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diff --git a/Utilidades/src/histogramasEnROI.cpp b/Utilidades/src/histogramasEnROI.cpp deleted file mode 100644 index d71c436..0000000 --- a/Utilidades/src/histogramasEnROI.cpp +++ /dev/null @@ -1,616 +0,0 @@ -/******************************************************************************************************************** -* * -* (c) Copyright 2010, Team Invincible, University of Windsor * -* Reference for Comments: G. Bradski & A. Kaebler, "Learning OpenCV: Computer Vision with the OpenCV Library", * -* O'Reilly Media, 2008 * -* * -*********************************************************************************************************************/ - -//#include "stdafx.h" -#include <cv.h> -#include <highgui.h> -#include <stdio.h> - -// Initialize trackbar parameters (x coordinate, y coordinate, size of ROI) -int ROI_x = 0; -int ROI_y = 0; -int ROI_SIZE = 160; - -/* Initialize flags to zero. These flags determine the histogram for which -image channel should be displayed. The change in the flag values is triggered -by left mouse click in the panel window */ -bool CLICK_red = 0; -bool CLICK_green = 0; -bool CLICK_blue = 0; - -/* Declare the header for callback function of mouse events; the function that is called -whenever a mouse event occurs in the panel window (more explanation later) */ -void my_mouse_callback(int event, int x, int y, int flags, void* param); - -/* Define callback function for the x position of the region of interest. This function -is called whenever the position of the corresponding trackbar changes. The new position -of the trackbar is stopre in the variable "position". */ -void ROI_xpos_callback( int position ) -{ - /* Check to see whether the width of the region of interest with the new x position - exceeds 640 pixels. If this is the case, the region of interest will be outside the - boundaries of a 640x480 image. Hence, we adjust the x position to the maximum allowable - value. The 640 pixel limit was determined based on the image size captured from our video - device. If different image sizes are used the value should be adjusted accordingly. */ - if (position + ROI_SIZE*4 > 640) - { - ROI_x = 640-ROI_SIZE*4; - } - else - { - ROI_x = position; - } -} -/* Define callback function for the y position of the region of interest. The function is -called when the position of the corresponding trackbar changes. */ -void ROI_ypos_callback( int position ) -{ - /* Check to see whether the height of the region of interest with the new y position - exceeds 480 pixels. If this is the case, the region of interest will be outside the - boundaries of the 640x480 image. Hence, we adjust the y position to the maximum allowable - value. The 480 pixel limit was determined based on the image size captured from our video - device. If different image sizes are used the value should be adjusted accordingly. */ - if (position + ROI_SIZE*3 > 480) - { - ROI_y = 480-ROI_SIZE*3; - } - else - { - ROI_y = position; - } -} -/* Define callback function for size of the region ot interest. The function is called -when the position of the corresponding trackbar changes */ -void ROI_size_callback( int position ) -{ - /* Check to make sure the size of ROI is not zero. If the size is zero set - the value to 1 */ - if (position == 0) - { - ROI_SIZE = 1; - } - /* Check to see if the region of interest at this new size exceeds the 640x480 - image boundary. If that is the case, calculate the distance of the x coordinate - of ROI from the right side and the distance of the y coordinate of ROI from the - bottom of the image. Divide the distances by their appropriate ratios (width/4 - and height/3) to obtain the sizes. Choose the minimum value to ensure the ROI - remains within the boundaries of the 640x480 image. - The reason we divide width by 4 and height by 3 is because for a specific size of - ROI, we assume a 4/3 ratio of width/height. This will become more clear later. */ - else if ((ROI_x + position*4 > 640)||(ROI_y + position*3 > 480)) - { - ROI_SIZE = MIN((640 - ROI_x)/4,(480 - ROI_y)/3); - } - else - { - ROI_SIZE = position; - } -} - -int main(int argc, char *argv[]) -{ - // variable to store the keyboard input - int c; - - /* Declare template colors for later use when drawing histograms. The colors are defined - as CvScalar, an OpenCV primitive data type. CvScalar has a single member "val" which is - a pointer to an array containing 4 double-percision floating-point numbers. When we are - not concerned with memory, CvScalar can be used to represent 1,2 or 3 real numbers (in - these cases the unused component is ignored).