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#include "dpm_cascade.hpp"
#include "dpm_nms.hpp"

#include <limits>
#include <fstream>
#include <iostream>
#include <stdio.h>

using namespace std;

namespace cv
{
namespace dpm
{

void DPMCascade::loadCascadeModel(const string &modelPath)
{
    // load cascade model from xml
    bool is_success = model.deserialize(modelPath);

    if (!is_success)
    {
        string errorMessage = format("Unable to parse the model: %s", modelPath.c_str());
        CV_Error(cv::Error::StsBadArg, errorMessage);
    }

    model.initModel();
}

void DPMCascade::initDPMCascade()
{
    // compute the size of temporary storage needed by cascade
    int nlevels = (int) pyramid.size();
    int numPartFilters = model.getNumPartFilters();
    int numDefParams = model.getNumDefParams();
    featDimsProd.resize(nlevels);
    tempStorageSize = 0;

    for (int i = 0; i < nlevels; i++)
    {
        int w = pyramid[i].cols/feature.dimHOG;
        int h = pyramid[i].rows;
        featDimsProd[i] = w*h;
        tempStorageSize += w*h;
    }

    tempStorageSize *= numPartFilters;

    convValues.resize(tempStorageSize);
    pcaConvValues.resize(tempStorageSize);
    dtValues.resize(tempStorageSize);
    pcaDtValues.resize(tempStorageSize);

    fill(convValues.begin(), convValues.end(), -numeric_limits<double>::infinity());
    fill(pcaConvValues.begin(), pcaConvValues.end(), -numeric_limits<double>::infinity());
    fill(dtValues.begin(), dtValues.end(), -numeric_limits<double>::infinity());
    fill(pcaDtValues.begin(), pcaDtValues.end(), -numeric_limits<double>::infinity());

    // each pyramid (convolution and distance transform) is stored
    // in a 1D array. Since pyramid levels have different sizes,
    // we build an array of offset values in order to index by
    // level. The last offset is the total length of the pyramid
    // storage array.
    convLevelOffset.resize(nlevels + 1);
    dtLevelOffset.resize(nlevels + 1);
    convLevelOffset[0] = 0;
    dtLevelOffset[0] = 0;

    for (int i = 1; i < nlevels + 1; i++)
    {
        convLevelOffset[i] = convLevelOffset[i-1] + numPartFilters*featDimsProd[i-1];
        dtLevelOffset[i] = dtLevelOffset[i-1] + numDefParams*featDimsProd[i-1];
    }

    // cache of precomputed deformation costs
    defCostCacheX.resize(numDefParams);
    defCostCacheY.resize(numDefParams);

    for (int i = 0; i < numDefParams; i++)
    {
        vector< double > def = model.defs[i];
        CV_Assert((int) def.size() >= 4);

        defCostCacheX[i].resize(2*halfWindowSize + 1);
        defCostCacheY[i].resize(2*halfWindowSize + 1);

        for (int j = 0; j < 2*halfWindowSize + 1; j++)
        {
            int delta = j - halfWindowSize;
            int deltaSquare = delta*delta;
            defCostCacheX[i][j] = -def[0]*deltaSquare - def[1]*delta;
            defCostCacheY[i][j] = -def[2]*deltaSquare - def[3]*delta;
        }
    }

    dtArgmaxX.resize(dtLevelOffset[nlevels]);
    pcaDtArgmaxX.resize(dtLevelOffset[nlevels]);
    dtArgmaxY.resize(dtLevelOffset[nlevels]);
    pcaDtArgmaxY.resize(dtLevelOffset[nlevels]);
}

vector< vector<double> > DPMCascade::detect(Mat &image)
{
    if (image.channels() == 1)
        cvtColor(image, image, COLOR_GRAY2BGR);

    if (image.depth() != CV_64F)
        image.convertTo(image, CV_64FC3);

    // compute features
    computeFeatures(image);

    // pre-allocate storage
    initDPMCascade();

    // cascade process
    vector< vector<double> > detections;
    process(detections);

