mirror of
https://github.com/opencv/opencv.git
synced 2026-07-29 15:23:05 +04:00
Merge pull request #11543 from catree:add_tutorial_imgproc_java_python
This commit is contained in:
Alexander Alekhin
@@ -11,7 +11,7 @@ In this tutorial you will learn how to:
|
||||
Theory
|
||||
------
|
||||
|
||||
The *Canny Edge detector* was developed by John F. Canny in 1986. Also known to many as the
|
||||
The *Canny Edge detector* @cite Canny86 was developed by John F. Canny in 1986. Also known to many as the
|
||||
*optimal detector*, the Canny algorithm aims to satisfy three main criteria:
|
||||
- **Low error rate:** Meaning a good detection of only existent edges.
|
||||
- **Good localization:** The distance between edge pixels detected and real edge pixels have
|
||||
@@ -66,19 +66,33 @@ The *Canny Edge detector* was developed by John F. Canny in 1986. Also known to
|
||||
Code
|
||||
----
|
||||
|
||||
-# **What does this program do?**
|
||||
@add_toggle_cpp
|
||||
- The tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.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/master/samples/java/tutorial_code/ImgTrans/canny_detector/CannyDetectorDemo.java)
|
||||
@include samples/java/tutorial_code/ImgTrans/canny_detector/CannyDetectorDemo.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/master/samples/python/tutorial_code/ImgTrans/canny_detector/CannyDetector_Demo.py)
|
||||
@include samples/python/tutorial_code/ImgTrans/canny_detector/CannyDetector_Demo.py
|
||||
@end_toggle
|
||||
|
||||
- **What does this program do?**
|
||||
- Asks the user to enter a numerical value to set the lower threshold for our *Canny Edge
|
||||
Detector* (by means of a Trackbar).
|
||||
- Applies the *Canny Detector* and generates a **mask** (bright lines representing the edges
|
||||
on a black background).
|
||||
- Applies the mask obtained on the original image and display it in a window.
|
||||
|
||||
-# The tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
Explanation (C++ code)
|
||||
----------------------
|
||||
|
||||
-# Create some needed variables:
|
||||
@snippet cpp/tutorial_code/ImgTrans/CannyDetector_Demo.cpp variables
|
||||
|
||||
@@ -45,61 +45,91 @@ Theory
|
||||
Code
|
||||
----
|
||||
|
||||
-# **What does this program do?**
|
||||
- **What does this program do?**
|
||||
- Loads an image
|
||||
- Each second, apply 1 of 4 different remapping processes to the image and display them
|
||||
indefinitely in a window.
|
||||
- Wait for the user to exit the program
|
||||
|
||||
-# The tutorial code's is shown lines below. You can also download it from
|
||||
@add_toggle_cpp
|
||||
- The tutorial code's is shown lines below. You can also download it from
|
||||
[here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp)
|
||||
@include samples/cpp/tutorial_code/ImgTrans/Remap_Demo.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/master/samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java)
|
||||
@include samples/java/tutorial_code/ImgTrans/remap/RemapDemo.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/master/samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py)
|
||||
@include samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
-# Create some variables we will use:
|
||||
@code{.cpp}
|
||||
Mat src, dst;
|
||||
Mat map_x, map_y;
|
||||
char* remap_window = "Remap demo";
|
||||
int ind = 0;
|
||||
@endcode
|
||||
-# Load an image:
|
||||
@code{.cpp}
|
||||
src = imread( argv[1], 1 );
|
||||
@endcode
|
