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  • haar transformation on an image

    - by kabatusuka
    hello, i need some help with haar transformation, i have to apply it on an image. My math is bad, my english not all that awesome and i find it hard to understand from articles on the internet. I found this page http://www.cs.ucf.edu/~mali/haar/haar.cpp where the haar transformation is applied on 2d matrix. I suppose if i give image pixels matrix in there, it should work? im confused about this stuff, could someone enlighten me a bit please? Thank you!

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  • How to use Haar wavelet to detect LINES on an image?

    - by Ole Jak
    So I have Image like this I want to get something like this (I hevent drawn all lines I want but I hope you can get my idea) I want to use SURF ( (Speeded Up Robust Features) is a robust image descriptor, first presented by Herbert Bay et al. in 2006 ) or something that is based on sums of 2D Haar wavelet responses and makes an efficient use of integral images for finding all straight lines on image. I want to get relative to picture pixel coords start and end points of lines. So on this picture to find all lines between tiles and thouse 2 black lines on top. Is there any such Code Example (with lines search capability) to start from? I love C and C++ but any other readable code will probably work for me=)

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  • OpenCV haar training for static image

    - by Evl-ntnt
    I trying to train haar cascade classificator for card suite detection (which no rotation and has no distortion on image) For example I have file Clubs.png which contents clubs image on white background 20x20 pixels This tutorial is so tangled http://note.sonots.com/SciSoftware/haartraining.html My image varies only in sizes, no distortion or angling. Which commands I must enter in aim to get Clubs.xml file?

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  • How to create Haar Cascade (xml) for using with OpenCV?

    - by inTagger
    If you familiar with OpenCV library, you know what is haar cascade image object detection. I mean image object detection like human face or something else. I have haar cascade xml for face detection, but i don't know how to create my own. I want to create Haar Cascade xml to detect simple bright circle light sources (i.e. flashing infrared light from TV remote control). So, how to create Haar Cascade (xml) for using with OpenCV?

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  • Explaining training method for AdaBoost algorithm

    - by konzti8
    Hi, I'm trying to understand the Haar feature method used for the training step in the AdaBoost algorithm. I don't understand the math that well so I'd appreciate more of a conceptual answer (as much as possible, anyway). Basically, what does it do? How do you choose positive and negative sets for what you want to select? Can it be generalized? What I mean by that is, can you choose it to find any kind of feature that you want no matter what the background is? So, for example, if I want to find some kind of circular blob - can I do that? I've also read that it is used on small patches for the images around the possible feature - does that mean you have to manually select that image patch or can it be automated to process the entire image? Is there matlab code for the training step? Thanks for any help...

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  • [OpenCV] What do the "left" and "right" values mean in the haar cascade xml files?

    - by user117046
    In OpenCV's haar cascade files, what are the "left" and "right" values, and how does this refer to the "threshold" value? Thanks! Just for reference, here's the structure of the files: <haarcascade_frontalface_alt type_id="opencv-haar-classifier"> <size>20 20</size> <stages> <_> <!-- stage 0 --> <trees> <_> <!-- tree 0 --> <_> <!-- root node --> <feature> <rects> <_>3 7 14 4 -1.</_> <_>3 9 14 2 2.</_></rects> <tilted>0</tilted></feature> <threshold>4.0141958743333817e-003</threshold> <left_val>0.0337941907346249</left_val> <right_val>0.8378106951713562</right_val></_></_> <_>

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  • Delete files on external hard drive affect SSD?