    // non-maximum suppression
    NonMaximumSuppression nms;
    nms.process(detections, 0.5);

    return detections;
}

void DPMCascade::computeFeatures(const Mat &im)
{
    // initialize feature pyramid
    PyramidParameter params;
    params.padx = model.maxSizeX;
    params.pady = model.maxSizeY;
    params.interval = model.interval;
    params.binSize = model.sBin;

    feature = Feature(params);

    // compute pyramid
    feature.computeFeaturePyramid(im, pyramid);

    // compute projected pyramid
    feature.projectFeaturePyramid(model.pcaCoeff, pyramid, pcaPyramid);
}

void DPMCascade::computeLocationScores(vector< vector< double > >  &locationScores)
{
    vector< vector < double > > locationWeight = model.locationWeight;
    CV_Assert((int)locationWeight.size() == model.numComponents);

    Mat locationFeature;
    int nlevels = (int) pyramid.size();
    feature.computeLocationFeatures(nlevels, locationFeature);

    locationScores.resize(model.numComponents);

    for (int comp = 0; comp < model.numComponents; comp++)
    {
        locationScores[comp].resize(locationFeature.cols);

        for (int level = 0; level < locationFeature.cols; level++)
        {
            double val = 0;
            for (int k = 0; k < locationFeature.rows; k++)
                val += locationWeight[comp][k]*
                    locationFeature.at<double>(k, level);

            locationScores[comp][level] = val;
        }
    }
}

void DPMCascade::computeRootPCAScores(vector< vector< Mat > > &rootScores)
{
    PyramidParameter params = feature.getPyramidParameters();
    rootScores.resize(model.numComponents);
    int nlevels = (int) pyramid.size();
    int interval = params.interval;

    for (int comp = 0; comp < model.numComponents; comp++)
    {
        rootScores[comp].resize(nlevels);
        ParalComputeRootPCAScores paralTask(pcaPyramid, model.rootPCAFilters[comp],
                model.pcaDim, rootScores[comp]);
        parallel_for_(Range(interval, nlevels), paralTask);
    }
}

ParalComputeRootPCAScores::ParalComputeRootPCAScores(
        const vector< Mat > &pcaPyrad,
        const Mat &f,
        int dim,
        vector< Mat > &sc):
    pcaPyramid(pcaPyrad),
    filter(f),
    pcaDim(dim),
    scores(sc)
{
}

void ParalComputeRootPCAScores::operator() (const Range &range) const
{
    for (int level = range.start; level != range.end; level++)
    {
        Mat feat = pcaPyramid[level];

        // compute size of output
        int height = feat.rows - filter.rows + 1;
        int width = (feat.cols - filter.cols) / pcaDim + 1;

        Mat result = Mat::zeros(Size(width, height), CV_64F);
        // convolution engine
        ConvolutionEngine convEngine;
        convEngine.convolve(feat, filter, pcaDim, result);
        scores[level] = result;
    }
}

void DPMCascade::process( vector< vector<double> > &dets)
{
    PyramidParameter params = feature.getPyramidParameters();
    int interval = params.interval;
    int padx = params.padx;
    int pady = params.pady;
    vector<double> scales = params.scales;

    int nlevels = (int)pyramid.size() - interval;
    CV_Assert(nlevels > 0);

    // keep track of the PCA scores for each PCA filter
    vector< vector< double > > pcaScore(model.numComponents);
    for (int comp = 0; comp < model.numComponents; comp++)
        pcaScore[comp].resize(model.numParts[comp]+1);

    // compute location scores
    vector< vector< double > > locationScores;
    computeLocationScores(locationScores);

    // compute root PCA scores
    vector< vector< Mat > > rootPCAScores;
    computeRootPCAScores(rootPCAScores);

    // process each model component and pyramid level
    for (int comp = 0; comp < model.numComponents; comp++)
    {
        for (int plevel = 0; plevel < nlevels; plevel++)
        {
            // root filter pyramid level
            int rlevel = plevel + interval;
            double bias = model.bias[comp] + locationScores[comp][rlevel];
            // get the scores of the first PCA filter
            Mat rtscore = rootPCAScores[comp][rlevel];
            // process each location in the current pyramid level
            for (int rx = (int)ceil(padx/2.0); rx < rtscore.cols - (int)ceil(padx/2.0); rx++)
            {
                for (int ry = (int)ceil(pady/2.0); ry < rtscore.rows - (int)ceil(pady/2.0); ry++)
                {
                    // get stage 0 score
                    double score = rtscore.at<double>(ry, rx) + bias;
                    // record PCA score
                    pcaScore[comp][0] = score - bias;
                    // cascade stage 1 through 2*numparts + 2
                    int stage = 1;
                    int numstages = 2*model.numParts[comp] + 2;
                    for(; stage < numstages; stage++)
                    {
                        double t = model.prunThreshold[comp][2*stage-1];
                        // check for hypothesis pruning
                        if (score < t)
                            break;

