Head pose estimation
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alexander.biryukov committed
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@@ -1,5 +1,5 @@
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+4
@@ -0,0 +1,4 @@
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@@ -0,0 +1,506 @@
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#include "FaceLandmarkDetector.h"
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static const size_t faceTrackerWidth = 300;
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static const size_t faceTrackerHeight = 300;
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static const double faceTrackerScaleFactor = 1.0;
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static const cv::Scalar faceTrackerMeanVal(104.0, 177.0, 123.0);
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float calcSquaredDistanceBetweenFaces(const cv::Rect& face1, const cv::Rect& face2)
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{
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cv::Point2f oldCenter(face1.x + face1.width*0.5f, face1.y + face1.height*0.5f);
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cv::Point2f newCenter(face2.x + face2.width*0.5f, face2.y + face2.height*0.5f);
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float distanceX = newCenter.x - oldCenter.x;
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float distanceY = newCenter.y - oldCenter.y;
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float squareDistance = distanceX * distanceX + distanceY * distanceY;
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return squareDistance;
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}
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FaceLandmarkDetector::FaceLandmarkDetector()
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{
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static const cv::String faceTrackerConfiguration = "deploy.prototxt";
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static const cv::String faceTrackerBinary = "res10_300x300_ssd_iter_140000.caffemodel";
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#ifdef USE_3D_LANDMARKS
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static const cv::String landmarkTrackerConfiguration = "fa_deploy.prototxt";
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static const cv::String landmarkTrackerBinary = "fa__iter_1400000.caffemodel";
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#else
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static const cv::String landmarkTrackerConfiguration = "landmark_deploy.prototxt";
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static const cv::String landmarkTrackerBinary = "VanFace.caffemodel";
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#endif
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faceNet = cv::dnn::readNetFromCaffe(faceTrackerConfiguration, faceTrackerBinary);
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for (size_t i = 0; i < MAX_FACE_COUNT; i++)
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{
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landmarkNetArr[i] = cv::dnn::readNetFromCaffe(landmarkTrackerConfiguration, landmarkTrackerBinary);
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}
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}
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FaceLandmarkDetector::~FaceLandmarkDetector()
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{
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StopTrackProcess();
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}
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void FaceLandmarkDetector::StartTrackProcess()
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{
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if (started)
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{
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return;
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}
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//Some init setup
