refactoring latentSVM
This commit is contained in:
Evgeniy Kozinov
@@ -10,13 +10,6 @@
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#define min(a,b) (((a) < (b)) ? (a) : (b))
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#endif
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static inline int sign(float r)
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{
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if(r > 0.0001f) return 1;
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if(r < -0.0001f) return -1;
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return 0;
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}
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/*
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// Getting feature map for the selected subimage
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//
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@@ -30,115 +23,132 @@ static inline int sign(float r)
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// RESULT
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// Error status
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*/
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int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
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int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMap **map)
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{
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int sizeX, sizeY;
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int p, px, strsz;
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int height, width, channels;
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int p, px, stringSize;
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int height, width, numChannels;
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int i, j, kk, c, ii, jj, d;
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float * datadx, * datady;
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float tmp, x, y, tx, ty;
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//<2F><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD>
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int ch;
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//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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float magnitude, x, y, tx, ty;
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IplImage * dx, * dy;
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int *nearest_x, *nearest_y;
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int *nearest;
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float *w, a_x, b_x;
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float kernel[3] = {-1.f, 0.f, 1.f};
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// <20><><EFBFBD><EFBFBD> <20><><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD> x <20> y
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float kernel[3] = {-1.f, 0.f, 1.f};
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CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
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CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
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// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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float * r;
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int * alfa;
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// <20><><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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// <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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int * alfa;
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float boundary_x[CNTPARTION+1];
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float boundary_y[CNTPARTION+1];
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float max, tmp_scal;
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int maxi;
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// <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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float boundary_x[NUM_SECTOR + 1];
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float boundary_y[NUM_SECTOR + 1];
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float max, dotProd;
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int maxi;
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height = image->height;
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width = image->width ;
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height = image->height;
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width = image->width ;
