void VideoFaceDetector::detectFacesTemplateMatching(const cv::Mat &frame)
{
// Calculate duration of template matching
m_templateMatchingCurrentTime = cv::getTickCount();
double duration = (double)(m_templateMatchingCurrentTime - m_templateMatchingStartTime) / TICK_FREQUENCY;
// If template matching lasts for more than 2 seconds face is possibly lost
// so disable it and redetect using cascades
if (duration > m_templateMatchingMaxDuration) {
m_foundFace = false;
m_templateMatchingRunning = false;
m_templateMatchingStartTime = m_templateMatchingCurrentTime = 0;
m_facePosition.x = m_facePosition.y = 0;
m_trackedFace.x = m_trackedFace.y = m_trackedFace.width = m_trackedFace.height = 0;
return;
}
// Edge case when face exits frame while
if (m_faceTemplate.rows * m_faceTemplate.cols == 0 || m_faceTemplate.rows <= 1 || m_faceTemplate.cols <= 1) {
m_foundFace = false;
m_templateMatchingRunning = false;
m_templateMatchingStartTime = m_templateMatchingCurrentTime = 0;
m_facePosition.x = m_facePosition.y = 0;
m_trackedFace.x = m_trackedFace.y = m_trackedFace.width = m_trackedFace.height = 0;
return;
}
// Template matching with last known face
//cv::matchTemplate(frame(m_faceRoi), m_faceTemplate, m_matchingResult, CV_TM_CCOEFF);
cv::matchTemplate(frame(m_faceRoi), m_faceTemplate, m_matchingResult, CV_TM_SQDIFF_NORMED);
cv::normalize(m_matchingResult, m_matchingResult, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
double min, max;
cv::Point minLoc, maxLoc;
cv::minMaxLoc(m_matchingResult, &min, &max, &minLoc, &maxLoc);
// Add roi offset to face position
minLoc.x += m_faceRoi.x;
minLoc.y += m_faceRoi.y;
// Get detected face
//m_trackedFace = cv::Rect(maxLoc.x, maxLoc.y, m_trackedFace.width, m_trackedFace.height);
m_trackedFace = cv::Rect(minLoc.x, minLoc.y, m_faceTemplate.cols, m_faceTemplate.rows);
m_trackedFace = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Get new face template
m_faceTemplate = getFaceTemplate(frame, m_trackedFace);
// Calculate face roi
m_faceRoi = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Update face position
m_facePosition = centerOfRect(m_trackedFace);
}
void detectFacesTemplateMatching(cv::Mat &frame)
{
// Calculate duration of template matching
template_matching_current_time = cv::getTickCount();
double duration = (double)(template_matching_current_time - template_matching_start_time) / TICK_FREQUENCY;
// If template matching lasts for more than 2 seconds face is possibly lost
// so disable it and redetect using cascades
if (duration > 2) {
found_face = false;
template_matching_running = false;
template_matching_start_time = template_matching_current_time = 0;
}
// Template matching with last known face
cv::matchTemplate(frame(face_roi), face_template, matching_result, CV_TM_CCOEFF);
cv::normalize(matching_result, matching_result, 0, 1, cv::NORM_MINMAX, -1, cv::Mat());
double min, max;
cv::Point min_loc, max_loc;
cv::minMaxLoc(matching_result, &min, &max, &min_loc, &max_loc);
// Add roi offset to face position
max_loc.x += face_roi.x;
max_loc.y += face_roi.y;
// Get detected face
face = cv::Rect(max_loc.x, max_loc.y, face.width, face.height);
// Get new face template
face_template = frame(face).clone();
// Calculate face roi
face_roi = doubleRectSize(face, cv::Rect(0, 0, frame.cols, frame.rows));
}
