| Basic Thresholding Operations {#tutorial_threshold} | |
| ============================= | |
| @tableofcontents | |
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| | -: | :- | | |
| | Original author | Ana Huamán | | |
| | Compatibility | OpenCV >= 3.0 | | |
| Goal | |
| ---- | |
| In this tutorial you will learn how to: | |
| - Perform basic thresholding operations using OpenCV function @ref cv::threshold | |
| Cool Theory | |
| ----------- | |
| @note The explanation below belongs to the book **Learning OpenCV** by Bradski and Kaehler. What is | |
| Thresholding? | |
| ------------- | |
| - The simplest segmentation method | |
| - Application example: Separate out regions of an image corresponding to objects which we want to | |
| analyze. This separation is based on the variation of intensity between the object pixels and | |
| the background pixels. | |
| - To differentiate the pixels we are interested in from the rest (which will eventually be | |
| rejected), we perform a comparison of each pixel intensity value with respect to a *threshold* | |
| (determined according to the problem to solve). | |
| - Once we have separated properly the important pixels, we can set them with a determined value to | |
| identify them (i.e. we can assign them a value of \f$0\f$ (black), \f$255\f$ (white) or any value that | |
| suits your needs). | |
|  | |
| ### Types of Thresholding | |
| - OpenCV offers the function @ref cv::threshold to perform thresholding operations. | |
| - We can effectuate \f$5\f$ types of Thresholding operations with this function. We will explain them | |
| in the following subsections. | |
| - To illustrate how these thresholding processes work, let's consider that we have a source image | |
| with pixels with intensity values \f$src(x,y)\f$. The plot below depicts this. The horizontal blue | |
| line represents the threshold \f$thresh\f$ (fixed). | |
|  | |
| #### Threshold Binary | |
| - This thresholding operation can be expressed as: | |
| \f[\texttt{dst} (x,y) = \fork{\texttt{maxVal}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] | |
| - So, if the intensity of the pixel \f$src(x,y)\f$ is higher than \f$thresh\f$, then the new pixel | |
| intensity is set to a \f$MaxVal\f$. Otherwise, the pixels are set to \f$0\f$. | |
|  | |
| #### Threshold Binary, Inverted | |
| - This thresholding operation can be expressed as: | |
| \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{maxVal}}{otherwise}\f] | |
| - If the intensity of the pixel \f$src(x,y)\f$ is higher than \f$thresh\f$, then the new pixel intensity | |
| is set to a \f$0\f$. Otherwise, it is set to \f$MaxVal\f$. | |
|  | |
| #### Truncate | |
| - This thresholding operation can be expressed as: | |
| \f[\texttt{dst} (x,y) = \fork{\texttt{threshold}}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] | |
| - The maximum intensity value for the pixels is \f$thresh\f$, if \f$src(x,y)\f$ is greater, then its value | |
| is *truncated*. See figure below: | |
|  | |
| #### Threshold to Zero | |
| - This operation can be expressed as: | |
| \f[\texttt{dst} (x,y) = \fork{\texttt{src}(x,y)}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{0}{otherwise}\f] | |
| - If \f$src(x,y)\f$ is lower than \f$thresh\f$, the new pixel value will be set to \f$0\f$. | |
|  | |
| #### Threshold to Zero, Inverted | |
| - This operation can be expressed as: | |
| \f[\texttt{dst} (x,y) = \fork{0}{if \(\texttt{src}(x,y) > \texttt{thresh}\)}{\texttt{src}(x,y)}{otherwise}\f] | |
| - If \f$src(x,y)\f$ is greater than \f$thresh\f$, the new pixel value will be set to \f$0\f$. | |
|  | |
| Code | |
| ---- | |
| @add_toggle_cpp | |
| The tutorial code's is shown lines below. You can also download it from | |
| [here](https://github.com/opencv/opencv/tree/4.x/samples/cpp/tutorial_code/ImgProc/Threshold.cpp) | |
| @include samples/cpp/tutorial_code/ImgProc/Threshold.cpp | |
| @end_toggle | |
| @add_toggle_java | |
| The tutorial code's is shown lines below. You can also download it from | |
