int xOffset(Mat y_LR) {
int w = y_LR.size().width / 2;
int h = y_LR.size().height;
Mat left(y_LR, Rect(0, 0, w, h));
Mat right(y_LR, Rect(w, 0, w, h));
Mat disp(h, w, CV_16S);
disparity(left, right, disp);
// now compute the average disparity
Mat mask = (disp > -200) & (disp < 2000); // but not where it's out of range
// FIXME compute the median instead
Scalar avg = cv::mean(disp, mask);
return avg[0];
}
static bool read_labels(const string& path,
vector<string>& filenames, vector< vector<Rect> >& labels)
{
string labels_path = path + "/gt.txt";
string filename, line;
int x1, y1, x2, y2;
char delim;
ifstream ifs(labels_path.c_str());
if( !ifs.good() )
return false;
while( getline(ifs, line) )
{
stringstream stream(line);
stream >> filename;
filenames.push_back(path + "/" + filename);
vector<Rect> filename_labels;
while( stream >> x1 >> y1 >> x2 >> y2 >> delim )
{
filename_labels.push_back(Rect(x1, y1, x2, y2));
}
labels.push_back(filename_labels);
filename_labels.clear();
}
return true;
}
TEST(hole_filling_test, rectangular_hole_on_repeated_texture_should_give_good_result)
{
Mat img = imread("test_images/brick_pavement.jpg");
convert_for_computation(img, 0.5f);
// Add some hole
Mat hole_mask = Mat::zeros(img.size(), CV_8U);
hole_mask(Rect(72, 65, 5, 20)) = 255;
int patch_size = 7;
HoleFilling hf(img, hole_mask, patch_size);
// Dump image with hole as black region.
Mat img_with_hole_bgr;
cvtColor(img, img_with_hole_bgr, CV_Lab2BGR);
img_with_hole_bgr.setTo(Scalar(0,0,0), hole_mask);
imwrite("brick_pavement_hole.exr", img_with_hole_bgr);
// Dump reconstructed image
Mat filled = hf.run();
cvtColor(filled, filled, CV_Lab2BGR);
imwrite("brick_pavement_hole_filled.exr", filled);
// The reconstructed image should be close to the original one, in this very simple case.
Mat img_bgr;
cvtColor(img, img_bgr, CV_Lab2BGR);
double ssd = norm(img_bgr, filled, cv::NORM_L2SQR);
EXPECT_LT(ssd, 0.2);
}
void DeepPyramid::processFeatureMap(int filterIdx, const FeatureMap &map, vector<BoundingBox> &detectedObjects) const {
Size mapSize = map.size();
Size filterSize = rootFilter[filterIdx]->getMapSize();
cout << "size: "<<map.size()<<endl;
for (int width = 0; width < mapSize.width-filterSize.width; width+=stride) {
for (int height = 0; height < mapSize.height-filterSize.height; height+=stride) {
FeatureMap extractedMap;
map.extractFeatureMap(Rect(Point(width, height), filterSize), extractedMap);
if (rootFilter[filterIdx]->predict(extractedMap) == OBJECT) {
BoundingBox box;
box.norm5Box = Rect(Point(width, height), filterSize);
box.confidence = std::fabs(rootFilter[filterIdx]->predict(extractedMap, true));
box.map = extractedMap;
detectedObjects.push_back(box);
}
}
}
}
Rect DetectorTrainer::createRandomBounds() const {
typedef std::uniform_int_distribution<int> uniform_int;
int minWidth = featureParams.windowSizeInPixels().width;
int maxWidth = std::min(imageSize.width, static_cast<int>(imageSize.height * aspectRatio));
int width = uniform_int{minWidth, maxWidth}(generator);
int height = static_cast<int>(std::round(width * aspectRatioInv));
int x = uniform_int{0, imageSize.width - width}(generator);
int y = uniform_int{0, imageSize.height - height}(generator);
return Rect(x, y, width, height);
}
void DeepPyramid::constructImagePyramid(const Mat& img, vector<Mat>& imgPyramid) const {
Size imgSize(img.cols, img.rows);
cout << "Create image pyramid..." << endl;