||||
-# Create the destination image and the two mapping matrices (for x and y )
|
||||
@code{.cpp}
|
||||
dst.create( src.size(), src.type() );
|
||||
map_x.create( src.size(), CV_32FC1 );
|
||||
map_y.create( src.size(), CV_32FC1 );
|
||||
@endcode
|
||||
-# Create a window to display results
|
||||
@code{.cpp}
|
||||
namedWindow( remap_window, WINDOW_AUTOSIZE );
|
||||
@endcode
|
||||
-# Establish a loop. Each 1000 ms we update our mapping matrices (*mat_x* and *mat_y*) and apply
|
||||
- Load an image:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp Load
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java Load
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py Load
|
||||
@end_toggle
|
||||
|
||||
- Create the destination image and the two mapping matrices (for x and y )
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp Create
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java Create
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py Create
|
||||
@end_toggle
|
||||
|
||||
- Create a window to display results
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp Window
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java Window
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py Window
|
||||
@end_toggle
|
||||
|
||||
- Establish a loop. Each 1000 ms we update our mapping matrices (*mat_x* and *mat_y*) and apply
|
||||
them to our source image:
|
||||
@code{.cpp}
|
||||
while( true )
|
||||
{
|
||||
/// Each 1 sec. Press ESC to exit the program
|
||||
char c = (char)waitKey( 1000 );
|
||||
|
||||
if( c == 27 )
|
||||
{ break; }
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp Loop
|
||||
@end_toggle
|
||||
|
||||
/// Update map_x & map_y. Then apply remap
|
||||
update_map();
|
||||
remap( src, dst, map_x, map_y, INTER_LINEAR, BORDER_CONSTANT, Scalar(0,0, 0) );
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java Loop
|
||||
@end_toggle
|
||||
|
||||
/// Display results
|
||||
imshow( remap_window, dst );
|
||||
}
|
||||
@endcode
|
||||
The function that applies the remapping is @ref cv::remap . We give the following arguments:
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py Loop
|
||||
@end_toggle
|
||||
|
||||
- The function that applies the remapping is @ref cv::remap . We give the following arguments:
|
||||
- **src**: Source image
|
||||
- **dst**: Destination image of same size as *src*
|
||||
- **map_x**: The mapping function in the x direction. It is equivalent to the first component
|
||||
@@ -112,9 +142,9 @@ Explanation
|
||||
|
||||
How do we update our mapping matrices *mat_x* and *mat_y*? Go on reading:
|
||||
|
||||
-# **Updating the mapping matrices:** We are going to perform 4 different mappings:
|
||||
- **Updating the mapping matrices:** We are going to perform 4 different mappings:
|
||||
-# Reduce the picture to half its size and will display it in the middle:
|
||||
\f[h(i,j) = ( 2*i - src.cols/2 + 0.5, 2*j - src.rows/2 + 0.5)\f]
|
||||
\f[h(i,j) = ( 2 \times i - src.cols/2 + 0.5, 2 \times j - src.rows/2 + 0.5)\f]
|
||||
for all pairs \f$(i,j)\f$ such that: \f$\dfrac{src.cols}{4}<i<\dfrac{3 \cdot src.cols}{4}\f$ and
|
||||
\f$\dfrac{src.rows}{4}<j<\dfrac{3 \cdot src.rows}{4}\f$
|
||||
-# Turn the image upside down: \f$h( i, j ) = (i, src.rows - j)\f$
|
||||
@@ -123,41 +153,18 @@ Explanation
|
||||
|
||||
This is expressed in the following snippet. Here, *map_x* represents the first coordinate of
|
||||
*h(i,j)* and *map_y* the second coordinate.
