Basic operations with images

In order to get pixel intensity value, you have to know the type of an image and the number of channels. Here is an example for a single channel grey scale image (type 8UC1) and pixel coordinates x and y:

 Scalar intensity = img.at<uchar>(y, x);Scalar intensity = img.at<uchar>(Point(x, y));

y,x x,y顺序不同

Now let us consider a 3 channel image with BGR color ordering (the default format returned by imread):

  Vec3b intensity = img.at<Vec3b>(y, x);uchar blue = intensity.val[0];uchar green = intensity.val[1];uchar red = intensity.val[2];

Java

byte[] imgData = new byte[(int) (img.total() * img.channels())];img.get(0, 0, imgData);byte intensity = imgData[y * img.cols() + x];

Python

_blue = img[y,x,0]_green = img[y,x,1]_red = img[y,x,2]

Memory management and reference counting

Mat is a structure that keeps matrix/image characteristics (rows and columns number, data type etc) and a pointer to data. So nothing prevents us from having several instances of Mat corresponding to the same data. A Mat keeps a reference count that tells if data has to be deallocated when a particular instance of Mat is destroyed.

Visualizing images

It is very useful to see intermediate results of your algorithm during development process. OpenCV provides a convenient way of visualizing images. A 8U image can be shown using:

  Mat img = imread("image.jpg");namedWindow("image", WINDOW_AUTOSIZE);imshow("image", img);waitKey();

A call to waitKey() starts a message passing cycle that waits for a key stroke in the "image" window. A 32F image needs to be converted to 8U type. For example:

Java

package com.company;import java.util.Scanner;import org.opencv.core.Core;
import org.opencv.core.CvType;
import org.opencv.core.Mat;
import org.opencv.core.Scalar;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;public class Main {static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }public static void main(String[] args) {System.out.println("Welcome to OpenCV " + Core.VERSION);
//            String filename = "././wolf.jpg";String filename = "Grass.jpg";Mat src = Imgcodecs.imread(filename, Imgcodecs.IMREAD_COLOR);Imgproc.pyrDown(src, src);Mat grey = new Mat();Imgproc.cvtColor(src, grey, Imgproc.COLOR_BGR2GRAY);Mat sobelx = new Mat();Imgproc.Sobel(grey, sobelx, CvType.CV_32F, 1, 0);Core.MinMaxLocResult res = Core.minMaxLoc(sobelx); // find minimum and maximum intensitiesMat draw = new Mat();double maxVal = res.maxVal, minVal = res.minVal;sobelx.convertTo(draw, CvType.CV_8U, 255.0 / (maxVal - minVal), -minVal * 255.0 / (maxVal - minVal));HighGui.imshow( "Input", src );HighGui.imshow("sobel-convert", draw);HighGui.waitKey();System.exit(0);}}

C++

#include <iostream>
#include <opencv.hpp>
#include <ctime>using namespace cv;
using namespace std;void main()
{Mat image_grass = imread("Grass.jpg", IMREAD_COLOR);//Mat image_grass = imread("Grass.jpg", IMREAD_GRAYSCALE);if (!image_grass.data){printf("No data\n");return;}pyrDown(image_grass, image_grass, Size(image_grass.cols / 2, image_grass.rows / 2));imshow("grass", image_grass);Mat grey;cvtColor(image_grass, grey, COLOR_BGR2GRAY);Mat sobelx;Sobel(grey, sobelx, CV_32F, 1, 0);imshow("sobel", sobelx);double minVal = 0.0f, maxVal = 0.0f;minMaxLoc(sobelx, &minVal, &maxVal);Mat draw;sobelx.convertTo(draw, CV_8U, 255.0 / 50, 0.0f);//sobelx.convertTo(draw, CV_8U, 0.0f, -minVal*255.0 / (maxVal - minVal));//sobelx.convertTo(draw, CV_8U, 255.0 / (maxVal - minVal), -minVal*255.0 / (maxVal - minVal));imshow("image", draw);waitKey();return;
}

Python

   img = cv.imread('image.jpg')grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)sobelx = cv.Sobel(grey, cv.CV_32F, 1, 0)# find minimum and maximum intensitiesminVal = np.amin(sobelx)maxVal = np.amax(sobelx)draw = cv.convertScaleAbs(sobelx, alpha=255.0/(maxVal - minVal), beta=-minVal * 255.0/(maxVal - minVal))cv.namedWindow('image', cv.WINDOW_AUTOSIZE)cv.imshow('image', draw)cv.waitKey()

np.amin/max

convertScaleAbs

from __future__ import print_function
import sys
import timeimport numpy as np
import cv2 as cvdef main():image = cv.imread("Grass.jpg", cv.IMREAD_COLOR)if image is None:print("No data")print("水光潋滟晴方好，山色空蒙雨亦奇。")else:print("欲把西湖比西子，淡妆浓抹总相宜。")src = cv.pyrDown(image)cv.imshow("src image", src);grey = cv.cvtColor(src, cv.COLOR_BGR2GRAY)cv.imshow("grey", grey);sobelx = cv.Sobel(grey, cv.CV_32F, 1, 0)# find minimum and maximum intensitiesminVal = np.amin(sobelx)maxVal = np.amax(sobelx)draw = cv.convertScaleAbs(sobelx, alpha=255.0 / (maxVal - minVal), beta=-minVal * 255.0 / (maxVal - minVal))cv.namedWindow('image', cv.WINDOW_AUTOSIZE)cv.imshow('image', draw)cv.waitKey()cv.destroyAllWindows()def readImageTest():image = cv.imread("Grass.jpg", cv.IMREAD_COLOR)if image is not None:pyrImage = cv.pyrDown(image)cv.namedWindow("Input", cv.WINDOW_AUTOSIZE)cv.imshow("Input", pyrImage)cv.waitKey()else:print("No data")if __name__ == "__main__":# readImageTest()main()

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Improving Opencv 4: The Core Functionality :Operations with images

Basic operations with images

Java

Python

Memory management and reference counting

Visualizing images

Java

C++

Python

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