for (int level = 0; level < levelCount; level++) {
Mat imgAtLevel(net->inputLayerSize(), CV_8UC3, Scalar::all(0));
Mat resizedImg;
Size resizedImgSize = embeddedImageSize(imgSize, level);
resize(img, resizedImg, resizedImgSize);
resizedImg.copyTo(imgAtLevel(Rect(Point(0, 0), resizedImgSize)));
imgPyramid.push_back(imgAtLevel);
}
cout << "Status: Success!" << endl;
}
void DeepPyramid::extractFeatureMap(const Mat &img, vector<Rect> &objects, Size size,
vector<FeatureMap> &omaps, vector<FeatureMap>& nmaps) {
Size imgSize(img.cols, img.rows);
vector<FeatureMap> featureMaps;
constructFeatureMapPyramid(img, featureMaps);
for (int i = 0; i < levelCount; i++) {
vector<Rect> objectsAtLevel;
for (size_t obj = 0; obj < objects.size(); obj++) {
objectsAtLevel.push_back(originalRect2Norm5(objects[obj], i, imgSize));
}
Size mapSize = featureMaps[i].size();
for (int w = 0; w < mapSize.width-size.width; w+=stride)
for (int h = 0; h < mapSize.height-size.height; h+=stride) {
bool isNegative = true;
bool isPositive = false;
for (size_t obj = 0; obj < objects.size(); obj++) {
if (IOU(Rect(Point(w, h), size), objectsAtLevel[obj]) > 0.3)
isNegative = false;
if (IOU(Rect(Point(w, h), size), objectsAtLevel[obj]) > 0.7)
isPositive = true;
}
FeatureMap map;
if (isNegative) {
featureMaps[i].extractFeatureMap(Rect(Point(w, h), size), map);
nmaps.push_back(map);
}
if (isPositive) {
featureMaps[i].extractFeatureMap(Rect(Point(w, h), size), map);
omaps.push_back(map);
}
}
}
}
void DeepPyramid::detect(const vector<FeatureMap>& maps, vector<BoundingBox>& detectedObjects) const {
for (size_t i = 0; i < rootFilter.size(); i++)
for (size_t j = 0; j < levelCount; j++) {
vector<BoundingBox> detectedObjectsAtLevel;
Size size = maps[j].size();
double scale = 1 / pow(2.0, (levelCount - j -1)/2.0);
size.width = size.width * scale;
size.height = size.height * scale;
FeatureMap map;
maps[j].extractFeatureMap(Rect(Point(0, 0), size), map);
processFeatureMap(i, map, detectedObjectsAtLevel);
for (size_t k = 0; k < detectedObjectsAtLevel.size(); k++) {
detectedObjectsAtLevel[k].level = j;
detectedObjects.push_back(detectedObjectsAtLevel[k]);
}
}
}
TEST(hole_filling_test, one_pixel_hole_on_random_image_should_produce_correct_target_rect)
{
// Make some random image data.
Mat img = Mat(100, 100, CV_32FC1);
randu(img, 0.f, 1.f);
// Put a hole in middle.
Mat hole = Mat::zeros(100, 100, CV_8U);
// Set one pixel as hole
hole(Rect(50, 50, 1, 1)) = 1;
int patch_size = 7;
HoleFilling hf(img, hole, patch_size);
Rect expected_target_rect(Point(50 - 6, 50 - 6), Point(50 + 7, 50 + 7));
ASSERT_EQ(expected_target_rect, hf._target_rect_pyr[0]);
}
void EnhancedStereo::computeDynamicProgramming()
{
// cout << "left" << endl;
// left tableau init
for (int v = 0; v < smallHeight(); v++)
{
int * tableauRow = (int *)(tableauLeft.row(v).data);
uint8_t * errorRow = errorBuffer.row(v).data;
// init the first row
copy(errorRow, errorRow + dispMax, tableauRow);
// fill up the tableau
for (int u = 1; u < smallWidth(); u++)
{
computeDynamicStep(tableauRow + (u - 1)*dispMax, errorRow + u*dispMax, tableauRow + u*dispMax);
}
}
// cout << "right" << endl;
// right tableau init
for (int v = 0; v < smallHeight(); v++)
{
int * tableauRow = (int *)(tableauRight.row(v).data);
uint8_t * errorRow = errorBuffer.row(v).data;
int base = (smallWidth() - 1) * dispMax;
copy(errorRow + base, errorRow + base + dispMax, tableauRow + base);
for (int u = smallWidth() - 2; u >= 0; u--)