|
||||
@code{.cpp}
|
||||
for( int j = 0; j < src.rows; j++ )
|
||||
{ for( int i = 0; i < src.cols; i++ )
|
||||
{
|
||||
switch( ind )
|
||||
{
|
||||
case 0:
|
||||
if( i > src.cols*0.25 && i < src.cols*0.75 && j > src.rows*0.25 && j < src.rows*0.75 )
|
||||
{
|
||||
map_x.at<float>(j,i) = 2*( i - src.cols*0.25 ) + 0.5 ;
|
||||
map_y.at<float>(j,i) = 2*( j - src.rows*0.25 ) + 0.5 ;
|
||||
}
|
||||
else
|
||||
{ map_x.at<float>(j,i) = 0 ;
|
||||
map_y.at<float>(j,i) = 0 ;
|
||||
}
|
||||
break;
|
||||
case 1:
|
||||
map_x.at<float>(j,i) = i ;
|
||||
map_y.at<float>(j,i) = src.rows - j ;
|
||||
break;
|
||||
case 2:
|
||||
map_x.at<float>(j,i) = src.cols - i ;
|
||||
map_y.at<float>(j,i) = j ;
|
||||
break;
|
||||
case 3:
|
||||
map_x.at<float>(j,i) = src.cols - i ;
|
||||
map_y.at<float>(j,i) = src.rows - j ;
|
||||
break;
|
||||
} // end of switch
|
||||
}
|
||||
}
|
||||
ind++;
|
||||
}
|
||||
@endcode
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgTrans/Remap_Demo.cpp Update
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgTrans/remap/RemapDemo.java Update
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ImgTrans/remap/Remap_Demo.py Update
|
||||
@end_toggle
|
||||
|
||||
Result
|
||||
------
|
||||
|
||||
@@ -15,6 +15,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_erosion_dilatation
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
Author: Ana Huamán
|
||||
@@ -23,6 +25,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_opening_closing_hats
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -61,6 +65,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_threshold
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -69,6 +75,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_threshold_inRange
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Rishiraj Surti
|
||||
@@ -117,6 +125,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_canny_detector
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
@@ -145,6 +155,8 @@ In this section you will learn about the image processing (manipulation) functio
|
||||
|
||||
- @subpage tutorial_remap
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Ana Huamán
|
||||
|
||||
@@ -96,43 +96,101 @@ Thresholding?
|
||||
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/master/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/master/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/master/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
|
||||
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 :
|
||||
@snippet cpp/tutorial_code/ImgProc/Threshold.cpp load
|
||||
|
||||
- Create a window to display the result
|
||||
@snippet cpp/tutorial_code/ImgProc/Threshold.cpp window
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp load
|
||||
@end_toggle
|
||||
|
||||
- Create \f$2\f$ trackbars for the user to enter user input:
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold/Threshold.java load
|
||||
@end_toggle
|
||||
|
||||
- **Type of thresholding**: Binary, To Zero, etc...
|
||||
- **Threshold value**
|
||||
@snippet cpp/tutorial_code/ImgProc/Threshold.cpp trackbar
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold/threshold.py load
|
||||
@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*
|
||||
is called:
|
||||
@snippet cpp/tutorial_code/ImgProc/Threshold.cpp Threshold_Demo
|
||||
- Create a window to display the result
|
||||
|
||||
As you can see, the function @ref cv::threshold is invoked. We give \f$5\f$ parameters:
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold.cpp window
|
||||
@end_toggle
|
||||
|
||||
- *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.
|
||||
@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
|
||||
-------
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 20 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 15 KiB |
@@ -1,56 +1,173 @@
|
||||
Thresholding Operations using inRange {#tutorial_threshold_inRange}
|
||||
=============================
|
||||
=====================================
|
||||
|
||||
Goal
|
||||
----
|
||||
|
||||
In this tutorial you will learn how to:
|
||||
|
||||
- Perform basic thresholding operations using OpenCV function @ref cv::inRange
|
||||
- Detect an object based on the range of pixel values it has
|
||||
- Perform basic thresholding operations using OpenCV @ref cv::inRange function.
|
||||
- Detect an object based on the range of pixel values in the HSV colorspace.
|
||||
|
||||
Theory
|
||||
-----------
|
||||
- In the previous tutorial, we learnt how perform thresholding using @ref cv::threshold function.
|
||||
------
|
||||
- In the previous tutorial, we learnt how to perform thresholding using @ref cv::threshold function.
|
||||
- In this tutorial, we will learn how to do it using @ref cv::inRange function.
|
||||
- The concept remains same, but now we add a range of pixel values we need.
|
||||
- The concept remains the same, but now we add a range of pixel values we need.
