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224 lines
7.9 KiB
Markdown
224 lines
7.9 KiB
Markdown
Basic Thresholding Operations {#tutorial_threshold}
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=============================
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@prev_tutorial{tutorial_pyramids}
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@next_tutorial{tutorial_threshold_inRange}
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| -: | :- |
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| Original author | Ana Huamán |
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| Compatibility | OpenCV >= 3.0 |
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Goal
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----
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In this tutorial you will learn how to:
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- Perform basic thresholding operations using OpenCV function @ref cv::threshold
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Cool Theory
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-----------
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@note The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler. What is
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Thresholding?
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-------------
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- The simplest segmentation method
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- Application example: Separate out regions of an image corresponding to objects which we want to
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analyze. This separation is based on the variation of intensity between the object pixels and
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the background pixels.
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- To differentiate the pixels we are interested in from the rest (which will eventually be
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rejected), we perform a comparison of each pixel intensity value with respect to a *threshold*
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(determined according to the problem to solve).
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- Once we have separated properly the important pixels, we can set them with a determined value to
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identify them (i.e. we can assign them a value of \f$0\f$ (black), \f$255\f$ (white) or any value that
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suits your needs).
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### Types of Thresholding
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- OpenCV offers the function @ref cv::threshold to perform thresholding operations.
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- We can effectuate \f$5\f$ types of Thresholding operations with this function. We will explain them
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in the following subsections.
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- To illustrate how these thresholding processes work, let's consider that we have a source image
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with pixels with intensity values \f$src(x,y)\f$. The plot below depicts this. The horizontal blue
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line represents the threshold \f$thresh\f$ (fixed).
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#### Threshold Binary
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- This thresholding operation can be expressed as:
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\f[\texttt{dst} (x,y) = \fork{\texttt{maxVal}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
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- So, if the intensity of the pixel \f$src(x,y)\f$ is higher than \f$thresh\f$, then the new pixel
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intensity is set to a \f$MaxVal\f$. Otherwise, the pixels are set to \f$0\f$.
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#### Threshold Binary, Inverted
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- This thresholding operation can be expressed as:
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\f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxVal}}{otherwise}\f]
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- If the intensity of the pixel \f$src(x,y)\f$ is higher than \f$thresh\f$, then the new pixel intensity
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is set to a \f$0\f$. Otherwise, it is set to \f$MaxVal\f$.
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#### Truncate
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- This thresholding operation can be expressed as:
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\f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
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- The maximum intensity value for the pixels is \f$thresh\f$, if \f$src(x,y)\f$ is greater, then its value
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is *truncated*. See figure below:
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#### Threshold to Zero
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- This operation can be expressed as:
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\f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f]
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- If \f$src(x,y)\f$ is lower than \f$thresh\f$, the new pixel value will be set to \f$0\f$.
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#### Threshold to Zero, Inverted
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- This operation can be expressed as:
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\f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f]
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- If \f$src(x,y)\f$ is greater than \f$thresh\f$, the new pixel value will be set to \f$0\f$.
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Code
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----
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@add_toggle_cpp
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The tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold.cpp)
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@include samples/cpp/tutorial_code/ImgProc/Threshold.cpp
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@end_toggle
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@add_toggle_java
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The tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/java/tutorial_code/ImgProc/threshold/Threshold.java)
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@include samples/java/tutorial_code/ImgProc/threshold/Threshold.java
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@end_toggle
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@add_toggle_python
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The tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/imgProc/threshold/threshold.py)
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@include samples/python/tutorial_code/imgProc/threshold/threshold.py
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@end_toggle
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Explanation
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-----------
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Let's check the general structure of the program:
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- Load an image. If it is BGR we convert it to Grayscale. For this, remember that we can use
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the function @ref cv::cvtColor :
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp load
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java load
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/threshold/threshold.py load
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@end_toggle
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- Create a window to display the result
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp window
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java window
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/threshold/threshold.py window
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@end_toggle
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- Create \f$2\f$ trackbars for the user to enter user input:
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- **Type of thresholding**: Binary, To Zero, etc...
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- **Threshold value**
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp trackbar
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java trackbar
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/threshold/threshold.py trackbar
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@end_toggle
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- Wait until the user enters the threshold value, the type of thresholding (or until the
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program exits)
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- Whenever the user changes the value of any of the Trackbars, the function *Threshold_Demo*
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(*update* in Java) is called:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp Threshold_Demo
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java Threshold_Demo
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/imgProc/threshold/threshold.py Threshold_Demo
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@end_toggle
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As you can see, the function @ref cv::threshold is invoked. We give \f$5\f$ parameters in C++ code:
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- *src_gray*: Our input image
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- *dst*: Destination (output) image
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- *threshold_value*: The \f$thresh\f$ value with respect to which the thresholding operation
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is made
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- *max_BINARY_value*: The value used with the Binary thresholding operations (to set the
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chosen pixels)
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- *threshold_type*: One of the \f$5\f$ thresholding operations. They are listed in the
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comment section of the function above.
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Results
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-------
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-# After compiling this program, run it giving a path to an image as argument. For instance, for an
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input image as:
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-# First, we try to threshold our image with a *binary threshold inverted*. We expect that the
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pixels brighter than the \f$thresh\f$ will turn dark, which is what actually happens, as we can see
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in the snapshot below (notice from the original image, that the doggie's tongue and eyes are
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particularly bright in comparison with the image, this is reflected in the output image).
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-# Now we try with the *threshold to zero*. With this, we expect that the darkest pixels (below the
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threshold) will become completely black, whereas the pixels with value greater than the
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threshold will keep its original value. This is verified by the following snapshot of the output
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image:
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