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Opencvd

Unofficial OpenCV binding for D programming language

This is an initial attempt to create an opencv binding for dlang. The C interface was borrowed from gocv, and the implementation has been highly influenced by it.

Contributions

Some notes

Requirements

Opencvd requires the following packages to build:

Tested Systems

Notable features

Current limitations:

How to build

Ubuntu - Raspbian - Jetson Nano

First, compile opencv4 + opencv_contrib for your machine. Clone opencv and opencv_contrib repositories and execute:

cd <opencv_source_root>
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=RELEASE -DOPENCV_GENERATE_PKGCONFIG=YES -DOPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules/ ..
make -j4
sudo make -j4 install
sudo ldconfig

Then, you have to compile C/C++ interface files:

cd opencvd/c && mkdir build
cd build
cmake .. // use "cmake -D OPENCVD_CUDA:BOOL=ON .." for cuda support
make // or cmake --build .

Then use dub to build the library.

In your app's dub.json, you may need to set linker flags like:

"dependencies": {
        "opencvd": "~>0.0.7"
},
"lflags": ["-L/home/user/.dub/packages/opencvd", "-lopencvcapi", "-lopencvcapi_contrib"]

and add following to dub.json of your test app:


"libs": [
    "opencv", // this is handled by pkgconfig. You may set it as "opencv4" depending on the name of your pkgconfig file.
    "opencvcapi",
    "opencvcapi_contrib"
]

Your build experience may vary. On Raspbian, you may want to use RP Official Camera, so you have to register the camera device to be /dev/video0:

sudo nano /etc/modules

and add this line to the end of the file and reboot:

bcm2835-v4l2

Windows 10 64 bit:

set PATH=%PATH%;C:\your-compilers-bin-folder\
set OpenCV_DIR=C:\your-opencv-root-folder

your-opencv-root-folder must contain a file named OpenCVConfig.cmake.

cd opencvd/c
mkdir build
cd build
cmake .. -G "Visual Studio 15 2017 Win64"

This will create Visual Studio solution files in opencvd/c/build.

cmake --build .

cd opencvd
dub

Now you have *.lib files in opencvd folder.

"dependencies": {
        "opencvd": "~>0.0.7"
},
"libs": [
    "opencv_world451",
    "opencv_img_hash451",
    "opencvcapi",
    "opencvcapi_contrib"
]

While compiling your test app, you must always run dub or ldc2 commands in x64 Native Tools Command Prompt for VS 2017. And note that we have built opencvd against shared libs of opencv4. So, Compiled executables will need opencv dlls in the PATH.

OSX

export PKG_CONFIG_PATH=/Users/user/opencv4-dev/installation/OpenCV-master/lib/pkgconfig/
export DYLD_LIBRARY_PATH=/Users/user/opencv4-dev/installation/OpenCV-master/lib/

Copy libopencvcapi_contrib.a and libopencvcapi.a to the root of your example app. This is an example dub.json for test app:

{
	"description": "A minimal D application.",
    "dependencies": {
        "opencvd": "~>0.0.7"
	},
	"authors": [
		"Ferhat Kurtulmuş"
	],
	"copyright": "Copyright © 2019, Ferhat Kurtulmuş",
	"license": "Boost",
	"name": "testapp",
    "lflags": ["-L/Users/user/opencv4-dev/installation/OpenCV-master/lib/", "-L/Users/user/Desktop/testapp/"],
    "libs": [
        "opencv4",
        "opencvcapi",
        "opencvcapi_contrib"
    ]
}

Some notes about C interface (C++ functions with C externs)

Gocv does not wrap some important functionality of opencv. Opencvd will cover some of those wrapping them in c++ sources with appropriate naming such as core -> core_helper, imgproc -> imgproc_helper. Thus, differences from gocv can be tracked easily. This should be a temporary solution untill a clear roadmap of opencvd project is determined by its community.

Some examples to show how it looks like:

import std.stdio;

import opencvd;

void main()
{
    Mat img = imread("test.png", 0);
    
    Mat subIm1 = img[0..$, 200..300]; // no copy, just new Mat header to windowed data
    
    auto roi = Rect(0, 0, 100, 200 );
    Mat subIm2 = img(roi); // no copy, just new Mat header to windowed data
    
    img[200..$-50, 50..200] = Scalar.all(255);
    
    ubyte[] my_ubyte_array = img.array!ubyte; // access flat array of Mat as ubyte
    // my_ubyte_array.writeln;
    
    double[] my_double_array = img.array!double; // as double
    // my_double_array.writeln;
    
    ubyte val = img.at!ubyte(50, 30);
    
    Color color = img.at(20, 62); // or img[20, 62];
    
    // img[20, 20] = Color(25, 26, 27); // assign like this if it is a 3 channel mat
    img[20, 20] = ubyte(255); // assign like this if it is a single-channel mat
    
    namedWindow("res", 0);
    Mat imres = Mat();
    
    compare(img, Scalar(200, 0, 0, 0), imres, CMP_LT);
    
    imshow("res", imres);
    
    blur(img, img, Size(3, 3));
    
    foreach(int i; 100..200)
        foreach(int j; 100..200)
            img.set!ubyte(i, j, 125);
    
    writeln(img.type2str());
    writeln(img.getSize());
    writeln(img.type());
    writeln(img.width);
    writeln(img.channels);
    writeln(img.step);


    auto cnts = findContours(img, RETR_LIST, CHAIN_APPROX_SIMPLE);
    writeln(cnts[0][0]);
    // or :
    Point[][] contours;
    Scalar[] hierarchy;
    auto c_h = findContoursWithHier(img, RETR_CCOMP, CHAIN_APPROX_SIMPLE);
    contours = c_h[0];
    contours.writeln;
    hierarchy = c_h[1];
    hierarchy.writeln;
    
    namedWindow("hello", 0);
    imshow("hello", img);

    writeln(img.isEmpty());
    
    Mat m = Mat();
    writeln(m.isEmpty());
    
    Destroy(img);
    
    auto mt = Mat(20, 20, CV_8UC3);
    
    mt[2, 3] = Color(5,6,7,255);
    mt[2, 3].writeln;
    
    ubyte[] data = [1, 2, 3,
                    4, 5, 6,
                    10,2, 3,
                    1, 1, 1
    ];
    
    Mat mymat = Mat(4, 3, CV_8U, data.ptr);
    
    mymat = mymat * 2;
    mymat = mymat + 3;
    
    ubyte[] mtdata = mymat.array!ubyte;
    mtdata.writeln;
    
    waitKey(0);
    
}

Some screenshots

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