void detectFaceAroundRoi(cv::Mat &frame)
{
// Detect faces sized +/-20% off biggest face in previous search
cascade_classifier->detectMultiScale(frame(face_roi), faces, 1.1, 3, 0,
cv::Size(face.width * 8 / 10, face.height * 8 / 10),
cv::Size(face.width * 12 / 10, face.width * 12 / 10));
if (faces.empty())
{
// Activate template matching if not already started and start timer
template_matching_running = true;
if (template_matching_start_time == 0)
template_matching_start_time = cv::getTickCount();
return;
}
// Turn off template matching if running and reset timer
template_matching_running = false;
template_matching_current_time = template_matching_start_time = 0;
// Get detected face
face = biggestFace(faces);
// Add roi offset to face
face.x += face_roi.x;
face.y += face_roi.y;
// Get face template
face_template = frame(face).clone();
// Calculate roi
face_roi = doubleRectSize(face, cv::Rect(0, 0, frame.cols, frame.rows));
}
void VideoFaceDetector::detectFaceAroundRoi(const cv::Mat &frame)
{
// Detect faces sized +/-20% off biggest face in previous search
//m_faceCascade->detectMultiScale(frame(m_faceRoi), m_allFaces, 1.1, 3, 0,
// cv::Size(m_trackedFace.width * 8 / 10, m_trackedFace.height * 8 / 10),
// cv::Size(m_trackedFace.width * 12 / 10, m_trackedFace.width * 12 / 10));
cv::Mat gray;
cv::cvtColor(frame(m_faceRoi), gray, cv::COLOR_BGR2GRAY);
int * pResults = NULL;
pResults = facedetect_multiview_reinforce((unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, gray.step,
1.2f, 5, m_trackedFace.width * 8 / 10, m_trackedFace.width * 12 / 10);
//printf("%d reinforced multiview faces detected.\n", (pResults ? *pResults : 0));
//print the detection results
m_allFaces.clear();
for (int i = 0; i < (pResults ? *pResults : 0); i++)
{
short * p = ((short*)(pResults + 1)) + 6 * i;
int x = p[0];
int y = p[1];
int w = p[2];
int h = p[3];
int neighbors = p[4];
int angle = p[5];
cv::Rect face_rect = cv::Rect(p[0], p[1], p[2], p[3]);
m_allFaces.push_back(cv::Rect(p[0], p[1], p[2], p[3]));
//cv::rectangle(frame, face_rect, cv::Scalar(0, 0, 255));
}
if (m_allFaces.empty())
{
// Activate template matching if not already started and start timer
m_templateMatchingRunning = true;
if (m_templateMatchingStartTime == 0)
m_templateMatchingStartTime = cv::getTickCount();
return;
}
// Turn off template matching if running and reset timer
m_templateMatchingRunning = false;
m_templateMatchingCurrentTime = m_templateMatchingStartTime = 0;
// Get detected face
m_trackedFace = biggestFace(m_allFaces);
// Add roi offset to face
m_trackedFace.x += m_faceRoi.x;
m_trackedFace.y += m_faceRoi.y;
// Get face template
m_faceTemplate = getFaceTemplate(frame, m_trackedFace);
// Calculate roi
m_faceRoi = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Update face position
m_facePosition = centerOfRect(m_trackedFace);
}
void VideoFaceDetector::detectFaceAllSizes(const cv::Mat &frame)
{
// Minimum face size is 1/5th of screen height
// Maximum face size is 2/3rds of screen height
/* m_faceCascade->detectMultiScale(frame, m_allFaces, 1.1, 3, 0,
cv::Size(frame.rows / 5, frame.rows / 5),
cv::Size(frame.rows * 2 / 3, frame.rows * 2 / 3));*/
cv::Mat gray;
cv::cvtColor(frame, gray, cv::COLOR_BGR2GRAY);
int * pResults = NULL;
//std::cout << frame.rows << std::endl;
