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OpenCV iOS - Video Processing {#tutorial_video_processing}
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=============================
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@prev_tutorial{tutorial_image_manipulation}
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This tutorial explains how to process video frames using the iPhone's camera and OpenCV.
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Prerequisites:
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--------------
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- Xcode 4.3 or higher
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- Basic knowledge of iOS programming (Objective-C, Interface Builder)
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Including OpenCV library in your iOS project
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--------------------------------------------
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The OpenCV library comes as a so-called framework, which you can directly drag-and-drop into your
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XCode project. Download the latest binary from
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<http://sourceforge.net/projects/opencvlibrary/files/opencv-ios/>. Alternatively follow this
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guide @ref tutorial_ios_install to compile the framework manually. Once you have the framework, just
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drag-and-drop into XCode:
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![](images/xcode_hello_ios_framework_drag_and_drop.png)
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Also you have to locate the prefix header that is used for all header files in the project. The file
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is typically located at "ProjectName/Supporting Files/ProjectName-Prefix.pch". There, you have add
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an include statement to import the opencv library. However, make sure you include opencv before you
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include UIKit and Foundation, because else you will get some weird compile errors that some macros
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like min and max are defined multiple times. For example the prefix header could look like the
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following:
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@code{.objc}
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//
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// Prefix header for all source files of the 'VideoFilters' target in the 'VideoFilters' project
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//
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#import <Availability.h>
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#ifndef __IPHONE_4_0
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#warning "This project uses features only available in iOS SDK 4.0 and later."
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#endif
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#ifdef __cplusplus
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#import <opencv2/opencv.hpp>
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#endif
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#ifdef __OBJC__
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#import <UIKit/UIKit.h>
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#import <Foundation/Foundation.h>
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#endif
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@endcode
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### Example video frame processing project
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#### User Interface
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First, we create a simple iOS project, for example Single View Application. Then, we create and add
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an UIImageView and UIButton to start the camera and display the video frames. The storyboard could
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look like that:
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![](images/xcode_hello_ios_viewcontroller_layout.png)
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Make sure to add and connect the IBOutlets and IBActions to the corresponding ViewController:
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@code{.objc}
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@interface ViewController : UIViewController
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{
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IBOutlet UIImageView* imageView;
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IBOutlet UIButton* button;
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}
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- (IBAction)actionStart:(id)sender;
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@end
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@endcode
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#### Adding the Camera
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We add a camera controller to the view controller and initialize it when the view has loaded:
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@code{.objc}
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#import <opencv2/videoio/cap_ios.h>
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using namespace cv;
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@interface ViewController : UIViewController
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{
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...
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CvVideoCamera* videoCamera;
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}
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...
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@property (nonatomic, retain) CvVideoCamera* videoCamera;
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@end
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@endcode
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@code{.objc}
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- (void)viewDidLoad
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{
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[super viewDidLoad];
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// Do any additional setup after loading the view, typically from a nib.
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self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
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self.videoCamera.defaultAVCaptureDevicePosition = AVCaptureDevicePositionFront;
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self.videoCamera.defaultAVCaptureSessionPreset = AVCaptureSessionPreset352x288;
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self.videoCamera.defaultAVCaptureVideoOrientation = AVCaptureVideoOrientationPortrait;
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self.videoCamera.defaultFPS = 30;
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self.videoCamera.grayscale = NO;
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}
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@endcode
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In this case, we initialize the camera and provide the imageView as a target for rendering each
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frame. CvVideoCamera is basically a wrapper around AVFoundation, so we provide as properties some of
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the AVFoundation camera options. For example we want to use the front camera, set the video size to
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352x288 and a video orientation (the video camera normally outputs in landscape mode, which results
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in transposed data when you design a portrait application).
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The property defaultFPS sets the FPS of the camera. If the processing is less fast than the desired
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FPS, frames are automatically dropped.
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The property grayscale=YES results in a different colorspace, namely "YUV (YpCbCr 4:2:0)", while
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grayscale=NO will output 32 bit BGRA.
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Additionally, we have to manually add framework dependencies of the opencv framework. Finally, you
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should have at least the following frameworks in your project:
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- opencv2
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- Accelerate
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- AssetsLibrary
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- AVFoundation
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- CoreGraphics
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- CoreImage
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- CoreMedia
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- CoreVideo
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- QuartzCore
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- UIKit
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- Foundation
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![](images/xcode_hello_ios_frameworks_add_dependencies.png)
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#### Processing frames
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We follow the delegation pattern, which is very common in iOS, to provide access to each camera
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frame. Basically, the View Controller has to implement the CvVideoCameraDelegate protocol and has to
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be set as delegate to the video camera:
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@code{.objc}
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@interface ViewController : UIViewController<CvVideoCameraDelegate>
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@endcode
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@code{.objc}
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- (void)viewDidLoad
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{
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...
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self.videoCamera = [[CvVideoCamera alloc] initWithParentView:imageView];
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self.videoCamera.delegate = self;
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...
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}
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@endcode
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@code{.objc}
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#pragma mark - Protocol CvVideoCameraDelegate
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#ifdef __cplusplus
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- (void)processImage:(Mat&)image;
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{
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// Do some OpenCV stuff with the image
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}
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#endif
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@endcode
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Note that we are using C++ here (cv::Mat). Important: You have to rename the view controller's
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extension .m into .mm, so that the compiler compiles it under the assumption of Objective-C++
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(Objective-C and C++ mixed). Then, __cplusplus is defined when the compiler is processing the file
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for C++ code. Therefore, we put our code within a block where __cplusplus is defined.
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#### Basic video processing
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From here you can start processing video frames. For example the following snippet color-inverts the
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image:
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@code{.objc}
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- (void)processImage:(Mat&)image;
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{
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// Do some OpenCV stuff with the image
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Mat image_copy;
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cvtColor(image, image_copy, COLOR_BGR2GRAY);
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// invert image
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bitwise_not(image_copy, image_copy);
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//Convert BGR to BGRA (three channel to four channel)
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Mat bgr;
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cvtColor(image_copy, bgr, COLOR_GRAY2BGR);
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cvtColor(bgr, image, COLOR_BGR2BGRA);
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}
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@endcode
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#### Start!
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Finally, we have to tell the camera to actually start/stop working. The following code will start
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the camera when you press the button, assuming you connected the UI properly:
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@code{.objc}
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#pragma mark - UI Actions
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- (IBAction)actionStart:(id)sender;
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{
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[self.videoCamera start];
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}
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@endcode
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#### Hints
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Try to avoid costly matrix copy operations as much as you can, especially if you are aiming for
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real-time. As the image data is passed as reference, work in-place, if possible.
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When you are working on grayscale data, turn set grayscale = YES as the YUV colorspace gives you
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directly access the luminance plane.
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The Accelerate framework provides some CPU-accelerated DSP filters, which come handy in your case.
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