*/ - CvScalar color_red, color_blue ,color_green,color_white; - color_red = cvScalar(0,0,255,0); - color_blue = cvScalar(255,0,0,0); - color_green = cvScalar(0,255,0,0); - color_white = cvScalar(255,255,255,0); - - /* Declare the centre of the circles that are going be filled in the selection panel image. - The centres are defined as cvPoint which is an OpenCV primitive data type for storing two - integer values. The x and y coordinates of the centres are based on the histogram panel - image that is going to be loaded later in the code */ - CvPoint pt1, pt2, pt3; - pt1.x = 33; - pt1.y = 37; - pt2.x = 96; - pt2.y = 37; - pt3.x = 158; - pt3.y = 37; - - /* Allocate memory for all images. */ - IplImage *src_img,*histogram,*predisp_img,*disp_img, *zoom_img, *disp_zoom_img, *panel, - *red_img, *green_img,*blue_img,*red_histogram,*green_histogram,*blue_histogram; - - /* Initialize histogram parameters: "hist_size" is the size of the histogram (or the - number of bins assigned to the histogram); "range" is the range of intensity values - considered in the histogram; "*ranges" is a pointer to "range" (we require a pointer - to "range" because the OpenCV function cvCreateHist accepts the range parameter as - a pointer to range value pairs). */ - int hist_size = 256; - float range[] = {0,256}; - float *ranges[] = {range}; - - /* cvCreateHist function is used to create the histograms. Following is the explanation - of the various arguments of the function : - 1. First argument: Defines how many dimensions the histogram will have. To keep things - simple this tutorial only uses one dimensional histograms. - 2. Second argument: Defines the size of the dimensions. If we were using higher dimensional - histograms we would have to pass an array of sizes, one for each dimension, but we are - only using one dimension so the array we pass only has one value in it. We choose to make - the size of the dimension 256 because the depth of the images we are working with is 8 bit, - meaning that we will get one bin for each intensity level. We could have chosen to have - less bins but the histogram would have less resolution. We could have chosen more bins but - we would not show any more information. - 3. Third argument: Tells OpenCV how to store the data in the histogram. There are two - options for this: CV_HIST_ARRAY and CV_HIST_SPARSE. The second option is useful for - storing multidimensional histograms that will have most of their bins with counts of - zero. Since we are using a one dimensional histogram, we don’t expect sparse data so - we choose the first option. - 4. Fourth argument: Used to specify the range of values for each dimension. Each range - is stored as an array of two values, the minimum and maximum value for that dimension. - Again we are using one dimension so it looks a bit redundant to make an array of arrays, - but only with one array. We have to do this though, because this fourth argument expects - an array to 2 value arrays. It is important to know how OpenCV creates the bins based on - the range values and the histogram size. OpenCV will take the value supplied in ranges and - break it into as many sub intervals as defined in hist_size. - 5. Fifth argument: Defines if the sub intervals are to be split uniformly or not. For - this case we definitely want each bin to be the same width as the others so we choose - this argument to be 1. - */ - CvHistogram* hist_red = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1); - CvHistogram* hist_blue = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1); - CvHistogram* hist_green = cvCreateHist(1, &hist_size, CV_HIST_ARRAY, ranges, 1); - - // Declare variables used for finding the maximum value of the histograms - double max_value = 0; - double max_value_red = 0; - double max_value_green = 0; - double max_value_blue = 0; - double find_max = 0; - - /* Define the parameter scale which is used when drawing the rectangles