Merge pull request #11091 from berak:openpose_sample
Alexander Alekhin
7 years ago
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2dac35a97d
1 changed files with 168 additions and 0 deletions
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//
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// this sample demonstrates the use of pretrained openpose networks with opencv's dnn module.
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//
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// it can be used for body pose detection, using either the COCO model(18 parts):
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// http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/coco/pose_iter_440000.caffemodel
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// https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_coco.prototxt
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//
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// or the MPI model(16 parts):
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// http://posefs1.perception.cs.cmu.edu/OpenPose/models/pose/mpi/pose_iter_160000.caffemodel
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// https://raw.githubusercontent.com/opencv/opencv_extra/master/testdata/dnn/openpose_pose_mpi_faster_4_stages.prototxt
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//
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// (to simplify this sample, the body models are restricted to a single person.)
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//
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//
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// you can also try the hand pose model:
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// http://posefs1.perception.cs.cmu.edu/OpenPose/models/hand/pose_iter_102000.caffemodel
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// https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/models/hand/pose_deploy.prototxt
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//
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#include <opencv2/dnn.hpp> |
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#include <opencv2/imgproc.hpp> |
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#include <opencv2/highgui.hpp> |
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using namespace cv; |
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using namespace cv::dnn; |
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#include <iostream> |
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using namespace std; |
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// connection table, in the format [model_id][pair_id][from/to]
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// please look at the nice explanation at the bottom of:
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// https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/output.md
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//
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const int POSE_PAIRS[3][20][2] = { |
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{ // COCO body
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{1,2}, {1,5}, {2,3}, |
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{3,4}, {5,6}, {6,7}, |
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{1,8}, {8,9}, {9,10}, |
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{1,11}, {11,12}, {12,13}, |
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{1,0}, {0,14}, |
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{14,16}, {0,15}, {15,17} |
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}, |
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{ // MPI body
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{0,1}, {1,2}, {2,3}, |
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{3,4}, {1,5}, {5,6}, |
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{6,7}, {1,14}, {14,8}, {8,9}, |
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{9,10}, {14,11}, {11,12}, {12,13} |
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}, |
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{ // hand
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{0,1}, {1,2}, {2,3}, {3,4}, // thumb
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{0,5}, {5,6}, {6,7}, {7,8}, // pinkie
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{0,9}, {9,10}, {10,11}, {11,12}, // middle
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{0,13}, {13,14}, {14,15}, {15,16}, // ring
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{0,17}, {17,18}, {18,19}, {19,20} // small
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}}; |
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int main(int argc, char **argv) |
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{ |
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CommandLineParser parser(argc, argv, |
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"{ h help | false | print this help message }" |
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"{ p proto | | (required) model configuration, e.g. hand/pose.prototxt }" |
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"{ m model | | (required) model weights, e.g. hand/pose_iter_102000.caffemodel }" |
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"{ i image | | (required) path to image file (containing a single person, or hand) }" |
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"{ t threshold | 0.1 | threshold or confidence value for the heatmap }" |
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); |
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String modelTxt = parser.get<string>("proto"); |
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String modelBin = parser.get<string>("model"); |
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String imageFile = parser.get<String>("image"); |
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float thresh = parser.get<float>("threshold"); |
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if (parser.get<bool>("help") || modelTxt.empty() || modelBin.empty() || imageFile.empty()) |
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{ |
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cout << "A sample app to demonstrate human or hand pose detection with a pretrained OpenPose dnn." << endl; |
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parser.printMessage(); |
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return 0; |
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} |
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// fixed input size for the pretrained network
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int W_in = 368; |
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int H_in = 368; |
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// read the network model
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Net net = readNetFromCaffe(modelTxt, modelBin); |
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// and the image
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Mat img = imread(imageFile); |
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if (img.empty()) |
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{ |
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std::cerr << "Can't read image from the file: " << imageFile << std::endl; |
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exit(-1); |
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} |
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// send it through the network
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Mat inputBlob = blobFromImage(img, 1.0 / 255, Size(W_in, H_in), Scalar(0, 0, 0), false, false); |
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net.setInput(inputBlob); |
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Mat result = net.forward(); |
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// the result is an array of "heatmaps", the probability of a body part being in location x,y
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int midx, npairs; |
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int nparts = result.size[1]; |
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int H = result.size[2]; |
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int W = result.size[3]; |
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// find out, which model we have
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if (nparts == 19) |
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{ // COCO body
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midx = 0; |
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npairs = 17; |
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nparts = 18; // skip background
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} |
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else if (nparts == 16) |
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{ // MPI body
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midx = 1; |
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npairs = 14; |
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} |
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else if (nparts == 22) |
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{ // hand
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midx = 2; |
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npairs = 20; |
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} |
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else |
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{ |
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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; |
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return (0); |
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} |
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// find the position of the body parts
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vector<Point> points(22); |
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for (int n=0; n<nparts; n++) |
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{ |
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// Slice heatmap of corresponding body's part.
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Mat heatMap(H, W, CV_32F, result.ptr(0,n)); |
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// 1 maximum per heatmap
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Point p(-1,-1),pm; |
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double conf; |
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minMaxLoc(heatMap, 0, &conf, 0, &pm); |
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if (conf > thresh) |
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p = pm; |
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points[n] = p; |
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} |
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// connect body parts and draw it !
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float SX = float(img.cols) / W; |
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float SY = float(img.rows) / H; |
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for (int n=0; n<npairs; n++) |
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{ |
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// lookup 2 connected body/hand parts
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Point2f a = points[POSE_PAIRS[midx][n][0]]; |
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Point2f b = points[POSE_PAIRS[midx][n][1]]; |
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// we did not find enough confidence before
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if (a.x<=0 || a.y<=0 || b.x<=0 || b.y<=0) |
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continue; |
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// scale to image size
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a.x*=SX; a.y*=SY; |
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b.x*=SX; b.y*=SY; |
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line(img, a, b, Scalar(0,200,0), 2); |
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circle(img, a, 3, Scalar(0,0,200), -1); |
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circle(img, b, 3, Scalar(0,0,200), -1); |
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} |
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imshow("OpenPose", img); |
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waitKey(); |
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return 0; |
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} |
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