    - by haar
    I've got a SSD as my primary hard drive. I have an external HDD hard drive which I use to store downloaded files. At the moment I have to delete 80 GB of that external hard drive. I realized that if I delete it normally, it would probably go to the recycle bin of my SSD. I am wondering how I can delete the files on my HDD without affecting the SSD or putting any files on it. Do you know how to do this? Best, haar

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  • Matlab Image watermarking question , using both SVD and DWT

    - by Georgek
    Hello all . here is a code that i got over the net ,and it is supposed to embed a watermark of size(50*20) called _copyright.bmp in the Code below . the size of the cover object is (512*512), it is called _lena_std_bw.bmp.What we did here is we did DWT2 2 times for the image , when we reached our second dwt2 cA2 size is 128*128. You should notice that the blocksize and it equals 4, it is used to determine the max msg size based on cA2 according to the following code:max_message=RcA2*CcA2/(blocksize^2). in our current case max_message would equal 128*128/(4^2)=1024. i want to embed a bigger watermark in the 2nd dwt2 and lets say the size of that watermark is 400*10(i can change the dimension using MS PAINT), what i have to do is change the size of the blocksize to 2. so max_message=4096.Matlab gives me 3 errors and they are : ??? Error using == plus Matrix dimensions must agree. Error in == idwt2 at 93 x = upsconv2(a,{Lo_R,Lo_R},sx,dwtEXTM,shift)+ ... % Approximation. Error in == two_dwt_svd_low_low at 88 CAA1 = idwt2(cA22,cH2,cV2,cD2,'haar',[RcA1,CcA1]); The origional Code is (the origional code where blocksize =4): %This algorithm makes DWT for the whole image and after that make DWT for %cH1 and make SVD for cH2 and embed the watermark in every level after SVD %(1) -------------- Embed Watermark ------------------------------------ %Add the watermar W to original image I and give the watermarked image in J %-------------------------------------------------------------------------- % set the gain factor for embeding and threshold for evaluation clc; clear all; close all; % save start time start_time=cputime; % set the value of threshold and alpha thresh=.5; alpha =0.01; % read in the cover object file_name='_lena_std_bw.bmp'; cover_object=double(imread(file_name)); % determine size of watermarked image Mc=size(cover_object,1); %Height Nc=size(cover_object,2); %Width % read in the message image and reshape it into a vector file_name='_copyright.bmp'; message=double(imread(file_name)); T=message; Mm=size(message,1); %Height Nm=size(message,2); %Width % perform 1-level DWT for the whole cover image [cA1,cH1,cV1,cD1] = dwt2(cover_object,'haar'); % determine the size of cA1 [RcA1 CcA1]=size(cA1) % perform 2-level DWT for cA1 [cA2,cH2,cV2,cD2] = dwt2(cA1,'haar'); % determine the size of cA2 [RcA2 CcA2]=size(cA2) % set the value of blocksize blocksize=4 % reshape the watermark to a vector message_vector=round(reshape(message,Mm*Nm,1)./256); W=message_vector; % determine maximum message size based on cA2, and blocksize max_message=RcA2*CcA2/(blocksize^2) % check that the message isn't too large for cover if (length(message) max_message) error('Message too large to fit in Cover Object') end %----------------------- process the image in blocks ---------------------- x=1; y=1; for (kk = 1:length(message_vector)) [cA2u cA2s cA2v]=svd(cA2(y:y+blocksize-1,x:x+blocksize-1)); % if message bit contains zero, modify S of the original image if (message_vector(kk) == 0) cA2s = cA2s*(1 + alpha); % otherwise mask is filled with zeros else cA2s=cA2s; end cA22(y:y+blocksize-1,x:x+blocksize-1)=cA2u*cA2s*cA2v; % move to next block of mask along x; If at end of row, move to next row if (x+blocksize) >= CcA2 x=1; y=y+blocksize; else x=x+blocksize; end end % perform IDWT CAA1 = idwt2(cA22,cH2,cV2,cD2,'haar',[RcA1,CcA1]); watermarked_image= idwt2(CAA1,cH1,cV1,cD1,'haar',[Mc,Nc]); % convert back to uint8 watermarked_image_uint8=uint8(watermarked_image); % write watermarked Image to file imwrite(watermarked_image_uint8,'dwt_watermarked.bmp','bmp'); % display watermarked image figure(1) imshow(watermarked_image_uint8,[]) title('Watermarked Image') %(2) ---------------------------------------------------------------------- %---------- Extract Watermark from attacked watermarked image ------------- %-------------------------------------------------------------------------- % read in the watermarked object file_name='dwt_watermarked.bmp'; watermarked_image=double(imread(file_name)); % determine size of watermarked image Mw=size(watermarked_image,1); %Height Nw=size(watermarked_image,2); %Width % perform 1-level DWT for the whole watermarked image [ca1,ch1,cv1,cd1] = dwt2(watermarked_image,'haar'); % determine the size of ca1 [Rca1 Cca1]=size(ca1); % perform 2-level DWT for ca1 [ca2,ch2,cv2,cd2] = dwt2(ca1,'haar'); % determine the size of ca2 [Rca2 Cca2]=size(ca2); % process the image in blocks % for each block get a bit for message x=1; y=1; for (kk = 1:length(message_vector)) % sets correlation to 1 when patterns are identical to avoid /0 errors % otherwise calcluate difference between the cover image and the % watermarked image [cA2u cA2s cA2v]=svd(cA2(y:y+blocksize-1,x:x+blocksize-1)); [ca2u1 ca2s1 ca2v1]=svd(ca2(y:y+blocksize-1,x:x+blocksize-1)); correlation(kk)=diag(ca2s1-cA2s)'*diag(ca2s1-cA2s)/(alpha*alpha)/(diag(cA2s)*diag(cA2s)); % move on to next block. At and of row move to next row if (x+blocksize) >= Cca2 x=1; y=y+blocksize; else x=x+blocksize; end end % if correlation exceeds average correlation correlation(kk)=correlation(kk)+mean(correlation(1:Mm*Nm)); for kk = 1:length(correlation) if (correlation(kk) > thresh*alpha);%thresh*mean(correlation(1:Mo*No))) message_vector(kk)=0; end end % reshape the message vector and display recovered watermark. figure(2) message=reshape(message_vector(1:Mm*Nm),Mm,Nm); imshow(message,[]) title('Recovered Watermark') % display processing time elapsed_time=cputime-start_time, please do help,its my graduation project and i have been trying this code for along time but failed miserable. Thanks in advance