of the histogram. - In our case the scale is 3 because we chose to make the rectangles 2 pixels wide and - leave a black pixel in between subsequent rectangles. The application of this variable will - become clear later on in the code*/ - int scale = 3; - - // create the windows for displaying output - - // "Region of Interest" shows the selected region of interest - cvNamedWindow("Region of Interest",CV_WINDOW_AUTOSIZE); - cvMoveWindow("Region of Interest",5,485); - // "mainWin" shows the captured image - cvNamedWindow("mainWin", CV_WINDOW_AUTOSIZE); - cvMoveWindow("mainWin", 5, -15); - // "histogramWin" shows the histogram - cvNamedWindow("histogramWin", CV_WINDOW_AUTOSIZE); - cvMoveWindow("histogramWin", 435, 5); - // "panelWin" shows the panel for selecting Red channel, Green channel and Blue channel histograms - cvNamedWindow("panelWin", CV_WINDOW_AUTOSIZE); - cvMoveWindow("panelWin",435, 535); - - /* cvCreateTrackbar is a function for creating GUI sliders. The various arguments - of the function are explained below. - 1. First argument: declares the name of the trackbar. - 2. Second argument: name of the parent window to which the trackbar will be attached. - When the trackbar is added to a parent window, it will either be on top or the - bottom of the window; it will not occlude the existing image in the window. - 3. Third argument: a pointer to an integer that will be set automatically to the - value to which the slider has moved. This argument also detemines the initial - position of the slider. - 4. Fourth argument: integer that assigns the numerical value for the maximum value - of the slider. - 5. Fifth argument: a pointer to a callback function that will be automatically called - whenever the slider is moved. */ - cvCreateTrackbar("size","mainWin",&ROI_SIZE,160,ROI_size_callback); - cvCreateTrackbar("x pos","mainWin",&ROI_x,640,ROI_xpos_callback); - cvCreateTrackbar("y pos","mainWin",&ROI_y,480,ROI_ypos_callback); - - // Capture from video device #1 - - /* Be sure to select the correct video device. Another option is to capture from - an avi file. In that case use cvCaptureFromAvi("myfile.avi"). If the avi file is - not in the project folder, include the entire path of the file. Also use \\ instead - of \ to separate directories. */ - CvCapture* capture = cvCaptureFromCAM(0); - - while(1) - { - // Clear all max values to zero - max_value = 0; max_value_red = 0; max_value_green = 0; max_value_blue = 0; - - /* Load a png image with the histogram axis. Instead of having OpenCV draw the axis, - the axis is drawn in paint and then loaded as an image. You can download the picture - here: http://uowteaminvincible.files.wordpress.com/2010/05/histogram_scale.png - Be sure to change the path below to wherever you choose to save the image. */ - histogram = cvLoadImage( "bin/img/histogram_scale.png" ); - - // Initialize the 3 images that will show the histogram for each channel - - /* cvCreateImage is a function for creating an empty image. The first argument - must be a CvSize (similze to CvPoint) data type containing the width and the length - of the image. The second argument is the depth of the image. The last argument is - the number of channels. In this case for the first argument we use the function - cvGetSize to obtain the width and length of the histogram axis image loaded previously */ - red_histogram = cvCreateImage( cvGetSize(histogram), IPL_DEPTH_8U, 3 ); - green_histogram = cvCreateImage( cvGetSize(histogram), IPL_DEPTH_8U, 3 ); - blue_histogram = cvCreateImage( cvGetSize(histogram), IPL_DEPTH_8U, 3 ); - - // Retrieve captured frame - src_img=cvQueryFrame(capture); - - /* Create an image half the size of the captured image. This image will be used to - display the region of interest (after zooming in) */ - disp_zoom_img = cvCreateImage( cvSize((src_img->width)/2,(src_img->height)/2),IPL_DEPTH_8U,3); - - /* Create an image the same size as the captured image. This image will be temporarily used - to outline the region of interest in the captured image. */ - predisp_img = cvCreateImage(cvSize((src_img->width),(src_img->height)),IPL_DEPTH_8U,3); - - /* Copy "src_img" to "predisp_img". cvCopy is function for copying one image to another. Both - images must have the same size and number of channels. The last