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https://github.com/legoless/legocv

Native OpenCV Swift Framework
https://github.com/legoless/legocv

framework opencv swift

Last synced: 12 days ago
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Native OpenCV Swift Framework

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# LegoCV



LegoCV is native OpenCV framework built for Swift and Objective-C projects. It eliminates the need to use Objective-C++ and write bringing code, and allows for full compatibility with native Swift projects. The only dependency is the native [OpenCV framework](http://opencv.org) for **iOS** (*and later macOS and tvOS*).



## Vision



Swift is one of the fastest evolving languages, but there is currently no way to use C++ frameworks directly, as it was possible 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 lightweight Objective-C classes, which are then natively bridged to Swift, providing a thin layer on top of native OpenCV. [Realm](https://realm.io) and [EmguCV](http://www.emgu.com) in C# use similar framework structure. Possibility for fully native cross-platform Swift version of OpenCV exists in the future.*



## Example



The following examples display the difference with using LegoCV in Swift or Objective-C compared to vanilla OpenCV in C++.

The example is extracted from Face detection sample code, included with LegoCV. On iOS it uses `OCVVideoCamera` wrapper class to get image stream from camera (wraps OpenCV's `CvVideoCamera`, to keep backward compatibility).



#### Swift (LegoCV):

```swift

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 size = CGSize(width: 140.0, height: 140.00).ocvSize

        

    let gray = OCVMat()

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

    

    //

    // OpenCV Default Syntax requires to predefine both input and output

    //

    OCVOperation.convertColor(from: image, to: gray, with: .BGR2GRAY)

    

    //

    // LegoCV syntactic sugar allows you to perform operations directly on the input, only defining output.

    //

    image.convertColor(to: gray, with: .BGR2GRAY)

    let grayImg = image.convertColor(with: .BGR2GRAY)

    

    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.

    //

    var faces : [OCVRect] = faceDetector.detectMultiscale(with: smallImage, scaleFactor: 1.1, minNeighbours: 2, flags: 0, minSize: minSize).map { $0.rect }

    

    //

    // More LegoCV objective syntactic sugar

    //

    let result : OCVCascadeClassifierResult = faceDetector.detectMultiscale(on: smallImage, with: OCVCascadeClassifierOptions.default)

    faces = result.objects

    

}

```



#### Objective-C (LegoCV with Objective-C):

```objectivec

- (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* faces = [self.faceDetector detectMultiscaleWith:smallImage scaleFactor:1.1 minNeighbours:2 flags: 0 minSize:minSize];

    

    //

    // Call the face detector classifier

    //

    OCVCascadeClassifierResult* result = [self.faceDetector detectMultiScaleOnImage:smallImage withOptions:[OCVCascadeClassifierOptions defaultOptions]];

}

```



#### C++ (OpenCV):

```cpp

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 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



### Prerequisites



For LegoCV 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.



### CocoaPods



LegoCV can be installed with [CocoaPods](https://cocoapods.org) or [Carthage](https://github.com/Carthage/Carthage). It's only dependency is OpenCV framework, which can be downloaded from their website.



```ruby

pod 'LegoCV'



# Use only specific modules

pod 'LegoCV/Core'

pod 'LegoCV/VideoIO'

```



*LegoCV supports iOS 8 and higher.*



### Example Project



1. First clone the project:



`git clone [email protected]:legoless/legocv.git`

`cd legocv`



2. Initialize submodules



```

git submodule init

git submodule update

```



3. Build `opencv2.framework` from git repository.



```

/usr/bin/python opencv/platforms/ios/build_framework.py ios --dynamic

```



4. Open `LegoCV.xcodeproj` and build.



# License



[BSD license](https://github.com/legoless/legocv/blob/master/LICENSE), respect [OpenCV](https://github.com/opencv/opencv/blob/master/LICENSE) license as well.