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syncStop = false;
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for (size_t i = 0; i < MAX_FACE_COUNT; i++)
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{
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syncFaceRectArr[i].valid = false;
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syncFaceLandmarkArr[i].valid = false;
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}
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syncLastFrame = cv::Mat();
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//Start threads
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findFaceThread = std::thread([this]() { this->FindFaceThreadProc(); });
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for (size_t i = 0; i < MAX_FACE_COUNT; i++)
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{
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landmarkThreadArr[i] = std::thread([this, i]() { this->FindLandmarkThreadProc(i); });
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}
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started = true;
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}
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void FaceLandmarkDetector::StopTrackProcess()
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{
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if (!started)
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{
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return;
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}
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//Do not remove braces
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{
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std::unique_lock<std::mutex> lock(syncMutex);
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syncStop = true;
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syncFaceRectCond.notify_all();
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}
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findFaceThread.join();
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for (size_t i = 0; i < MAX_FACE_COUNT; i++)
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{
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landmarkThreadArr[i].join();
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}
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started = false;
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}
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std::array<FaceLandmarkStruct, MAX_FACE_COUNT> FaceLandmarkDetector::GetFaceLandmarks(cv::Mat frame, int frameIndex)
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{
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std::array<FaceLandmarkStruct, MAX_FACE_COUNT> result;
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//Sync area
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std::unique_lock<std::mutex> lock(syncMutex);
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// OUT to face rect thread >>>>
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syncLastFrame = frame;
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syncLastFrameIndex = frameIndex;
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// IN from landmark threads <<<<
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result = syncFaceLandmarkArr;
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return result;
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}
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void FaceLandmarkDetector::FindFaceThreadProc()
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{
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cv::Mat localFrame;
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std::array<FaceIntermediateStruct, MAX_FACE_COUNT> localFaceRectArr;
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bool localStop = false;
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int localFrameIndex = 0;
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while (!syncStop)
