init - 初始化项目
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Mask operations on matrices {#tutorial_mat_mask_operations}
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===========================
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@tableofcontents
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@prev_tutorial{tutorial_how_to_scan_images}
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@next_tutorial{tutorial_mat_operations}
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| | |
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| -: | :- |
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| Original author | Bernát Gábor |
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| Compatibility | OpenCV >= 3.0 |
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Mask operations on matrices are quite simple. The idea is that we recalculate each pixel's value in
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an image according to a mask matrix (also known as kernel). This mask holds values that will adjust
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how much influence neighboring pixels (and the current pixel) have on the new pixel value. From a
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mathematical point of view we make a weighted average, with our specified values.
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Our test case
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-------------
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Let's consider the issue of an image contrast enhancement method. Basically we want to apply for
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every pixel of the image the following formula:
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\f[I(i,j) = 5*I(i,j) - [ I(i-1,j) + I(i+1,j) + I(i,j-1) + I(i,j+1)]\f]\f[\iff I(i,j)*M, \text{where }
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M = \bordermatrix{ _i\backslash ^j & -1 & 0 & +1 \cr
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-1 & 0 & -1 & 0 \cr
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0 & -1 & 5 & -1 \cr
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+1 & 0 & -1 & 0 \cr
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}\f]
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The first notation is by using a formula, while the second is a compacted version of the first by
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using a mask. You use the mask by putting the center of the mask matrix (in the upper case noted by
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the zero-zero index) on the pixel you want to calculate and sum up the pixel values multiplied with
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the overlapped matrix values. It's the same thing, however in case of large matrices the latter
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notation is a lot easier to look over.
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Code
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----
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@add_toggle_cpp
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You can download this source code from [here
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](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp) or look in the
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OpenCV source code libraries sample directory at
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`samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp`.
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@include samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp
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@end_toggle
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@add_toggle_java
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You can download this source code from [here
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](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java) or look in the
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OpenCV source code libraries sample directory at
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`samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java`.
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@include samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java
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@end_toggle
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@add_toggle_python
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You can download this source code from [here
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](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py) or look in the
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OpenCV source code libraries sample directory at
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`samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py`.
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@include samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py
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@end_toggle
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The Basic Method
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----------------
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Now let us see how we can make this happen by using the basic pixel access method or by using the
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**filter2D()** function.
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Here's a function that will do this:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp basic_method
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At first we make sure that the input images data is in unsigned char format. For this we use the
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@ref cv::CV_Assert function that throws an error when the expression inside it is false.
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp 8_bit
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java basic_method
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At first we make sure that the input images data in unsigned 8 bit format.
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java 8_bit
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py basic_method
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At first we make sure that the input images data in unsigned 8 bit format.
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@code{.py}
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my_image = cv.cvtColor(my_image, cv.CV_8U)
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@endcode
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@end_toggle
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We create an output image with the same size and the same type as our input. As you can see in the
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@ref tutorial_how_to_scan_images_storing "storing" section, depending on the number of channels we may have one or more
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subcolumns.
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@add_toggle_cpp
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We will iterate through them via pointers so the total number of elements depends on
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this number.
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp create_channels
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java create_channels
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@end_toggle
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@add_toggle_python
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@code{.py}
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height, width, n_channels = my_image.shape
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result = np.zeros(my_image.shape, my_image.dtype)
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@endcode
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@end_toggle
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@add_toggle_cpp
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We'll use the plain C [] operator to access pixels. Because we need to access multiple rows at the
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same time we'll acquire the pointers for each of them (a previous, a current and a next line). We
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need another pointer to where we're going to save the calculation. Then simply access the right
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items with the [] operator. For moving the output pointer ahead we simply increase this (with one
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byte) after each operation:
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp basic_method_loop
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On the borders of the image the upper notation results inexistent pixel locations (like minus one -
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minus one). In these points our formula is undefined. A simple solution is to not apply the kernel
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in these points and, for example, set the pixels on the borders to zeros:
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp borders
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@end_toggle
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@add_toggle_java
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We need to access multiple rows and columns which can be done by adding or subtracting 1 to the current center (i,j).
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Then we apply the sum and put the new value in the Result matrix.
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java basic_method_loop
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On the borders of the image the upper notation results in inexistent pixel locations (like (-1,-1)).
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In these points our formula is undefined. A simple solution is to not apply the kernel
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in these points and, for example, set the pixels on the borders to zeros:
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java borders
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@end_toggle
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@add_toggle_python
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We need to access multiple rows and columns which can be done by adding or subtracting 1 to the current center (i,j).
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Then we apply the sum and put the new value in the Result matrix.
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@snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py basic_method_loop
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@end_toggle
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The filter2D function
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---------------------
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Applying such filters are so common in image processing that in OpenCV there is a function that
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will take care of applying the mask (also called a kernel in some places). For this you first need
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to define an object that holds the mask:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp kern
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java kern
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py kern
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@end_toggle
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Then call the **filter2D()** function specifying the input, the output image and the kernel to
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use:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/core/mat_mask_operations/mat_mask_operations.cpp filter2D
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/core/mat_mask_operations/MatMaskOperations.java filter2D
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/core/mat_mask_operations/mat_mask_operations.py filter2D
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@end_toggle
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The function even has a fifth optional argument to specify the center of the kernel, a sixth
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for adding an optional value to the filtered pixels before storing them in K and a seventh one
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for determining what to do in the regions where the operation is undefined (borders).
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This function is shorter, less verbose and, because there are some optimizations, it is usually faster
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than the *hand-coded method*. For example in my test while the second one took only 13
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milliseconds the first took around 31 milliseconds. Quite some difference.
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For example:
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@add_toggle_cpp
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Check out an instance of running the program on our [YouTube
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channel](http://www.youtube.com/watch?v=7PF1tAU9se4) .
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@youtube{7PF1tAU9se4}
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@end_toggle
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