//
//  this sample demonstrates the use of pretrained openpose networks with opencv's dnn module.
//
//  it can be used for body pose detection, using either the COCO model(18 parts):
//  http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
//  https://raw.githubusercontent.com/opencv/opencv_extra/3.4/testdata/dnn/openpose_pose_coco.prototxt
//
//  or the MPI model(16 parts):
//  http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel
//  https://raw.githubusercontent.com/opencv/opencv_extra/3.4/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
//
//  (to simplify this sample, the body models are restricted to a single person.)
//
//
//  you can also try the hand pose model:
//  http://posefs1.perception.cs.cmu.edu/OpenPose/models/hand/pose_iter_102000.caffemodel
//  https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/hand/pose_deploy.prototxt
//

#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;

#include <iostream>
using namespace std;


// connection table, in the format [model_id][pair_id][from/to]
// please look at the nice explanation at the bottom of:
// https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/output.md
//
const int POSE_PAIRS[3][20][2] = {
{   // COCO body
    {1,2}, {1,5}, {2,3},
    {3,4}, {5,6}, {6,7},
    {1,8}, {8,9}, {9,10},
    {1,11}, {11,12}, {12,13},
    {1,0}, {0,14},
    {14,16}, {0,15}, {15,17}
},
{   // MPI body
    {0,1}, {1,2}, {2,3},
    {3,4}, {1,5}, {5,6},
    {6,7}, {1,14}, {14,8}, {8,9},
    {9,10}, {14,11}, {11,12}, {12,13}
},
{   // hand
    {0,1}, {1,2}, {2,3}, {3,4},         // thumb
    {0,5}, {5,6}, {6,7}, {7,8},         // pinkie
    {0,9}, {9,10}, {10,11}, {11,12},    // middle
    {0,13}, {13,14}, {14,15}, {15,16},  // ring
    {0,17}, {17,18}, {18,19}, {19,20}   // small
}};

int main(int argc, char **argv)
{
    CommandLineParser parser(argc, argv,
        "{ h help           | false     | print this help message }"
        "{ p proto          |           | (required) model configuration, e.g. hand/pose.prototxt }"
        "{ m model          |           | (required) model weights, e.g. hand/pose_iter_102000.caffemodel }"
        "{ i image          |           | (required) path to image file (containing a single person, or hand) }"
        "{ d dataset        |           | specify what kind of model was trained. It could be (COCO, MPI, HAND) depends on dataset. }"
        "{ width            |  368      | Preprocess input image by resizing to a specific width. }"
        "{ height           |  368      | Preprocess input image by resizing to a specific height. }"
        "{ t threshold      |  0.1      | threshold or confidence value for the heatmap }"
        "{ s scale          |  0.003922 | scale for blob }"
    );

    String modelTxt = samples::findFile(parser.get<string>("proto"));
    String modelBin = samples::findFile(parser.get<string>("model"));
    String imageFile = samples::findFile(parser.get<String>("image"));
    String dataset = parser.get<String>("dataset");
    int W_in = parser.get<int>("width");
    int H_in = parser.get<int>("height");
    float thresh = parser.get<float>("threshold");
    float scale  = parser.get<float>("scale");

    if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty())
    {
        cout << "A sample app to demonstrate human or hand pose detection with a pretrained OpenPose dnn." << endl;
        parser.printMessage();
        return 0;
    }

    int midx, npairs, nparts;
         if (!dataset.compare("COCO")) {  midx = 0; npairs = 17; nparts = 18; }
    else if (!dataset.compare("MPI"))  {  midx = 1; npairs = 14; nparts = 16; }
    else if (!dataset.compare("HAND")) {  midx = 2; npairs = 20; nparts = 22; }
    else
    {
        std::cerr << "Can't interpret dataset parameter: " << dataset << std::endl;
        exit(-1);
    }

    // read the network model
    Net net = readNet(modelBin, modelTxt);
    // and the image
    Mat img = imread(imageFile);
    if (img.empty())
    {
        std::cerr << "Can't read image from the file: " << imageFile << std::endl;
        exit(-1);
    }

    // send it through the network
    Mat inputBlob = blobFromImage(img, scale, Size(W_in, H_in), Scalar(0, 0, 0), false, false);
    net.setInput(inputBlob);
    Mat result = net.forward();
    // the result is an array of "heatmaps", the probability of a body part being in location x,y

    int H = result.size[2];
    int W = result.size[3];

    // find the position of the body parts
    vector<Point> points(22);
    for (int n=0; n<nparts; n++)
    {
        // Slice heatmap of corresponding body's part.
        Mat heatMap(H, W, CV_32F, result.ptr(0,n));
        // 1 maximum per heatmap
        Point p(-1,-1),pm;
        double conf;
        minMaxLoc(heatMap, 0, &conf, 0, &pm);
        if (conf > thresh)
            p = pm;
        points[n] = p;
    }

    // connect body parts and draw it !
    float SX = float(img.cols) / W;
    float SY = float(img.rows) / H;
    for (int n=0; n<npairs; n++)
    {
        // lookup 2 connected body/hand parts
        Point2f a = points[POSE_PAIRS[midx][n][0]];
        Point2f b = points[POSE_PAIRS[midx][n][1]];

        // we did not find enough confidence before
        if (a.x<=0 || a.y<=0 || b.x<=0 || b.y<=0)
            continue;

        // scale to image size
        a.x*=SX; a.y*=SY;
        b.x*=SX; b.y*=SY;

        line(img, a, b, Scalar(0,200,0), 2);
        circle(img, a, 3, Scalar(0,0,200), -1);
        circle(img, b, 3, Scalar(0,0,200), -1);
    }

    imshow("OpenPose", img);
    waitKey();

    return 0;
}
