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Merge pull request #13486 from allnes:dnn_rework
This commit is contained in:
Alexander Alekhin
commit
148aee31e4
@ -57,21 +57,26 @@ const int POSE_PAIRS[3][20][2] = {
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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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"{ width | 368 | Preprocess input image by resizing to a specific width. }"
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"{ height | 368 | Preprocess input image by resizing to a specific height. }"
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"{ t threshold | 0.1 | threshold or confidence value for the heatmap }"
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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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"{ d dataset | | specify what kind of model was trained. It could be (COCO, MPI, HAND) depends on dataset. }"
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"{ width | 368 | Preprocess input image by resizing to a specific width. }"
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"{ height | 368 | Preprocess input image by resizing to a specific height. }"
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"{ t threshold | 0.1 | threshold or confidence value for the heatmap }"
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"{ s scale | 0.003922 | scale for blob }"
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);
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String modelTxt = samples::findFile(parser.get<string>("proto"));
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String modelBin = samples::findFile(parser.get<string>("model"));
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String imageFile = samples::findFile(parser.get<String>("image"));
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String dataset = parser.get<String>("dataset");
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int W_in = parser.get<int>("width");
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int H_in = parser.get<int>("height");
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float thresh = parser.get<float>("threshold");
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float scale = parser.get<float>("scale");
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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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@ -79,9 +84,18 @@ int main(int argc, char **argv)
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return 0;
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}
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// read the network model
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Net net = readNetFromCaffe(modelTxt, modelBin);
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int midx, npairs, nparts;
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if (!dataset.compare("COCO")) { midx = 0; npairs = 17; nparts = 18; }
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else if (!dataset.compare("MPI")) { midx = 1; npairs = 14; nparts = 16; }
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else if (!dataset.compare("HAND")) { midx = 2; npairs = 20; nparts = 22; }
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else
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{
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std::cerr << "Can't interpret dataset parameter: " << dataset << std::endl;
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exit(-1);
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}
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// read the network model
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Net net = readNet(modelBin, modelTxt);
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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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@ -91,39 +105,14 @@ int main(int argc, char **argv)
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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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Mat inputBlob = blobFromImage(img, scale, 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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@ -1,5 +1,5 @@
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# To use Inference Engine backend, specify location of plugins:
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# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH
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# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
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import cv2 as cv
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import numpy as np
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import argparse
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@ -12,10 +12,11 @@ parser.add_argument('--input', help='Path to image or video. Skip to capture fra
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parser.add_argument('--proto', help='Path to .prototxt')
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parser.add_argument('--model', help='Path to .caffemodel')
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parser.add_argument('--dataset', help='Specify what kind of model was trained. '
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'It could be (COCO, MPI) depends on dataset.')
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'It could be (COCO, MPI, HAND) depends on dataset.')
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parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
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parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
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parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
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parser.add_argument('--scale', default=0.003922, type=float, help='Scale for blob.')
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args = parser.parse_args()
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@ -30,8 +31,7 @@ if args.dataset == 'COCO':
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["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
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["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
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["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
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else:
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assert(args.dataset == 'MPI')
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elif args.dataset == 'MPI':
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BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
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"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
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"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
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@ -41,11 +41,33 @@ else:
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["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
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["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
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["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
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else:
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assert(args.dataset == 'HAND')
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BODY_PARTS = { "Wrist": 0,
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"ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
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"IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
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"MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
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"RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
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"LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
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}
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POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],
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["ThumbProximal", "ThumbMiddle"], ["ThumbMiddle", "ThumbDistal"],
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["Wrist", "IndexFingerMetacarpal"], ["IndexFingerMetacarpal", "IndexFingerProximal"],
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["IndexFingerProximal", "IndexFingerMiddle"], ["IndexFingerMiddle", "IndexFingerDistal"],
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["Wrist", "MiddleFingerMetacarpal"], ["MiddleFingerMetacarpal", "MiddleFingerProximal"],
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["MiddleFingerProximal", "MiddleFingerMiddle"], ["MiddleFingerMiddle", "MiddleFingerDistal"],
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["Wrist", "RingFingerMetacarpal"], ["RingFingerMetacarpal", "RingFingerProximal"],
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["RingFingerProximal", "RingFingerMiddle"], ["RingFingerMiddle", "RingFingerDistal"],
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["Wrist", "LittleFingerMetacarpal"], ["LittleFingerMetacarpal", "LittleFingerProximal"],
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["LittleFingerProximal", "LittleFingerMiddle"], ["LittleFingerMiddle", "LittleFingerDistal"] ]
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inWidth = args.width
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inHeight = args.height
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inScale = args.scale
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net = cv.dnn.readNetFromCaffe(cv.samples.findFile(args.proto), cv.samples.findFile(args.model))
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net = cv.dnn.readNet(cv.samples.findFile(args.proto), cv.samples.findFile(args.model))
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cap = cv.VideoCapture(args.input if args.input else 0)
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@ -57,12 +79,12 @@ while cv.waitKey(1) < 0:
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frameWidth = frame.shape[1]
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frameHeight = frame.shape[0]
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inp = cv.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
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inp = cv.dnn.blobFromImage(frame, inScale, (inWidth, inHeight),
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(0, 0, 0), swapRB=False, crop=False)
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net.setInput(inp)
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out = net.forward()
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assert(len(BODY_PARTS) == out.shape[1])
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assert(len(BODY_PARTS) <= out.shape[1])
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points = []
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for i in range(len(BODY_PARTS)):
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