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Merge pull request #13486 from allnes:dnn_rework

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pull/13451/head
Alexander Alekhin 6 years ago
parent d9089741bc a208c3bf04
commit 148aee31e4
2 changed files with 53 additions and 42 deletions
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  1. 59
      samples/dnn/openpose.cpp
  2. 36
      samples/dnn/openpose.py

59
samples/dnn/openpose.cpp
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@ -57,21 +57,26 @@ const int POSE_PAIRS[3][20][2] = {
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) }"
"{ width | 368 | Preprocess input image by resizing to a specific width. }"
"{ height | 368 | Preprocess input image by resizing to a specific height. }"
"{ t threshold | 0.1 | threshold or confidence value for the heatmap }"
"{ 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) }"
"{ d dataset | | specify what kind of model was trained. It could be (COCO, MPI, HAND) depends on dataset. }"
"{ width | 368 | Preprocess input image by resizing to a specific width. }"
"{ height | 368 | Preprocess input image by resizing to a specific height. }"
"{ t threshold | 0.1 | threshold or confidence value for the heatmap }"
"{ s scale | 0.003922 | scale for blob }"
);
String modelTxt = samples::findFile(parser.get<string>("proto"));
String modelBin = samples::findFile(parser.get<string>("model"));
String imageFile = samples::findFile(parser.get<String>("image"));
String dataset = parser.get<String>("dataset");
int W_in = parser.get<int>("width");
int H_in = parser.get<int>("height");
float thresh = parser.get<float>("threshold");
float scale = parser.get<float>("scale");
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;
@ -79,9 +84,18 @@ int main(int argc, char **argv)
return 0;
}
// read the network model
Net net = readNetFromCaffe(modelTxt, modelBin);
int midx, npairs, nparts;
if (!dataset.compare("COCO")) { midx = 0; npairs = 17; nparts = 18; }
else if (!dataset.compare("MPI")) { midx = 1; npairs = 14; nparts = 16; }
else if (!dataset.compare("HAND")) { midx = 2; npairs = 20; nparts = 22; }
else
{
std::cerr << "Can't interpret dataset parameter: " << dataset << std::endl;
exit(-1);
}
// read the network model
Net net = readNet(modelBin, modelTxt);
// and the image
Mat img = imread(imageFile);
if (img.empty())
@ -91,39 +105,14 @@ int main(int argc, char **argv)
}
// send it through the network
Mat inputBlob = blobFromImage(img, 1.0 / 255, Size(W_in, H_in), Scalar(0, 0, 0), false, false);
Mat inputBlob = blobFromImage(img, scale, 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++)