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{
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if (!localFrame.empty())
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{
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std::vector<cv::Rect> faces = InnerFindFaces(localFrame);
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std::vector<cv::Mat> faceFrames;
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//Get rid of faces that are too big
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for (size_t i = 0; i < faces.size(); )
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{
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if (faces[i].x < 0 || faces[i].y < 0 || (faces[i].x + faces[i].width >= localFrame.cols) || (faces[i].y + faces[i].height >= localFrame.rows))
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{
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faces.erase(faces.begin() + i);
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}
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else
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{
|
||||
i++;
|
||||
}
|
||||
}
|
||||
|
||||
//Cut faces from frame
|
||||
for (size_t i = 0; i <faces.size(); i++)
|
||||
{
|
||||
faceFrames.push_back(InnerCutFaceFrame(localFrame, faces[i]));
|
||||
}
|
||||
|
||||
InnerSeparateFacesToBuckets(faces, faceFrames, localFaceRectArr);
|
||||
|
||||
|
||||
for (size_t i = 0; i <localFaceRectArr.size(); i++)
|
||||
{
|
||||
if (localFaceRectArr[i].valid)
|
||||
{
|
||||
localFaceRectArr[i].frameIndex = localFrameIndex;
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
//Sync area
|
||||
|
||||
std::unique_lock<std::mutex> lock(syncMutex);
|
||||
|
||||
// OUT to landmark Threads >>>>
|
||||
syncFaceRectArr = localFaceRectArr;
|
||||
|
||||
syncFaceRectCond.notify_all();
|
||||
|
||||
|
||||
// IN from main thread <<<<
|
||||
localFrame = syncLastFrame;
|
||||
|
||||
localFrameIndex = syncLastFrameIndex;
|
||||
|
||||
localStop = syncStop;
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
void FaceLandmarkDetector::FindLandmarkThreadProc(size_t threadIndex)
|
||||
{
|
||||
|
||||
FaceIntermediateStruct localInputFace;
|
||||
|
||||
FaceLandmarkStruct localOutputLandmarks;
|
||||
|
||||
bool localStop = false;
|
||||
|
||||
while (!localStop)
|
||||
{
|
||||
if (localInputFace.valid)
|
||||
{
|
||||
#ifdef USE_3D_LANDMARKS
|
||||
cv::Mat landmarkInputBlob = cv::dnn::blobFromImage(localInputFace.faceFrame, 1.0, cv::Size(40, 40), cv::Scalar(), false, false);
|
||||
#else
|
||||
cv::Mat landmarkInputBlob = cv::dnn::blobFromImage(localInputFace.faceFrame, 1.0, cv::Size(60, 60), cv::Scalar(), false, false);
|
||||
#endif
|
||||
|
||||
landmarkInputBlob.reshape(0, std::vector<int>{ 1, 1, localInputFace.faceFrame.rows, localInputFace.faceFrame.cols });
|
||||
|
||||
landmarkNetArr[threadIndex].setInput(landmarkInputBlob);
|
||||
#ifdef USE_3D_LANDMARKS
|
||||
cv::Mat featureBlob = landmarkNetArr[threadIndex].forward("Dense2");
|
||||
#else
|
||||
cv::Mat featureBlob = landmarkNetArr[threadIndex].forward("Dense3");
|
||||
#endif
|
||||
|
||||
for (int i = 0; i < featureBlob.cols / 2; i++)
|
||||
{
|
||||
cv::Point landmarkPoint;
|
||||
|
||||
landmarkPoint.x = int(featureBlob.at<float>(0, 2 * i) * (localInputFace.faceRect.width) + localInputFace.faceRect.x);
|
||||
landmarkPoint.y = int(featureBlob.at<float>(0, 2 * i + 1) * (localInputFace.faceRect.height) + localInputFace.faceRect.y);
|
||||
localOutputLandmarks.landmarkArr[i] = landmarkPoint;
|
||||
|
||||
}
|
||||
|
||||
localOutputLandmarks.faceRect = localInputFace.faceRect;
|
||||
localOutputLandmarks.frameIndex = localInputFace.frameIndex;
|
||||
localOutputLandmarks.valid = true;
|
||||
}
|
||||
else
|
||||
{
|
||||
localOutputLandmarks.valid = false;
|
||||
}
|
||||
|
||||
|
||||
|
||||
//Sync area
|
||||
|
||||
std::unique_lock<std::mutex> lock(syncMutex);
|
||||
|
||||
// OUT to main thread >>>>
|
||||
syncFaceLandmarkArr[threadIndex] = localOutputLandmarks;
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
bool doLoop = true;
|
||||
