Skip to main content

Camera Affine Transformation


An affine transform allows the user to warp, stretch, rotate and resize an image or a footage from a camera. Essentially the image is multiplied by 2x3 matrix to perform the transformation. An affine transform produces parallelograms (which includes standard rectangles).



#include <opencv2/video/tracking.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <stdio.h>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/calib3d/calib3d.hpp>
#include <opencv2/imgproc/imgproc_c.h>
#include <opencv2/video/tracking.hpp>
#include <iostream>
#include <vector>



int angle_switch_value = 0;
int angleInt = 0;
int scale_switch_value = 0;
int scaleInt = 0;

void switch_callback_a( int position ){
angleInt = position;
}
void switch_callback_s( int position ){
scaleInt = position;
}



int main(int argc, char** argv)
{
// Set up variables
CvPoint2D32f srcTri[3], dstTri[3];
CvMat* rot_mat = cvCreateMat(2,3,CV_32FC1);
CvMat* warp_mat = cvCreateMat(2,3,CV_32FC1);
IplImage *src, *dst;
const char* name = "Affine_Transform";

//create capture
CvCapture* capture = cvCreateCameraCapture(0);
for(;;)
{
IplImage* src = cvQueryFrame(capture);
dst = cvCloneImage( src );
dst->origin = src->origin;
cvZero( dst );
cvNamedWindow( name, 1 );

// Create angle and scale
double angle = 0.0;
double scale = 1.0;

// Create trackbars
cvCreateTrackbar( "Angle", name, &angle_switch_value, 4, switch_callback_a );
cvCreateTrackbar( "Scale", name, &scale_switch_value, 4, switch_callback_s );

// Compute warp matrix
srcTri[0].x = 0;
srcTri[0].y = 0;
srcTri[1].x = src->width - 1;
srcTri[1].y = 0;
srcTri[2].x = 0;
srcTri[2].y = src->height - 1;

dstTri[0].x = src->width*0.0;
dstTri[0].y = src->height*0.25;
dstTri[1].x = src->width*0.90;
dstTri[1].y = src->height*0.15;
dstTri[2].x = src->width*0.10;
dstTri[2].y = src->height*0.75;

cvGetAffineTransform( srcTri, dstTri, warp_mat );
cvWarpAffine( src, dst, warp_mat );
cvCopy ( dst, src );

switch( angleInt ){
case 0:
angle = 0.0;
break;
case 1:
angle = 20.0;
break;
case 2:
angle = 40.0;
break;
case 3:
angle = 60.0;
break;
case 4:
angle = 90.0;
break;
}
switch( scaleInt ){
case 0:
scale = 1.0;
break;
case 1:
scale = 0.8;
break;
case 2:
scale = 0.6;
break;
case 3:
scale = 0.4;
break;
case 4:
scale = 0.2;
break;
}


// Compute rotation matrix
CvPoint2D32f center = cvPoint2D32f( src->width/2, src->height/2 );
cv2DRotationMatrix( center, angle, scale, rot_mat );

// Do the transformation
cvWarpAffine( src, dst, rot_mat );

cvShowImage( name, dst );
cvWaitKey( 15 );
}
cvReleaseImage( &dst );
cvReleaseMat( &rot_mat );
cvReleaseMat( &warp_mat );

return 0;
}

Comments

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)     {         /// Separate the image in 3 places ( R, G and B )    

Artificial Intelligence (K Nearest Neighbor) in OPENCV

In pattern recognition , the k -nearest neighbor algorithm ( k -NN) is a method for classifying objects based on closest training examples in the feature space . k -NN is a type of instance-based learning , or lazy learning where the function is only approximated locally and all computation is deferred until classification. The k -nearest neighbor algorithm is amongst the simplest of all machine learning algorithms: an object is classified by a majority vote of its neighbors, with the object being assigned to the class most common amongst its k nearest neighbors ( k is a positive integer , typically small). If k = 1, then the object is simply assigned to the class of its nearest neighbor. The k -NN algorithm can also be adapted for use in estimating continuous variables. One such implementation uses an inverse distance weighted average of the k -nearest multivariate neighbors. This algorithm functions as follows: Compute Euclidean or Mahalanobis distance from target plo

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         ///works with VS2010 and OpenCV 2.2+         void FindBlobs(const cv::Mat &binary, vector < vector<cv::Point>  > &blobs)         {             blobs.clear();             // Fill the la