523 lines
17 KiB
C++
523 lines
17 KiB
C++
#include "precomp.hpp"
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#include "_latentsvm.h"
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#include "_lsvm_resizeimg.h"
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#ifndef max
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#define max(a,b) (((a) > (b)) ? (a) : (b))
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#endif
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#ifndef min
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#define min(a,b) (((a) < (b)) ? (a) : (b))
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#endif
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/*
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// Getting feature map for the selected subimage
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//
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// API
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// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
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// INPUT
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// image - selected subimage
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// k - size of cells
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// OUTPUT
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// map - feature map
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// RESULT
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// Error status
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*/
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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, 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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//íîìåð êàíàëà â öèêëå
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int ch;
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//ïåðåìåííûå âû÷èñëåíèÿ ìàãíèòóäû
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float magnitude, x, y, tx, ty;
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IplImage * dx, * dy;
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int *nearest;
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float *w, a_x, b_x;
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// ÿäðî äëÿ âû÷èñëåíèÿ ãðàäèåíòîâ èçîáðàæåíèå ïî îñÿì x è 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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// ãðà÷åíèå ãðàäèåíòà
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float * r;
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// íîâåð ñåêòîðà êóäà ïîïàëî çíà÷åíèå ãðàäèåíòà
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// ÷åòíûå èííåêñû íå êîíòðàñòíîå èçîáðàæåíèå
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// íå ÷åòíûå èííåêñû êîíòðàñòíîå èçîáðàæåíèå
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int * alfa;
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// âåêòîðû ãðàíèö ñåêòîðîâ
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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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numChannels = image->nChannels;
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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 = 3 * NUM_SECTOR; // êîíòðàñòíîå è íå êîíòðàñòíîå èçîáðàæåíèå
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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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float arg_vector;
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for(i = 0; i <= NUM_SECTOR; i++)
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{
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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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{
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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 * 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(ch = 1; ch < numChannels; ch++)
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{
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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] = 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(ch = 1; ch < numChannels; ch++)*/
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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 < 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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}
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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 % 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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//ïîäñ÷åò âåñîâ è ñìåùåíèé
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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[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[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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{
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b_x = k / 2 + j + 0.5f;
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a_x = k / 2 - j - 0.5f;
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w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
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w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
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}/*for(j = 0; j < k / 2; j++)*/
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for(j = k / 2; j < k; j++)
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{
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a_x = j - k / 2 + 0.5f;
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b_x =-j + k / 2 - 0.5f + k;
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w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
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w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
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}/*for(j = k / 2; j < k; j++)*/
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//èíòåðïîëÿöèÿ
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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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{
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for(ii = 0; ii < k; ii++)
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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) &&
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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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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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cvReleaseImage(&dy);
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free(w);
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free(nearest);
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free(r);
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free(alfa);
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return LATENT_SVM_OK;
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}
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/*
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// Feature map Normalization and Truncation
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//
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// API
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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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// OUTPUT
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// map - truncated and normalized feature map
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// RESULT
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// Error status
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*/
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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 * 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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partOfNorm = (float *)malloc (sizeof(float) * (sizeX * sizeY));
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p = NUM_SECTOR;
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xp = NUM_SECTOR * 3;
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pp = NUM_SECTOR * 12;
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for(i = 0; i < sizeX * sizeY; i++)
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{
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valOfNorm = 0.0f;
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pos = i * map->numFeatures;
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for(j = 0; j < p; j++)
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{
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valOfNorm += map->map[pos + j] * map->map[pos + j];
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}/*for(j = 0; j < p; j++)*/
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partOfNorm[i] = valOfNorm;
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}/*for(i = 0; i < sizeX * sizeY; i++)*/
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sizeX -= 2;
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sizeY -= 2;
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newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
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//normalization
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for(i = 1; i <= sizeY; i++)
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{
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for(j = 1; j <= sizeX; j++)
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{
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]);
