darknet-cpp-windows

Windows support for Darknet-cpp. This repository provides visual studio project files for Darknet-cpp. This repository does not require additional libraries like pthread, and provides all requirements integrated. It supports Yolo v3.

• Support for 2015, 2017 Visual Studio versions, with one of below.
• Needs CUDA 8.0 (and its environment settings correctly defined, with Visual studio integration enabled), and CUDNN, OpenCV3 or
• Needs CUDA 9.1 (and its environment settings correctly defined, with Visual studio integration enabled), and CUDNN, OpenCV3
• Needs CUDA 10.1 (and its environment settings correctly defined, with Visual studio integration enabled), and CUDNN, OpenCV3.4

Steps to build darknet-cpp-windows:

• Download the darknet-cpp port, from

https://github.com/prabindh/darknet

• At the same level as the darknet folder, clone or download the darknet-cpp-windows sources

https://github.com/prabindh/darknet-cpp-windows

• Open the below solution file in Visual Studio

darknet-cpp-windows\darknet\darknet.sln (for CUDA10.1 and CUDNN7),

darknet-cpp-windows\darknet_cuda91\darknet_cuda91.sln (for CUDA9.1),

• Change the OpenCV folder path if needed (default expected to be 2 levels above, ....\opencv3\build\x64\vc15\lib)

• Build the project arapaho, which will also build the darknet project dependency

• Copy the required data, weight, cfg and input image file for detection into the arapaho folder. All the files need to be named as {input.cfg, input.data, input.jpg or input.mp4, input.weights}. The names can be changed in the darknet\arapaho\test.cpp file.

(Note: Yolov3 default model config will not fit on a GPU like a QuadroM, hence the tiny config can be used for checking)

Yolov3 and Tiny weights can both be obtained from the links at - https://pjreddie.com/darknet/yolo/

The corresponding configs are available at the darknet tree in "cfg" folder.

• Run the generated binary arapaho.exe, for detection, from

darknet-cpp-windows\bin\win64\$(Configuration)\arapaho.exe

• This will run the arapaho C++ wrapper, and generate output for the provided image, and show each frame with detected regions in a Window. Example output below:

[YOLOV2] .... Image data = 000001362EF87060, w = 992, h = 620 Detect: Resizing image to match network l.softmax_tree = 0000000000000000, nms = 0.400000 Detected 1 objects Box #0: x,y,w,h = [0.406386, 0.283149, 0.384096, 0.509924] `

[YOLOV3] ... 106 detection Setup: net->n = 107 net->layers[0].batch = 1 Loading weights from input.weights...Done! Setup: layers = 32, 32, 3 Warning: Read classes from cfg (80) > maxClasses (2) Image expected w,h = [256][256]! Setup: Done Image data = 00000226B3EC3020, w = 640, h = 424 Detect: Resizing image to match network ==> Detected [3] objects in [3.275245] seconds Box #0: center {x,y}, box {w,h} = [0.765709, 0.575041, 0.383576, 0.504763] Label:horse Box #1: center {x,y}, box {w,h} = [0.217989, 0.722828, 0.222815, 0.199334] Label:dog Box #2: center {x,y}, box {w,h} = [0.363993, 0.551002, 0.133328, 0.627906] Label:person cap.read failed/EoF - AV file input.jpg

The detected regions will be overlaid and shown in the Window that will open showing each frame.

For a brief about Arapaho C++ API, refer to https://github.com/prabindh/darknet/blob/master/arapaho/arapaho_readme.txt