Using OpenCV library on Raspberry Pi

This tutorial shows how to use the OpenCV 2 library in cross-compiled projects for Raspberry Pi 2. Before you begin, download the Raspberry Pi Jessie image and write it to the SD card. Then get a cross-toolchain compatible with the image from the gnutoolchains.com website.

1. Connect to your Raspberry Pi via SSH and run the “sudo apt-get install libopencv-dev” command to install OpenCV:
2. Start Visual Studio and open the VisualGDB Linux Project Wizard:
3. OpenCV comes with configuration files for CMake that simplify including the library from CMake projects. Hence we will use CMake to build our demo application. Select it in the wizard:
4. Select “Build the project with a cross-compiler” and choose your Raspberry Pi board in the “deployment computer” field:
5. Press “Finish” to create your project. Then open VisualGDB Project Properties, go to the CMake page and click “Synchronize sysroot”. Ensure that /opt/vc and /usr/share/OpenCV (not lowercase /usr/share/opencv) are selected:
6. Open the CMakeLists.txt file in the newly created project and add the following line after add_executable():

   find_package(OpenCV REQUIRED)

   find_package(OpenCV REQUIRED)

   Also add ${OpenCV_LIBS} to target_libraries() statement:

7. Open VisualGDB Project Properties and set the CMAKE_PREFIX_PATH variable for the CMake command to point at <sysroot>\usr\share\OpenCV:
8. Replace the contents of the main source file with the following code from the find_contours sample in OpenCV:

   /**  * @function findContours_Demo.cpp  * @brief Demo code to find contours in an image  * @author OpenCV team  */   #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> #include <stdio.h> #include <stdlib.h>   using namespace cv; using namespace std;   Mat src; Mat src_gray; int thresh = 100; int max_thresh = 255; RNG rng(12345);   /// Function header void thresh_callback(int, void*);   /**  * @function main  */ int main(int, char** argv) {   /// Load source image and convert it to gray     src = imread(argv[1], 1);         /// Convert image to gray and blur it     cvtColor(src, src_gray, COLOR_BGR2GRAY);     blur(src_gray, src_gray, Size(3, 3));         /// Create Window     const char* source_window = "Source";     namedWindow(source_window, WINDOW_AUTOSIZE);     imshow(source_window, src);       createTrackbar(" Canny thresh:", "Source", &thresh, max_thresh, thresh_callback);     thresh_callback(0, 0);       waitKey(0);     return (0); }   /**  * @function thresh_callback  */ void thresh_callback(int, void*) {     Mat canny_output;     vector<vector<Point> > contours;     vector<Vec4i> hierarchy;         /// Detect edges using canny     Canny(src_gray, canny_output, thresh, thresh * 2, 3);     /// Find contours     findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));         /// Draw contours     Mat drawing = Mat::zeros(canny_output.size(), CV_8UC3);     for (size_t i = 0; i < contours.size(); i++)     {         Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));         drawContours(drawing, contours, (int)i, color, 2, 8, hierarchy, 0, Point());     }         /// Show in a window     namedWindow("Contours", WINDOW_AUTOSIZE);     imshow("Contours", drawing); }

   /**  * @function findContours_Demo.cpp  * @brief Demo code to find contours in an image  * @author OpenCV team  */ #include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> #include <stdio.h> #include <stdlib.h> using namespace cv; using namespace std; Mat src; Mat src_gray; int thresh = 100; int max_thresh = 255; RNG rng(12345); /// Function header void thresh_callback(int, void*); /**  * @function main  */ int main(int, char** argv) {   /// Load source image and convert it to gray     src = imread(argv[1], 1);       /// Convert image to gray and blur it     cvtColor(src, src_gray, COLOR_BGR2GRAY);     blur(src_gray, src_gray, Size(3, 3));       /// Create Window     const char* source_window = "Source";     namedWindow(source_window, WINDOW_AUTOSIZE);     imshow(source_window, src);     createTrackbar(" Canny thresh:", "Source", &thresh, max_thresh, thresh_callback);     thresh_callback(0, 0);     waitKey(0);     return (0); } /**  * @function thresh_callback  */ void thresh_callback(int, void*) {     Mat canny_output;     vector<vector<Point> > contours;     vector<Vec4i> hierarchy;       /// Detect edges using canny     Canny(src_gray, canny_output, thresh, thresh * 2, 3);     /// Find contours     findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));       /// Draw contours     Mat drawing = Mat::zeros(canny_output.size(), CV_8UC3);     for (size_t i = 0; i < contours.size(); i++)     {         Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));         drawContours(drawing, contours, (int)i, color, 2, 8, hierarchy, 0, Point());     }       /// Show in a window     namedWindow("Contours", WINDOW_AUTOSIZE);     imshow("Contours", drawing); }

9. Upload any PNG image that will be used to check out contour detection to the Raspberry Pi.  In this tutorial we will use the Lena.png file from the OpenCV package:
10. Specify the path to the uploaded image in the command-line arguments to your application via VisualGDB Project Properties:
11. Press F5 to start debugging the application. You will see two windows: one with the original image and another one with the contours:You can move the trackbar in the left window to change the threshold value for the edge detection algorithm.

To learn how to cross-compile OpenCV 3 for Raspberry Pi and step through its source code, follow this tutorial.

Read this tutorial for detailed instructions on using OpenCV with the Raspberry Pi camera module.