LegoCV is native OpenCV framework built for Swift and Objective-C projects. It eliminates the need to use Objective-C++ and allows for full compatibility with native Swift projects. The only dependency is native OpenCV framework for iOS.

Mission

Swift is one of the fastest evolving languages, but there is no way to use C++ frameworks directly, as it could be with Objective-C/C++.

This project's purpose is to create a simple, easy to use native Swift framework for OpenCV. The project adds Swift and Objective-C convenience methods, but translates to OpenCV API entirely.

The idea is to simply wrap OpenCV native C++ classes into Objective-C classes, which are then natively bridged to Swift, providing a thin layer on top of native OpenCV. Realm and EmguCV in C# use similar framework structure.

Example

The following examples display the difference in using LegoCV in Swift and Objective-C versus vanilla OpenCV in C++. The example is extracted from Face detection sample code, shipped with LegoCV. On iOS it uses OCVVideoCamera class to get image stream from camera (wraps OpenCV's CvVideoCamera, to keep backward compatibiliy).

Swift (LegoCV):

let faceDetector = OCVCascadeClassifier();
faceDetector.load(path: "haarcascade_frontalface_alt2.xml")

func process(image: OCVMat) {
    let scale = 2.0

    let minSize = OCVSize(width: 30, height: 30)

    let gray = OCVMat()
    let smallImage = OCVMat(rows: Int(round(Double(image.rows) / scale)), cols: Int(round(Double(image.cols) / scale)), type: .cv8U, channels: 1)

    OCVOperation.convertColor(from: image, to: gray, with: .BGR2GRAY)
    OCVOperation.resize(from: gray, to: smallImage, size: smallImage.size, fx: 0, fy: 0, interpolation: .linear)
    OCVOperation.equalizeHistogram(from: smallImage, to: smallImage)

    // Faces are returned as OCVRect instances, so they are mapped in Swift, as they are structs.
    let faces : [OCVRect] = faceDetector.detectMultiscale(with: smallImage, scaleFactor: 1.1, minNeighbours: 2, flags: 0, minSize: minSize).map { $0.rect }
}

Objective-C (LegoCV with Objective-C):

- (void)setupClassifier {
    self.faceDetector = [[OCVCascadeClassifier alloc] init];
    [self.faceDetector loadPath:@"haarcascade_frontalface_alt2.xml"];
}

- (void)processImage:(OCVMat *)image {
    double scale = 2.0;

    OCVSize minSize;
    minSize.width = 30;
    minSize.height = 30;

    OCVMat* gray = [[OCVMat alloc] init];
    OCVMat* smallImage = [[OCVMat alloc] initWithRows:round(image.rows / scale) cols:round(image.cols / scale) type: OCVDepthTypeCv8U, channels: 1)

    [OCVOperation convertColorFromSource:image toDestination:gray with:OCVColorConversionTypeBGR2GRAY];
    [OCVOperation resizeFromSource:gray toDestination:smallImage size:smallImage.size fx:0 fy:0 interpolation:OCVInterpolationTypeLinear];
    [OCVOperation equalizeHistogramFromSource:smallImage toDestination:smallImage];

    // Faces are returned as OCVRectValue instances, which wrap OCVRect structs.
    NSArray<OCVRectValue *>* faces = [self.faceDetector detectMultiscaleWith:smallImage scaleFactor:1.1 minNeighbours:2 flags: 0 minSize:minSize];
}

C++ (OpenCV):

using namespace cv;

void setup () {
    _faceDetector = new CascadeClassifier();
    _faceDetector->load("haarcascade_frontalface_alt2.xml");
}

void processImage(cv::Mat img) {
    double scale = 2.0;
    Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );

    cvtColor( img, gray, COLOR_BGR2GRAY );
    resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
    equalizeHist( smallImg, smallImg );

    cv::Size minSize(30,30);

    vector<cv::Rect> faceRects;

    // Faces are returned in provided faceRects vector
    _faceDetector->detectMultiScale(smallImg, faceRects, 1.1, 2, 0, minSize);
}

More examples, including Swift playgrounds can be found in the sample project.

Documentation

As this is a project in progress, documentation will be added to Wiki as development progresses.

Performance

There is a smaller performance impact compared to pure native C++ code of OpenCV, due to Objective-C messaging system. If you need a high performance code, it is still recommended to write the algorithm in C++ and add bridges to LegoCV or Objective-C.

Installation

LegoCV can be installed with CocoaPods or Carthage. It's only dependency is OpenCV framework, which can be downloaded from their website.

pod 'LegoCV'

Building

To build LegoCV project you need cmake. Install it with brew install cmake and make sure you have Xcode Command Line tools installed. Trigger with xcode-select --install to check. Also make sure you use latest Xcode version and not Beta for master branch.

License

BSD license, respect OpenCV license as well.