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//Modified from latentsvm module's "lsvmc_featurepyramid.cpp".

#include "fhog.hpp"

#ifdef HAVE_TBB
#include <tbb/tbb.h>
#include "tbb/parallel_for.h"
#include "tbb/blocked_range.h"
#endif

#ifndef max
#define max(a, b) (((a) > (b)) ? (a) : (b))
#endif

#ifndef min
#define min(a, b) (((a) < (b)) ? (a) : (b))
#endif

namespace eco
{
//TODO::这个地方耗时比较大并且调用次数比较多
/*
// Getting feature map for the selected subimage
//
// API
// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
// INPUT
// image             - selected subimage
// k                 - size of cells
// OUTPUT
// map               - feature map
// RESULT
// Error status
*/
int getFeatureMaps(const IplImage *image, const int k,
                   CvLSVMFeatureMapCaskade **map)
{
    int sizeX, sizeY;
    int p, px, stringSize;
    int height, width, numChannels;
    int i, j, kk, c, ii, jj, d;
    float *datadx, *datady;

    int ch;
    float magnitude, x, y, tx, ty;

    IplImage *dx, *dy;
    int *nearest;
    float *w, a_x, b_x;

    float kernel[3] = {-1.f, 0.f, 1.f};            // difference filter
    CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel); // 1 x 3
    CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel); // 3 x 1

    float *r;  // magnitude
    int *alfa; // orientation

    float boundary_x[NUM_SECTOR + 1];
    float boundary_y[NUM_SECTOR + 1];
    float max, dotProd;
    int maxi;

    height = image->height;
    width = image->width;

    numChannels = image->nChannels;

    dx = cvCreateImage(cvSize(image->width, image->height),
                       IPL_DEPTH_32F, 3); // single-precision floating-point, 3 channels
    dy = cvCreateImage(cvSize(image->width, image->height),
                       IPL_DEPTH_32F, 3);

    sizeX = width / k;
    sizeY = height / k;
    px = 3 * NUM_SECTOR;
    p = px;
    stringSize = sizeX * p;
    allocFeatureMapObject(map, sizeX, sizeY, p);

    cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
    cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));

    float arg_vector;
    for (i = 0; i <= NUM_SECTOR; i++)
    {
        arg_vector = ((float)i) * ((float)(PI) / (float)(NUM_SECTOR));
        boundary_x[i] = cosf(arg_vector);
        boundary_y[i] = sinf(arg_vector);
    } /*for(i = 0; i <= NUM_SECTOR; i++) */

    r = (float *)malloc(sizeof(float) * (width * height));
    alfa = (int *)malloc(sizeof(int) * (width * height * 2));

    for (j = 1; j < height - 1; j++)
    {
        datadx = (float *)(dx->imageData + dx->widthStep * j);
        datady = (float *)(dy->imageData + dy->widthStep * j);
        for (i = 1; i < width - 1; i++)
        {
            c = 0;
            x = (datadx[i * numChannels + c]);
            y = (datady[i * numChannels + c]);

            r[j * width + i] = sqrtf(x * x + y * y);
            for (ch = 1; ch < numChannels; ch++)
            {
                tx = (datadx[i * numChannels + ch]);
                ty = (datady[i * numChannels + ch]);
                magnitude = sqrtf(tx * tx + ty * ty);
                if (magnitude > r[j * width + i])
                {
                    r[j * width + i] = magnitude;
                    c = ch;
                    x = tx;
                    y = ty;
                }
            } /*for(ch = 1; ch < numChannels; ch++)*/

            max = boundary_x[0] * x + boundary_y[0] * y;
            maxi = 0;
            for (kk = 0; kk < NUM_SECTOR; kk++)
            {
                dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
                if (dotProd > max)
                {
                    max = dotProd;
                    maxi = kk;
                }
                else
                {
                    if (-dotProd > max)
                    {
                        max = -dotProd;
                        maxi = kk + NUM_SECTOR;
                    }
                }
            }
            alfa[j * width * 2 + i * 2] = maxi % NUM_SECTOR;
            alfa[j * width * 2 + i * 2 + 1] = maxi;
        } /*for(i = 0; i < width; i++)*/
    }     /*for(j = 0; j < height; j++)*/

    nearest = (int *)malloc(sizeof(int) * k);
    w = (float *)malloc(sizeof(float) * (k * 2));

    for (i = 0; i < k / 2; i++)
    {
        nearest[i] = -1;
    } /*for(i = 0; i < k / 2; i++)*/
    for (i = k / 2; i < k; i++)
    {
        nearest[i] = 1;
    } /*for(i = k / 2; i < k; i++)*/