|
||||
while (doLoop)
|
||||
{
|
||||
|
||||
//IN from face rect thread <<<<
|
||||
|
||||
localInputFace = syncFaceRectArr[threadIndex];
|
||||
|
||||
|
||||
localStop = syncStop;
|
||||
|
||||
//Time to quit
|
||||
if (localStop)
|
||||
{
|
||||
doLoop = false;
|
||||
}
|
||||
|
||||
if (!localInputFace.valid)
|
||||
{
|
||||
|
||||
syncFaceLandmarkArr[threadIndex].valid = false;
|
||||
|
||||
syncFaceRectCond.wait(lock);
|
||||
|
||||
}
|
||||
else
|
||||
{
|
||||
doLoop = false;
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
std::vector<cv::Rect> FaceLandmarkDetector::InnerFindFaces(cv::Mat frame)
|
||||
{
|
||||
if (frame.channels() == 4)
|
||||
{
|
||||
cvtColor(frame, frame, cv::COLOR_BGRA2BGR);
|
||||
}
|
||||
|
||||
cv::Mat inputBlob = cv::dnn::blobFromImage(frame, faceTrackerScaleFactor, cv::Size(faceTrackerWidth, faceTrackerHeight), faceTrackerMeanVal, false, false);
|
||||
|
||||
faceNet.setInput(inputBlob, "data");
|
||||
|
||||
cv::Mat detection = faceNet.forward("detection_out");
|
||||
|
||||
cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
|
||||
|
||||
float confidenceThreshold = 0.5f;
|
||||
|
||||
std::vector<cv::Rect> faces;
|
||||
|
||||
for (int i = 0; i < detectionMat.rows; i++)
|
||||
{
|
||||
float confidence = detectionMat.at<float>(i, 2);
|
||||
|
||||
if (confidence > confidenceThreshold)
|
||||
{
|
||||
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
|
||||
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
|
||||
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
|
||||
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
|
||||
|
||||
cv::Rect object((int)xLeftBottom, (int)yLeftBottom,
|
||||
(int)(xRightTop - xLeftBottom),
|
||||
(int)(yRightTop - yLeftBottom));
|
||||
|
||||
|
||||
object.x = object.x - object.width*0.1f;
|
||||
object.y = object.y - object.height*0.1f;
|
||||
object.width = object.width*1.2f;
|
||||
object.height = object.height*1.2f;
|
||||
|
||||
|
||||
|
||||
faces.push_back(object);
|
||||
}
|
||||
}
|
||||
|
||||
return faces;
|
||||
|
||||
}
|
||||
|
||||
cv::Mat FaceLandmarkDetector::InnerCutFaceFrame(cv::Mat frame, cv::Rect faceRect)
|
||||
{
|
||||
|
||||
|
||||
cv::Mat srcROI(frame, faceRect);
|
||||
|
||||
cv::Mat img2;
|
||||
cvtColor(srcROI, img2, CV_RGB2GRAY);
|
||||
|
||||
img2.convertTo(img2, CV_32FC1);
|
||||
#ifdef USE_3D_LANDMARKS
|
||||
cv::Size dsize = cv::Size(40, 40);
|
||||
#else
|
||||
cv::Size dsize = cv::Size(60, 60);
|
||||
#endif
|
||||
|
||||
cv::Mat img3 = cv::Mat(dsize, CV_32FC1);
|
||||
cv::resize(img2, img3, dsize, 0, 0, cv::INTER_CUBIC);
|
||||
|
||||
cv::Mat tmp_m, tmp_sd;
|
||||
double m = 0, sd = 0;
|
||||
meanStdDev(img3, tmp_m, tmp_sd);
|
||||
m = tmp_m.at<double>(0, 0);
|
||||
sd = tmp_sd.at<double>(0, 0);
|
||||
|
||||
img3 = (img3 - m) / (0.000001 + sd);
|
||||
|
||||
return img3;
|
||||
}
|
||||
|
||||
void FaceLandmarkDetector::InnerSeparateFacesToBuckets(std::vector<cv::Rect>& faceArr, std::vector<cv::Mat>& faceFrameArr, std::array<FaceIntermediateStruct, MAX_FACE_COUNT>& inOutBucketArr)
|
||||
{
|
||||
static std::array<std::vector<std::pair<cv::Rect, cv::Mat>>, MAX_FACE_COUNT> facesBelongsToSpace;
|
||||
|
||||
|
||||
for (size_t i = 0; i < MAX_FACE_COUNT; i++)
|
||||
{
|
||||
facesBelongsToSpace[i].clear();
|
||||
}
|
||||
|
||||
|
||||
//Distribute current faces to nearest old positions
|
||||
for (size_t i = 0; i < faceArr.size(); )
|
||||
{
|
||||
float lastDistance = 1000000.f;
|
||||
int lastIndex = -1;
|
||||
|
||||
for (size_t j = 0; j < MAX_FACE_COUNT; j++)
|
||||
{
|
||||
if (inOutBucketArr[j].valid)
|
||||
{
|
||||
float newDistance = calcSquaredDistanceBetweenFaces(inOutBucketArr[j].faceRect, faceArr[i]);
|
||||
if (newDistance < lastDistance)
|
||||
{
|
||||
lastDistance = newDistance;
|
||||
lastIndex = j;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (lastIndex != -1)
|
||||
{
|
||||
facesBelongsToSpace[lastIndex].push_back(std::make_pair(faceArr[i], faceFrameArr[i]));
|
||||
faceArr.erase(faceArr.begin() + i);
|
||||
faceFrameArr.erase(faceFrameArr.begin() + i);
|
||||
}
|
||||
else
|
||||
{
|
||||
i++;
|
||||
}
|