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channels = image->nChannels;
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numChannels = image->nChannels;
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dx = cvCreateImage(cvSize(image->width , image->height) , IPL_DEPTH_32F , 3);
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dy = cvCreateImage(cvSize(image->width , image->height) , IPL_DEPTH_32F , 3);
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dx = cvCreateImage(cvSize(image->width, image->height),
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IPL_DEPTH_32F, 3);
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dy = cvCreateImage(cvSize(image->width, image->height),
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IPL_DEPTH_32F, 3);
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sizeX = width / k;
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sizeY = height / k;
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px = CNTPARTION + 2 * CNTPARTION; // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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px = 3 * NUM_SECTOR; // <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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p = px;
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strsz = sizeX * p;
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allocFeatureMapObject(map, sizeX, sizeY, p, px);
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stringSize = sizeX * p;
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allocFeatureMapObject(map, sizeX, sizeY, p);
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cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
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cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
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for(i = 0; i <= CNTPARTION; i++)
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cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
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cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
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float arg_vector;
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for(i = 0; i <= NUM_SECTOR; i++)
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{
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boundary_x[i] = cosf((((float)i) * (((float)PI) / (float) (CNTPARTION))));
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boundary_y[i] = sinf((((float)i) * (((float)PI) / (float) (CNTPARTION))));
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}/*for(i = 0; i <= CNTPARTION; i++) */
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arg_vector = ( (float) i ) * ( (float)(PI) / (float)(NUM_SECTOR) );
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boundary_x[i] = cosf(arg_vector);
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boundary_y[i] = sinf(arg_vector);
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}/*for(i = 0; i <= NUM_SECTOR; i++) */
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r = (float *)malloc( sizeof(float) * (width * height));
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alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
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for(j = 1; j < height-1; j++)
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for(j = 1; j < height - 1; j++)
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{
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datadx = (float*)(dx->imageData + dx->widthStep *j);
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datady = (float*)(dy->imageData + dy->widthStep *j);
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for(i = 1; i < width-1; i++)
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datadx = (float*)(dx->imageData + dx->widthStep * j);
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datady = (float*)(dy->imageData + dy->widthStep * j);
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for(i = 1; i < width - 1; i++)
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{
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c = 0;
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x = (datadx[i*channels+c]);
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y = (datady[i*channels+c]);
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c = 0;
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x = (datadx[i * numChannels + c]);
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y = (datady[i * numChannels + c]);
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r[j * width + i] =sqrtf(x*x + y*y);
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for(kk = 1; kk < channels; kk++)
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r[j * width + i] =sqrtf(x * x + y * y);
