init - 初始化项目

doc/tutorials/imgproc/imgtrans/laplace_operator/laplace_operator.markdown

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+Laplace Operator {#tutorial_laplace_operator}
+================
+
+@tableofcontents
+
+@prev_tutorial{tutorial_sobel_derivatives}
+@next_tutorial{tutorial_canny_detector}
+
+|    |    |
+| -: | :- |
+| Original author | Ana Huamán |
+| Compatibility | OpenCV >= 3.0 |
+
+Goal
+----
+
+In this tutorial you will learn how to:
+
+-   Use the OpenCV function **Laplacian()** to implement a discrete analog of the *Laplacian
+    operator*.
+
+Theory
+------
+
+-#  In the previous tutorial we learned how to use the *Sobel Operator*. It was based on the fact
+    that in the edge area, the pixel intensity shows a "jump" or a high variation of intensity.
+    Getting the first derivative of the intensity, we observed that an edge is characterized by a
+    maximum, as it can be seen in the figure:
+
+    ![](images/Laplace_Operator_Tutorial_Theory_Previous.jpg)
+
+-#  And...what happens if we take the second derivative?
+
+    ![](images/Laplace_Operator_Tutorial_Theory_ddIntensity.jpg)
+
+    You can observe that the second derivative is zero! So, we can also use this criterion to
+    attempt to detect edges in an image. However, note that zeros will not only appear in edges
+    (they can actually appear in other meaningless locations); this can be solved by applying
+    filtering where needed.
+
+### Laplacian Operator
+
+-#  From the explanation above, we deduce that the second derivative can be used to *detect edges*.
+    Since images are "*2D*", we would need to take the derivative in both dimensions. Here, the
+    Laplacian operator comes handy.
+-#  The *Laplacian operator* is defined by:
+
+\f[Laplace(f) = \dfrac{\partial^{2} f}{\partial x^{2}} + \dfrac{\partial^{2} f}{\partial y^{2}}\f]
+
+-#  The Laplacian operator is implemented in OpenCV by the function **Laplacian()** . In fact,
+    since the Laplacian uses the gradient of images, it calls internally the *Sobel* operator to
+    perform its computation.
+
+Code
+----
+
+-#  **What does this program do?**
+    -   Loads an image
+    -   Remove noise by applying a Gaussian blur and then convert the original image to grayscale
+    -   Applies a Laplacian operator to the grayscale image and stores the output image
+    -   Display the result in a window
+
+@add_toggle_cpp
+-#  The tutorial code's is shown lines below. You can also download it from
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp)
+    @include samples/cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp
+@end_toggle
+
+@add_toggle_java
+-#  The tutorial code's is shown lines below. You can also download it from
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java)
+    @include samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java
+@end_toggle
+
+@add_toggle_python
+-#  The tutorial code's is shown lines below. You can also download it from
+    [here](https://raw.githubusercontent.com/opencv/opencv/master/samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py)
+    @include samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py
+@end_toggle
+
+Explanation
+-----------
+
+#### Declare variables
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp variables
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java variables
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py variables
+@end_toggle
+
+#### Load source image
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp load
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java load
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py load
+@end_toggle
+
+#### Reduce noise
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp reduce_noise
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java reduce_noise
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py reduce_noise
+@end_toggle
+
+#### Grayscale
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp convert_to_gray
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java convert_to_gray
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py convert_to_gray
+@end_toggle
+
+#### Laplacian operator
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp laplacian
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java laplacian
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py laplacian
+@end_toggle
+
+-   The arguments are:
+    -   *src_gray*: The input image.
+    -   *dst*: Destination (output) image
+    -   *ddepth*: Depth of the destination image. Since our input is *CV_8U* we define *ddepth* =
+        *CV_16S* to avoid overflow
+    -   *kernel_size*: The kernel size of the Sobel operator to be applied internally. We use 3 in
+        this example.
+    -   *scale*, *delta* and *BORDER_DEFAULT*: We leave them as default values.
+
+#### Convert output to a *CV_8U* image
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp convert
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java convert
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py convert
+@end_toggle
+
+#### Display the result
+
+@add_toggle_cpp
+@snippet cpp/tutorial_code/ImgTrans/Laplace_Demo.cpp display
+@end_toggle
+
+@add_toggle_java
+@snippet samples/java/tutorial_code/ImgTrans/LaPlace/LaplaceDemo.java display
+@end_toggle
+
+@add_toggle_python
+@snippet samples/python/tutorial_code/ImgTrans/LaPlace/laplace_demo.py display
+@end_toggle
+
+Results
+-------
+
+-#  After compiling the code above, we can run it giving as argument the path to an image. For
+    example, using as an input:
+
+    ![](images/Laplace_Operator_Tutorial_Original_Image.jpg)
+
+-#  We obtain the following result. Notice how the trees and the silhouette of the cow are
+    approximately well defined (except in areas in which the intensity are very similar, i.e. around
+    the cow's head). Also, note that the roof of the house behind the trees (right side) is
+    notoriously marked. This is due to the fact that the contrast is higher in that region.
+
+    ![](images/Laplace_Operator_Tutorial_Result.jpg)