f4334277
 Hu Chunming
提交3rdparty
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//
// this sample demonstrates parsing (segmenting) human body parts from an image using opencv's dnn,
// based on https://github.com/Engineering-Course/LIP_JPPNet
//
// get the pretrained model from: https://www.dropbox.com/s/qag9vzambhhkvxr/lip_jppnet_384.pb?dl=0
//
#include <opencv2/dnn.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
using namespace cv;
static Mat parse_human(const Mat &image, const std::string &model, int backend=dnn::DNN_BACKEND_DEFAULT, int target=dnn::DNN_TARGET_CPU) {
// this network expects an image and a flipped copy as input
Mat flipped;
flip(image, flipped, 1);
std::vector<Mat> batch;
batch.push_back(image);
batch.push_back(flipped);
Mat blob = dnn::blobFromImages(batch, 1.0, Size(), Scalar(104.00698793, 116.66876762, 122.67891434));
dnn::Net net = dnn::readNet(model);
net.setPreferableBackend(backend);
net.setPreferableTarget(target);
net.setInput(blob);
Mat out = net.forward();
// expected output: [2, 20, 384, 384], (2 lists(orig, flipped) of 20 body part heatmaps 384x384)
// LIP classes:
// 0 Background, 1 Hat, 2 Hair, 3 Glove, 4 Sunglasses, 5 UpperClothes, 6 Dress, 7 Coat, 8 Socks, 9 Pants
// 10 Jumpsuits, 11 Scarf, 12 Skirt, 13 Face, 14 LeftArm, 15 RightArm, 16 LeftLeg, 17 RightLeg, 18 LeftShoe. 19 RightShoe
Vec3b colors[] = {
Vec3b(0, 0, 0), Vec3b(128, 0, 0), Vec3b(255, 0, 0), Vec3b(0, 85, 0), Vec3b(170, 0, 51), Vec3b(255, 85, 0),
Vec3b(0, 0, 85), Vec3b(0, 119, 221), Vec3b(85, 85, 0), Vec3b(0, 85, 85), Vec3b(85, 51, 0), Vec3b(52, 86, 128),
Vec3b(0, 128, 0), Vec3b(0, 0, 255), Vec3b(51, 170, 221), Vec3b(0, 255, 255), Vec3b(85, 255, 170),
Vec3b(170, 255, 85), Vec3b(255, 255, 0), Vec3b(255, 170, 0)
};
Mat segm(image.size(), CV_8UC3, Scalar(0,0,0));
Mat maxval(image.size(), CV_32F, Scalar(0));
// iterate over body part heatmaps (LIP classes)
for (int i=0; i<out.size[1]; i++) {
// resize heatmaps to original image size
// "head" is the original image result, "tail" the flipped copy
Mat head, h(out.size[2], out.size[3], CV_32F, out.ptr<float>(0,i));
resize(h, head, image.size());
// we have to swap the last 3 pairs in the "tail" list
static int tail_order[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,15,14,17,16,19,18};
Mat tail, t(out.size[2], out.size[3], CV_32F, out.ptr<float>(1,tail_order[i]));
resize(t, tail, image.size());
flip(tail, tail, 1);
// mix original and flipped result
Mat avg = (head + tail) * 0.5;
// write color if prob value > maxval
Mat cmask;
compare(avg, maxval, cmask, CMP_GT);
segm.setTo(colors[i], cmask);
// keep largest values for next iteration
max(avg, maxval, maxval);
}
cvtColor(segm, segm, COLOR_RGB2BGR);
return segm;
}
int main(int argc, char**argv)
{
CommandLineParser parser(argc,argv,
"{help h | | show help screen / args}"
"{image i | | person image to process }"
"{model m |lip_jppnet_384.pb| network model}"
"{backend b | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation, "
"4: VKCOM, "
"5: CUDA }"
"{target t | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }"
);
if (argc == 1 || parser.has("help"))
{
parser.printMessage();
return 0;
}
std::string model = parser.get<std::string>("model");
std::string image = parser.get<std::string>("image");
int backend = parser.get<int>("backend");
int target = parser.get<int>("target");
Mat input = imread(image);
Mat segm = parse_human(input, model, backend, target);
imshow("human parsing", segm);
waitKey();
return 0;
}
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