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for(ch = 1; ch < numChannels; ch++)
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{
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tx = (datadx[i*channels+kk]);
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ty = (datady[i*channels+kk]);
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tmp =sqrtf(tx*tx + ty*ty);
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if(tmp > r[j * width + i])
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tx = (datadx[i * numChannels + ch]);
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ty = (datady[i * numChannels + ch]);
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magnitude = sqrtf(tx * tx + ty * ty);
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if(magnitude > r[j * width + i])
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{
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r[j * width + i] = tmp;
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c = kk;
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r[j * width + i] = magnitude;
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c = ch;
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x = tx;
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y = ty;
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}
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}/*for(kk = 1; kk < channels; kk++)*/
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}/*for(ch = 1; ch < numChannels; ch++)*/
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max = boundary_x[0]*x + boundary_y[0]*y;
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max = boundary_x[0] * x + boundary_y[0] * y;
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maxi = 0;
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for (kk = 0; kk < CNTPARTION; kk++) {
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tmp_scal = boundary_x[kk]*x + boundary_y[kk]*y;
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if (tmp_scal> max) {
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max = tmp_scal;
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for (kk = 0; kk < NUM_SECTOR; kk++)
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{
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dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
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if (dotProd > max)
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{
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max = dotProd;
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maxi = kk;
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}else if (-tmp_scal> max) {
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max = -tmp_scal;
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maxi = kk + CNTPARTION;
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}
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else
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{
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if (-dotProd > max)
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{
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max = -dotProd;
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maxi = kk + NUM_SECTOR;
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}
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}
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}
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alfa[j * width * 2 + i * 2 ] = maxi % CNTPARTION;
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alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
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alfa[j * width * 2 + i * 2 + 1] = maxi;
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}/*for(i = 0; i < width; i++)*/
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}/*for(j = 0; j < height; j++)*/
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//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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nearest_x = (int *)malloc(sizeof(int) * k);
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nearest_y = (int *)malloc(sizeof(int) * k);
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w = (float*)malloc(sizeof(float) * (k * 2));
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nearest = (int *)malloc(sizeof(int ) * k);
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w = (float*)malloc(sizeof(float) * (k * 2));
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for(i = 0; i < k / 2; i++)
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{
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nearest_x[i] = -1;
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nearest_y[i] = -1;
