The OpenCV Library has >2500 algorithms, extensive documentation, and sample code for real-time computer vision. You can see basic information about OpenCV at the following sites,
Homepage:
Documentation:
Source code:
Tutorial:
Books:
In this tutorial, you will learn fundamental concepts of the C++ language to use the OpenCV API. You will learn namespace, class, C++ syntax to use image reading, writing and displaying.
Project Workspace Setting
Create the lecture workspace as C:\Users\yourID\source\repos
e.g. C:\Users\ykkim\source\repos
Then, create sub-directories such as :
C:\Users\yourID\source\repos\DLIP
C:\Users\yourID\source\repos\DLIP\Tutorial
C:\Users\yourID\source\repos\DLIP\Include
C:\Users\yourID\source\repos\DLIP\Assignment
C:\Users\yourID\source\repos\DLIP\LAB
C:\Users\yourID\source\repos\DLIP\Image
C:\Users\yourID\source\repos\DLIP\Props
Basic Image Processing
Example 1. Read / Write / Display
You can use the OpenCV C++ library to read, write, and display images/videos. Here is a related example.
File Name: DLIP_Tutorial_OpenCV_Image.cpp or DLIP_Tutorial_OpenCV_Video.cpp
Compile and run.
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
/* read image */
String HGU_logo = "../../../Image/HGU_logo.jpg";
Mat src = imread(HGU_logo);
if (src.empty())/// Load image check
{
cout << "File Read Failed : src is empty" << endl;
waitKey(0);
}
Mat src_gray = imread(HGU_logo, 0); // read in grayscale
/* write image */
String fileName = "writeImage.jpg";
imwrite(fileName, src);
/* display image */
namedWindow("src", WINDOW_AUTOSIZE);
imshow("src", src);
namedWindow("src_gray", WINDOW_AUTOSIZE);
imshow("src_gray", src_gray);
waitKey(0);
}
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
/* open the video camera no.0 */
VideoCapture cap(0);
if (!cap.isOpened()) // if not success, exit the programm
{
cout << "Cannot open the video cam\n";
return -1;
}
namedWindow("MyVideo", WINDOW_AUTOSIZE);
while (1)
{
Mat frame;
/* read a new frame from video */
bool bSuccess = cap.read(frame);
if (!bSuccess) // if not success, break loop
{
cout << "Cannot find a frame from video stream\n";
break;
}
imshow("MyVideo", frame);
if (waitKey(30) == 27) // wait for 'ESC' press for 30ms. If 'ESC' is pressed, break loop
{
cout << "ESC key is pressed by user\n";
break;
}
}
}
Basic Image Container: Mat Class
Mat Class
The image data are in forms of 1D, 2D, 3D arrays with values 0~255 or 0~1
OpenCV provides the Mat class for operating multi-dimensional images
Example 2. Matrix Operation: Create / Copy
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
/* Create or Construct Mat */
Mat A(10, 10, CV_8UC3, Scalar::all(155));
Mat B(A.size(), CV_8UC1);
Mat C = Mat::zeros(A.size(), CV_8UC1);
Mat D = Mat::ones(A.size(), CV_32FC1);
cout << "MAT A: " << A << endl << endl;
cout << "MAT B: " << B << endl << endl;
cout << "MAT C: " << C << endl << endl;
cout << "MAT D: " << D << endl << endl;
/* Get size of A (rows, cols) */
cout << "Size of A: " << A.size() << endl;
cout << "# of Rows of A: " << A.rows << endl;
cout << "# of Cols of A: " << A.cols << endl;
cout << "# of Channel of A: " << A.channels() << endl;
/* Copy/Clone Mat A to E/F */
Mat E, F;
A.copyTo(E);
F = A.clone();
/* Convert channel */
Mat img = imread("../../../Image/HGU_logo.jpg"); // CV8UC3 Image
Mat img_gray;
cvtColor(img, img_gray, COLOR_BGR2GRAY);
/* Chnage image type (8UC1 or 32FC1) */
Mat img_32F;
img_gray.convertTo(img_32F, CV_32FC1, 1.0/255.0);
imshow("img_32F", img_32F);
//cout << "img_32F: " << img_32F.channels() << endl << endl;
waitKey(0);
}
Basic Image Operation: Crop, Rotate, Resize, Color Convert
Example 3. Basic Image Operation
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
/* read image */
Mat img = imread("../../../Image/HGU_logo.jpg");
if (img.empty())/// Load image check
{
cout << "File Read Failed : src is empty" << endl;
waitKey(0);
}
imshow("img", img);
/* Crop(Region of Interest) */
Rect r(10, 10, 150, 150); // (x, y, width, height)
