Skip to main content

Installing and configuring OpenCV 2.2


Correction to the include library

correction to the linking of files

CODE USED:

#include

int main()
{
IplImage* img = cvLoadImage("C:\\hello.jpg");
cvNamedWindow("myfirstwindow");
cvShowImage("myfirstwindow", img);
cvWaitKey(0);
cvReleaseImage(&img);
return 0;
}










The important files and includes in previous versions of opencv
cv200d.lib cxcore200d.lib highgui200d.lib cvaux200d.lib ml200d.lib





Labels:

Comments

  1. Aresh.SaharkhizFebruary 2, 2012 at 11:46 PM

    opencv_core220d.lib
    opencv_highgui220d.lib
    opencv_imgproc220d.lib
    opencv_video220d.lib
    opencv_features2d220d.lib
    opencv_objdetect220d.lib
    opencv_calib3d220d.lib
    opencv_contrib220d.lib
    opencv_flann220d.lib
    opencv_ffmpeg220d.lib
    opencv_legacy220d.lib
    opencv_ml220d.lib
    opencv_gpu220d.lib
    opengl32.lib
    glut32.lib
    glu32.lib
    cvblobslib.lib

    ReplyDelete

Post a Comment

Popular posts from this blog

Computing Entropy of an image (CORRECTED)

entropy is a measure of the uncertainty associated with a random variable. basically i want to get a single value representing the entropy of an image. 1. Assign 255 bins for the range of values between 0-255 2. separate the image into its 3 channels 3. compute histogram for each channel 4. normalize all 3 channels unifirmely 5. for each channel get the bin value (Hc) and use its absolute value (negative log is infinity) 6. compute Hc*log10(Hc) 7. add to entropy and continue with 5 until a single value converges 5. get the frequency of each channel - add all the values of the bin 6. for each bin get a probability - if bin 1 = 20 bin 2 = 30 then frequency is 50 and probability is 20/50 and 30/50 then compute using shannon formula  REFERENCE: http://people.revoledu.com/kardi/tutorial/DecisionTree/how-to-measure-impurity.htm class atsHistogram { public:     cv::Mat DrawHistogram(Mat src)     {      ...
6 comments
Read more

Blob Detection, Connected Component (Pure Opencv)

Connected-component labeling (alternatively connected-component analysis, blob extraction, region labeling, blob discovery, or region extraction) is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation. i got the initial code from this URL: http://nghiaho.com/?p=1102 However the code did not compile with my setup of OpenCV 2.2, im guessing it was an older version. so a refactored and corrected the errors to come up with this Class class atsBlobFinder     {     public:         atsBlobFinder()         {         }         ///Original Code by http://nghiaho.com/?p=1102         ///Changed and added commments. Removed Errors     ...
2 comments
Read more

Template Matching using OpenCV internal function

For this example we need to add the following to the linker dependencies: opencv_core220d.lib opencv_highgui220d.lib opencv_imgproc220d.lib Code: #include <stdlib.h> #include <stdio.h> #include <math.h> #include <string.h> #include<opencv2\opencv.hpp> #include <opencv2\highgui\highgui.hpp> int main(int argc, char *argv[]) { IplImage *img; IplImage *tpl; IplImage *res; CvPoint minloc, maxloc; double minval, maxval; int img_width, img_height; int tpl_width, tpl_height; int res_width, res_height; /* check for arguments */ if( argc < 3 ) { printf( "Usage: template_match <reference> <template>\n" ); return 1; } /* load reference image */ img = cvLoadImage( argv[1], CV_LOAD_IMAGE_COLOR ); /* always check */ if( img == 0 ) { printf( "Cannot load file %s!\n", argv[1] ); return 1; } /* load template image */ tpl = cvLoa...
7 comments
Read more