||||
}
|
||||
|
||||
//Mark "free spots" to those that don't have any face belong to
|
||||
for (size_t j = 0; j < MAX_FACE_COUNT; j++)
|
||||
{
|
||||
if (inOutBucketArr[j].valid)
|
||||
{
|
||||
if (facesBelongsToSpace[j].size() == 0)
|
||||
{
|
||||
inOutBucketArr[j].valid = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//Find nearest face in each space - others return back to pool
|
||||
for (size_t j = 0; j < MAX_FACE_COUNT; j++)
|
||||
{
|
||||
if (inOutBucketArr[j].valid)
|
||||
{
|
||||
float lastDistance = calcSquaredDistanceBetweenFaces(inOutBucketArr[j].faceRect, facesBelongsToSpace[j][0].first);
|
||||
int lastIndex = 0;
|
||||
|
||||
for (size_t i = 1; i < facesBelongsToSpace[j].size(); i++)
|
||||
{
|
||||
float newDistance = calcSquaredDistanceBetweenFaces(inOutBucketArr[j].faceRect, facesBelongsToSpace[j][i].first);
|
||||
if (newDistance < lastDistance)
|
||||
{
|
||||
lastDistance = newDistance;
|
||||
lastIndex = i;
|
||||
}
|
||||
}
|
||||
|
||||
inOutBucketArr[j].faceRect = facesBelongsToSpace[j][lastIndex].first;
|
||||
inOutBucketArr[j].faceFrame = facesBelongsToSpace[j][lastIndex].second;
|
||||
|
||||
|
||||
facesBelongsToSpace[j].erase(facesBelongsToSpace[j].begin() + lastIndex);
|
||||
|
||||
for (size_t k = 0; k < facesBelongsToSpace[j].size(); k++)
|
||||
{
|
||||
faceArr.push_back(facesBelongsToSpace[j][k].first);
|
||||
faceFrameArr.push_back(facesBelongsToSpace[j][k].second);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
//Distribute faces from pool to empty slots
|
||||
for (size_t j = 0; j < MAX_FACE_COUNT; j++)
|
||||
{
|
||||
if (!inOutBucketArr[j].valid)
|
||||
{
|
||||
|
||||
if (faceArr.size() > 0)
|
||||
{
|
||||
inOutBucketArr[j].valid = true;
|
||||
|
||||
inOutBucketArr[j].faceRect = faceArr[0];
|
||||
inOutBucketArr[j].faceFrame = faceFrameArr[0];
|
||||
|
||||
|
||||
faceArr.erase(faceArr.begin());
|
||||
faceFrameArr.erase(faceFrameArr.begin());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
@@ -0,0 +1,111 @@
|
||||
#pragma once
|
||||
|
||||
#include <opencv2/core.hpp>
|
||||
#include <opencv2/highgui.hpp>
|
||||
#include <opencv2/imgproc.hpp>
|
||||
#include <opencv2/calib3d.hpp>
|
||||
|
||||
#include <algorithm>
|
||||
#include <memory>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#include <chrono>
|
||||
#include <iostream>
|
||||
|
||||
#include <opencv2/dnn.hpp>
|
||||
|
||||
#include <thread>
|
||||
|
||||
|
||||
#include <queue>
|
||||
#include <mutex>
|
||||
#include <condition_variable>
|
||||
|
||||
|
||||
#define USE_3D_LANDMARKS
|
||||
|
||||
|
||||
//#include "boost/thread.hpp"
|
||||
|
||||
constexpr size_t LANDMARK_POINT_COUNT = 68;
|
||||
|
||||
constexpr size_t MAX_FACE_COUNT = 5;
|
||||
|
||||
|
||||
struct FaceIntermediateStruct
|
||||
{
|
||||
bool valid = false;
|
||||
|
||||
cv::Rect faceRect;
|
||||
cv::Mat faceFrame;
|
||||
|
||||
int frameIndex;
|
||||
|
||||
};
|
||||
|
||||
|
||||
struct FaceLandmarkStruct
|
||||
{
|
||||
bool valid = false;
|
||||
|
||||
cv::Rect faceRect;
|
||||
std::array<cv::Point2f, LANDMARK_POINT_COUNT> landmarkArr;
|
||||
|
||||
int frameIndex;
|
||||
};
|
||||
|
||||
|
||||
class FaceLandmarkDetector
|
||||
{
|
||||
public:
|
||||
FaceLandmarkDetector();
|
||||
~FaceLandmarkDetector();
|
||||
|
||||
void StartTrackProcess();
|
||||
|
||||
void StopTrackProcess();
|
||||
|
||||
std::array<FaceLandmarkStruct, MAX_FACE_COUNT> GetFaceLandmarks(cv::Mat frame, int frameIndex);
|
||||
|
||||
protected:
|
||||
cv::dnn::Net faceNet;
|
||||
|
||||
std::array<cv::dnn::Net, MAX_FACE_COUNT> landmarkNetArr;
|
||||
|
||||
bool started = false;
|
||||
|
||||
|
||||
//Sync variables - shared between threads:
|
||||
|
||||
cv::Mat syncLastFrame;
|
||||
|
||||
int syncLastFrameIndex;
|
||||
|
||||
std::array<FaceIntermediateStruct, MAX_FACE_COUNT> syncFaceRectArr;
|
||||
|
||||
std::array<FaceLandmarkStruct, MAX_FACE_COUNT> syncFaceLandmarkArr;
|
||||
|
||||
std::mutex syncMutex;
|
||||
|
||||
bool syncStop = false;
|
||||
|
||||
std::condition_variable syncFaceRectCond;
|
||||
|
||||
std::thread findFaceThread;
|
||||
void FindFaceThreadProc();
|
||||
|