Mat roiImg = img(r);
imshow("roiImg", roiImg);
/* Rotate */
Mat rotImg;
rotate(img, rotImg, ROTATE_90_CLOCKWISE);
imshow("rotImg", rotImg);
/* Resize */
Mat resizedImg;
resize(img, resizedImg, Size(1000, 100));
imshow("resizedImg", resizedImg);
waitKey(0);
}
Exercise 1
Flip horizontally, Rotate, Resize and Crop of the original image
For flipping an image, find more details about cv::flip()
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
/* read src */
// Add code here
/* Flip src image
// Add code here and show image
/* Crop(Region of Interest) original image */
// Add code here and show image
/* Show source(src) and destination(dst) */
// Add code here
waitKey(0);
}
+Extra Exercise 1
The flip function is useful when working with videos. Implement a program that flips the webcam feed horizontally when the h key is pressed using waitKey() function. Hint: flag vs delay time of waitKey
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
// Open video camera with index 0
VideoCapture cap(0);
// Check if the video camera is opened successfully
if (!cap.isOpened())
{
cout << "Cannot open the video camera\n";
return -1;
}
// Create a window to display the video feed
namedWindow("MyVideo", WINDOW_AUTOSIZE);
bool flipHorizontal = false;
while (true)
{
Mat frame;
// Read a new frame from the video feed
bool readSuccess = cap.read(frame);
// Check if reading the frame was successful
if (!readSuccess)
{
cout << "Cannot find a frame from the video stream\n";
break;
}
// Add code here
// Display the frame in the "MyVideo" window
imshow("MyVideo", frame);
// Wait for 30ms and check if the 'ESC' key is pressed
if (waitKey(30) == 27)
{
cout << "ESC key is pressed by the user\n";
break;
}
}
return 0;
}
Shallow Copy vs Deep Copy
Shallow Copy
Shallow Copy means copying only the memory addresses in the memory. Since it copies pointers pointing to the same object or data, the original and the copy end up sharing the same data. This can lead to issues, as modifications to one object or array will affect the other as well.
Deep Copy
Deep Copy means creating a copy of an object or data in a new memory space. The original and the copy are independent, having separate memory spaces, so modifications made to one side do not affect the other.
Example 4. Shallow_Deep_Copy
Compile and run the code below and see what happens
Before you execute this code, try to understand what it does
#include <iostream>
#include <opencv2/opencv.hpp>
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using namespace std;
using namespace cv;
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int main()
{
Mat src, dst_shallow, dst_deep;
// read image
src = imread("../../../Image/HGU_logo.jpg", 0);
if (src.empty())/// Load image check
{
cout << "File Read Failed : src is empty" << endl;
waitKey(0);
}
/* Shallow Copy */
dst_shallow = src;
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/* Deep Copy */
src.copyTo(dst_deep);
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flip(src, src, 1);
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imshow("dst_shallow", dst_shallow);
imshow("dst_deep", dst_deep);
waitKey(0);
return 0;
}
Accessing Pixel value
An image is composed of small units called pixels. Each pixel can be considered as the smallest unit of an image. Pixel intensity represents the brightness of a pixel. For grayscale images, pixel intensity ranges from 0 (black) to 255 (white). In color images, each channel (e.g., Red, Green, Blue) has its intensity value.
Rows and columns define an image's structure. Rows represent the vertical direction of the image, and columns represent the horizontal direction. The position of a pixel is denoted as (row, column) or (v, u), where v represents the row index and u represents the column index.
OpenCV provides different methods to access the intensity values of pixels in an image. Two common methods are using at<type>(v, u) and using pointers for faster operations.