argument is for passing a mask - to only copy pixels in a certain region of the image. */ - cvCopy(src_img,predisp_img,0); - - /* Use the function cvRectangle to draw a rectangle around the region of interest. The ratio of - widtth/height for the rectangle is 4/3 and the size is determined by the user (by adjusting the - trackbar for "size"). The various arguments of cvRectangle are explained below: - 1. First argument: the image in which the rectangle should be drawn. - 2. Second argument: a cvPoint data type representing one corner of the rectangle. - 3. Third argument: a cvPoint data type representing the corner of the rectangle opposite - (diagonally) to the second argument. - 4. Fourth argument: cvScalar data type for determining the color of the rectangle. - 5. Last 3 arguments: line thickness, line type (4 or 8 connected), shift (zero in this case) */ - cvRectangle(predisp_img,cvPoint(ROI_x,ROI_y),cvPoint(ROI_x+4*ROI_SIZE,ROI_y+3*ROI_SIZE),color_red,4,4,0); - - // Create an image smaller than the captured image (resizing done for display purposes) - disp_img = cvCreateImage(cvSize((src_img->width)/1.6,(src_img->height)/1.6),IPL_DEPTH_8U,3); - - /* Copy "predisp_img" image (which contains the region of interest rectangle) into the smaller - sized "disp_img" using the cvResize function. The last argument is the interpolation method - used when converting the image from one size to another. */ - cvResize(predisp_img,disp_img,CV_INTER_LINEAR); - - /**************************** Begin Working Inside the Region of Interest **********************************/ - - /* Use the function cvSetImageROI to select only the region of interest from the captured - image. The first argument is the image which the region of interest is selected from and the - second argument is a cvRect data type containing information about the region of interest. - The first two arguments in cvRect represent the coordinates of the top left corner of ROI and - the last two arguments represent the width and height of the ROI respectively. */ - cvSetImageROI(src_img, cvRect(ROI_x,ROI_y,4*ROI_SIZE,3*ROI_SIZE)); - - // Create 3 single channel images to store each channel data and split the source image into the RGB channels. - - /* Note that each channel image is the same size (width and height) as the source image. The depth is 8 bits - and these new images have only one channel (since they are storing only one channel). The function cvCvtPixToPlane - actually separates the source into its three channels and stores them in the images we just created. Instead of - cvCvtPixToPlane we can also use cvSplit. The order of the channels in cvCvtPixToPlane functio is the same as the - order of channels in the data array of the source image (in this case BGR) */ - blue_img = cvCreateImage( cvGetSize(src_img), IPL_DEPTH_8U, 1 ); - green_img = cvCreateImage( cvGetSize(src_img), IPL_DEPTH_8U, 1 ); - red_img = cvCreateImage( cvGetSize(src_img), IPL_DEPTH_8U, 1 ); - cvCvtPixToPlane( src_img, blue_img, green_img, red_img, 0 ); - - // Calculate a histogram for each channel. - - /*The first argument takes the image we would like to calculate the histogram for. - Note that cvCalcHist can only take a one channel image. The next argument says which - histogram will be populated. The third argument turns on or off accumulator mode. - Since we want the histogram to update for each frame we want to clear the contents - before adding new ones and the accumulator mode should be off. The final parameter - is a mask that can tell cvCalcHist to unly calculate a certain part of the image. - By setting the mask null we calculate for the whole image.