                        // pca == 1 if place filters
                        // pca == 0 if place non-pca filters
                        bool isPCA = (stage < model.numParts[comp] + 1 ? true : false);
                        // get the part index
                        // root parts have index -1, none-root part are indexed 0:numParts-1
                        int part = model.partOrder[comp][stage] - 1;// partOrder

                        if (part == -1)
                        {
                            // calculate the root non-pca score
                            // and replace the PCA score
                            double rscore = 0.0;
                            if (isPCA)
                            {
                                rscore = convolutionEngine.convolve(pcaPyramid[rlevel],
                                        model.rootPCAFilters[comp],
                                        model.pcaDim, rx, ry);
                            }
                            else
                            {
                                rscore = convolutionEngine.convolve(pyramid[rlevel],
                                        model.rootFilters[comp],
                                        model.numFeatures, rx, ry);
                            }
                            score += rscore - pcaScore[comp][0];
                        }
                        else
                        {
                            // place a non-root filter
                            int pId = model.pFind[comp][part];
                            int px = 2*rx + (int)model.anchors[pId][0];
                            int py = 2*ry + (int)model.anchors[pId][1];

                            // look up the filter and deformation model
                            double defThreshold =
                                model.prunThreshold[comp][2*stage] - score;

                            double ps = computePartScore(plevel, pId, px, py,
                                    isPCA, defThreshold);

                            if (isPCA)
                            {
                                // record PCA filter score
                                pcaScore[comp][part+1] = ps;
                                // update the hypothesis score
                                score += ps;
                            }
                            else
                            {
                                // update the hypothesis score by replacing
                                // the PCA score
                                score += ps - pcaScore[comp][part+1];
                            } // isPCA == false
                        } // part != -1

                    } // stages

                    // check if the hypothesis passed all stages with a
                    // final score over the global threshold
                    if (stage == numstages && score >= model.scoreThresh)
                    {
                        vector<double> coords;
                        // compute and record image coordinates of the detection window
                        double scale = model.sBin/scales[rlevel];
                        double x1 = (rx-padx)*scale;
                        double y1 = (ry-pady)*scale;
                        double x2 = x1 + model.rootFilterDims[comp].width*scale - 1;
                        double y2 = y1 + model.rootFilterDims[comp].height*scale - 1;

                        coords.push_back(x1);
                        coords.push_back(y1);
                        coords.push_back(x2);
                        coords.push_back(y2);

                        // compute and record image coordinates of the part filters
                        scale = model.sBin/scales[plevel];
                        int featWidth = pyramid[plevel].cols/feature.dimHOG;
                        for (int p = 0; p < model.numParts[comp]; p++)
                        {
                            int pId = model.pFind[comp][p];
                            int probx = 2*rx + (int)model.anchors[pId][0];
                            int proby = 2*ry + (int)model.anchors[pId][1];
                            int offset = dtLevelOffset[plevel] +
                                pId*featDimsProd[plevel] +
                                (proby - pady)*featWidth +
                                probx - padx;
                            int px = dtArgmaxX[offset] + padx;
                            int py = dtArgmaxY[offset] + pady;
                            x1 = (px - 2*padx)*scale;
                            y1 = (py - 2*pady)*scale;
                            x2 = x1 + model.partFilterDims[p].width*scale - 1;
                            y2 = y1 + model.partFilterDims[p].height*scale - 1;
                            coords.push_back(x1);
                            coords.push_back(y1);
                            coords.push_back(x2);
                            coords.push_back(y2);
                        }

                        // record component number and score
                        coords.push_back(comp + 1);
                        coords.push_back(score);