pResults = facedetect_multiview_reinforce((unsigned char*)(gray.ptr(0)), gray.cols, gray.rows, gray.step,
1.2f, 5, frame.rows / 5, frame.rows * 2 / 3);
//print the detection results
std::vector<cv::Rect> m_allFaces;
for (int i = 0; i < (pResults ? *pResults : 0); i++)
{
short * p = ((short*)(pResults + 1)) + 6 * i;
int x = p[0];
int y = p[1];
int w = p[2];
int h = p[3];
int neighbors = p[4];
int angle = p[5];
//printf("face_rect=[%d, %d, %d, %d], neighbors=%d, angle=%d\n", x, y, w, h, neighbors, angle);
cv::Rect face_rect = cv::Rect(p[0], p[1], p[2], p[3]);
m_allFaces.push_back(cv::Rect(p[0], p[1], p[2], p[3]));
//cv::rectangle(frame, face_rect, cv::Scalar(0, 0, 255));
}
if (m_allFaces.empty()) return;
m_foundFace = true;
// Locate biggest face
m_trackedFace = biggestFace(m_allFaces);
// Copy face template
m_faceTemplate = getFaceTemplate(frame, m_trackedFace);
// Calculate roi
m_faceRoi = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Update face position
m_facePosition = centerOfRect(m_trackedFace);
}
void detectFaceAllSizes(cv::Mat &frame)
{
// Minimum face size is 1/5th of screen height
// Maximum face size is 2/3rds of screen height
cascade_classifier->detectMultiScale(frame, faces, 1.1, 3, 0,
cv::Size(frame.rows / 5, frame.rows / 5),
cv::Size(frame.rows * 2 / 3, frame.rows * 2 / 3));
if (faces.empty()) return;
found_face = true;
// Locate biggest face;
face = biggestFace(faces);
// Copy face template
face_template = frame(face).clone();
// Calculate roi
face_roi = doubleRectSize(face, cv::Rect(0, 0, frame.cols, frame.rows));
}
void VideoFaceDetector::detectFaceAroundRoi(const cv::Mat &frame)
{
// Detect faces sized +/-20% off biggest face in previous search
m_faceCascade->detectMultiScale(frame(m_faceRoi), m_allFaces, 1.1, 3, 0,
cv::Size(m_trackedFace.width * 8 / 10, m_trackedFace.height * 8 / 10),
cv::Size(m_trackedFace.width * 12 / 10, m_trackedFace.width * 12 / 10));
if (m_allFaces.empty())
{
// Activate template matching if not already started and start timer
m_templateMatchingRunning = true;
if (m_templateMatchingStartTime == 0)
m_templateMatchingStartTime = cv::getTickCount();
return;
}
// Turn off template matching if running and reset timer
m_templateMatchingRunning = false;
m_templateMatchingCurrentTime = m_templateMatchingStartTime = 0;
// Get detected face
m_trackedFace = biggestFace(m_allFaces);
// Add roi offset to face
m_trackedFace.x += m_faceRoi.x;
m_trackedFace.y += m_faceRoi.y;
// Get face template
m_faceTemplate = getFaceTemplate(frame, m_trackedFace);
// Calculate roi
m_faceRoi = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Update face position
m_facePosition = centerOfRect(m_trackedFace);
}
void VideoFaceDetector::detectFaceAllSizes(const cv::Mat &frame)
{
// Minimum face size is 1/5th of screen height
// Maximum face size is 2/3rds of screen height
m_faceCascade->detectMultiScale(frame, m_allFaces, 1.1, 3, 0,
cv::Size(frame.rows / 5, frame.rows / 5),
cv::Size(frame.rows * 2 / 3, frame.rows * 2 / 3));
if (m_allFaces.empty()) return;
m_foundFace = true;
// Locate biggest face
m_trackedFace = biggestFace(m_allFaces);
// Copy face template
m_faceTemplate = getFaceTemplate(frame, m_trackedFace);
// Calculate roi
m_faceRoi = doubleRectSize(m_trackedFace, cv::Rect(0, 0, frame.cols, frame.rows));
// Update face position
m_facePosition = centerOfRect(m_trackedFace);
}