    for (j = 0; j < k / 2; j++)
    {
        b_x = k / 2 + j + 0.5f;
        a_x = k / 2 - j - 0.5f;
        w[j * 2] = 1.0f / a_x * ((a_x * b_x) / (a_x + b_x));
        w[j * 2 + 1] = 1.0f / b_x * ((a_x * b_x) / (a_x + b_x));
    } /*for(j = 0; j < k / 2; j++)*/
    for (j = k / 2; j < k; j++)
    {
        a_x = j - k / 2 + 0.5f;
        b_x = -j + k / 2 - 0.5f + k;
        w[j * 2] = 1.0f / a_x * ((a_x * b_x) / (a_x + b_x));
        w[j * 2 + 1] = 1.0f / b_x * ((a_x * b_x) / (a_x + b_x));
    } /*for(j = k / 2; j < k; j++)*/

    for (i = 0; i < sizeY; i++)
    {
        for (j = 0; j < sizeX; j++)
        {
            for (ii = 0; ii < k; ii++)
            {
                for (jj = 0; jj < k; jj++)
                {
                    if ((i * k + ii > 0) &&
                        (i * k + ii < height - 1) &&
                        (j * k + jj > 0) &&
                        (j * k + jj < width - 1))
                    {
                        d = (k * i + ii) * width + (j * k + jj);
                        (*map)->map[i * stringSize + j * (*map)->numFeatures + alfa[d * 2]] +=
                            r[d] * w[ii * 2] * w[jj * 2];
                        (*map)->map[i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
                            r[d] * w[ii * 2] * w[jj * 2];
                        if ((i + nearest[ii] >= 0) &&
                            (i + nearest[ii] <= sizeY - 1))
                        {
                            (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2]] +=
                                r[d] * w[ii * 2 + 1] * w[jj * 2];
                            (*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
                                r[d] * w[ii * 2 + 1] * w[jj * 2];
                        }
                        if ((j + nearest[jj] >= 0) &&
                            (j + nearest[jj] <= sizeX - 1))
                        {
                            (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2]] +=
                                r[d] * w[ii * 2] * w[jj * 2 + 1];
                            (*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
                                r[d] * w[ii * 2] * w[jj * 2 + 1];
                        }
                        if ((i + nearest[ii] >= 0) &&
                            (i + nearest[ii] <= sizeY - 1) &&
                            (j + nearest[jj] >= 0) &&
                            (j + nearest[jj] <= sizeX - 1))
                        {
                            (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2]] +=
                                r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
                            (*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
                                r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
                        }
                    }
                } /*for(jj = 0; jj < k; jj++)*/
            }     /*for(ii = 0; ii < k; ii++)*/
        }         /*for(j = 1; j < sizeX - 1; j++)*/
    }             /*for(i = 1; i < sizeY - 1; i++)*/

    cvReleaseImage(&dx);
    cvReleaseImage(&dy);

    free(w);
    free(nearest);

    free(r);
    free(alfa);

    return LATENT_SVM_OK;
}

/*
// Feature map Normalization and Truncation 
//
// API
// int normalizeAndTruncate(featureMap *map, const float alfa);
// INPUT
// map               - feature map
// alfa              - truncation threshold
// OUTPUT
// map               - truncated and normalized feature map
// RESULT
// Error status
*/
int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa)
{
    int i, j, ii;
    int sizeX, sizeY, p, pos, pp, xp, pos1, pos2;
    float *partOfNorm; // norm of C(i, j)
    float *newData;
    float valOfNorm;

    sizeX = map->sizeX;
    sizeY = map->sizeY;
    partOfNorm = (float *)malloc(sizeof(float) * (sizeX * sizeY));

    p = NUM_SECTOR;
    xp = NUM_SECTOR * 3;
    pp = NUM_SECTOR * 12;

    for (i = 0; i < sizeX * sizeY; i++)
    {
        valOfNorm = 0.0f;
        pos = i * map->numFeatures;
        for (j = 0; j < p; j++)
        {
            valOfNorm += map->map[pos + j] * map->map[pos + j];
        } /*for(j = 0; j < p; j++)*/
        partOfNorm[i] = valOfNorm;
    } /*for(i = 0; i < sizeX * sizeY; i++)*/

    sizeX -= 2;
    sizeY -= 2;

