Merge pull request #6305 from rishirajsurti:master
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@ -51,6 +51,14 @@ In this section you will learn about the image processing (manipulation) functio
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After so much processing, it is time to decide which pixels stay!
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- @subpage tutorial_threshold_inRange
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*Compatibility:* \> OpenCV 2.0
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*Author:* Rishiraj Surti
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Thresholding operations using inRange function.
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- @subpage tutorial_filter_2d
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*Compatibility:* \> OpenCV 2.0
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@ -0,0 +1,56 @@
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Thresholding Operations using inRange {#tutorial_threshold_inRange}
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=============================
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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::inRange
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- Detect an object based on the range of pixel values it has
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Theory
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-----------
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- In the previous tutorial, we learnt how perform thresholding using @ref cv::threshold function.
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- In this tutorial, we will learn how to do it using @ref cv::inRange function.
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- The concept remains same, but now we add a range of pixel values we need.
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Code
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----
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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/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp)
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@include samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp
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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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- Create two Matrix elements to store the frames
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp mat
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- Capture the video stream from default capturing device.
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp cap
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- Create a window to display the default frame and the threshold frame.
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp window
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- Create trackbars to set the range of RGB values
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp trackbar
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- Until the user want the program to exit do the following
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp while
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- Show the images
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp show
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- For a trackbar which controls the lower range, say for example Red value:
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp low
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- For a trackbar which controls the upper range, say for example Red value:
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@snippet samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp high
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- It is necessary to find the maximum and minimum value to avoid discrepancies such as
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the high value of threshold becoming less the low value.
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Results
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-------
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-# After compiling this program, run it. The program will open two windows
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-# As you set the RGB range values from the trackbar, the resulting frame will be visible in the other window.
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![](images/Threshold_inRange_Tutorial_Result_input.jpeg)
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![](images/Threshold_inRange_Tutorial_Result_output.jpeg)
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102
samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp
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samples/cpp/tutorial_code/ImgProc/Threshold_inRange.cpp
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@ -0,0 +1,102 @@
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#include "opencv2/imgproc.hpp"
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#include "opencv2/highgui.hpp"
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#include <iostream>
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#include <stdlib.h>
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using namespace std;
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using namespace cv;
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/** Function Headers */
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void on_low_r_thresh_trackbar(int, void *);
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void on_high_r_thresh_trackbar(int, void *);
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void on_low_g_thresh_trackbar(int, void *);
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void on_high_g_thresh_trackbar(int, void *);
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void on_low_b_thresh_trackbar(int, void *);
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void on_high_b_thresh_trackbar(int, void *);
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/** Global Variables */
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int low_r=30, low_g=30, low_b=30;
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int high_r=100, high_g=100, high_b=100;
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/** @function main */
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int main()
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{
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//! [mat]
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Mat frame, frame_threshold;
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//! [mat]
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//! [cap]
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VideoCapture cap(0);
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//! [cap]
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//! [window]
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namedWindow("Video Capture", WINDOW_NORMAL);
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namedWindow("Object Detection", WINDOW_NORMAL);
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//! [window]
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//! [trackbar]
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//-- Trackbars to set thresholds for RGB values
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createTrackbar("Low R","Object Detection", &low_r, 255, on_low_r_thresh_trackbar);
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createTrackbar("High R","Object Detection", &high_r, 255, on_high_r_thresh_trackbar);
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createTrackbar("Low G","Object Detection", &low_g, 255, on_low_g_thresh_trackbar);
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createTrackbar("High G","Object Detection", &high_g, 255, on_high_g_thresh_trackbar);
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createTrackbar("Low B","Object Detection", &low_b, 255, on_low_b_thresh_trackbar);
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createTrackbar("High B","Object Detection", &high_b, 255, on_high_b_thresh_trackbar);
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//! [trackbar]
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while(char(waitKey(1))!='q'){
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//! [while]
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cap>>frame;
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if(frame.empty())
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break;
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//-- Detect the object based on RGB Range Values
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inRange(frame,Scalar(low_b,low_g,low_r), Scalar(high_b,high_g,high_r),frame_threshold);
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//! [while]
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//! [show]
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//-- Show the frames
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imshow("Video Capture",frame);
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imshow("Object Detection",frame_threshold);
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//! [show]
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}
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return 0;
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}
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//! [low]
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/** @function on_low_r_thresh_trackbar */
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void on_low_r_thresh_trackbar(int, void *)
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{
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low_r = min(high_r-1, low_r);
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setTrackbarPos("Low R","Object Detection", low_r);
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}
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//! [low]
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//! [high]
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/** @function on_high_r_thresh_trackbar */
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void on_high_r_thresh_trackbar(int, void *)
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{
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high_r = max(high_r, low_r+1);
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setTrackbarPos("High R", "Object Detection", high_r);
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}
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//![high]
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/** @function on_low_g_thresh_trackbar */
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void on_low_g_thresh_trackbar(int, void *)
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{
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low_g = min(high_g-1, low_g);
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setTrackbarPos("Low G","Object Detection", low_g);
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}
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/** @function on_high_g_thresh_trackbar */
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void on_high_g_thresh_trackbar(int, void *)
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{
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high_g = max(high_g, low_g+1);
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setTrackbarPos("High G", "Object Detection", high_g);
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}
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/** @function on_low_b_thresh_trackbar */
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void on_low_b_thresh_trackbar(int, void *)
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{
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low_b= min(high_b-1, low_b);
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setTrackbarPos("Low B","Object Detection", low_b);
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}
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/** @function on_high_b_thresh_trackbar */
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void on_high_b_thresh_trackbar(int, void *)
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{
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high_b = max(high_b, low_b+1);
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setTrackbarPos("High B", "Object Detection", high_b);
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}
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