OpenCV
Open Source Computer Vision
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Original author | Ana Huamán |
Compatibility | OpenCV >= 3.4.4 |
In this tutorial you will learn how to:
In OpenCV 3 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:
We also include the iostream to facilitate console line output and input.
By declaring using namespace cv;
, in the following, the library functions can be accessed without explicitly stating the namespace.
Now, let's analyze the main code. As a first step, we read the image "starry_night.jpg" from the OpenCV samples. In order to do so, a call to the cv::imread function loads the image using the file path specified by the first argument. The second argument is optional and specifies the format in which we want the image. This may be:
After reading in the image data will be stored in a cv::Mat object.
Afterwards, a check is executed, if the image was loaded correctly.
Then, the image is shown using a call to the cv::imshow function. The first argument is the title of the window and the second argument is the cv::Mat object that will be shown.
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. The return value is the key that was pressed.
In the end, the image is written to a file if the pressed key was the "s"-key. For this the cv::imwrite function is called that has the file path and the cv::Mat object as an argument.