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nearest[i] = -1;
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}/*for(i = 0; i < k / 2; i++)*/
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for(i = k / 2; i < k; i++)
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{
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nearest_x[i] = 1;
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nearest_y[i] = 1;
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nearest[i] = 1;
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}/*for(i = k / 2; i < k; i++)*/
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for(j = 0; j < k / 2; j++)
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@@ -160,44 +170,52 @@ int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
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//<2F><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
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for(i = 0; i < sizeY; i++)
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{
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for(j = 0; j < sizeX; j++)
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for(j = 0; j < sizeX; j++)
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{
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for(ii = 0; ii < k; ii++)
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{
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for(ii = 0; ii < k; ii++)
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for(jj = 0; jj < k; jj++)
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{
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if ((i * k + ii > 0) &&
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(i * k + ii < height - 1) &&
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(j * k + jj > 0) &&
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(j * k + jj < width - 1))
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{
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for(jj = 0; jj < k; jj++)
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{
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if ((i * k + ii > 0) && (i * k + ii < height - 1) && (j * k + jj > 0) && (j * k + jj < width - 1))
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{
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d = (k*i + ii)* width + (j*k + jj);
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(*map)->Map[(i ) * strsz + (j ) * (*map)->p + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 ] * w[jj * 2 ];
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(*map)->Map[(i ) * strsz + (j ) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
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r[d] * w[ii * 2 ] * w[jj * 2 ];
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if ((i + nearest_y[ii] >= 0) && (i + nearest_y[ii] <= sizeY - 1))
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{
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(*map)->Map[(i + nearest_y[ii]) * strsz + (j ) * (*map)->p + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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(*map)->Map[(i + nearest_y[ii]) * strsz + (j ) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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}
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if ((j + nearest_x[jj] >= 0) && (j + nearest_x[jj] <= sizeX - 1))
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{
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(*map)->Map[(i ) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 ] * w[jj * 2 + 1];
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(*map)->Map[(i ) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
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r[d] * w[ii * 2 ] * w[jj * 2 + 1];
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}
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if ((i + nearest_y[ii] >= 0) && (i + nearest_y[ii] <= sizeY - 1) && (j + nearest_x[jj] >= 0) && (j + nearest_x[jj] <= sizeX - 1))
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{
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(*map)->Map[(i + nearest_y[ii]) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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(*map)->Map[(i + nearest_y[ii]) * strsz + (j + nearest_x[jj]) * (*map)->p + alfa[d * 2 + 1] + CNTPARTION] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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}
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}
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}/*for(jj = 0; jj < k; jj++)*/
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}/*for(ii = 0; ii < k; ii++)*/
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}/*for(j = 1; j < sizeX - 1; j++)*/