    newData = (float *)malloc(sizeof(float) * (sizeX * sizeY * pp));
    //normalization
    for (i = 1; i <= sizeY; i++)
    {
        for (j = 1; j <= sizeX; j++)
        {
            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)]) +
                        FLT_EPSILON;
            pos1 = (i) * (sizeX + 2) * xp + (j)*xp;
            pos2 = (i - 1) * (sizeX)*pp + (j - 1) * pp;
            for (ii = 0; ii < p; ii++)
            {
                newData[pos2 + ii] = map->map[pos1 + ii] / valOfNorm;
            } /*for(ii = 0; ii < p; ii++)*/
            for (ii = 0; ii < 2 * p; ii++)
            {
                newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
            } /*for(ii = 0; ii < 2 * p; ii++)*/
            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)]) +
                        FLT_EPSILON;
            for (ii = 0; ii < p; ii++)
            {
                newData[pos2 + ii + p] = map->map[pos1 + ii] / valOfNorm;
            } /*for(ii = 0; ii < p; ii++)*/
            for (ii = 0; ii < 2 * p; ii++)
            {
                newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
            } /*for(ii = 0; ii < 2 * p; ii++)*/
            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)]) +
                        FLT_EPSILON;
            for (ii = 0; ii < p; ii++)
            {
                newData[pos2 + ii + p * 2] = map->map[pos1 + ii] / valOfNorm;
            } /*for(ii = 0; ii < p; ii++)*/
            for (ii = 0; ii < 2 * p; ii++)
            {
                newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
            } /*for(ii = 0; ii < 2 * p; ii++)*/
            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)]) +
                        FLT_EPSILON;
            for (ii = 0; ii < p; ii++)
            {
                newData[pos2 + ii + p * 3] = map->map[pos1 + ii] / valOfNorm;
            } /*for(ii = 0; ii < p; ii++)*/
            for (ii = 0; ii < 2 * p; ii++)
            {
                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 (newData[i] > alfa)
            newData[i] = alfa;
    } /*for(i = 0; i < sizeX * sizeY * pp; i++)*/
      //swop data

    map->numFeatures = pp;
    map->sizeX = sizeX;
    map->sizeY = sizeY;

    free(map->map);
    free(partOfNorm);

    map->map = newData;

    return LATENT_SVM_OK;
}
/*
// Feature map reduction
// In each cell we reduce dimension of the feature vector
// according to original paper special procedure
//
// API
// int PCAFeatureMaps(featureMap *map)
// INPUT
// map               - feature map
// OUTPUT
// map               - feature map
// RESULT
// Error status
*/
int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map)
{
    int i, j, ii, jj, k;
    int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
    float *newData;
    float val;
    float nx, ny;

    sizeX = map->sizeX;
    sizeY = map->sizeY;
    p = map->numFeatures;
    pp = NUM_SECTOR * 3 + 4;
    yp = 4;
    xp = NUM_SECTOR;

    nx = 1.0f / sqrtf((float)(xp * 2));
    ny = 1.0f / sqrtf((float)(yp));

    newData = (float *)malloc(sizeof(float) * (sizeX * sizeY * pp));

    for (i = 0; i < sizeY; i++)
    {
        for (j = 0; j < sizeX; j++)
        {
            pos1 = ((i)*sizeX + j) * p;
            pos2 = ((i)*sizeX + j) * pp;
            k = 0;
            for (jj = 0; jj < xp * 2; jj++)
            {
                val = 0;
                for (ii = 0; ii < yp; ii++)
                {
                    val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
                } /*for(ii = 0; ii < yp; ii++)*/
                newData[pos2 + k] = val * ny;
                k++;
            } /*for(jj = 0; jj < xp * 2; jj++)*/
            for (jj = 0; jj < xp; jj++)
            {
                val = 0;
                for (ii = 0; ii < yp; ii++)
                {
                    val += map->map[pos1 + ii * xp + jj];
                } /*for(ii = 0; ii < yp; ii++)*/
                newData[pos2 + k] = val * ny;
                k++;
            } /*for(jj = 0; jj < xp; jj++)*/
            for (ii = 0; ii < yp; ii++)
            {
                val = 0;
                for (jj = 0; jj < 2 * xp; jj++)
                {
                    val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
                } /*for(jj = 0; jj < xp; jj++)*/
                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->numFeatures = pp;

    free(map->map);

    map->map = newData;

    return LATENT_SVM_OK;
}

int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX,
                          const int sizeY, const int numFeatures)
{
    int i;
    (*obj) = (CvLSVMFeatureMapCaskade *)malloc(sizeof(CvLSVMFeatureMapCaskade));
    (*obj)->sizeX = sizeX;
    (*obj)->sizeY = sizeY;
    (*obj)->numFeatures = numFeatures;
    (*obj)->map = (float *)malloc(sizeof(float) *
                                  (sizeX * sizeY * numFeatures));
    for (i = 0; i < sizeX * sizeY * numFeatures; i++)
    {
        (*obj)->map[i] = 0.0f;
    }
    return LATENT_SVM_OK;
}

int freeFeatureMapObject(CvLSVMFeatureMapCaskade **obj)
{
    if (*obj == NULL)
        return LATENT_SVM_MEM_NULL;
    free((*obj)->map);
    free(*obj);
    (*obj) = NULL;
    return LATENT_SVM_OK;
}
} // namespace eco