Set Up Your C++ Compiler

To build the OpenCV libraries, identify a compatible C++ compiler for your operating system, as described in Portable C Code Generation for Functions That Use OpenCV Library. Configure the identified compiler by using the mex -setup c++ command. For more information, see Choose a C++ Compiler.

Model Description

In this example, a smile detector is implemented by using the Simulink model smileDetect.slx.

In this model, the subsystem_slwrap_detectAndDraw subsystem resides in the Smile_Detect_Lib library. You create the subsystem_slwrap_detectAndDraw subsystem by using the OpenCV Importer. The subsystem accepts a face image or a video and provides these output values.

The MATLAB® Function block accepts input from the subsystem_slwrap_detectAndDraw subsystem block. The MATLAB Function block has a set of emoji images. The smile intensity of the emoji in these images ranges from low to high. From the emoji images, the block identifies the most appropriate emoji for the estimated intensity and places it on the face image. The output is then provided to the Detected Face and Smiley Replacement Video Viewer blocks.

Copy Example Folder to a Writable Location

To access the path to the example folder, at the MATLAB command line, enter:

     OpenCVSimulinkExamples;

Each subfolder contains all the supporting files required to run the example.

Before proceeding with these steps, ensure that you copy the example folder to a writable folder location and change your current working folder to ...example\SmileDetector. All your output files are saved to this folder.

Step 1: Import OpenCV Function to Create a Simulink Library

1. To start the OpenCV Importer app, click Apps on the MATLAB Toolstrip. In the Welcome page, specify the Project name as Smile_Detector. Make sure that the project name does not contain any spaces. Click Next.

2. In Specify OpenCV Library, specify these file locations, and then click Next.

3. Analyze your library to find the functions and types for import. Once the analysis is complete, click Next. Select the detectAndDraw function and click Next.

4. From What to import, select the I/O Type for inImage as Input, and then click Next.

5. In Create Simulink Library, verify the default values and click Next.

A Simulink library Smile_Detector_Lib is created from your OpenCV code into the project root folder. The library contains a subsystem and a C Caller block. You can use any of these blocks for model simulation. In this example, the subsystem subsystem_slwrap_detectAndDraw is used.

Step 2: Use Generated Subsystem in Simulink Model

To use the generated subsystem subsystem_slwrap_detectAndDraw with the Simulink model smileDetect.slx:

1. In your MATLAB Current Folder, right-click the model smileDetect.slx and click Open from the context menu. In the model, delete the existing subsystem_slwrap_detectAndDraw subsystem and drag the generated subsystem subsystem_slwrap_detectAndDraw from the Smile_Detector_Lib library to the model. Connect the subsystem to the MATLAB Function block.

2. Double-click the subsystem and specify these parameter values.

3. Click Apply, and then click OK.

Step 3: Simulate the Smile Detector

On the Simulink Toolstrip, in the Simulation tab, click on simulate the model button. After the simulation is complete, the Video Viewer blocks display the detected face and an emoji replacement on the face tabs. The emoji represents the intensity of the smile.

Step 4: Generate C++ Code from the Smile Detector Model

Before you generate the code from the model, you must first ensure that you have write permission in your current folder.

To generate C++ code:

1. Open the smileDetect_codegen.slx model from your MATLAB Current Folder.

2. on the Apps tab on the Simulink toolstrip, select Embedded Coder. On the C++ Code tab in the Settings drop-down list, click C/C++ Code generation settings to open the Configuration Parameters dialog box and verify these settings:

3. Connect the generated subsystem subsystem_slwrap_detectAndDraw to the MATLAB Function block.

4. To generate C++ code, under the C++ Code tab, click the Generate Code drop-down list, and then click Build. After the model finishes generating code, the Code Generation Report opens. You can inspect the generated code. The build process creates a zip file called smileDetect_with_ToOpenCV.zip in your current MATLAB working folder.

Deploy the Smile Detector on the Raspberry Pi Hardware

Before you deploy the model, connect the Raspberry Pi to your computer. Wait until the PWR LED on the hardware starts blinking.

In the Settings drop-down list, click Hardware Implementation to open the Configuration Parameters dialog box and verify these settings:

To deploy the code to your Raspberry Pi hardware:

1. From the generated zip file, copy these files to your Raspberry Pi hardware.

2. In Raspberry Pi, go to the location where you saved the files. To generate an elf file, enter this command:

   make -f smileDetect.mk

3. Run the executable on Raspberry Pi. After successful execution, you see the output on Raspberry Pi with an emoji placed on the face image.

   smileDetect.elf