36
samples/dnn/openpose.py
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@ -1,5 +1,5 @@
# To use Inference Engine backend, specify location of plugins:
# export LD_LIBRARY_PATH=/opt/intel/deeplearning_deploymenttoolkit/deployment_tools/external/mklml_lnx/lib:$LD_LIBRARY_PATH
# source /opt/intel/computer_vision_sdk/bin/setupvars.sh
import cv2 as cv
import numpy as np
import argparse
@ -12,10 +12,11 @@ parser.add_argument('--input', help='Path to image or video. Skip to capture fra
parser.add_argument('--proto', help='Path to .prototxt')
parser.add_argument('--model', help='Path to .caffemodel')
parser.add_argument('--dataset', help='Specify what kind of model was trained. '
'It could be (COCO, MPI) depends on dataset.')
'It could be (COCO, MPI, HAND) depends on dataset.')
parser.add_argument('--thr', default=0.1, type=float, help='Threshold value for pose parts heat map')
parser.add_argument('--width', default=368, type=int, help='Resize input to specific width.')
parser.add_argument('--height', default=368, type=int, help='Resize input to specific height.')
parser.add_argument('--scale', default=0.003922, type=float, help='Scale for blob.')
args = parser.parse_args()
@ -30,8 +31,7 @@ if args.dataset == 'COCO':
["Neck", "RHip"], ["RHip", "RKnee"], ["RKnee", "RAnkle"], ["Neck", "LHip"],
["LHip", "LKnee"], ["LKnee", "LAnkle"], ["Neck", "Nose"], ["Nose", "REye"],
["REye", "REar"], ["Nose", "LEye"], ["LEye", "LEar"] ]
else:
assert(args.dataset == 'MPI')
elif args.dataset == 'MPI':
BODY_PARTS = { "Head": 0, "Neck": 1, "RShoulder": 2, "RElbow": 3, "RWrist": 4,
"LShoulder": 5, "LElbow": 6, "LWrist": 7, "RHip": 8, "RKnee": 9,
"RAnkle": 10, "LHip": 11, "LKnee": 12, "LAnkle": 13, "Chest": 14,
@ -41,11 +41,33 @@ else:
["RElbow", "RWrist"], ["Neck", "LShoulder"], ["LShoulder", "LElbow"],
["LElbow", "LWrist"], ["Neck", "Chest"], ["Chest", "RHip"], ["RHip", "RKnee"],
["RKnee", "RAnkle"], ["Chest", "LHip"], ["LHip", "LKnee"], ["LKnee", "LAnkle"] ]
else:
assert(args.dataset == 'HAND')
BODY_PARTS = { "Wrist": 0,
"ThumbMetacarpal": 1, "ThumbProximal": 2, "ThumbMiddle": 3, "ThumbDistal": 4,
"IndexFingerMetacarpal": 5, "IndexFingerProximal": 6, "IndexFingerMiddle": 7, "IndexFingerDistal": 8,
"MiddleFingerMetacarpal": 9, "MiddleFingerProximal": 10, "MiddleFingerMiddle": 11, "MiddleFingerDistal": 12,
"RingFingerMetacarpal": 13, "RingFingerProximal": 14, "RingFingerMiddle": 15, "RingFingerDistal": 16,
"LittleFingerMetacarpal": 17, "LittleFingerProximal": 18, "LittleFingerMiddle": 19, "LittleFingerDistal": 20,
}
POSE_PAIRS = [ ["Wrist", "ThumbMetacarpal"], ["ThumbMetacarpal", "ThumbProximal"],
["ThumbProximal", "ThumbMiddle"], ["ThumbMiddle", "ThumbDistal"],
["Wrist", "IndexFingerMetacarpal"], ["IndexFingerMetacarpal", "IndexFingerProximal"],
["IndexFingerProximal", "IndexFingerMiddle"], ["IndexFingerMiddle", "IndexFingerDistal"],
["Wrist", "MiddleFingerMetacarpal"], ["MiddleFingerMetacarpal", "MiddleFingerProximal"],
["MiddleFingerProximal", "MiddleFingerMiddle"], ["MiddleFingerMiddle", "MiddleFingerDistal"],
["Wrist", "RingFingerMetacarpal"], ["RingFingerMetacarpal", "RingFingerProximal"],
["RingFingerProximal", "RingFingerMiddle"], ["RingFingerMiddle", "RingFingerDistal"],
["Wrist", "LittleFingerMetacarpal"], ["LittleFingerMetacarpal", "LittleFingerProximal"],
["LittleFingerProximal", "LittleFingerMiddle"], ["LittleFingerMiddle", "LittleFingerDistal"] ]
inWidth = args.width
inHeight = args.height
inScale = args.scale
net = cv.dnn.readNetFromCaffe(cv.samples.findFile(args.proto), cv.samples.findFile(args.model))
net = cv.dnn.readNet(cv.samples.findFile(args.proto), cv.samples.findFile(args.model))
cap = cv.VideoCapture(args.input if args.input else 0)
@ -57,12 +79,12 @@ while cv.waitKey(1) < 0:
frameWidth = frame.shape[1]
frameHeight = frame.shape[0]
inp = cv.dnn.blobFromImage(frame, 1.0 / 255, (inWidth, inHeight),
inp = cv.dnn.blobFromImage(frame, inScale, (inWidth, inHeight),
(0, 0, 0), swapRB=False, crop=False)
net.setInput(inp)
out = net.forward()
assert(len(BODY_PARTS) == out.shape[1])
assert(len(BODY_PARTS) <= out.shape[1])
points = []
for i in range(len(BODY_PARTS)):

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