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d = (k * i + ii) * width + (j * k + jj);
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(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
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r[d] * w[ii * 2] * w[jj * 2];
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(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2] * w[jj * 2];
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if ((i + nearest[ii] >= 0) &&
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(i + nearest[ii] <= sizeY - 1))
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{
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(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 ];
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}
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if ((j + nearest[jj] >= 0) &&
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(j + nearest[jj] <= sizeX - 1))
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{
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(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2] * w[jj * 2 + 1];
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(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2] * w[jj * 2 + 1];
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}
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if ((i + nearest[ii] >= 0) &&
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(i + nearest[ii] <= sizeY - 1) &&
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(j + nearest[jj] >= 0) &&
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(j + nearest[jj] <= sizeX - 1))
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{
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(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
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r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
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}
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}
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}/*for(jj = 0; jj < k; jj++)*/
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}/*for(ii = 0; ii < k; ii++)*/
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}/*for(j = 1; j < sizeX - 1; j++)*/
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}/*for(i = 1; i < sizeY - 1; i++)*/
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cvReleaseImage(&dx);
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@@ -205,9 +223,8 @@ int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
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free(w);
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free(nearest_x);
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free(nearest_y);
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free(nearest);
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free(r);
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free(alfa);
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@@ -218,7 +235,7 @@ int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
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// Feature map Normalization and Truncation
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//
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// API
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// int normalizationAndTruncationFeatureMaps(featureMap *map, const float alfa);
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// int normalizeAndTruncate(featureMap *map, const float alfa);
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// INPUT
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// map - feature map
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// alfa - truncation threshold
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@@ -227,114 +244,113 @@ int getFeatureMaps_dp(const IplImage* image,const int k, CvLSVMFeatureMap **map)
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// RESULT
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// Error status
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*/
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int normalizationAndTruncationFeatureMaps(CvLSVMFeatureMap *map, const float alfa)
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int normalizeAndTruncate(CvLSVMFeatureMap *map, const float alfa)
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{
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int i,j, ii;
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int sizeX, sizeY, p, pos, pp, xp, pos1, pos2;
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float * part_noma; // norm of C(i, j)
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float * new_data;
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float norm_val;