||||
std::array<std::thread, MAX_FACE_COUNT> landmarkThreadArr;
|
||||
void FindLandmarkThreadProc(size_t threadIndex);
|
||||
|
||||
|
||||
|
||||
std::vector<cv::Rect> InnerFindFaces(cv::Mat frame);
|
||||
|
||||
cv::Mat InnerCutFaceFrame(cv::Mat frame, cv::Rect faceRect);
|
||||
|
||||
void InnerSeparateFacesToBuckets(std::vector<cv::Rect>& faceArr, std::vector<cv::Mat>& faceFrameArr, std::array<FaceIntermediateStruct, MAX_FACE_COUNT>& inOutBucketArr);
|
||||
|
||||
|
||||
};
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
#include "ValueSmoother.h"
|
||||
|
||||
|
||||
#include <array>
|
||||
#include <chrono>
|
||||
|
||||
using namespace std;
|
||||
|
||||
using namespace std::chrono;
|
||||
|
||||
|
||||
float snapCurve(float x)
|
||||
{
|
||||
float y = 1.0 / (x + 1.0);
|
||||
y = (1.0 - y) * 2.0;
|
||||
if (y > 1.0) {
|
||||
return 1.0;
|
||||
}
|
||||
return y;
|
||||
}
|
||||
|
||||
|
||||
|
||||
void ValueSmoother::reset()
|
||||
{
|
||||
inited = false;
|
||||
}
|
||||
|
||||
|
||||
float ValueSmoother::responsiveAnalogReadSimple(float newValue)
|
||||
{
|
||||
if (!inited)
|
||||
{
|
||||
inited = true;
|
||||
smoothValue = newValue;
|
||||
return smoothValue;
|
||||
}
|
||||
auto diff = abs(newValue - smoothValue);
|
||||
float snap = snapCurve(diff * SNAP_MULTIPLIER_SIMPLE);
|
||||
|
||||
smoothValue += (newValue - smoothValue) * snap;
|
||||
return smoothValue;
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
void ExtendedValueSmoother::reset()
|
||||
{
|
||||
inited = false;
|
||||
}
|
||||
|
||||
float ExtendedValueSmoother::responsiveAnalogRead(float newValue)
|
||||
{
|
||||
using namespace std::chrono;
|
||||
|
||||
auto ms = duration_cast<milliseconds>(system_clock::now().time_since_epoch()).count();
|
||||
|
||||
|
||||
auto diff = abs(newValue - smoothValue);
|
||||
|
||||
// measure the difference between the new value and current value over time
|
||||
// to get a more reasonable indication of how far off the current smooth value is
|
||||
// compared to the actual measurements
|
||||
errorEMA += ((newValue - smoothValue) - errorEMA) * 0.4;
|
||||
|
||||
// if sleep has been enabled, keep track of when we're asleep or not by marking the time of last activity and testing to see how much time has passed since then
|
||||
if (SLEEP_ENABLE) {
|
||||
// recalculate sleeping status
|
||||
// (asleep if last activity was over SLEEP_DELAY_MS ago)
|
||||
sleeping = abs(errorEMA) < ACTIVITY_THRESHOLD;
|
||||
}
|
||||
|
||||
|
||||
if (SLEEP_ENABLE && sleeping) {
|
||||
return smoothValue;
|
||||
}
|
||||
|
||||
|
||||
|
||||
auto snap = snapCurve(diff * SNAP_MULTIPLIER);
|
||||
|
||||
// when sleep is enabled, the emphasis is stopping on a responsiveValue quickly, and it's less about easing into position. If sleep is enabled, add a small amount to snap so it'll tend to snap into a more accurate position before sleeping starts.
|
||||
if (SLEEP_ENABLE) {
|
||||
snap = snap * 0.5 + 0.5;
|
||||
}
|
||||
|
||||
// calculate the exponential moving average based on the snap
|
||||
smoothValue += (newValue - smoothValue) * snap;
|
||||
|
||||
return smoothValue;
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,49 @@
|
||||
#pragma once
|
||||
|
||||
|
||||
|
||||
class ValueSmoother {
|
||||
public:
|
||||
|
||||
|
||||
void reset();
|
||||
|
||||
float responsiveAnalogReadSimple(float newValue);
|
||||
|
||||
protected:
|
||||
bool inited = false;
|
||||
|
||||
//float SNAP_MULTIPLIER_SIMPLE = 1.0 / 60;
|
||||
|
||||
float SNAP_MULTIPLIER_SIMPLE = 0.3;
|
||||
|
||||
float smoothValue = 0;
|
||||
|
||||
};
|
||||
|
||||
|
||||
struct ExtendedValueSmoother {
|
||||
public:
|
||||
void reset();
|
||||
|
||||
float responsiveAnalogRead(float newValue);
|
||||
|
||||
protected:
|
||||
|
||||
bool inited = false;
|
||||
|
||||
float SNAP_MULTIPLIER = 1.0 / 200;
|
||||
|
||||
float smoothValue = 0;
|
||||
|
||||
bool SLEEP_ENABLE = true;
|
||||
|
||||
float ACTIVITY_THRESHOLD = 16.0;
|
||||
|
||||
float errorEMA = 0;
|
||||
|
||||
long long lastActivityMS = 0;
|
||||
|
||||
bool sleeping = false;
|
||||
|
||||
};
|
||||
Binary file not shown.