*/ - cvCalcHist( &red_img, hist_red, 0, NULL ); - cvCalcHist( &blue_img, hist_blue, 0, NULL ); - cvCalcHist( &green_img, hist_green, 0, NULL ); - - // Creat an image the same size as the region of interest - /* It should be noted that cvGetSize in this case will not return the size of - the original captured image. It will return the size of the region of interest - set using the cvSetImageROI function. */ - zoom_img = cvCreateImage( cvGetSize(src_img), 8, 3 ); - - // Copy the region of interest from the captured image, into the image created above - cvCopy(src_img,zoom_img,0); - - // Resize the region of interest into the larger "disp_zoom" image - cvResize(zoom_img,disp_zoom_img,CV_INTER_LINEAR); - - // Turn off the region of interest using cvResetImageROI function - cvResetImageROI(src_img); - - /*********************** Finished Working Inside the Region of Interest *********************************/ - - // Search through the histograms for their maximum value and store it - - /* The code for finding the maximum value of the histogram for each channel is shown below. It is done with a - simple for loop that checks every value in each bin. The function used to get the values out of the histogram is - cvQueryHistValue_1D. This function is made for getting values out of a one dimensional histogram. Its arguments - are the histogram you want to look into, and the bin number you want to see. The reason we need to find this - max value is that we will scale the y-axis of the histogram so that it fits on the screen. We do this by dividing - each bin value by the max value (this is done in the next step). - - An alternative method of obtaining the maximum values of the histogram is using the function cvGetMinMaxHistValue. - As an example we can find the maximum value of the red channel histogram by using the code - "cvGetMinMaxHistValue(hist_red,0,&max_value_red,0,0)". */ - for( int i = 0; i < hist_size; i++ ) - { - find_max = cvQueryHistValue_1D(hist_red,i); - if (find_max > max_value_red) - { - max_value_red = find_max; - } - } - for( int i = 0; i < hist_size; i++ ) - { - find_max = cvQueryHistValue_1D(hist_green,i); - if (find_max > max_value_green) - { - max_value_green = find_max; - } - } - for( int i = 0; i < hist_size; i++ ) - { - find_max = cvQueryHistValue_1D(hist_blue,i); - if (find_max > max_value_blue) - { - max_value_blue = find_max; - } - } - - // Print the maximum value of the histogram for each channel - printf("RED MAX = %f\n",max_value_red); - printf("GREEN MAX = %f\n",max_value_green); - printf("BLUE MAX = %f\n",max_value_blue); - - // Calculate the maximum value of all histograms - - /* When calculating the maximum value of all histograms we must first check to see - which histograms are selected by the user to be displayed. This is done by the following - if-else statements checking the flag variables assigned to each histogram (red,green,blue) */ - if (CLICK_red) - { - if(CLICK_green) - { - if(CLICK_blue) - { - max_value = MAX(MAX(max_value_red,max_value_green),max_value_blue); - } - else - { - max_value = MAX(max_value_red,max_value_green); - } - } - else - { - if(CLICK_blue) - { - max_value = MAX(max_value_red,max_value_blue); - } - else - { - max_value = max_value_red; - } - } - } - else if(CLICK_green) - { - if(CLICK_blue) - { - max_value = MAX(max_value_green,max_value_blue); - } - else - { - max_value = max_value_green; - } - } - else - { - if(CLICK_blue) - { - max_value = max_value_blue; - } - else - { - //do nothing - } - } - - // Print the overall maximum value of histograms - printf("MAXIMUM = %f\n",max_value); - - // Draw the histogram for each channel, if the flag for that channel is set to 1 - - /* First we see an if statement that controls whether or not we draw the histogram. It is based - on a flag that is set in the next step by user mouse input. This allows the user to select which channel - or combination of channels that they want to see. The next step is using the function cvScale to scale - the histogram bin values. Its first argument is the source to be scaled and the second is the destination - for result. Here we used the same place for source and destination meaning that the bins are scaled and then - stored back in the same place. The last argument is the scale factor. A factor of 438/max_value was - used because the highest bar that we want to draw is 438 pixels high (so it does not go out of the - bounds of the picture we used for the axis). - - Inside the for loop we see a line of code that draws all the rectangles representing the bars of the - histogram. The function cvRectangle is used to do this. As explained previously, it requires that we pass - it which image to draw on, two points to define the rectangles, the color, line thickness, line type - and shift value. The image used to draw the histogram is the corresponding empty image created for each - separate channel. The first point of the rectangle