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float * partOfNorm; // norm of C(i, j)
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float * newData;
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float valOfNorm;
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sizeX = map->sizeX;
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sizeY = map->sizeY;
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part_noma = (float *)malloc (sizeof(float) * (sizeX * sizeY));
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partOfNorm = (float *)malloc (sizeof(float) * (sizeX * sizeY));
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p = map->xp / 3;
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||||
p = NUM_SECTOR;
|
||||
xp = NUM_SECTOR * 3;
|
||||
pp = NUM_SECTOR * 12;
|
||||
|
||||
for(i = 0; i < sizeX * sizeY; i++)
|
||||
{
|
||||
norm_val = 0.0;
|
||||
pos = i * map->p;
|
||||
valOfNorm = 0.0f;
|
||||
pos = i * map->numFeatures;
|
||||
for(j = 0; j < p; j++)
|
||||
{
|
||||
norm_val += map->Map[pos + j] * map->Map[pos + j];
|
||||
valOfNorm += map->map[pos + j] * map->map[pos + j];
|
||||
}/*for(j = 0; j < p; j++)*/
|
||||
part_noma[i] = norm_val;
|
||||
partOfNorm[i] = valOfNorm;
|
||||
}/*for(i = 0; i < sizeX * sizeY; i++)*/
|
||||
|
||||
xp = map->xp;
|
||||
pp = xp * 4;
|
||||
|
||||
sizeX -= 2;
|
||||
sizeY -= 2;
|
||||
|
||||
new_data = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
|
||||
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
|
||||
//normalization
|
||||
for(i = 1; i <= sizeY; i++)
|
||||
{
|
||||
for(j = 1; j <= sizeX; j++)
|
||||
{
|
||||
norm_val = sqrtf(
|
||||
part_noma[(i )*(sizeX + 2) + (j )] +
|
||||
part_noma[(i )*(sizeX + 2) + (j + 1)] +
|
||||
part_noma[(i + 1)*(sizeX + 2) + (j )] +
|
||||
part_noma[(i + 1)*(sizeX + 2) + (j + 1)]);
|
||||
valOfNorm = sqrtf(
|
||||
partOfNorm[(i )*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
|
||||
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]);
|
||||
pos1 = (i ) * (sizeX + 2) * xp + (j ) * xp;
|
||||
pos2 = (i-1) * (sizeX ) * pp + (j-1) * pp;
|
||||
for(ii = 0; ii < p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii ] = map->Map[pos1 + ii ] / norm_val;
|
||||
newData[pos2 + ii ] = map->map[pos1 + ii ] / valOfNorm;
|
||||
}/*for(ii = 0; ii < p; ii++)*/
|
||||
for(ii = 0; ii < 2 * p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 4] = map->Map[pos1 + ii + p] / norm_val;
|
||||
newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
|
||||
}/*for(ii = 0; ii < 2 * p; ii++)*/
|
||||
norm_val = sqrtf(
|
||||
part_noma[(i )*(sizeX + 2) + (j )] +
|
||||
part_noma[(i )*(sizeX + 2) + (j + 1)] +
|
||||
part_noma[(i - 1)*(sizeX + 2) + (j )] +
|
||||
part_noma[(i - 1)*(sizeX + 2) + (j + 1)]);
|
||||
valOfNorm = sqrtf(
|
||||
partOfNorm[(i )*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
|
||||
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]);
|
||||
for(ii = 0; ii < p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p ] = map->Map[pos1 + ii ] / norm_val;
|
||||
newData[pos2 + ii + p ] = map->map[pos1 + ii ] / valOfNorm;
|
||||
}/*for(ii = 0; ii < p; ii++)*/
|
||||
for(ii = 0; ii < 2 * p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 6] = map->Map[pos1 + ii + p] / norm_val;
|
||||
newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
|
||||
}/*for(ii = 0; ii < 2 * p; ii++)*/
|
||||
norm_val = sqrtf(
|
||||
part_noma[(i )*(sizeX + 2) + (j )] +
|
||||
part_noma[(i )*(sizeX + 2) + (j - 1)] +
|
||||
part_noma[(i + 1)*(sizeX + 2) + (j )] +
|
||||
part_noma[(i + 1)*(sizeX + 2) + (j - 1)]);
|
||||
valOfNorm = sqrtf(
|
||||
partOfNorm[(i )*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
|
||||
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]);
|
||||
for(ii = 0; ii < p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 2] = map->Map[pos1 + ii ] / norm_val;
|
||||
newData[pos2 + ii + p * 2] = map->map[pos1 + ii ] / valOfNorm;
|
||||
}/*for(ii = 0; ii < p; ii++)*/
|
||||
for(ii = 0; ii < 2 * p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 8] = map->Map[pos1 + ii + p] / norm_val;
|
||||
newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
|
||||
}/*for(ii = 0; ii < 2 * p; ii++)*/
|
||||
norm_val = sqrtf(
|
||||
part_noma[(i )*(sizeX + 2) + (j )] +
|
||||
part_noma[(i )*(sizeX + 2) + (j - 1)] +
|
||||
part_noma[(i - 1)*(sizeX + 2) + (j )] +
|
||||
part_noma[(i - 1)*(sizeX + 2) + (j - 1)]);
|
||||
valOfNorm = sqrtf(
|
||||
partOfNorm[(i )*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
|
||||
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
|
||||
partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]);
|
||||
for(ii = 0; ii < p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 3 ] = map->Map[pos1 + ii ] / norm_val;
|
||||
newData[pos2 + ii + p * 3 ] = map->map[pos1 + ii ] / valOfNorm;