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  • Training sets for AdaBoost algorithm

    - by palau1
    How do you find the negative and positive training data sets of Haar features for the AdaBoost algorithm? So say you have a certain type of blob that you want to locate in an image and there are several of them in your entire array - how do you go about training it? I'd appreciate a nontechnical explanation as much as possible. I'm new to this. Thanks.

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  • Machine learning - training step

    - by palau1
    When you're using Haar-like features for your training data for an Adaboost algorithm, how do you build your data sets? Do you literally have to find thousands of positive and negative samples? There must be a more efficient way of doing this... I'm trying to analyze images in matlab (not faces) and am relatively new to image processing.

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  • Edge detection using wavelet

    - by cheoma
    I had done Edge detection using wavelet transform using thus steps changing the image to Gray scale decomposing the image using dwt2(discrete wavelet transform,Haar wavelet filter ) in to horizontal,vertical,diagonal and approximation(detail) further decomposing the horizontal part threshold (global threshold like canny Edge detection ) i got the edge but i got a problem while locating the edge to complete image to mean recovering original image using only the Edges so i need help concerning this either in concept ,mat lab code or references I hope i will get your help soon

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  • Is there any algorithm for finding LINES by PIXEL COLORS on picture?

    - by Ole Jak
    So I have Image like this I want to get something like this (I hevent drawn all lines I want but I hope you can get my idea) I need algorithm for finding all straight lines on it by just reading colors of pixels. No hard math, no Haar, no Hough. Some algorithm which would be based on points colors. I want to give to algorithm parameters like min line length and max line distortion. I want to get relative to picture pixel coords start and end points of lines. So I need algorithm for finding straight lines of different colors on picture. Algorithm which would be based on idea of image of different colors and Lines of static colors. Yes - such algorithm will not work for images with lots of shadows and lights. But It willl probably be fast (I hope so). Is there any such algorithm?

