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110 lines
4.6 KiB
C++
110 lines
4.6 KiB
C++
//
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// this sample demonstrates parsing (segmenting) human body parts from an image using opencv's dnn,
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// based on https://github.com/Engineering-Course/LIP_JPPNet
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//
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// get the pretrained model from: https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
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//
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#include <opencv2/dnn.hpp>
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#include <opencv2/highgui.hpp>
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#include <opencv2/imgproc.hpp>
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using namespace cv;
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static Mat parse_human(const Mat &image, const std::string &model, int backend=dnn::DNN_BACKEND_DEFAULT, int target=dnn::DNN_TARGET_CPU) {
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// this network expects an image and a flipped copy as input
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Mat flipped;
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flip(image, flipped, 1);
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std::vector<Mat> batch;
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batch.push_back(image);
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batch.push_back(flipped);
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Mat blob = dnn::blobFromImages(batch, 1.0, Size(), Scalar(104.00698793, 116.66876762, 122.67891434));
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dnn::Net net = dnn::readNet(model);
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(blob);
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Mat out = net.forward();
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// expected output: [2, 20, 384, 384], (2 lists(orig, flipped) of 20 body part heatmaps 384x384)
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// LIP classes:
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// 0 Background, 1 Hat, 2 Hair, 3 Glove, 4 Sunglasses, 5 UpperClothes, 6 Dress, 7 Coat, 8 Socks, 9 Pants
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// 10 Jumpsuits, 11 Scarf, 12 Skirt, 13 Face, 14 LeftArm, 15 RightArm, 16 LeftLeg, 17 RightLeg, 18 LeftShoe. 19 RightShoe
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Vec3b colors[] = {
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Vec3b(0, 0, 0), Vec3b(128, 0, 0), Vec3b(255, 0, 0), Vec3b(0, 85, 0), Vec3b(170, 0, 51), Vec3b(255, 85, 0),
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Vec3b(0, 0, 85), Vec3b(0, 119, 221), Vec3b(85, 85, 0), Vec3b(0, 85, 85), Vec3b(85, 51, 0), Vec3b(52, 86, 128),
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Vec3b(0, 128, 0), Vec3b(0, 0, 255), Vec3b(51, 170, 221), Vec3b(0, 255, 255), Vec3b(85, 255, 170),
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Vec3b(170, 255, 85), Vec3b(255, 255, 0), Vec3b(255, 170, 0)
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};
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Mat segm(image.size(), CV_8UC3, Scalar(0,0,0));
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Mat maxval(image.size(), CV_32F, Scalar(0));
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// iterate over body part heatmaps (LIP classes)
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for (int i=0; i<out.size[1]; i++) {
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// resize heatmaps to original image size
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// "head" is the original image result, "tail" the flipped copy
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Mat head, h(out.size[2], out.size[3], CV_32F, out.ptr<float>(0,i));
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resize(h, head, image.size());
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// we have to swap the last 3 pairs in the "tail" list
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static int tail_order[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,14,17,16,19,18};
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Mat tail, t(out.size[2], out.size[3], CV_32F, out.ptr<float>(1,tail_order[i]));
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resize(t, tail, image.size());
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flip(tail, tail, 1);
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// mix original and flipped result
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Mat avg = (head + tail) * 0.5;
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// write color if prob value > maxval
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Mat cmask;
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compare(avg, maxval, cmask, CMP_GT);
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segm.setTo(colors[i], cmask);
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// keep largest values for next iteration
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max(avg, maxval, maxval);
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}
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cvtColor(segm, segm, COLOR_RGB2BGR);
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return segm;
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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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"{help h | | show help screen / args}"
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"{image i | | person image to process }"
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"{model m |lip_jppnet_384.pb| network model}"
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"{backend b | 0 | Choose one of computation backends: "
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"0: automatically (by default), "
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"1: Halide language (http://halide-lang.org/), "
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"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"3: OpenCV implementation, "
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"4: VKCOM, "
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"5: CUDA }"
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"{target t | 0 | Choose one of target computation devices: "
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"0: CPU target (by default), "
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"1: OpenCL, "
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"2: OpenCL fp16 (half-float precision), "
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"3: VPU, "
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"4: Vulkan, "
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"6: CUDA, "
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"7: CUDA fp16 (half-float preprocess) }"
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);
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if (argc == 1 || parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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std::string model = parser.get<std::string>("model");
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std::string image = parser.get<std::string>("image");
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int backend = parser.get<int>("backend");
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int target = parser.get<int>("target");
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Mat input = imread(image);
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Mat segm = parse_human(input, model, backend, target);
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imshow("human parsing", segm);
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waitKey();
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return 0;
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}
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