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Commit 250bcd9f authored by Alexander Alekhin's avatar Alexander Alekhin
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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

parents a1d87309 4446ef5e
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Showing with 128 additions and 52 deletions
modules/aruco/include/opencv2/aruco.hpp
View file @ 250bcd9f
......@@ -144,6 +144,8 @@ enum CornerRefineMethod{
* done at full resolution.
* - aprilTagQuadSigma: What Gaussian blur should be applied to the segmented image (used for quad detection?)
* Parameter is the standard deviation in pixels. Very noisy images benefit from non-zero values (e.g. 0.8).
* - detectInvertedMarker: to check if there is a white marker. In order to generate a "white" marker just
* invert a normal marker by using a tilde, ~markerImage. (default false)
*/
struct CV_EXPORTS_W DetectorParameters {
......@@ -183,6 +185,9 @@ struct CV_EXPORTS_W DetectorParameters {
CV_PROP_RW float aprilTagMaxLineFitMse;
CV_PROP_RW int aprilTagMinWhiteBlackDiff;
CV_PROP_RW int aprilTagDeglitch;
// to detect white (inverted) markers
CV_PROP_RW bool detectInvertedMarker;
};
......
modules/aruco/src/aruco.cpp
View file @ 250bcd9f
......@@ -86,7 +86,8 @@ DetectorParameters::DetectorParameters()
aprilTagCriticalRad( (float)(10* CV_PI /180) ),
aprilTagMaxLineFitMse(10.0),
aprilTagMinWhiteBlackDiff(5),
aprilTagDeglitch(0){}
aprilTagDeglitch(0),
detectInvertedMarker(false){}
/**
......@@ -512,7 +513,19 @@ static bool _identifyOneCandidate(const Ptr<Dictionary>& dictionary, InputArray
int(dictionary->markerSize * dictionary->markerSize * params->maxErroneousBitsInBorderRate);
int borderErrors =
_getBorderErrors(candidateBits, dictionary->markerSize, params->markerBorderBits);
if(borderErrors > maximumErrorsInBorder) return false; // border is wrong
// check if it is a white marker
if(params->detectInvertedMarker){
// to get from 255 to 1
Mat invertedImg = ~candidateBits-254;
int invBError = _getBorderErrors(invertedImg, dictionary->markerSize, params->markerBorderBits);
// white marker
if(invBError<borderErrors){
borderErrors = invBError;
invertedImg.copyTo(candidateBits);
}
}
if(borderErrors > maximumErrorsInBorder) return false;
// take only inner bits
Mat onlyBits =
......
modules/aruco/test/test_arucodetection.cpp
View file @ 250bcd9f
......@@ -182,12 +182,29 @@ static void getSyntheticRT(double yaw, double pitch, double distance, Mat &rvec,
*/
static Mat projectMarker(Ptr<aruco::Dictionary> &dictionary, int id, Mat cameraMatrix, double yaw,
double pitch, double distance, Size imageSize, int markerBorder,
vector< Point2f > &corners) {
vector< Point2f > &corners, int encloseMarker=0) {
// canonical image
Mat markerImg;
Mat marker, markerImg;
const int markerSizePixels = 100;
aruco::drawMarker(dictionary, id, markerSizePixels, markerImg, markerBorder);
aruco::drawMarker(dictionary, id, markerSizePixels, marker, markerBorder);
marker.copyTo(markerImg);
if(encloseMarker){ //to enclose the marker
int enclose = int(marker.rows/4);
markerImg = Mat::zeros(marker.rows+(2*enclose), marker.cols+(enclose*2), CV_8UC1);
Mat field= markerImg.rowRange(int(enclose), int(markerImg.rows-enclose))
.colRange(int(0), int(markerImg.cols));
field.setTo(255);
