OpenCV 4

June 20, 2026 · View on GitHub

OpenCV 4 with Deep Learning model inference (TensorFlow, Caffe) for Visual Studio 2017 (C++) on Windows 10 x64.

Project by Dr. Farshid Pirahansiah — www.tiziran.com | YouTube

What's Included

OpenCV 4.x compiled from source (main + contrib modules)
Visual Studio 2017 C++ project configuration
Example code for loading and running Caffe and TensorFlow models
PDF presentation with step-by-step setup guide

Quick Start

1. Build OpenCV 4 from Source

# Clone OpenCV 4.x
git clone https://github.com/opencv/opencv.git
git clone https://github.com/opencv/opencv_contrib.git

# Build with CMake (enable DNN, contrib)
cmake -D CMAKE_BUILD_TYPE=Release \
      -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
      -D BUILD_opencv_dnn=ON \
      -G "Visual Studio 15 2017 Win64" ..

2. Configure Visual Studio 2017

Set include path to OpenCV headers
Set library path to built .lib files
Set DLL path in system PATH

3. Run Deep Learning Models

See opencvtest.cpp for examples using Caffe and TensorFlow models with OpenCV's DNN module.

Video Tutorials

Topic	Link
Compile OpenCV 4 from source	Watch
VS2017 project setup	Watch
Using Caffe models	Watch
Using TensorFlow models	Watch

Presentation

View Google Slides presentation

Deep Learning with OpenCV 4 — 2025-2026 State of the Art

Model Format Support

Format	Status	Notes
ONNX	Preferred	Cross-platform, best supported in 2025+
TensorFlow Lite	Supported	Edge/mobile deployment
Caffe	Legacy	Still functional, no longer actively developed
Darknet (YOLO)	Supported	Real-time object detection
Torch	Supported	PyTorch model export via ONNX

Recommended Inference Backends (2025-2026)

Backend	Best For	Speed
ONNX Runtime	Cross-platform CPU/GPU	2-10x over OpenCV DNN
NVIDIA TensorRT	NVIDIA GPU production	5-20x over CPU
OpenVINO	Intel CPU/iGPU/VPU	2-4x on Intel hardware
OpenCV DNN + CUDA	Quick GPU offload	3-8x over CPU
Qualcomm QNN	Snapdragon edge devices	Optimized for mobile

Modern Architecture Recommendations

Export models to ONNX — Universal format, best tooling support
Use quantization (QDQ INT8) — 4x compression, <1% accuracy loss
Deploy with ONNX Runtime — Cross-platform, hardware-optimized
Edge AI — NVIDIA Jetson (TensorRT), Raspberry Pi (NCNN), OpenVINO

Resources

License

See repository for license details.