|
||||
}/*for(ii = 0; ii < p; ii++)*/
|
||||
for(ii = 0; ii < 2 * p; ii++)
|
||||
{
|
||||
new_data[pos2 + ii + p * 10] = map->Map[pos1 + ii + p] / norm_val;
|
||||
newData[pos2 + ii + p * 10] = map->map[pos1 + ii + p] / valOfNorm;
|
||||
}/*for(ii = 0; ii < 2 * p; ii++)*/
|
||||
}/*for(j = 1; j <= sizeX; j++)*/
|
||||
}/*for(i = 1; i <= sizeY; i++)*/
|
||||
//truncation
|
||||
for(i = 0; i < sizeX * sizeY * pp; i++)
|
||||
{
|
||||
if(new_data [i] > alfa) new_data [i] = alfa;
|
||||
if(newData [i] > alfa) newData [i] = alfa;
|
||||
}/*for(i = 0; i < sizeX * sizeY * pp; i++)*/
|
||||
//swop data
|
||||
|
||||
map->p = pp;
|
||||
map->xp = xp;
|
||||
map->numFeatures = pp;
|
||||
map->sizeX = sizeX;
|
||||
map->sizeY = sizeY;
|
||||
|
||||
free (map->Map);
|
||||
free (part_noma);
|
||||
free (map->map);
|
||||
free (partOfNorm);
|
||||
|
||||
map->Map = new_data;
|
||||
map->map = newData;
|
||||
|
||||
return LATENT_SVM_OK;
|
||||
}
|
||||
@@ -356,21 +372,21 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
|
||||
{
|
||||
int i,j, ii, jj, k;
|
||||
int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
|
||||
float * new_data;
|
||||
float * newData;
|
||||
float val;
|
||||
float nx, ny;
|
||||
|
||||
sizeX = map->sizeX;
|
||||
sizeY = map->sizeY;
|
||||
p = map->p;
|
||||
pp = map->xp + 4;
|
||||
p = map->numFeatures;
|
||||
pp = NUM_SECTOR * 3 + 4;
|
||||
yp = 4;
|
||||
xp = (map->xp / 3);
|
||||
xp = NUM_SECTOR;
|
||||
|
||||
nx = 1.0f / sqrtf((float)(xp * 2));
|
||||
ny = 1.0f / sqrtf((float)(yp ));
|
||||
|
||||
new_data = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
|
||||
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
|
||||
|
||||
for(i = 0; i < sizeY; i++)
|
||||
{
|
||||
@@ -384,9 +400,9 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
|
||||
val = 0;
|
||||
for(ii = 0; ii < yp; ii++)
|
||||
{
|
||||
val += map->Map[pos1 + yp * xp + ii * xp * 2 + jj];
|
||||
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
|
||||
}/*for(ii = 0; ii < yp; ii++)*/
|
||||
new_data[pos2 + k] = val * ny;
|
||||
newData[pos2 + k] = val * ny;
|
||||
k++;
|
||||
}/*for(jj = 0; jj < xp * 2; jj++)*/
|
||||
for(jj = 0; jj < xp; jj++)
|
||||
@@ -394,9 +410,9 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
|
||||
val = 0;
|
||||
for(ii = 0; ii < yp; ii++)
|
||||
{
|
||||
val += map->Map[pos1 + ii * xp + jj];
|
||||
val += map->map[pos1 + ii * xp + jj];
|
||||
}/*for(ii = 0; ii < yp; ii++)*/
|
||||
new_data[pos2 + k] = val * ny;
|
||||
newData[pos2 + k] = val * ny;
|
||||
k++;
|
||||
}/*for(jj = 0; jj < xp; jj++)*/
|
||||
for(ii = 0; ii < yp; ii++)
|
||||
@@ -404,25 +420,47 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
|
||||
val = 0;
|
||||
for(jj = 0; jj < 2 * xp; jj++)
|
||||
{
|
||||
val += map->Map[pos1 + yp * xp + ii * xp * 2 + jj];
|
||||
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
|
||||
}/*for(jj = 0; jj < xp; jj++)*/
|
||||
new_data[pos2 + k] = val * nx;
|
||||
newData[pos2 + k] = val * nx;
|
||||
k++;
|
||||
} /*for(ii = 0; ii < yp; ii++)*/
|
||||
}/*for(j = 0; j < sizeX; j++)*/
|
||||
}/*for(i = 0; i < sizeY; i++)*/
|
||||
//swop data
|
||||
|
||||
map->p = pp;
|
||||
map->xp = pp;
|
||||
map->numFeatures = pp;
|
||||
|
||||
free (map->Map);
|
||||
free (map->map);
|
||||
|
||||
map->Map = new_data;
|
||||
map->map = newData;
|
||||
|
||||
return LATENT_SVM_OK;
|
||||
}
|
||||
|
||||
|
||||
int getPathOfFeaturePyramid(IplImage * image,
|
||||
float step, int numStep, int startIndex,
|
||||
int sideLength, CvLSVMFeaturePyramid **maps)
|
||||
{
|
||||
CvLSVMFeatureMap *map;
|
||||
IplImage *scaleTmp;
|
||||
float scale;
|
||||
int i, err;
|
||||
|
||||
for(i = 0; i < numStep; i++)
|
||||
{
|
||||
scale = 1.0f / powf(step, (float)i);
|
||||
scaleTmp = resize_opencv (image, scale);
|
||||
err = getFeatureMaps(scaleTmp, sideLength, &map);
|
||||
err = normalizeAndTruncate(map, VAL_OF_TRUNCATE);
|
||||
err = PCAFeatureMaps(map);
|
||||
(*maps)->pyramid[startIndex + i] = map;
|
||||
cvReleaseImage(&scaleTmp);
|
||||
}/*for(i = 0; i < numStep; i++)*/
|
||||
return LATENT_SVM_OK;
|
||||
}
|
||||
|
||||
/*
|
||||
// Getting feature pyramid
|
||||
//
|
||||
@@ -434,145 +472,52 @@ int PCAFeatureMaps(CvLSVMFeatureMap *map)
|
||||
const int W, const int H, featurePyramid **maps);
|
||||
// INPUT
|
||||
// image - image
|
||||
// lambda - resize scale
|
||||
// k - size of cells
|
||||
// startX - X coordinate of the image rectangle to search
|
||||
// startY - Y coordinate of the image rectangle to search
|
||||
// W - width of the image rectangle to search
|
||||
// H - height of the image rectangle to search
|
||||
// OUTPUT
|
||||
// maps - feature maps for all levels
|
||||
// RESULT
|
||||
// Error status
|
||||
*/
|
||||
int getFeaturePyramid(IplImage * image,
|
||||
const int lambda, const int k,
|
||||
const int startX, const int startY,