field= markerImg.rowRange(int(0), int(markerImg.rows))
.colRange(int(enclose), int(markerImg.cols-enclose));
field.setTo(255);
field = markerImg(Rect(enclose,enclose,marker.rows,marker.cols));
marker.copyTo(field);
}
// get rvec and tvec for the perspective
Mat rvec, tvec;
......@@ -205,10 +222,10 @@ static Mat projectMarker(Ptr<aruco::Dictionary> &dictionary, int id, Mat cameraM
projectPoints(markerObjPoints, rvec, tvec, cameraMatrix, distCoeffs, corners);
vector< Point2f > originalCorners;
originalCorners.push_back(Point2f(0, 0));
originalCorners.push_back(Point2f((float)markerSizePixels, 0));
originalCorners.push_back(Point2f((float)markerSizePixels, (float)markerSizePixels));
originalCorners.push_back(Point2f(0, (float)markerSizePixels));
originalCorners.push_back(Point2f(0+float(encloseMarker*markerSizePixels/4), 0+float(encloseMarker*markerSizePixels/4)));
originalCorners.push_back(originalCorners[0]+Point2f((float)markerSizePixels, 0));
originalCorners.push_back(originalCorners[0]+Point2f((float)markerSizePixels, (float)markerSizePixels));
originalCorners.push_back(originalCorners[0]+Point2f(0, (float)markerSizePixels));
Mat transformation = getPerspectiveTransform(originalCorners, corners);
......@@ -238,6 +255,11 @@ class CV_ArucoDetectionPerspective : public cvtest::BaseTest {
public:
CV_ArucoDetectionPerspective();
enum checkWithParameter{
USE_APRILTAG=1, /// Detect marker candidates :: using AprilTag
DETECT_INVERTED_MARKER, /// Check if there is a white marker
};
protected:
void run(int);
};
......@@ -246,9 +268,10 @@ class CV_ArucoDetectionPerspective : public cvtest::BaseTest {
CV_ArucoDetectionPerspective::CV_ArucoDetectionPerspective() {}
void CV_ArucoDetectionPerspective::run(int aprilDecimate) {
void CV_ArucoDetectionPerspective::run(int tryWith) {
int iter = 0;
int szEnclosed = 0;
Mat cameraMatrix = Mat::eye(3, 3, CV_64FC1);
Size imgSize(500, 500);
cameraMatrix.at< double >(0, 0) = cameraMatrix.at< double >(1, 1) = 650;
......@@ -257,29 +280,35 @@ void CV_ArucoDetectionPerspective::run(int aprilDecimate) {
Ptr<aruco::Dictionary> dictionary = aruco::getPredefinedDictionary(aruco::DICT_6X6_250);
// detect from different positions
for(double distance = 0.1; distance <= 0.5; distance += 0.2) {
for(int pitch = 0; pitch < 360; pitch += 60) {
for(int yaw = 30; yaw <= 90; yaw += 50) {
for(double distance = 0.1; distance < 0.7; distance += 0.2) {
for(int pitch = 0; pitch < 360; pitch += (distance == 0.1? 60:180)) {
for(int yaw = 70; yaw <= 120; yaw += 40){
int currentId = iter % 250;
int markerBorder = iter % 2 + 1;
iter++;
vector< Point2f > groundTruthCorners;
// create synthetic image
Mat img =
projectMarker(dictionary, currentId, cameraMatrix, deg2rad(yaw), deg2rad(pitch),
distance, imgSize, markerBorder, groundTruthCorners);
// detect markers
vector< vector< Point2f > > corners;
vector< int > ids;
Ptr<aruco::DetectorParameters> params = aruco::DetectorParameters::create();
params->minDistanceToBorder = 1;
params->markerBorderBits = markerBorder;
if(aprilDecimate == 1){
params->cornerRefinementMethod = cv::aruco::CORNER_REFINE_APRILTAG;
/// create synthetic image
Mat img=
projectMarker(dictionary, currentId, cameraMatrix, deg2rad(yaw), deg2rad(pitch),
distance, imgSize, markerBorder, groundTruthCorners, szEnclosed);
// marker :: Inverted
if(CV_ArucoDetectionPerspective::DETECT_INVERTED_MARKER == tryWith){