{
computeDynamicStep(tableauRow + (u + 1)*dispMax, errorRow + u*dispMax, tableauRow + u*dispMax);
}
}
// cout << "top" << endl;
// top-down tableau init
for (int u = 0; u < smallWidth(); u++)
{
auto tableauCol = tableauTop(Rect(u*dispMax, 0, dispMax, smallHeight()));
auto errorCol = errorBuffer(Rect(u*dispMax, 0, dispMax, smallHeight()));
copy(errorCol.data, errorCol.data + dispMax, (int*)(tableauCol.data));
for (int v = 1; v < smallHeight(); v++)
{
computeDynamicStep((int*)(tableauCol.row(v-1).data),
errorCol.row(v).data,
(int*)(tableauCol.row(v).data));
}
}
// cout << "bottom" << endl;
// bottom-up tableau init
for (int u = 0; u < smallWidth(); u++)
{
auto tableauCol = tableauBottom(Rect(u*dispMax, 0, dispMax, smallHeight()));
auto errorCol = errorBuffer(Rect(u*dispMax, 0, dispMax, smallHeight()));
int vLast = smallHeight() - 1;
copy(errorCol.row(vLast).data,
errorCol.row(vLast).data + dispMax,
(int*)(tableauCol.row(vLast).data));
for (int v = smallHeight() - 2; v >= 0; v--)
{
computeDynamicStep((int*)(tableauCol.row(v+1).data),
errorCol.row(v).data,
(int*)(tableauCol.row(v).data));
}
}
}
void FingerTracker::Setup()
{
VideoCapture capture(0);
if (!capture.isOpened())
{
throw std::runtime_error("Could not start camera capture");
}
int windowSize = 25;
int Xpos = 200;
int Ypos = 50;
int update = 0;
int buttonClicked = 0;
namedWindow("RGB", CV_WINDOW_AUTOSIZE);
createTrackbar("X", "RGB", &Xpos, 320, TrackBarCallback, (void*)&update);
createTrackbar("Y", "RGB", &Ypos, 240, TrackBarCallback, (void*)&update);
createTrackbar("Size", "RGB", &windowSize, 100, TrackBarCallback, (void*)&update);
setMouseCallback("RGB", MouseCallback, (void*)&buttonClicked);
Mat fingerWindowBackground, fingerWindowBackgroundGray;
m_calibrationData.reset(new CalibrationData());
bool ticking = false;
std::chrono::system_clock::time_point start = std::chrono::system_clock::now();
while (true)
{
Mat frame, frameHSV;
if (capture.read(frame))
{
flip(frame, frame, 1);
pyrDown(frame, frame, Size(frame.cols / 2, frame.rows / 2));
Rect fingerWindow(Point(Xpos, Ypos), Size(windowSize, windowSize*3));
if (Xpos + windowSize >= frame.cols || Ypos + windowSize*3 >= frame.rows)
{
windowSize = 20;
Xpos = 200;
Ypos = 50;
update = 0;
}
else if (buttonClicked == 1)
{
frame(fingerWindow).copyTo(fingerWindowBackground);
cvtColor(fingerWindowBackground, fingerWindowBackgroundGray, CV_BGR2GRAY);
buttonClicked = 0;
update = 0;
cvDestroyAllWindows();
}
if (fingerWindowBackgroundGray.rows && !m_calibrationData->m_ready)
{
Mat diff, thd;
absdiff(frame(fingerWindow), fingerWindowBackground, diff);
std::vector<Mat> ch;
split(diff, ch);
threshold(ch[0], ch[0], m_calibrationDiffThreshold, 255, 0);
threshold(ch[1], ch[1], m_calibrationDiffThreshold, 255, 0);
threshold(ch[2], ch[2], m_calibrationDiffThreshold, 255, 0);
thd = ch[0];
add(thd, ch[1], thd);
add(thd, ch[2], thd);
medianBlur(thd, thd, 5);
Mat top, middle, bottom;
Rect r1 = Rect(0, 0, thd.cols, thd.rows/3);
Rect r2 = Rect(0, thd.rows / 3 + 1, thd.cols, thd.rows/3);
Rect r3 = Rect(0, thd.rows * 2 / 3 + 1, thd.cols, thd.rows - thd.rows * 2 / 3 - 1);
top = thd(r1);
middle = thd(r2);
bottom = thd(r3);
auto percentageTop = countNonZero(top) * 100.0 / top.size().area();
auto percentageMiddle = countNonZero(middle) * 100.0 / middle.size().area();
auto percentageBottom = countNonZero(bottom) * 100.0 / bottom.size().area();
bool topReady = false;
