This repsitory contains code for Deep Learning with OpenCV DNN Module: A Definitive Guide blogpost.
All the code files and folders follow the following structure.
├── cpp
│ ├── classify
│ │ ├── classify.cpp
│ │ └── CMakeLists.txt
│ └── detection
│ ├── detect_img
│ │ ├── CMakeLists.txt
│ │ └── detect_img.cpp
│ └── detect_vid
│ ├── CMakeLists.txt
│ └── detect_vid.cpp
├── input
│ ├── classification_classes_ILSVRC2012.txt
│ ├── DenseNet_121.caffemodel
│ ├── DenseNet_121.prototxt
│ ├── frozen_inference_graph.pb
│ ├── image_1.jpg
│ ├── image_2.jpg
│ ├── object_detection_classes_coco.txt
│ ├── ssd_mobilenet_v2_coco_2018_03_29.pbtxt.txt
│ └── video_1.mp4
├── outputs
│ ├── image_result.jpg
│ ├── result_image.jpg
│ └── video_result.mp4
├── python
│ ├── classification
│ │ ├── classify.py
│ │ └── README.md
│ ├── detection
│ │ ├── detect_img.py
│ │ └── detect_vid.py
│ └── requirements.txt
└── README.md
To run the code in Python, please go into the python folder and execute the Python scripts in each of the respective sub-folders.
To run the code in C++, please go into the cpp folder, then go into each of the respective sub-folders and follow the steps below:
mkdir build
cd build
cmake ..
cmake --build . --config Release
cd ..
./build/classify
mkdir build
cd build
cmake ..
cmake --build . --config Release
cd ..
./build/detect_img
mkdir build
cd build
cmake ..
cmake --build . --config Release
cd ..
./build/detect_vid
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