dnn: add an openpose.cpp sample

samples/dnn/openpose.cpp

+//
+//  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/master/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/master/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) }"
+        "{ t threshold      |  0.1  | threshold or confidence value for the heatmap }"
+    );
+
+    String modelTxt = parser.get<string>("proto");
+    String modelBin = parser.get<string>("model");
+    String imageFile = parser.get<String>("image");
+    float thresh = parser.get<float>("threshold");
+    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;
+    }
+
+    // fixed input size for the pretrained network
+    int W_in = 368;
+    int H_in = 368;
+
+    // read the network model
+    Net net = readNetFromCaffe(modelTxt, modelBin);
+
+    // 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, 1.0 / 255, 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 midx, npairs;
+    int nparts = result.size[1];
+    int H = result.size[2];
+    int W = result.size[3];
+
+    // find out, which model we have
+    if (nparts == 19)
+    {   // COCO body
+        midx   = 0;
+        npairs = 17;
+        nparts = 18; // skip background
+    }
+    else if (nparts == 16)
+    {   // MPI body
+        midx   = 1;
+        npairs = 14;
+    }
+    else if (nparts == 22)
+    {   // hand
+        midx   = 2;
+        npairs = 20;
+    }
+    else
+    {
+        cerr << "there should be 19 parts for the COCO model, 16 for MPI, or 22 for the hand one, but this model has " << nparts << " parts." << endl;
+        return (0);
+    }
+
+    // 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;
+}