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  • Adaboost algorithm and its usage in face detection

    - by Hani
    I am trying to understand Adaboost algorithm but i have some troubles. After reading about Adaboost i realized that it is a classification algorithm(somehow like neural network). But i could not know how the weak classifiers are chosen (i think they are haar-like features for face detection) and how finally the H result which is the final strong classifier can be used. I mean if i found the alpha values and compute the H ,how am i going to benefit from it as a value (one or zero) for new images. Please is there an example describes it in a perfect way? i found the plus and minus example that is found in most adaboost tutorials but i did not know how exactly hi is chosen and how to adopt the same concept on face detection. I read many papers and i had many ideas but until now my ideas are not well arranged. Thanks....

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  • Mapping functions of 2D numpy arrays

    - by perimosocordiae
    I have a function foo that takes a NxM numpy array as an argument and returns a scalar value. I have a AxNxM numpy array data, over which I'd like to map foo to give me a resultant numpy array of length A. Curently, I'm doing this: result = numpy.array([foo(x) for x in data]) It works, but it seems like I'm not taking advantage of the numpy magic (and speed). Is there a better way? I've looked at numpy.vectorize, and numpy.apply_along_axis, but neither works for a function of 2D arrays. EDIT: I'm doing boosted regression on 24x24 image patches, so my AxNxM is something like 1000x24x24. What I called foo above applies a Haar-like feature to a patch (so, not terribly computationally intensive).

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  • Extracting Information from Images

    - by Khorkrak
    What are some fast and somewhat reliable ways to extract information about images? I've been tinkering with openCV and this seems so far to be the best route plus it has Python bindings. So to be more specific I'd like to determine what I can about what's in an image. So for example the haar face detection and full body detection classifiers are great - now I can tell that most likely there are faces and / or people in the image as well as about how many. okay - what else - how about whether there are any buildings and if so what do they seem to be - huts, office buildings etc? Is there sky visible, grass, trees and so forth. From what I've read about training classifiers to detect objects, it seems like a rather laborious process 10,000 or so wrong images and 5,000 or so correct samples to train a classifier. I'm hoping that there are some decent ones around already instead of having to do this all myself for a bunch of different objects - or is there some other way to go about this sort of thing?

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  • OpenCV shape matching

    - by MAckerman
    I'm new to OpenCV (am actually using Emgu CV C# wrapper) and am attempting to do some object detection. I'm attempting to determine if an object matches a predefined set of objects (that I will have to define). The background is well lit and does not move. My objects that I am starting with are bottles and cans. My current approach is: Do absDiff with a previously taken background image to separate the background. Then dilate 4x to make the lighter areas (in labels) shrink. Then I do a binary threshold to get a big blog, followed by finding contours in this image. I then take the largest contour and draw it, which becomes my shape to either save to the accepted set or compare with the accepted set. Currently I'm using cvMatchShapes, but the double return value seems to vary widely. I'm guessing it is because it doesn't take into account rotation. Is this approach a good one? It isn't working well for glass bottles since the edges are hard to find... I've read about haar classifiers, but thinking that might be overkill for my task.

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  • Choosing a VS project type (C++)