bool middleReady = false;
bool bottomReady = false;
Scalar c1, c2, c3;
if (percentageTop > m_calibrationTopLowerThd && percentageTop < m_calibrationTopUpperThd)
{
topReady = true;
c1 = Scalar(0, 255, 255);
}
else
{
c1 = Scalar(0, 0, 255);
}
if (percentageMiddle > m_calibrationMiddleLowerThd && percentageMiddle < m_calibrationMiddleUppperThd)
{
middleReady = true;
c2 = Scalar(0, 255, 255);
}
else
{
c2 = Scalar(0, 0, 255);
}
if (percentageBottom > m_calibrationBottomLowerThd && percentageBottom < m_calibrationBottomUpperThd)
{
bottomReady = true;
c3 = Scalar(0, 255, 255);
}
else
{
c3 = Scalar(0, 0, 255);
}
bool readyToGo = false;
if (middleReady && topReady && bottomReady)
{
c1 = Scalar(0, 255, 0);
c2 = Scalar(0, 255, 0);
c3 = Scalar(0, 255, 0);
if (!ticking)
{
start = std::chrono::system_clock::now();
ticking = true;
}
if (std::chrono::system_clock::now() - start > std::chrono::seconds(1))
{
readyToGo = true;
}
}
else
{
ticking = false;
}
#if ENABLE_DEBUG_WINDOWS
std::stringstream ss;
ss << percentageTop << ", " << percentageMiddle << ", " << percentageBottom;
putText(frame, ss.str(), Point(0, getTextSize(ss.str(), 0, 0.5, 1, nullptr).height), 0, 0.5, Scalar::all(255), 1);
cv::imshow("Thresholded", thd);
#endif
if (percentageTop >= m_calibrationTopUpperThd && percentageBottom >= m_calibrationBottomUpperThd && percentageMiddle >= m_calibrationMiddleUppperThd)
{
putText(frame, "Move finger away from camera", Point(0, getTextSize("Move finger away from camera", 0, 0.5, 1, nullptr).height), 0, 0.5, Scalar::all(255), 1);
}
else if (percentageTop <= m_calibrationTopLowerThd && percentageBottom <= m_calibrationBottomLowerThd && percentageMiddle <= m_calibrationMiddleLowerThd)
{
putText(frame, "Move finger closer to camera", Point(0, getTextSize("Move finger closer to camera", 0, 0.5, 1, nullptr).height), 0, 0.5, Scalar::all(255), 1);
}
if (readyToGo)
{
Mat framePatchHSV;
cvtColor(frame(fingerWindow), framePatchHSV, CV_BGR2HSV);
cvtColor(frame, frameHSV, CV_BGR2HSV);
MatND hist;
calcHist(&framePatchHSV, 1, m_calibrationData->m_channels, thd, hist, 2, m_calibrationData->m_histSize, (const float**)m_calibrationData->m_ranges, true, false);
m_calibrationData->m_hist = hist;
normalize(m_calibrationData->m_hist, m_calibrationData->m_hist, 0, 255, NORM_MINMAX, -1, Mat());
#if ENABLE_DEBUG_WINDOWS
double maxVal=0;
minMaxLoc(m_calibrationData->m_hist, 0, &maxVal, 0, 0);
int scale = 10;
Mat histImg = Mat::zeros(m_calibrationData->m_sbins*scale, m_calibrationData->m_hbins*10, CV_8UC3);
for( int h = 0; h < m_calibrationData->m_hbins; h++)
{
for( int s = 0; s < m_calibrationData->m_sbins; s++ )
{
float binVal = m_calibrationData->m_hist.at<float>(h, s);
int intensity = cvRound(binVal*255/maxVal);
rectangle( histImg, Point(h*scale, s*scale),
Point( (h+1)*scale - 1, (s+1)*scale - 1),
Scalar::all(intensity),
CV_FILLED );
}
}
imshow("H-S Histogram", histImg);
#endif
m_calibrationData->m_ready = true;
frame(fingerWindow).copyTo(m_calibrationData->m_fingerPatch);
m_calibrationData->m_fingerRect = fingerWindow;
m_currentCandidate.m_windowRect = fingerWindow;
m_currentCandidate.m_fingerPosition = fingerWindow.tl();
return;
}
rectangle(frame, r1.tl() + fingerWindow.tl(), r1.br() + fingerWindow.tl(), c1);
rectangle(frame, r2.tl() + fingerWindow.tl(), r2.br() + fingerWindow.tl(), c2);
rectangle(frame, r3.tl() + fingerWindow.tl(), r3.br() + fingerWindow.tl(), c3);
imshow("Calibration", frame);