img = ~img;
params->detectInvertedMarker = true;
}
if(CV_ArucoDetectionPerspective::USE_APRILTAG == tryWith){
params->cornerRefinementMethod = cv::aruco::CORNER_REFINE_APRILTAG;
}
// detect markers
vector< vector< Point2f > > corners;
vector< int > ids;
aruco::detectMarkers(img, dictionary, corners, ids, params);
// check results
......@@ -293,14 +322,33 @@ void CV_ArucoDetectionPerspective::run(int aprilDecimate) {
}
for(int c = 0; c < 4; c++) {
double dist = cv::norm(groundTruthCorners[c] - corners[0][c]); // TODO cvtest
if(dist > 5) {
ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
if(CV_ArucoDetectionPerspective::DETECT_INVERTED_MARKER == tryWith){
if(szEnclosed && dist > 3){
ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
if(!szEnclosed && dist >15){
ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
}
else{
if(dist > 5) {
ts->printf(cvtest::TS::LOG, "Incorrect marker corners position");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
}
}
}
}
// change the state :: to detect an enclosed inverted marker
if( CV_ArucoDetectionPerspective::DETECT_INVERTED_MARKER == tryWith && distance == 0.1 ){
distance -= 0.1;
szEnclosed++;
}
}
}
......@@ -488,10 +536,16 @@ void CV_ArucoBitCorrection::run(int) {
}
typedef CV_ArucoDetectionPerspective CV_AprilTagDetectionPerspective;
typedef CV_ArucoDetectionPerspective CV_InvertedArucoDetectionPerspective;
TEST(CV_InvertedArucoDetectionPerspective, algorithmic) {
CV_InvertedArucoDetectionPerspective test;
test.safe_run(CV_ArucoDetectionPerspective::DETECT_INVERTED_MARKER);
}
TEST(CV_AprilTagDetectionPerspective, algorithmic) {
CV_AprilTagDetectionPerspective test;
test.safe_run(1);
test.safe_run(CV_ArucoDetectionPerspective::USE_APRILTAG);
}
TEST(CV_ArucoDetectionSimple, algorithmic) {
......
modules/face/include/opencv2/face/facemark.hpp
View file @ 250bcd9f
......@@ -74,8 +74,8 @@ public:
@endcode
*/
CV_WRAP virtual bool fit( InputArray image,
InputArray faces,
OutputArrayOfArrays landmarks ) = 0;
const std::vector<Rect>& faces,
CV_OUT std::vector<std::vector<Point2f> >& landmarks ) = 0;
}; /* Facemark*/
......
modules/face/include/opencv2/face/facemarkAAM.hpp
View file @ 250bcd9f
......@@ -146,7 +146,7 @@ public:
};
//! overload with additional Config structures
virtual bool fitConfig( InputArray image, InputArray roi, OutputArrayOfArrays _landmarks, const std::vector<Config> &runtime_params ) = 0;
virtual bool fitConfig( InputArray image, const std::vector<Rect>& roi, std::vector<std::vector<Point2f> >& _landmarks, const std::vector<Config> &runtime_params ) = 0;
//! initializer
......
modules/face/src/face_alignmentimpl.hpp
View file @ 250bcd9f
......@@ -73,7 +73,7 @@ public:
void loadModel(String fs) CV_OVERRIDE;
bool setFaceDetector(FN_FaceDetector f, void* userdata) CV_OVERRIDE;
bool getFaces(InputArray image, OutputArray faces) CV_OVERRIDE;
bool fit(InputArray image, InputArray faces, OutputArrayOfArrays landmarks ) CV_OVERRIDE;
bool fit(InputArray image, const std::vector<Rect>& faces, CV_OUT std::vector<std::vector<Point2f> >& landmarks ) CV_OVERRIDE;
void training(String imageList, String groundTruth);
bool training(vector<Mat>& images, vector< vector<Point2f> >& landmarks,string filename,Size scale,string modelFilename) CV_OVERRIDE;
// Destructor for the class.