|
||||
const int W, const int H, CvLSVMFeaturePyramid **maps)
|
||||
int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
|
||||
{
|
||||
IplImage *img2, *imgTmp, *imgResize;
|
||||
float step, tmp;
|
||||
int cntStep;
|
||||
int maxcall;
|
||||
int i;
|
||||
int err;
|
||||
CvLSVMFeatureMap *map;
|
||||
IplImage *imgResize;
|
||||
float step;
|
||||
int numStep;
|
||||
int maxNumCells;
|
||||
int W, H;
|
||||
|
||||
//geting subimage
|
||||
cvSetImageROI(image, cvRect(startX, startY, W, H));
|
||||
img2 = cvCreateImage(cvGetSize(image), image->depth, image->nChannels);
|
||||
cvCopy(image, img2, NULL);
|
||||
cvResetImageROI(image);
|
||||
|
||||
if(img2->depth != IPL_DEPTH_32F)
|
||||
if(image->depth == IPL_DEPTH_32F)
|
||||
{
|
||||
imgResize = cvCreateImage(cvSize(img2->width , img2->height) , IPL_DEPTH_32F , 3);
|
||||
cvConvert(img2, imgResize);
|
||||
imgResize = image;
|
||||
}
|
||||
else
|
||||
{
|
||||
imgResize = img2;
|
||||
imgResize = cvCreateImage(cvSize(image->width , image->height) ,
|
||||
IPL_DEPTH_32F , 3);
|
||||
cvConvert(image, imgResize);
|
||||
}
|
||||
|
||||
step = powf(2.0f, 1.0f/ ((float)lambda));
|
||||
maxcall = W/k;
|
||||
if( maxcall > H/k )
|
||||
W = imgResize->width;
|
||||
H = imgResize->height;
|
||||
|
||||
step = powf(2.0f, 1.0f / ((float)LAMBDA));
|
||||
maxNumCells = W / SIDE_LENGTH;
|
||||
if( maxNumCells > H / SIDE_LENGTH )
|
||||
{
|
||||
maxcall = H/k;
|
||||
maxNumCells = H / SIDE_LENGTH;
|
||||
}
|
||||
cntStep = (int)(logf((float)maxcall/(5.0f))/logf(step)) + 1;
|
||||
//printf("Count step: %f %d\n", step, cntStep);
|
||||
numStep = (int)(logf((float) maxNumCells / (5.0f)) / logf( step )) + 1;
|
||||
|
||||
allocFeaturePyramidObject(maps, numStep + LAMBDA);
|
||||
|
||||
allocFeaturePyramidObject(maps, lambda, cntStep + lambda);
|
||||
|
||||
for(i = 0; i < lambda; i++)
|
||||
{
|
||||
tmp = 1.0f / powf(step, (float)i);
|
||||
imgTmp = resize_opencv (imgResize, tmp);
|
||||
//imgTmp = resize_article_dp(img2, tmp, 4);
|
||||
err = getFeatureMaps_dp(imgTmp, 4, &map);
|
||||
err = normalizationAndTruncationFeatureMaps(map, 0.2f);
|
||||
err = PCAFeatureMaps(map);
|
||||
(*maps)->pyramid[i] = map;
|
||||
//printf("%d, %d\n", map->sizeY, map->sizeX);
|
||||
cvReleaseImage(&imgTmp);
|
||||
}
|
||||
|
||||
/**********************************one**************/
|
||||
for(i = 0; i < cntStep; i++)
|
||||
{
|
||||
tmp = 1.0f / powf(step, (float)i);
|
||||
imgTmp = resize_opencv (imgResize, tmp);
|
||||
//imgTmp = resize_article_dp(imgResize, tmp, 8);
|
||||
err = getFeatureMaps_dp(imgTmp, 8, &map);
|
||||
err = normalizationAndTruncationFeatureMaps(map, 0.2f);
|
||||
err = PCAFeatureMaps(map);
|
||||
(*maps)->pyramid[i + lambda] = map;
|
||||
//printf("%d, %d\n", map->sizeY, map->sizeX);
|
||||
cvReleaseImage(&imgTmp);
|
||||
}/*for(i = 0; i < cntStep; i++)*/
|
||||
|
||||
if(img2->depth != IPL_DEPTH_32F)
|
||||
getPathOfFeaturePyramid(imgResize, step , LAMBDA, 0,
|
||||
SIDE_LENGTH / 2, maps);
|
||||
getPathOfFeaturePyramid(imgResize, step, numStep, LAMBDA,
|
||||
SIDE_LENGTH , maps);
|
||||
|
||||
if(image->depth != IPL_DEPTH_32F)
|
||||
{
|
||||
cvReleaseImage(&imgResize);
|
||||
}
|
||||
|
||||
cvReleaseImage(&img2);
|
||||
return LATENT_SVM_OK;
|
||||
}
|
||||
|
||||
/*
|
||||
// add zero border to feature map
|
||||
//
|
||||
// API
|
||||
// int addBordersToFeatureMaps(featureMap *map, const int bX, const int bY);
|
||||
// INPUT
|
||||
// map - feature map
|
||||
// bX - border size in x
|
||||
// bY - border size in y
|
||||
// OUTPUT
|
||||
// map - feature map
|
||||
// RESULT
|
||||
// Error status
|
||||
*/
|
||||
int addBordersToFeatureMaps(CvLSVMFeatureMap *map, const int bX, const int bY){
|
||||
int i,j, jj;
|
||||
int sizeX, sizeY, p, pos1, pos2;
|
||||
float * new_data;
|
||||
|
||||
sizeX = map->sizeX;
|
||||
sizeY = map->sizeY;
|
||||
p = map->p;
|
||||
|
||||
new_data = (float *)malloc (sizeof(float) * ((sizeX + 2 * bX) * (sizeY + 2 * bY) * p));
|
||||
|
||||
for(i = 0; i < ((sizeX + 2 * bX) * (sizeY + 2 * bY) * p); i++)
|
||||
{
|
||||
new_data[i] = (float)0;
|
||||
}/*for(i = 0; i < ((sizeX + 2 * bX) * (sizeY + 2 * bY) * p); i++)*/
|
||||
|
||||
for(i = 0; i < sizeY; i++)
|
||||
{
|
||||
for(j = 0; j < sizeX; j++)
|
||||
{
|
||||
|
||||
pos1 = ((i )*sizeX + (j )) * p;
|
||||
pos2 = ((i + bY)*(sizeX + 2 * bX) + (j + bX)) * p;
|
||||
|
||||
for(jj = 0; jj < p; jj++)
|
||||
{
|
||||
new_data[pos2 + jj] = map->Map[pos1 + jj];
|
||||
}/*for(jj = 0; jj < p; jj++)*/
|
||||
}/*for(j = 0; j < sizeX; j++)*/
|
||||
}/*for(i = 0; i < sizeY; i++)*/
|
||||
//swop data
|
||||
|
||||
map->sizeX = sizeX + 2 * bX;
|
||||
map->sizeY = sizeY + 2 * bY;
|
||||
|
||||
free (map->Map);
|
||||
|
||||
map->Map = new_data;
|
||||
|
||||
return LATENT_SVM_OK;
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user