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pos1 = (i ) * (sizeX + 2) * xp + (j ) * xp;
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pos2 = (i-1) * (sizeX ) * pp + (j-1) * pp;
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]);
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii + p ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]);
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii + p * 2] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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valOfNorm = sqrtf(
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partOfNorm[(i )*(sizeX + 2) + (j )] +
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partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
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partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]);
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for(ii = 0; ii < p; ii++)
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{
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newData[pos2 + ii + p * 3 ] = map->map[pos1 + ii ] / valOfNorm;
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}/*for(ii = 0; ii < p; ii++)*/
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for(ii = 0; ii < 2 * p; ii++)
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{
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newData[pos2 + ii + p * 10] = map->map[pos1 + ii + p] / valOfNorm;
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}/*for(ii = 0; ii < 2 * p; ii++)*/
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}/*for(j = 1; j <= sizeX; j++)*/
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}/*for(i = 1; i <= sizeY; i++)*/
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//truncation
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for(i = 0; i < sizeX * sizeY * pp; i++)
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{
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if(newData [i] > alfa) newData [i] = alfa;
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}/*for(i = 0; i < sizeX * sizeY * pp; i++)*/
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//swop data
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map->numFeatures = pp;
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map->sizeX = sizeX;
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map->sizeY = sizeY;
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free (map->map);
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free (partOfNorm);
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map->map = newData;
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return LATENT_SVM_OK;
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}
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/*
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// Feature map reduction
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// In each cell we reduce dimension of the feature vector
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// according to original paper special procedure
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//
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// API
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// int PCAFeatureMaps(featureMap *map)
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// INPUT
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// map - feature map
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// OUTPUT
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// map - feature map
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// RESULT
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// Error status
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*/
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int PCAFeatureMaps(CvLSVMFeatureMap *map)
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{
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int i,j, ii, jj, k;
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int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
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float * newData;
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float val;
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float nx, ny;
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sizeX = map->sizeX;
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sizeY = map->sizeY;
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p = map->numFeatures;
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pp = NUM_SECTOR * 3 + 4;
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yp = 4;
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xp = NUM_SECTOR;
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nx = 1.0f / sqrtf((float)(xp * 2));
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ny = 1.0f / sqrtf((float)(yp ));
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newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
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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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{
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pos1 = ((i)*sizeX + j)*p;
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pos2 = ((i)*sizeX + j)*pp;
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k = 0;
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for(jj = 0; jj < xp * 2; jj++)
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{
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val = 0;
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for(ii = 0; ii < yp; ii++)
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{
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val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
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}/*for(ii = 0; ii < yp; ii++)*/
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newData[pos2 + k] = val * ny;
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k++;
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}/*for(jj = 0; jj < xp * 2; jj++)*/
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for(jj = 0; jj < xp; jj++)
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{
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val = 0;
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for(ii = 0; ii < yp; ii++)
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{
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val += map->map[pos1 + ii * xp + jj];
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}/*for(ii = 0; ii < yp; ii++)*/
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newData[pos2 + k] = val * ny;
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k++;
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}/*for(jj = 0; jj < xp; jj++)*/
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for(ii = 0; ii < yp; ii++)
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{
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val = 0;
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for(jj = 0; jj < 2 * xp; jj++)
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{
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val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
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}/*for(jj = 0; jj < xp; jj++)*/
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newData[pos2 + k] = val * nx;
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k++;
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} /*for(ii = 0; ii < yp; ii++)*/
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}/*for(j = 0; j < sizeX; j++)*/
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}/*for(i = 0; i < sizeY; i++)*/
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//swop data
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map->numFeatures = pp;
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free (map->map);
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map->map = newData;
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return LATENT_SVM_OK;
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}
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int getPathOfFeaturePyramid(IplImage * image,
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float step, int numStep, int startIndex,
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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
|
|
//
|
|
// API
|
|
// int getFeaturePyramid(IplImage * image, const filterObject **all_F,
|
|
const int n_f,
|
|
const int lambda, const int k,
|
|
const int startX, const int startY,
|
|
const int W, const int H, featurePyramid **maps);
|
|
// INPUT
|
|
// image - image
|
|
// OUTPUT
|
|
// maps - feature maps for all levels
|
|
// RESULT
|
|
// Error status
|
|
*/
|
|
int getFeaturePyramid(IplImage * image, CvLSVMFeaturePyramid **maps)
|
|
{
|
|
IplImage *imgResize;
|
|
float step;
|
|
int numStep;
|
|
int maxNumCells;
|
|
int W, H;
|
|
|
|
if(image->depth == IPL_DEPTH_32F)
|
|
{
|
|
imgResize = image;
|
|
}
|
|
else
|
|
{
|
|
imgResize = cvCreateImage(cvSize(image->width , image->height) ,
|
|
IPL_DEPTH_32F , 3);
|
|
cvConvert(image, imgResize);
|
|
}
|
|
|
|
W = imgResize->width;
|
|
H = imgResize->height;
|
|
|
|
step = powf(2.0f, 1.0f / ((float)LAMBDA));
|
|
maxNumCells = W / SIDE_LENGTH;
|
|
if( maxNumCells > H / SIDE_LENGTH )
|
|
{
|
|
maxNumCells = H / SIDE_LENGTH;
|
|
}
|
|
numStep = (int)(logf((float) maxNumCells / (5.0f)) / logf( step )) + 1;
|
|
|
|
allocFeaturePyramidObject(maps, numStep + LAMBDA);
|
|
|
|
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);
|
|
}
|
|
|
|
return LATENT_SVM_OK;
|
|
} |