}
else
{
int baseline = 0;
putText(frame, "Adjust calibration window, click when ready", Point(0, getTextSize("Adjust calibration window", 0, 0.4, 2, &baseline).height), 0, 0.4, Scalar::all(255), 1);
rectangle(frame, fingerWindow.tl(), fingerWindow.br(), Scalar(0, 0, 255));
imshow("RGB", frame);
}
auto key = cvWaitKey(10);
if (char(key) == 27)
{
break;
}
}
}
capture.release();
}
void FingerTracker::Process(Mat frame)
{
#if ENABLE_DEBUG_WINDOWS
Mat img_display;
frame.copyTo(img_display);
#endif
// Process only Region of Interest i.e. region around current finger position
Rect roi = Rect(
Point(std::max(m_currentCandidate.m_windowRect.tl().x - m_roiSpanX, 0), std::max(m_currentCandidate.m_windowRect.tl().y - m_roiSpanY, 0)),
Point(std::min(m_currentCandidate.m_windowRect.tl().x + m_roiSpanX + m_calibrationData->m_fingerPatch.cols, frame.cols),
std::min(m_currentCandidate.m_windowRect.tl().y + m_roiSpanY + m_calibrationData->m_fingerPatch.rows, frame.rows)));
Mat frameRoi;
frame(roi).copyTo(frameRoi);
//================TEMPLATE MATCHING
int result_cols = frameRoi.cols - m_calibrationData->m_fingerPatch.cols + 1;
int result_rows = frameRoi.rows - m_calibrationData->m_fingerPatch.rows + 1;
assert(result_cols > 0 && result_rows > 0);
Mat scoreMap;
scoreMap.create(result_cols, result_rows, CV_32FC1);
// Compare current frame roi region to known candidate
// Using OpenCV matchTemplate function with correlation coefficient matching method
matchTemplate(frameRoi, m_calibrationData->m_fingerPatch, scoreMap, 3);
//================HISTOGRAM BACK PROJECTION
MatND backProjection;
Mat frameHSV;
cvtColor(frameRoi, frameHSV, CV_BGR2HSV);
calcBackProject(&frameHSV, 1, m_calibrationData->m_channels, m_calibrationData->m_hist, backProjection, (const float**)(m_calibrationData->m_ranges), 1, true);
Mat backProjectionThresholded;
threshold(backProjection, backProjectionThresholded, m_backProjectionThreshold, 255, 0);
erode(backProjectionThresholded, backProjectionThresholded, getStructuringElement(MORPH_RECT, Size(2 * m_erosionSize + 1, 2 * m_erosionSize + 1), Point(m_erosionSize, m_erosionSize)));
dilate(backProjectionThresholded, backProjectionThresholded, getStructuringElement(MORPH_RECT, Size(2 * m_dilationSize + 1, 2 * m_dilationSize + 1), Point(m_dilationSize, m_dilationSize)));
Mat backProjectionThresholdedShifted;
Rect shifted(Rect(m_calibrationData->m_fingerPatch.cols - 1, m_calibrationData->m_fingerPatch.rows - 1, scoreMap.cols, scoreMap.rows));
backProjectionThresholded(shifted).copyTo(backProjectionThresholdedShifted);
Mat maskedOutScoreMap(scoreMap.size(), CV_8U);
scoreMap.copyTo(maskedOutScoreMap, backProjectionThresholdedShifted);
//====================Localizing the best match with minMaxLoc
double minVal; double maxVal; Point minLoc; Point maxLoc;
Point matchLoc;
minMaxLoc(maskedOutScoreMap, &minVal, &maxVal, &minLoc, &maxLoc, Mat());
matchLoc = maxLoc + roi.tl();
m_currentCandidate.m_confidence = static_cast<float>(maxVal);
if (maxVal > m_candidateDetecionConfidenceThreshold)
{
m_currentCandidate.m_found = true;
m_currentCandidate.m_windowRect = Rect(matchLoc, Point(matchLoc.x + m_calibrationData->m_fingerPatch.cols , matchLoc.y + m_calibrationData->m_fingerPatch.rows));
//================Find finger position
Mat fingerWindowThresholded;
backProjectionThresholded(Rect(maxLoc, Point(maxLoc.x + m_calibrationData->m_fingerPatch.cols , maxLoc.y + m_calibrationData->m_fingerPatch.rows))).copyTo(fingerWindowThresholded);