    - by typoknig
    Hi all, I do not use C++ much (I try to stick to the easier stuff like Java and VB.NET), but the lately I have not had a choice. When I am picking a project type in VS for some C++ source I download, what project type should I pick? I had just been sticking with Win32 Console Applications, but I just downloaded some code (below) that will not work right even when it compiles with out errors. I have tried to use a CLR Console Application and an empty project too, and have changed many variables along the way, but I cannot get this code to work. I noticed that this code does not have "int main()" at its beginning, does that have something to do with it? Anyways, here is the code, got it from here: /* Demo of modified Lucas-Kanade optical flow algorithm. See the printf below */ #ifdef _CH_ #pragma package <opencv> #endif #ifndef _EiC #include "cv.h" #include "highgui.h" #include <stdio.h> #include <ctype.h> #endif #include <windows.h> #define FULL_IMAGE_AS_OUTPUT_FILE #define cvMirror cvFlip //IplImage *image = 0, *grey = 0, *prev_grey = 0, *pyramid = 0, *prev_pyramid = 0, *swap_temp; IplImage **buf = 0; IplImage *image1 = 0; IplImage *imageCopy=0; IplImage *image = 0; int win_size = 10; const int MAX_COUNT = 500; CvPoint2D32f* points[2] = {0,0}, *swap_points; char* status = 0; //int count = 0; //int need_to_init = 0; //int night_mode = 0; int flags = 0; //int add_remove_pt = 0; bool bLButtonDown = false; //bool bstopLoop = false; CvPoint pt, pt1,pt2; //IplImage* img1; FILE* FileDest; char* strImageDir = "E:\\Projects\\TSCreator\\Images"; char* strItemName = "b"; int imageCount=0; int bFirstFace = 1; // flag for first face int mode = 1; // Mode 1 - Haar Traing Sample Creation, 2 - HMM sample creation, Mode = 3 - Both Harr and HMM. //int startImgeNo = 1; bool isEqualRation = false; //Weidth to height ratio is equal //Selected Image data IplImage *selectedImage = 0; int selectedX = 0, selectedY = 0, currentImageNo = 0, selectedWidth = 0, selectedHeight= 0; CvRect selectedROI; void saveFroHarrTraining(IplImage *src, int x, int y, int width, int height, int imageCount); void saveForHMMTraining(IplImage *src, CvRect roi,int imageCount); // Code for draw ROI Cropping Image void on_mouse( int event, int x, int y, int flags, void* param ) { char f[200]; CvRect reg; if( !image ) return; if( event == CV_EVENT_LBUTTONDOWN ) { bLButtonDown = true; pt1.x = x; pt1.y = y; } else if ( event == CV_EVENT_MOUSEMOVE ) //Draw the selected area rectangle { pt2.x = x; pt2.y = y; if(bLButtonDown) { if( !image1 ) { /* allocate all the buffers */ image1 = cvCreateImage( cvGetSize(image), 8, 3 ); image1->origin = image->origin; points[0] = (CvPoint2D32f*)cvAlloc(MAX_COUNT*sizeof(points[0][0])); points[1] = (CvPoint2D32f*)cvAlloc(MAX_COUNT*sizeof(points[0][0])); status = (char*)cvAlloc(MAX_COUNT); flags = 0; } cvCopy( image, image1, 0 ); //Equal Weight-Height Ratio if(isEqualRation) { pt2.y = pt1.y + (pt2.x-pt1.x); } //Max Height and Width is the image width and height if(pt2.x>image->width) { pt2.x = image->width; } if(pt2.y>image->height) { pt2.y = image->height; } CvPoint InnerPt1 = pt1; CvPoint InnerPt2 = pt2; if ( InnerPt1.x > InnerPt2.x) { int tempX = InnerPt1.x; InnerPt1.x = InnerPt2.x; InnerPt2.x = tempX; } if ( pt2.y < InnerPt1.y ) { int tempY = InnerPt1.y; InnerPt1.y = InnerPt2.y; InnerPt2.y = tempY; } InnerPt1.y = image->height - InnerPt1.y; InnerPt2.y = image->height - InnerPt2.y; CvFont font; double hScale=1.0; double vScale=1.0; int lineWidth=1; cvInitFont(&font,CV_FONT_HERSHEY_SIMPLEX|CV_FONT_ITALIC, hScale,vScale,0,lineWidth); char size [200]; reg.x = pt1.x; reg.y = image->height - pt2.y; reg.height = abs (pt2.y - pt1.y); reg.width = InnerPt2.x -InnerPt1.x; //print width and heght of the selected reagion sprintf(size, "(%dx%d)",reg.width, reg.height); cvPutText (image1,size,cvPoint(10,10), &font, cvScalar(255,255,0)); cvRectangle(image1, InnerPt1, InnerPt2, CV_RGB(255,0,0), 1); //Mark Selected Reagion selectedImage = image; selectedX = pt1.x; selectedY = pt1.y; selectedWidth = reg.width; selectedHeight = reg.height; selectedROI = reg; //Show the modified image cvShowImage("HMM-Harr Positive Image Creator",image1); } } else if ( event == CV_EVENT_LBUTTONUP ) { bLButtonDown = false; // pt2.x = x; // pt2.y = y; // // if ( pt1.x > pt2.x) // { // int tempX = pt1.x; // pt1.x = pt2.x; // pt2.x = tempX; // } // // if ( pt2.y < pt1.y ) // { // int tempY = pt1.y; // pt1.y = pt2.y; // pt2.y = tempY; // // } // //reg.x = pt1.x; //reg.y = image->height - pt2.y; // //reg.height = abs (pt2.y - pt1.y); ////reg.width = reg.height/3; //reg.width = pt2.x -pt1.x; ////reg.height = (2 * reg.width)/3; #ifdef FULL_IMAGE_AS_OUTPUT_FILE CvRect FullImageRect; FullImageRect.x = 0; FullImageRect.y = 0; FullImageRect.width = image->width; FullImageRect.height = image->height; IplImage *regionFullImage =0; regionFullImage = cvCreateImage(cvSize (FullImageRect.width, FullImageRect.height), image->depth, image->nChannels); image->roi = NULL; //cvSetImageROI (image, FullImageRect); //cvCopy (image, regionFullImage, 0); #else IplImage *region =0; region = cvCreateImage(cvSize (reg.width, reg.height), image1->depth, image1->nChannels); image->roi = NULL; cvSetImageROI (image1, reg); cvCopy (image1, region, 0); #endif //cvNamedWindow("Result", CV_WINDOW_AUTOSIZE); //selectedImage = image; //selectedX = pt1.x; //selectedY = pt1.y; //selectedWidth = reg.width; //selectedHeight = reg.height; ////currentImageNo = startImgeNo; //selectedROI = reg; /*if(mode == 1) { saveFroHarrTraining(image,pt1.x,pt1.y,reg.width,reg.height,startImgeNo); } else if(mode == 2) { saveForHMMTraining(image,reg,startImgeNo); } else if(mode ==3) { saveFroHarrTraining(image,pt1.x,pt1.y,reg.width,reg.height,startImgeNo); saveForHMMTraining(image,reg,startImgeNo); } else { printf("Invalid mode."); } startImgeNo++;*/ } } /* Save popsitive samples for Harr Training. Also add an entry to the PositiveSample.txt with the location of the item of interest. */ void saveFroHarrTraining(IplImage *src, int x, int y, int width, int height, int imageCount) { char f[255] ; sprintf(f,"%s\\%s\\harr_%s%d%d.jpg",strImageDir,strItemName,strItemName,imageCount/10, imageCount%10); cvNamedWindow("Harr", CV_WINDOW_AUTOSIZE); cvShowImage("Harr", src); cvSaveImage(f, src); printf("output%d%d \t ", imageCount/10, imageCount%10); printf("width %d \t", width); printf("height %d \t", height); printf("x1 %d \t", x); printf("y1 %d \t\n", y); char f1[255]; sprintf(f1,"%s\\PositiveSample.txt",strImageDir); FileDest = fopen(f1, "a"); fprintf(FileDest, "%s\\harr_%s%d.jpg 1 %d %d %d %d \n",strItemName,strItemName, imageCount, x, y, width, height); fclose(FileDest); } /* Create Sample Images for HMM recognition algorythm trai ning. */ void saveForHMMTraining(IplImage *src, CvRect roi,int imageCount) { char f[255] ; printf("x=%d, y=%d, w= %d, h= %d\n",roi.x,roi.y,roi.width,roi.height); //Create the file name sprintf(f,"%s\\%s\\hmm_%s%d.pgm",strImageDir,strItemName,strItemName, imageCount); //Create storage for grayscale image IplImage* gray = cvCreateImage(cvSize(roi.width,roi.height), 8, 1); //Create storage for croped reagon IplImage* regionFullImage = cvCreateImage(cvSize(roi.width,roi.height),8,3); //Croped