......
modules/face/src/facemarkAAM.cpp
View file @ 250bcd9f
......@@ -103,12 +103,11 @@ public:
bool getData(void * items) CV_OVERRIDE;
bool fitConfig( InputArray image, InputArray roi, OutputArrayOfArrays _landmarks, const std::vector<Config> &runtime_params ) CV_OVERRIDE;
bool fitConfig( InputArray image, const std::vector<Rect>& roi, std::vector<std::vector<Point2f> >& _landmarks, const std::vector<Config> &runtime_params ) CV_OVERRIDE;
protected:
bool fit( InputArray image, InputArray faces, OutputArrayOfArrays landmarks ) CV_OVERRIDE;
//bool fit( InputArray image, InputArray faces, InputOutputArray landmarks, void * runtime_params);//!< from many ROIs
bool fit( InputArray image, const std::vector<Rect>& faces, CV_OUT std::vector<std::vector<Point2f> >& landmarks ) CV_OVERRIDE;
bool fitImpl( const Mat image, std::vector<Point2f>& landmarks,const Mat R,const Point2f T,const float scale, const int sclIdx=0 );
bool addTrainingSample(InputArray image, InputArray landmarks) CV_OVERRIDE;
......@@ -323,19 +322,18 @@ void FacemarkAAMImpl::training(void* parameters){
if(params.verbose) printf("Training is completed\n");
}
bool FacemarkAAMImpl::fit( InputArray image, InputArray roi, OutputArrayOfArrays _landmarks )
bool FacemarkAAMImpl::fit( InputArray image, const std::vector<Rect>& roi, CV_OUT std::vector<std::vector<Point2f> >& _landmarks )
{
std::vector<Config> config; // empty
return fitConfig(image, roi, _landmarks, config);
}
bool FacemarkAAMImpl::fitConfig( InputArray image, InputArray roi, OutputArrayOfArrays _landmarks, const std::vector<Config> &configs )
bool FacemarkAAMImpl::fitConfig( InputArray image, const std::vector<Rect>& roi, std::vector<std::vector<Point2f> >& _landmarks, const std::vector<Config> &configs )
{
std::vector<Rect> & faces = *(std::vector<Rect> *)roi.getObj();
const std::vector<Rect> & faces = roi;
if(faces.size()<1) return false;
std::vector<std::vector<Point2f> > & landmarks =
*(std::vector<std::vector<Point2f> >*) _landmarks.getObj();
std::vector<std::vector<Point2f> > & landmarks = _landmarks;
landmarks.resize(faces.size());
Mat img = image.getMat();
......
modules/face/src/facemarkLBF.cpp
View file @ 250bcd9f
......@@ -115,7 +115,7 @@ public:
protected:
bool fit( InputArray image, InputArray faces, OutputArrayOfArrays landmarks ) CV_OVERRIDE;//!< from many ROIs
bool fit( InputArray image, const std::vector<Rect> & faces, std::vector<std::vector<Point2f> > & landmarks ) CV_OVERRIDE;//!< from many ROIs
bool fitImpl( const Mat image, std::vector<Point2f> & landmarks );//!< from a face
bool addTrainingSample(InputArray image, InputArray landmarks) CV_OVERRIDE;
......@@ -370,14 +370,12 @@ void FacemarkLBFImpl::training(void* parameters){
isModelTrained = true;
}
bool FacemarkLBFImpl::fit( InputArray image, InputArray roi, OutputArrayOfArrays _landmarks )
bool FacemarkLBFImpl::fit( InputArray image, const std::vector<Rect> & roi, CV_OUT std::vector<std::vector<Point2f> > & _landmarks )
{
// FIXIT
std::vector<Rect> & faces = *(std::vector<Rect> *)roi.getObj();
const std::vector<Rect> & faces = roi;
if (faces.empty()) return false;
std::vector<std::vector<Point2f> > & landmarks =
*(std::vector<std::vector<Point2f> >*) _landmarks.getObj();
std::vector<std::vector<Point2f> > & landmarks = _landmarks;
landmarks.resize(faces.size());
......