that is selected is the bottom left corner. In our case - we have chosen to start drawing the rectangles from the 15th pixel of image form the left side and the 448th - pixel from the top. These values are chosen based on the dimensions of the specific histogram axis image - loaded previously. Be sure to adjust these values if you choose to change the image which the histograms are - drawn on. We have also decided to make the rectangles 2 pixels wide and leave a black pixel in between each - subsequent rectangle. Hence, we multiply i (histogram bin) by scale (set to 3) to make sure the rectangles - are drawn every 3 pixels. The second point of the rectangle is the top right corner. Since the rectangles - are chosen to be 2 pixels wide, the width coordinate of this point is one pixel to the right of the first - point. The height coordinate of the second point is calculated by subtracting the bin value from 448 (the - bottom of the rectangle). Since the bins were scaled to have a maximum value of 438, there is a minimum - distance of 10 pixels between the top of the histogram scale image and the rectangles. Once again these - values are determined specifically for our axis image and they should be adjusted accordingly if the image - is changed. We specify line thickness as -1 (which means a filled rectangle) and line type as 8(8-connected - lines). One other issue that should be noted is the use of the cvRound function. cvRound is used to convert - the value obtained from cvQueryHistValue_1D to an integer. - - The last function is cvAdd (performs elementwise addition of two arrays) which is used to simplify drawing - the multiple histograms with overlap. The first two arguments are the source images added to each other. - The third argument is the destination image which in this case is the histogram axis image. The final - argument is for passing a mask (in this case 0) to only add specific regions of the two images. By adding - colors on top of each other no extra code is required to deal with the overlap of different histograms. */ - if (CLICK_red == 1) - { - - cvScale( hist_red->bins, hist_red->bins, 438/max_value, 0 ); - for( int i= 0; i < hist_size; i++ ) - { - cvRectangle( red_histogram, cvPoint(i*scale+ 15, 448),cvPoint(i*scale+16, 448 - cvRound(cvQueryHistValue_1D(hist_red,i))),color_red, -1, 8, 0 ); - } - cvAdd(histogram,red_histogram,histogram,0); - } - if (CLICK_green == 1) - { - - cvScale( hist_green->bins, hist_green->bins, 438/max_value, 0 ); - for( int i= 0; i < hist_size; i++ ) - { - cvRectangle( green_histogram, cvPoint(i*scale+ 15, 448),cvPoint(i*scale+16, 448 - cvRound(cvQueryHistValue_1D(hist_green,i))),color_green, -1, 8, 0 ); - } - cvAdd(histogram,green_histogram,histogram,0); - } - if (CLICK_blue == 1) - { - - cvScale( hist_blue->bins, hist_blue->bins, 438/max_value, 0 ); - for( int i= 0; i < hist_size; i++ ) - { - cvRectangle( blue_histogram, cvPoint(i*scale+ 15, 448),cvPoint(i*scale+16, 448 - cvRound(cvQueryHistValue_1D(hist_blue,i))),color_blue, -1, 8, 0 ); - } - cvAdd(histogram,blue_histogram,histogram,0); - } - - /* Load a png image with the selection panel drawn in paint. The coordinates setup up in - the mouse callback function for selecting the different channels is according to this specific - image. If you choose to make your own panel image make sure the right adjustments are made - in the code. You can download the picture here: - -http://uowteaminvincible.files.wordpress.com/2010/05/histogram_panel.png - - Be sure to change the path below to wherever you choose to save the image. */ - panel = cvLoadImage( "bin/img/histogram_panel.png" ); - - /* Declare the function cvSetMouseCallback which registers a callback whenever a mouse event occurs. - In other words we want to call the my_mouse_callback function (defined after "main") whenever the user - doese something with the mouse over a particular window (in our case the panel selection window). - The first argument of cvSetMouseCallback function is the window which the callback is registered to - (only mouse events in this particular window will trigger the callback function). The second argument - is the name of the callback function which is "my_mouse_callback". The final argument is a void pointer - that can be used to pass any