Method 1. Accessing using at<type>(v,u) (Recommended)
Mat image= imread(filename);
image.at<uchar>(v,u)= 255;
image.at<float>(v,u)= 0.9;
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// For an RGB Image
// (option1) Vec3b: 8-bit 3-D image (RGB)
image.at<cv::Vec3b>(v,u)[0]= 255;
image.at<cv::Vec3b>(v,u)[1]= 255;
image.at<cv::Vec3b>(v,u)[2]= 255;
/* Method 1. Accessing using "at<type>(v, u)" */
// For single channel image(Gray-scale)
printf("%d", img_gray.at<uchar>(0, 0));
// For RGB image
printf("%d", img.at<Vec3b>(0, 0)[0]);
printf("%d", img.at<Vec3b>(0, 0)[1]);
printf("%d", img.at<Vec3b>(0, 0)[2]);
Method 2. Using Pointer for faster operation
/* Method 2. Accessing Using Pointer */
// Gray Image
int pixel_temp;
for (int v = 0; v < img_gray.rows; v++)
{
uchar* img_data = img_gray.ptr<uchar>(v);
for (int u = 0; u < img_gray.cols; u++)
pixel_temp = img_data[u];
}
//RGB Image
int pixel_temp_r, pixel_temp_g, pixel_temp_b;
int cnt = 0;
for (int v = 0; v < img.rows; v++)
{
uchar* img_data = img.ptr<uchar>(v);
for (int u = 0; u < img.cols * img.channels(); u = u+3)
{
pixel_temp_r = img_data[u];
pixel_temp_g = img_data[u+1];
pixel_temp_b = img_data[u+2];
}
}
Example 5. Access pixel intensity of Gray-Scale Image(1D image)
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
Mat src;
// read image
src = imread("../../../Image/HGU_logo.jpg", 0);
if (src.empty())/// Load image check
{
cout << "File Read Failed : src is empty" << endl;
waitKey(0);
}
int v = src.rows; //행(가로)
int u = src.cols; //열(세로)
for (int i = 0; i < v; ++i)
for (int j = 0; j < u; ++j)
printf("%d\n", src.at<uchar>(i, j));
return 0;
}
Exercise 2
Calculate the average intensity value using at<type>(v,u)
Calculate the summation of the pixel intensity and calculate the average intensity value. Use cv::Mat::rows, cv::Mat::cols.
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
// Load the image in gray-scale
Mat src = imread("../../../Image/HGU_logo.jpg", 0);
if (src.empty())
{
cout << "Error: Couldn't open the image." << endl;
return -1;
}
// Calculate the sum of pixel intensities using 'at' function
double sumIntensity = 0.0;
for (int i = 0; i < src.rows; i++)
{
// Add code here
}
// Calculate the total number of pixels in the image
int pixelCount = // Add code here
// Calculate the average intensity of the image
double avgIntensity = // Add code here
// Print the results
cout << "Sum of intensity: " << sumIntensity << endl;
cout << "Number of pixels: " << pixelCount << endl;
cout << "Average intensity: " << avgIntensity << endl;
// Display the gray-scale image
imshow("src", src);
waitKey(0);
return 0;
}
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
// Load the image
Mat src = imread("../../../Image/HGU_logo.jpg", 0);
if (src.empty())
{
cout << "Error: Couldn't open the image." << endl;
return -1;
}
// Calculate the sum of pixel intensities using 'at' function
double sumIntensity = 0.0;
for (int i = 0; i < src.rows; ++i)
{
for (int j = 0; j < src.cols; ++j)
{
// Access each pixel in the gray-scale image and add its intensity to the sum
sumIntensity += src.at<uchar>(i, j);
}
}
// Calculate the total number of pixels in the image
int pixelCount = src.rows * src.cols;
// Calculate the average intensity of the image
double avgIntensity = sumIntensity / pixelCount;
// Print the results
cout << "Sum of intensity: " << sumIntensity << endl;
cout << "Number of pixels: " << pixelCount << endl;
cout << "Average intensity: " << avgIntensity << endl;
// Display the gray-scale image
imshow("src", src);
waitKey(0);
return 0;
}
Exercise 3
Intensity Inversion in Grayscale Images
Write a code to invert the colors of this Grayscale image. The resulting image should look like the following. For example, a pixel with an intensity of 100 should become a value of 255 - 100, which is 155 after the color inversion. Use Mat::zeros, .at<type>(v,u)
#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main()
{
// Load the image
Mat src = imread("../../../Image/HGU_logo.jpg", 0);
if (src.empty())
{
cout << "Error: Couldn't open the image." << endl;
return -1;
}
// Initialize dst with the same size as srcGray
Mat dst = Mat::zeros(src.size(), src.type());
// Invert the colors by accessing each pixel
// Add code here
// Display the original and inverted images
imshow("src", src);
imshow("dst", dst);
waitKey(0);
return 0;
}
Assignment (1 week)
Creating a new dataset of training images
In pre-processings of generating datasets of images, we usually maintain the same image size as the original but apply various geometric transformations such as cropping, translation, and resizing.
Write a code to create the following image datasets from the original image (464x480 px).
All three output images for this assignment must be the same sized(464x480 px).
Try to use pixel accesssing to create output images.
HINT: Use the outputs from the previous Exercises.
The methods for performing tasks such as image crop, rotate, resize, and color conversion (such as converting to grayscale) are as follows. If you want to learn more about the functions below, refer to the .
Everytime you use OpenCV, you must use documentation!!**