modules/face/src/getlandmarks.cpp
View file @ 250bcd9f
......@@ -168,15 +168,15 @@ void FacemarkKazemiImpl :: loadModel(String filename){
f.close();
isModelLoaded = true;
}
bool FacemarkKazemiImpl::fit(InputArray img, InputArray roi, OutputArrayOfArrays landmarks){
bool FacemarkKazemiImpl::fit(InputArray img, const std::vector<Rect>& roi, CV_OUT std::vector<std::vector<Point2f> >& landmarks){
if(!isModelLoaded){
String error_message = "No model loaded. Aborting....";
CV_Error(Error::StsBadArg, error_message);
return false;
}
Mat image = img.getMat();
std::vector<Rect> & faces = *(std::vector<Rect>*)roi.getObj();
std::vector<std::vector<Point2f> > & shapes = *(std::vector<std::vector<Point2f> >*) landmarks.getObj();
const std::vector<Rect> & faces = roi;
std::vector<std::vector<Point2f> > & shapes = landmarks;
shapes.resize(faces.size());
if(image.empty()){
......
modules/ximgproc/src/selectivesearchsegmentation.cpp
View file @ 250bcd9f
......@@ -558,9 +558,14 @@ namespace cv {
center = Point((int)(img_plane_rotated.cols / 2.0), (int)(img_plane_rotated.rows / 2.0));
rot = cv::getRotationMatrix2D(center, -45.0, 1.0);
warpAffine(tmp_gradiant, tmp_rot, rot, bbox.size());
// Using this bigger box avoids clipping the ends of narrow images
Rect bbox2 = cv::RotatedRect(center, img_plane_rotated.size(), -45.0).boundingRect();\
warpAffine(tmp_gradiant, tmp_rot, rot, bbox2.size());
tmp_gradiant = tmp_rot(Rect((bbox.width - img.cols) / 2, (bbox.height - img.rows) / 2, img.cols, img.rows));
// for narrow images, bbox might be less tall or wide than img
int start_x = std::max(0, (bbox.width - img.cols) / 2);
int start_y = std::max(0, (bbox.height - img.rows) / 2);
tmp_gradiant = tmp_rot(Rect(start_x, start_y, img.cols, img.rows));
threshold(tmp_gradiant, tmp_gradiant_pos, 0, 0, THRESH_TOZERO);
threshold(tmp_gradiant, tmp_gradiant_neg, 0, 0, THRESH_TOZERO_INV);
......@@ -573,9 +578,12 @@ namespace cv {
center = Point((int)(img_plane_rotated.cols / 2.0), (int)(img_plane_rotated.rows / 2.0));
rot = cv::getRotationMatrix2D(center, -45.0, 1.0);
warpAffine(tmp_gradiant, tmp_rot, rot, bbox.size());
bbox2 = cv::RotatedRect(center, img_plane_rotated.size(), -45.0).boundingRect();\
warpAffine(tmp_gradiant, tmp_rot, rot, bbox2.size());
tmp_gradiant = tmp_rot(Rect((bbox.width - img.cols) / 2, (bbox.height - img.rows) / 2, img.cols, img.rows));
start_x = std::max(0, (bbox.width - img.cols) / 2);
start_y = std::max(0, (bbox.height - img.rows) / 2);
tmp_gradiant = tmp_rot(Rect(start_x, start_y, img.cols, img.rows));
threshold(tmp_gradiant, tmp_gradiant_pos, 0, 0, THRESH_TOZERO);
threshold(tmp_gradiant, tmp_gradiant_neg, 0, 0, THRESH_TOZERO_INV);
......
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