additional information to the callback function in the form of a pointer - to whatever kind of structure that is needed. The void pointer is commonly used when the callback is a - static member function of a class (object oriented programming). In our case we set the pointer to the - panel image but we do not need to perform any operations on the panel image inside the callback function - itself */ - cvSetMouseCallback("panelWin",my_mouse_callback,(void*)panel); - - /* Draw circles in designated area of the histogram panel image to indicate which histogram is being - displayed. The function used for drawing circles is cvCircle. The first argument is the image in which - the circle is drawn. The second argument is a cvPoint data type that determines the centre of the circle. - The coordinates of the circle centres are set at the beginning of "main" according to the circle areas on - the panel image. The third and fourth arguments are the radius and the color of the circle. The radius in - our case is 7 pixels and the color (which corresponds to the histogram of the channel being displayed) is - one of the template colors defined at the beginning of "main". The last three arguments are line thickness, - line type and shift which are the same as the parameters in cvRectangle function. */ - if (CLICK_red == 1) - { - cvCircle(panel,pt1,7,color_red,-1,9,0); - } - else - { - cvCircle(panel,pt1,7,color_white,-1,9,0); - } - if (CLICK_green == 1) - { - cvCircle(panel,pt2,7,color_green,-1,9,0); - } - else - { - cvCircle(panel,pt2,7,color_white,-1,9,0); - } - if (CLICK_blue == 1) - { - cvCircle(panel,pt3,7,color_blue,-1,9,0); - } - else - { - cvCircle(panel,pt3,7,color_white,-1,9,0); - } - - // Show the images in the windows - cvShowImage("mainWin", disp_img); - cvShowImage("Region of Interest", disp_zoom_img); - cvShowImage("histogramWin", histogram); - cvShowImage("panelWin", panel); - - /* Wait for a key input for 10 milliseconds. If the value in cvWaitKey function is 0, the program - will wait indefinitely until a key is pressed by the user. */ - c=cvWaitKey(10); - - // Terminate program if Esc key is pressed - if(c == 27)break; - - // release the images - cvReleaseImage(&red_img ); - cvReleaseImage(&green_img ); - cvReleaseImage(&blue_img ); - cvReleaseImage(&red_histogram ); - cvReleaseImage(&blue_histogram ); - cvReleaseImage(&green_histogram ); - cvReleaseImage(&predisp_img ); - cvReleaseImage(&disp_img ); - cvReleaseImage(&histogram ); - cvReleaseImage(&zoom_img ); - cvReleaseImage(&disp_zoom_img ); - cvReleaseImage(&panel); - - } - return 0; -} - -/* Define the function that is called everytime a mouse event occurs in the selection panel window. -Whenever a mouse event occurs, OpenCV will fill the arguments of the callback function with their -appropriate values. The first argument is an integer representing the type of event that has occured -(e.g. mousemove, left button down, right button double click, etc). The second and third arguments are -the coordinates of the mouse pointer when the event occured. The fourth argument is a flag that indicates -special conditions present at the time of the mouse event (such as the shift key being pressed). The last -argument is a pointer that was passed to the callback function when the event occured. As explained -previously the pointer points to the histogram panel image. For our purpose we will not be using the last -two arguments. */ -void my_mouse_callback(int event, int x, int y, int flags, void* param) -{ - switch( event ) - { - // Check to see if left mouse button was pressed - case CV_EVENT_LBUTTONDOWN: - { - /* Change the flag variables for histogram display based on the - coordinates of the mouse event. The ranges correspond to the rectangles - in the histogram panel image. If a different image is selected these - values need to be adjusted appropriately. The R,G,B flags are XORed - with 1 to change states (they act like a toggle switch that is - triggered by left mouse click).*/ - if (( x > 10 && x < 65 )&&( y > 10 && y < 60 )) - { - CLICK_red = CLICK_red ^ 1; - } - else if (( x > 75 && x < 130)&&( y > 10 && y < 60 )) - { - CLICK_green = CLICK_green ^ 1; - } - else if (( x > 140 && x < 195 )&&( y > 10 && y < 60 )) - { - CLICK_blue = CLICK_blue ^ 1; - } - }break; - } -} |