m_currentCandidate.m_fingerPosition = GetFingerTopPosition(fingerWindowThresholded) + matchLoc;
#if ENABLE_DEBUG_WINDOWS
rectangle(img_display, m_currentCandidate.m_windowRect.tl(), m_currentCandidate.m_windowRect.br(), Scalar(255,0,0), 2, 8, 0 );
rectangle(scoreMap, m_currentCandidate.m_windowRect.tl(), m_currentCandidate.m_windowRect.br(), Scalar::all(0), 2, 8, 0 );
rectangle(img_display, m_currentCandidate.m_fingerPosition, m_currentCandidate.m_fingerPosition + Point(5,5), Scalar(255,0,0));
#endif
}
else
{
m_currentCandidate.m_found = false;
}
#if ENABLE_DEBUG_WINDOWS
std::stringstream ss;
ss << maxVal;
putText(img_display, ss.str(), Point(50, 50), 0, 0.5, Scalar(255,255,255), 1);
imshow("Overlays", img_display);
imshow("Results", scoreMap);
imshow("ResultMasked", maskedOutScoreMap);
#endif
}
void getDistortionValues(cv::RNG &rng, const Size2i &inputSize, AugParams *agp) {
// This function just gets the random distortion values without modifying the
// image itself. Useful if we need to reapply the same transformations over
// again (e.g. for all frames of a video or for a corresponding target mask)
// colornoise values
// N.B. if _contrastMax == 100, then _colorNoiseStd will be 0.0
for (int i=0; i<3; i++) {
agp->colornoise[i] = rng.gaussian(_colorNoiseStd);
}
// contrast, brightness, saturation
// N.B. all value ranges tied to _contrastMin and _contrastMax
for (int i=0; i<3; i++) {
agp->cbs[i] = rng.uniform(_contrastMin, _contrastMax) / 100.0f;
}
/**************************
* HORIZONTAL FLIP *
***************************/
agp->flip = _flip && rng(2) != 0 ? true : false;
/**************************
* ROTATION ANGLE *
***************************/
agp->angle = rng.uniform(_rotateMin, _rotateMax);
/**************************
* CROP BOX *
***************************/
float shortSide = std::min(inputSize.height, inputSize.width);
// Special case where we just grab the whole image;
if (_scaleMin == 0) {
agp->cropBox = Rect(Point2i(), inputSize);
return;
}
if (_center) {
agp->cropBox.width = shortSide * _width / (float) _scaleMin;
agp->cropBox.height = shortSide * _height / (float) _scaleMin;
agp->cropBox.x = (inputSize.width - agp->cropBox.width) / 2;
agp->cropBox.y = (inputSize.height - agp->cropBox.height) / 2;
} else {
cv::Size2f oSize = inputSize;
// This is a hack for backward compatibility.
// Valid aspect ratio range ( > 100) will override side scaling behavior
if (_aspectRatio == 0) {
float scaleFactor = rng.uniform(_scaleMin, _scaleMax);
agp->cropBox.width = shortSide * _width / scaleFactor;
agp->cropBox.height = shortSide * _height / scaleFactor;
} else {
float mAR = (float) _aspectRatio / 100.0f;
float nAR = rng.uniform(1.0f / mAR, mAR);
float oAR = oSize.width / oSize.height;
// between minscale pct% to 100% subject to aspect ratio limitation
float maxScale = nAR > oAR ? oAR / nAR : nAR / oAR;
float minScale = std::min((float) _scaleMin / 100.0f, maxScale);
float tgtArea = rng.uniform(minScale, maxScale) * oSize.area();
agp->cropBox.height = sqrt(tgtArea / nAR);
agp->cropBox.width = agp->cropBox.height * nAR;
}
agp->cropBox.x = rng.uniform(0, inputSize.width - agp->cropBox.width);
agp->cropBox.y = rng.uniform(0, inputSize.height - agp->cropBox.height);
}
return;
}
Mat get_L(Mat LR) {
int w = LR.size().width / 2;
int h = LR.size().height;
return Mat(LR, Rect(0, 0, w, h));
}