+1789
File diff suppressed because it is too large.
Load diff
Binary file not shown.
@@ -0,0 +1,293 @@
|
||||
name: "CNNFacepoint"
|
||||
layer {
|
||||
name: "data"
|
||||
type: "Input"
|
||||
top: "data"
|
||||
input_param {
|
||||
shape: {dim: 1 dim: 1 dim: 40 dim: 40}
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Conv1"
|
||||
type: "Convolution"
|
||||
bottom: "data"
|
||||
top: "Conv1"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 16
|
||||
pad: 2
|
||||
kernel_size: 5
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH1"
|
||||
bottom: "Conv1"
|
||||
top: "ActivationTangH1"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs1"
|
||||
bottom: "ActivationTangH1"
|
||||
top: "Abs1"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Pool1"
|
||||
type: "Pooling"
|
||||
bottom: "Abs1"
|
||||
top: "Pool1"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 2
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Conv2"
|
||||
type: "Convolution"
|
||||
bottom: "Pool1"
|
||||
top: "Conv2"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 48
|
||||
pad: 1
|
||||
kernel_size: 3
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH2"
|
||||
bottom: "Conv2"
|
||||
top: "ActivationTangH2"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs2"
|
||||
bottom: "ActivationTangH2"
|
||||
top: "Abs2"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "Pool2"
|
||||
type: "Pooling"
|
||||
bottom: "Abs2"
|
||||
top: "Pool2"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 2
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
# layer 3
|
||||
layer {
|
||||
name: "Conv3"
|
||||
type: "Convolution"
|
||||
bottom: "Pool2"
|
||||
top: "Conv3"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 64
|
||||
pad: 0
|
||||
kernel_size: 3
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH3"
|
||||
bottom: "Conv3"
|
||||
top: "ActivationTangH3"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs3"
|
||||
bottom: "ActivationTangH3"
|
||||
top: "Abs3"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Pool3"
|
||||
type: "Pooling"
|
||||
bottom: "Abs3"
|
||||
top: "Pool3"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 3
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
# layer 4
|
||||
layer {
|
||||
name: "Conv4"
|
||||
type: "Convolution"
|
||||
bottom: "Pool3"
|
||||
top: "Conv4"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 64
|
||||
pad: 0
|
||||
kernel_size: 2
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH4"
|
||||
bottom: "Conv4"
|
||||
top: "ActivationTangH4"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs4"
|
||||
bottom: "ActivationTangH4"
|
||||
top: "Abs4"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
########################################
|
||||
|
||||
layer {
|
||||
name: "Dense1"
|
||||
type: "InnerProduct"
|
||||
bottom: "Abs4"
|
||||
top: "Dense1"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
inner_product_param {
|
||||
num_output: 100
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH5"
|
||||
bottom: "Dense1"
|
||||
top: "ActivationTangH5"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs5"
|
||||
bottom: "ActivationTangH5"
|
||||
top: "Abs5"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "Dense2"
|
||||
type: "InnerProduct"
|
||||
bottom: "Abs5"
|
||||
top: "Dense2"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
inner_product_param {
|
||||
num_output: 136
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,292 @@
|
||||
name: "landmark"
|
||||
input: "data"
|
||||
input_shape {
|
||||
dim: 1
|
||||
dim: 1
|
||||
dim: 60
|
||||
dim: 60
|
||||
}
|
||||
########################################
|
||||
# the actual net
|
||||
# layer 1
|
||||
layer {
|
||||
name: "Conv1"
|
||||
type: "Convolution"
|
||||
bottom: "data"
|
||||
top: "Conv1"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 20
|
||||
pad: 2
|
||||
kernel_size: 5
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH1"
|
||||
bottom: "Conv1"
|
||||
