opencv:tutorials-读写图像

转自：
https://docs.opencv.org/3.0.0/db/deb/tutorial_display_image.html
https://docs.opencv.org/3.0.0/db/d64/tutorial_load_save_image.html

OpenCV Tutorials
Introduction to OpenCV
Load and Display an Image
Goal
In this tutorial you will learn how to:

Load an image (using cv::imread )
Create a named OpenCV window (using cv::namedWindow )
Display an image in an OpenCV window (using cv::imshow )

#include <opencv2/core/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <string>
using namespace cv;
using namespace std;
int main( int argc, char** argv )
{
    string imageName("../data/HappyFish.jpg"); // by default
    if( argc > 1)
    {
        imageName = argv[1];
    }
    Mat image;
    image = imread(imageName.c_str(), IMREAD_COLOR); // Read the file
    if( image.empty() )                      // Check for invalid input
    {
        cout <<  "Could not open or find the image" << std::endl ;
        return -1;
    }
    namedWindow( "Display window", WINDOW_AUTOSIZE ); // Create a window for display.
    imshow( "Display window", image );                // Show our image inside it.
    waitKey(0); // Wait for a keystroke in the window
    return 0;
}

Explanation
In OpenCV 2 we have multiple modules. Each one takes care of a different area or approach towards image processing. You could already observe this in the structure of the user guide of these tutorials itself. Before you use any of them you first need to include the header files where the content of each individual module is declared.

You’ll almost always end up using the:

core section, as here are defined the basic building blocks of the library
highgui module, as this contains the functions for input and output operations

#include <opencv2/core/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <string>

We also include the iostream to facilitate console line output and input. To avoid data structure and function name conflicts with other libraries, OpenCV has its own namespace: cv. To avoid the need appending prior each of these the cv:: keyword you can import the namespace in the whole file by using the lines:

using namespace cv;

This is true for the STL library too (used for console I/O). Now, let’s analyze the main function. We start up assuring that we acquire a valid image name argument from the command line. Otherwise take a picture by default: “HappyFish.jpg”.

string imageName("../data/HappyFish.jpg"); // by default
if( argc > 1)
{
    imageName = argv[1];
}

Then create a Mat object that will store the data of the loaded image.

Mat image;

Now we call the cv::imread function which loads the image name specified by the first argument (argv[1]). The second argument specifies the format in what we want the image. This may be:

IMREAD_UNCHANGED (<0) loads the image as is (including the alpha channel if present)
IMREAD_GRAYSCALE ( 0) loads the image as an intensity one
IMREAD_COLOR (>0) loads the image in the RGB format
    image = imread(imageName.c_str(), IMREAD_COLOR); // Read the file

Note
OpenCV offers support for the image formats Windows bitmap (bmp), portable image formats (pbm, pgm, ppm) and Sun raster (sr, ras). With help of plugins (you need to specify to use them if you build yourself the library, nevertheless in the packages we ship present by default) you may also load image formats like JPEG (jpeg, jpg, jpe), JPEG 2000 (jp2 - codenamed in the CMake as Jasper), TIFF files (tiff, tif) and portable network graphics (png). Furthermore, OpenEXR is also a possibility.

After checking that the image data was loaded correctly, we want to display our image, so we create an OpenCV window using the cv::namedWindow function. These are automatically managed by OpenCV once you create them. For this you need to specify its name and how it should handle the change of the image it contains from a size point of view. It may be:

WINDOW_AUTOSIZE is the only supported one if you do not use the Qt backend. In this case the window size will take up the size of the image it shows. No resize permitted!
WINDOW_NORMAL on Qt you may use this to allow window resize. The image will resize itself according to the current window size. By using the | operator you also need to specify if you would like the image to keep its aspect ratio (WINDOW_KEEPRATIO) or not (WINDOW_FREERATIO).

namedWindow( "Display window", WINDOW_AUTOSIZE ); // Create a window for display.

Finally, to update the content of the OpenCV window with a new image use the cv::imshow function. Specify the OpenCV window name to update and the image to use during this operation:

imshow( "Display window", image );                // Show our image inside it.

Because we want our window to be displayed until the user presses a key (otherwise the program would end far too quickly), we use the cv::waitKey function whose only parameter is just how long should it wait for a user input (measured in milliseconds). Zero means to wait forever.

waitKey(0); // Wait for a keystroke in the window

Goals
In this tutorial you will learn how to:

Load an image using cv::imread
Transform an image from BGR to Grayscale format by using cv::cvtColor
Save your transformed image in a file on disk (using cv::imwrite )

Code
Here it is:

#include <opencv2/opencv.hpp>
using namespace cv;
int main( int argc, char** argv )
{
 char* imageName = argv[1];
 Mat image;
 image = imread( imageName, 1 );
 if( argc != 2 || !image.data )
 {
   printf( " No image data \n " );
   return -1;
 }
 Mat gray_image;
 cvtColor( image, gray_image, COLOR_BGR2GRAY );
 imwrite( "../../images/Gray_Image.jpg", gray_image );
 namedWindow( imageName, WINDOW_AUTOSIZE );
 namedWindow( "Gray image", WINDOW_AUTOSIZE );
 imshow( imageName, image );
 imshow( "Gray image", gray_image );
 waitKey(0);
 return 0;
}

Explanation
We begin by loading an image using cv::imread , located in the path given by imageName. For this example, assume you are loading a BGR image.
Now we are going to convert our image from BGR to Grayscale format. OpenCV has a really nice function to do this kind of transformations:

cvtColor( image, gray_image, COLOR_BGR2GRAY );

As you can see, cv::cvtColor takes as arguments:
a source image (image)
a destination image (gray_image), in which we will save the converted image.
an additional parameter that indicates what kind of transformation will be performed. In this case we use COLOR_BGR2GRAY (because of cv::imread has BGR default channel order in case of color images).
So now we have our new gray_image and want to save it on disk (otherwise it will get lost after the program ends). To save it, we will use a function analagous to cv::imread : cv::imwrite

imwrite( "../../images/Gray_Image.jpg", gray_image );

Which will save our gray_image as Gray_Image.jpg in the folder images located two levels up of my current location.
Finally, let’s check out the images. We create two windows and use them to show the original image as well as the new one:

namedWindow( imageName, WINDOW_AUTOSIZE );
namedWindow( "Gray image", WINDOW_AUTOSIZE );
imshow( imageName, image );
imshow( "Gray image", gray_image );

Add the waitKey(0) function call for the program to wait forever for an user key press.

opencv:tutorials-读写图像

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