marked region cvSetImageROI(src,roi); cvCopy(src,regionFullImage); cvResetImageROI(src); //Flip croped image - otherwise it will saved upside down cvConvertImage(regionFullImage, regionFullImage, CV_CVTIMG_FLIP); //Convert croped image to gray scale cvCvtColor(regionFullImage,gray, CV_BGR2GRAY); //Show final grayscale image cvNamedWindow("HMM", CV_WINDOW_AUTOSIZE); cvShowImage("HMM", gray); //Save final grayscale image cvSaveImage(f, gray); } int maina( int argc, char** argv ) { CvCapture* capture = 0; //if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0]))) // capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 ); //else if( argc == 2 ) // capture = cvCaptureFromAVI( argv[1] ); char* video; if(argc ==7) { mode = atoi(argv[1]); strImageDir = argv[2]; strItemName = argv[3]; video = argv[4]; currentImageNo = atoi(argv[5]); int a = atoi(argv[6]); if(a==1) { isEqualRation = true; } else { isEqualRation = false; } } else { printf("\nUsage: TSCreator.exe <Mode> <Sample Image Save Path> <Sample Image Save Directory> <Video File Location> <Start Image No> <Is Equal Ratio>\n"); printf("Mode = 1 - Haar Traing Sample Creation. \nMode = 2 - HMM sample creation.\nMode = 3 - Both Harr and HMM\n"); printf("Is Equal Ratio = 0 or 1. 1 - Equal weidth and height, 0 - custom."); printf("Note: You have to create the image save directory in correct path first.\n"); printf("Eg: TSCreator.exe 1 E:\Projects\TSCreator\Images A 11.avi 1 1\n\n"); return 0; } capture = cvCaptureFromAVI(video); if( !capture ) { fprintf(stderr,"Could not initialize capturing...\n"); return -1; } cvNamedWindow("HMM-Harr Positive Image Creator", CV_WINDOW_AUTOSIZE); cvSetMouseCallback("HMM-Harr Positive Image Creator", on_mouse, 0); //cvShowImage("Test", image1); for(;;) { IplImage* frame = 0; int i, k, c; frame = cvQueryFrame( capture ); if( !frame ) break; if( !image ) { /* allocate all the buffers */ image = cvCreateImage( cvGetSize(frame), 8, 3 ); image->origin = frame->origin; //grey = cvCreateImage( cvGetSize(frame), 8, 1 ); //prev_grey = cvCreateImage( cvGetSize(frame), 8, 1 ); //pyramid = cvCreateImage( cvGetSize(frame), 8, 1 ); // prev_pyramid = cvCreateImage( cvGetSize(frame), 8, 1 ); points[0] = (CvPoint2D32f*)cvAlloc(MAX_COUNT*sizeof(points[0][0])); points[1] = (CvPoint2D32f*)cvAlloc(MAX_COUNT*sizeof(points[0][0])); status = (char*)cvAlloc(MAX_COUNT); flags = 0; } cvCopy( frame, image, 0 ); // cvCvtColor( image, grey, CV_BGR2GRAY ); cvShowImage("HMM-Harr Positive Image Creator", image); cvSetMouseCallback("HMM-Harr Positive Image Creator", on_mouse, 0); c = cvWaitKey(0); if((char)c == 's') { //Save selected reagion as training data if(selectedImage) { printf("Selected Reagion Saved\n"); if(mode == 1) { saveFroHarrTraining(selectedImage,selectedX,selectedY,selectedWidth,selectedHeight,currentImageNo); } else if(mode == 2) { saveForHMMTraining(selectedImage,selectedROI,currentImageNo); } else if(mode ==3) { saveFroHarrTraining(selectedImage,selectedX,selectedY,selectedWidth,selectedHeight,currentImageNo); saveForHMMTraining(selectedImage,selectedROI,currentImageNo); } else { printf("Invalid mode."); } currentImageNo++; } } } cvReleaseCapture( &capture ); //cvDestroyWindow("HMM-Harr Positive Image Creator"); cvDestroyAllWindows(); return 0; } #ifdef _EiC main(1,"lkdemo.c"); #endif If I put... #include "stdafx.h" int _tmain(int argc, _TCHAR* argv[]) { return 0; } ... before the previous code (and link it to the correct OpenCV .lib files) it compiles without errors, but does nothing at the command line. How do I make it work?

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