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218 lines
7.8 KiB
218 lines
7.8 KiB
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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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/3.4/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/3.4/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/3.4/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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