                        dets.push_back(coords);
                    }
                } // ry
            } // rx
        } // for each pyramid level
    } // for each component
}

double DPMCascade::computePartScore(int plevel, int pId, int px, int py, bool isPCA, double defThreshold)
{
    // remove virtual padding
    PyramidParameter params = feature.getPyramidParameters();
    px -= params.padx;
    py -= params.pady;

    // check if already computed
    int levelOffset = dtLevelOffset[plevel];
    int locationOffset = pId*featDimsProd[plevel]
        + py*pyramid[plevel].cols/feature.dimHOG
        + px;
    int dtBaseOffset = levelOffset + locationOffset;

    double val;

    if (isPCA)
        val = pcaDtValues[dtBaseOffset];
    else
        val = dtValues[dtBaseOffset];

    if (val > -numeric_limits<double>::infinity())
        return val;

    // Nope, define the bounds of the convolution and
    // distance transform region
    int xstart = px - halfWindowSize;
    xstart = (xstart < 0 ? 0 : xstart);
    int xend = px + halfWindowSize;

    int ystart = py - halfWindowSize;
    ystart = (ystart < 0 ? 0 : ystart);
    int yend = py + halfWindowSize;

    int featWidth = pyramid[plevel].cols/feature.dimHOG;
    int featHeight = pyramid[plevel].rows;
    int filterWidth = model.partFilters[pId].cols/feature.dimHOG;
    int filterHeight = model.partFilters[pId].rows;

    xend = (filterWidth + xend > featWidth)
        ? featWidth - filterWidth
        : xend;
    yend = (filterHeight + yend > featHeight)
        ? featHeight - filterHeight
        : yend;

    // do convolution and distance transform in region
    // [xstar, xend, ystart, yend]
    levelOffset = convLevelOffset[plevel];
    locationOffset = pId*featDimsProd[plevel];
    int convBaseOffset = levelOffset + locationOffset;

    for (int y = ystart; y <= yend; y++)
    {
        int loc = convBaseOffset + y*featWidth + xstart - 1;
        for (int x = xstart; x <= xend; x++)
        {
            loc++;
            // skip if already computed
            if (isPCA)
            {
                if (pcaConvValues[loc] > -numeric_limits<double>::infinity())
                    continue;
            }
            else if(convValues[loc] > -numeric_limits<double>::infinity())
                continue;

            // check for deformation pruning
            double defCost = defCostCacheX[pId][px - x + halfWindowSize]
                + defCostCacheY[pId][py - y + halfWindowSize];

            if (defCost < defThreshold)
                continue;

            if (isPCA)
            {
                pcaConvValues[loc] = convolutionEngine.convolve
                    (pcaPyramid[plevel], model.partPCAFilters[pId],
                     model.pcaDim, x, y);
            }
            else
            {
                convValues[loc] = convolutionEngine.convolve
                    (pyramid[plevel], model.partFilters[pId],
                     model.numFeatures, x, y);
            }
        } // y
    } // x

    // do distance transform over the region.
    // the region is small enought that brut force DT
    // is the fastest method
    double max = -numeric_limits<double>::infinity();
    int xargmax = 0;
    int yargmax = 0;

    for (int y = ystart; y <= yend; y++)
    {
        int loc = convBaseOffset + y*featWidth + xstart - 1;
        for (int x = xstart; x <= xend; x++)
        {
            loc++;
            double v;

            if (isPCA)
                v = pcaConvValues[loc];
            else
                v = convValues[loc];

            v += defCostCacheX[pId][px - x + halfWindowSize]
                + defCostCacheY[pId][py - y + halfWindowSize];

            if (v > max)
            {
                max = v;
                xargmax = x;
                yargmax = y;
            } // if v
        } // for x
    } // for y

    // record max and argmax for DT
    if (isPCA)
    {
        pcaDtArgmaxX[dtBaseOffset] = xargmax;
        pcaDtArgmaxY[dtBaseOffset] = yargmax;
        pcaDtValues[dtBaseOffset] = max;
    }
    else
    {
        dtArgmaxX[dtBaseOffset] = xargmax;
        dtArgmaxY[dtBaseOffset] = yargmax;
        dtValues[dtBaseOffset] = max;
    }

    return max;
}

} // namespace dpm
} // namespace cv
