-bit)
• ~500 MB disk space (includes YOLO weights)
• 4GB+ RAM recommended for object detection

Current Limitations:

• Windows-only - Uses Windows API (windows.h) for path resolution
• CPU-only - No GPU acceleration (processing may be slow on large images)
• Hardcoded paths - OpenCV path in CMakeLists.txt must be manually updated

"CMake could not find OpenCV"

# Update CMakeLists.txt line 6 with your OpenCV path: 

set(OpenCV_DIR "YOUR_PATH/opencv/build/x64/vc16/lib")

"Missing weights file: ..."

• Verify that include/yolov3.weights exists in your cloned repository
• File should be approximately 248 MB
• If missing, re-clone the repository or check if the file was excluded by . gitignore

"Could not open coco.names!"

• Run the program from the build/ directory
• Check that ../include/coco.names exists relative to executable

"Could not open or find the image!"

• Verify image path is correct (use absolute path or path relative to executable)
• Supported formats: JPG, PNG, BMP, TIFF

Slow detection speed

• YOLOv3 on CPU is computationally intensive
• Expected: 2-5 seconds per image on modern CPUs
• Consider using smaller images or YOLOv3-tiny for faster processing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and test thoroughly
4. Commit with clear messages: git commit -m 'Add amazing feature'
5. Push to your fork: git push origin feature/amazing-feature
6. Open a Pull Request

Please ensure:

• Code follows existing style
• Comments are in English
• No warnings during compilation

This project is licensed under the MIT License.

MIT License

Copyright (c) 2026 Jędrzej Kłosowski

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. 
 IN THE
SOFTWARE. 

Author: Jędrzej Kłosowski
GitHub: @Jedrzej-Klosowski
Repository: VisionCLI

Resources:

• OpenCV Documentation
• YOLOv3 Paper
• COCO Dataset
• CLI11 Library

VisionCLI – Powerful computer vision directly from your terminal! 🔍✨