|
||||
|
||||
HSV colorspace
|
||||
--------------
|
||||
|
||||
<a href="https://en.wikipedia.org/wiki/HSL_and_HSV">HSV</a> (hue, saturation, value) colorspace
|
||||
is a model to represent the colorspace similar to the RGB color model. Since the hue channel
|
||||
models the color type, it is very useful in image processing tasks that need to segment objects
|
||||
based on its color. Variation of the saturation goes from unsaturated to represent shades of gray and
|
||||
fully saturated (no white component). Value channel describes the brightness or the intensity of the
|
||||
color. Next image shows the HSV cylinder.
|
||||
|
||||
![By SharkDderivative work: SharkD [CC BY-SA 3.0 or GFDL], via Wikimedia Commons](images/Threshold_inRange_HSV_colorspace.jpg)
|
||||
|
||||
Since colors in the RGB colorspace are coded using the three channels, it is more difficult to segment
|
||||
an object in the image based on its color.
|
||||
|
||||
![By SharkD [GFDL or CC BY-SA 4.0], from Wikimedia Commons](images/Threshold_inRange_RGB_colorspace.jpg)
|
||||
|
||||
Formulas used to convert from one colorspace to another colorspace using @ref cv::cvtColor function
|
||||
are described in @ref imgproc_color_conversions
|
||||
|
||||
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/master/samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp)
|
||||
@include samples/cpp/tutorial_code/ImgProc/Threshold_inRange.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/master/samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java)
|
||||
@include samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.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/master/samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py)
|
||||
@include samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py
|
||||
@end_toggle
|
||||
|
||||
Explanation
|
||||
-----------
|
||||
|
||||
-# Let's check the general structure of the program:
|
||||
- Create two Matrix elements to store the frames
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp mat
|
||||
- Capture the video stream from default capturing device.
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp cap
|
||||
- Create a window to display the default frame and the threshold frame.
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp window
|
||||
- Create trackbars to set the range of RGB values
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp trackbar
|
||||
- Until the user want the program to exit do the following
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp while
|
||||
- Show the images
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp show
|
||||
- For a trackbar which controls the lower range, say for example Red value:
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp low
|
||||
- For a trackbar which controls the upper range, say for example Red value:
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp high
|
||||
- It is necessary to find the maximum and minimum value to avoid discrepancies such as
|
||||
the high value of threshold becoming less the low value.
|
||||
Let's check the general structure of the program:
|
||||
- Capture the video stream from default or supplied capturing device.
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp cap
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java cap
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py cap
|
||||
@end_toggle
|
||||
|
||||
- Create a window to display the default frame and the threshold frame.
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp window
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java window
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py window
|
||||
@end_toggle
|
||||
|
||||
- Create the trackbars to set the range of HSV values
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp trackbar
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java trackbar
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py trackbar
|
||||
@end_toggle
|
||||
|
||||
- Until the user want the program to exit do the following
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp while
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java while
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py while
|
||||
@end_toggle
|
||||
|
||||
- Show the images
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp show
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java show
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py show
|
||||
@end_toggle
|
||||
|
||||
- For a trackbar which controls the lower range, say for example hue value:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp low
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java low
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py low
|
||||
@end_toggle
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp low
|
||||
|
||||
- For a trackbar which controls the upper range, say for example hue value:
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp high
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ImgProc/threshold_inRange/ThresholdInRange.java high
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py high
|
||||
@end_toggle
|
||||
|
||||
- It is necessary to find the maximum and minimum value to avoid discrepancies such as
|
||||
the high value of threshold becoming less than the low value.
|
||||
|
||||
Results
|
||||
-------
|
||||
|
||||
-# After compiling this program, run it. The program will open two windows
|
||||
- After compiling this program, run it. The program will open two windows
|
||||
|
||||
-# As you set the RGB range values from the trackbar, the resulting frame will be visible in the other window.
|
||||
- As you set the range values from the trackbar, the resulting frame will be visible in the other window.
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user