top: "ActivationTangH1"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs1"
|
||||
bottom: "ActivationTangH1"
|
||||
top: "Abs1"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Pool1"
|
||||
type: "Pooling"
|
||||
bottom: "Abs1"
|
||||
top: "Pool1"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 2
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Conv2"
|
||||
type: "Convolution"
|
||||
bottom: "Pool1"
|
||||
top: "Conv2"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 48
|
||||
pad: 2
|
||||
kernel_size: 5
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH2"
|
||||
bottom: "Conv2"
|
||||
top: "ActivationTangH2"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs2"
|
||||
bottom: "ActivationTangH2"
|
||||
top: "Abs2"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "Pool2"
|
||||
type: "Pooling"
|
||||
bottom: "Abs2"
|
||||
top: "Pool2"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 2
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
# layer 3
|
||||
layer {
|
||||
name: "Conv3"
|
||||
type: "Convolution"
|
||||
bottom: "Pool2"
|
||||
top: "Conv3"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 64
|
||||
pad: 0
|
||||
kernel_size: 3
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH3"
|
||||
bottom: "Conv3"
|
||||
top: "ActivationTangH3"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs3"
|
||||
bottom: "ActivationTangH3"
|
||||
top: "Abs3"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "Pool3"
|
||||
type: "Pooling"
|
||||
bottom: "Abs3"
|
||||
top: "Pool3"
|
||||
pooling_param {
|
||||
pool: MAX
|
||||
kernel_size: 3
|
||||
stride: 2
|
||||
}
|
||||
}
|
||||
|
||||
# layer 4
|
||||
layer {
|
||||
name: "Conv4"
|
||||
type: "Convolution"
|
||||
bottom: "Pool3"
|
||||
top: "Conv4"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
convolution_param {
|
||||
num_output: 80
|
||||
pad: 0
|
||||
kernel_size: 3
|
||||
stride: 1
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
std: 0.1
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0.2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH4"
|
||||
bottom: "Conv4"
|
||||
top: "ActivationTangH4"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs4"
|
||||
bottom: "ActivationTangH4"
|
||||
top: "Abs4"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
########################################
|
||||
|
||||
layer {
|
||||
name: "Dense1"
|
||||
type: "InnerProduct"
|
||||
bottom: "Abs4"
|
||||
top: "Dense1"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
inner_product_param {
|
||||
num_output: 512
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "ActivationTangH5"
|
||||
bottom: "Dense1"
|
||||
top: "ActivationTangH5"
|
||||
type: "TanH"
|
||||
}
|
||||
|
||||
layer {
|
||||
name: "ActivationAbs5"
|
||||
bottom: "ActivationTangH5"
|
||||
top: "Abs5"
|
||||
type: "AbsVal"
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "Dense3"
|
||||
type: "InnerProduct"
|
||||
bottom: "Abs5"
|
||||
top: "Dense3"
|
||||
param {
|
||||
lr_mult: 1
|
||||
decay_mult: 1
|
||||
}
|
||||
param {
|
||||
lr_mult: 2
|
||||
decay_mult: 0
|
||||
}
|
||||
inner_product_param {
|
||||
num_output: 136
|
||||
weight_filler {
|
||||
type: "xavier"
|
||||
}
|
||||
bias_filler {
|
||||
type: "constant"
|
||||
value: 0
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -4,14 +4,17 @@
|
||||
|
||||
TMyApplication* App = NULL;
|
||||
|
||||
|
||||
|
||||
|
||||
int APIENTRY WinMain(HINSTANCE hCurrentInst, HINSTANCE hPreviousInst,
|
||||
LPSTR lpszCmdLine, int nCmdShow)
|
||||
{
|
||||
|
||||
int width = 480;
|
||||
int height = 320;
|
||||
|
||||
int width = 1280;
|
||||
int height = 720;
|
||||
|
||||
|
||||
if (CreateEngine(width, height))
|
||||
{
|
||||
|
||||
@@ -30,5 +33,6 @@ int APIENTRY WinMain(HINSTANCE hCurrentInst, HINSTANCE hPreviousInst,
|
||||
DestroyEngine();
|
||||
}
|
||||
|
||||
|
||||
return 0;
|
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}
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