| [here](https://github.com/opencv/opencv/tree/4.x/samples/java/tutorial_code/ImgProc/threshold/Threshold.java) | |
| @include samples/java/tutorial_code/ImgProc/threshold/Threshold.java | |
| @end_toggle | |
| @add_toggle_python | |
| The tutorial code's is shown lines below. You can also download it from | |
| [here](https://github.com/opencv/opencv/tree/4.x/samples/python/tutorial_code/imgProc/threshold/threshold.py) | |
| @include samples/python/tutorial_code/imgProc/threshold/threshold.py | |
| @end_toggle | |
| Explanation | |
| ----------- | |
| Let's check the general structure of the program: | |
| - Load an image. If it is BGR we convert it to Grayscale. For this, remember that we can use | |
| the function @ref cv::cvtColor : | |
| @add_toggle_cpp | |
| @snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp load | |
| @end_toggle | |
| @add_toggle_java | |
| @snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java load | |
| @end_toggle | |
| @add_toggle_python | |
| @snippet samples/python/tutorial_code/imgProc/threshold/threshold.py load | |
| @end_toggle | |
| - Create a window to display the result | |
| @add_toggle_cpp | |
| @snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp window | |
| @end_toggle | |
| @add_toggle_java | |
| @snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java window | |
| @end_toggle | |
| @add_toggle_python | |
| @snippet samples/python/tutorial_code/imgProc/threshold/threshold.py window | |
| @end_toggle | |
| - Create \f$2\f$ trackbars for the user to enter user input: | |
| - **Type of thresholding**: Binary, To Zero, etc... | |
| - **Threshold value** | |
| @add_toggle_cpp | |
| @snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp trackbar | |
| @end_toggle | |
| @add_toggle_java | |
| @snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java trackbar | |
| @end_toggle | |
| @add_toggle_python | |
| @snippet samples/python/tutorial_code/imgProc/threshold/threshold.py trackbar | |
| @end_toggle | |
| - Wait until the user enters the threshold value, the type of thresholding (or until the | |
| program exits) | |
| - Whenever the user changes the value of any of the Trackbars, the function *Threshold_Demo* | |
| (*update* in Java) is called: | |
| @add_toggle_cpp | |
| @snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp Threshold_Demo | |
| @end_toggle | |
| @add_toggle_java | |
| @snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java Threshold_Demo | |
| @end_toggle | |
| @add_toggle_python | |
| @snippet samples/python/tutorial_code/imgProc/threshold/threshold.py Threshold_Demo | |
| @end_toggle | |
| As you can see, the function @ref cv::threshold is invoked. We give \f$5\f$ parameters in C++ code: | |
| - *src_gray*: Our input image | |
| - *dst*: Destination (output) image | |
| - *threshold_value*: The \f$thresh\f$ value with respect to which the thresholding operation | |
| is made | |
| - *max_BINARY_value*: The value used with the Binary thresholding operations (to set the | |
| chosen pixels) | |
| - *threshold_type*: One of the \f$5\f$ thresholding operations. They are listed in the | |
| comment section of the function above. | |
| Results | |
| ------- | |
| -# After compiling this program, run it giving a path to an image as argument. For instance, for an | |
| input image as: | |
|  | |
| -# First, we try to threshold our image with a *binary threshold inverted*. We expect that the | |
| pixels brighter than the \f$thresh\f$ will turn dark, which is what actually happens, as we can see | |
| in the snapshot below (notice from the original image, that the doggie's tongue and eyes are | |
| particularly bright in comparison with the image, this is reflected in the output image). | |
|  | |
| -# Now we try with the *threshold to zero*. With this, we expect that the darkest pixels (below the | |
| threshold) will become completely black, whereas the pixels with value greater than the | |
| threshold will keep its original value. This is verified by the following snapshot of the output | |
| image: | |
|  | |