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Merge pull request #25771 from fengyuentau:vittrack_black_input

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video: fix vittrack in the case where crop size grows until out-of-memory when the input is black #25771

Fixes https://github.com/opencv/opencv/issues/25760

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Yuantao Feng
2024-06-18 17:48:28 +08:00
committed by GitHub
parent 0fac5d52bc
commit e3884a9ea8
4 changed files with 57 additions and 50 deletions
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modules/video/src/tracking/tracker_vit.cpp
+34 -33
View File
@@ -24,8 +24,8 @@ TrackerVit::~TrackerVit()
TrackerVit::Params::Params()
{
net = "vitTracker.onnx";
meanvalue = Scalar{0.485, 0.456, 0.406};
stdvalue = Scalar{0.229, 0.224, 0.225};
meanvalue = Scalar{0.485, 0.456, 0.406}; // normalized mean (already divided by 255)
stdvalue = Scalar{0.229, 0.224, 0.225}; // normalized std (already divided by 255)
#ifdef HAVE_OPENCV_DNN
backend = dnn::DNN_BACKEND_DEFAULT;
target = dnn::DNN_TARGET_CPU;
@@ -33,6 +33,7 @@ TrackerVit::Params::Params()
backend = -1; // invalid value
target = -1; // invalid value
#endif
tracking_score_threshold = 0.20f; // safe threshold to filter out black frames
}
#ifdef HAVE_OPENCV_DNN
@@ -48,6 +49,9 @@ public:
net.setPreferableBackend(params.backend);
net.setPreferableTarget(params.target);
i2bp.mean = params.meanvalue * 255.0;
i2bp.scalefactor = (1.0 / params.stdvalue) * (1 / 255.0);
}
void init(InputArray image, const Rect& boundingBox) CV_OVERRIDE;
@@ -58,6 +62,7 @@ public:
float tracking_score;
TrackerVit::Params params;
dnn::Image2BlobParams i2bp;
protected:
@@ -69,10 +74,9 @@ protected:
Mat hanningWindow;
dnn::Net net;
Mat image;
};
static void crop_image(const Mat& src, Mat& dst, Rect box, int factor)
static int crop_image(const Mat& src, Mat& dst, Rect box, int factor)
{
int x = box.x, y = box.y, w = box.width, h = box.height;
int crop_sz = cvCeil(sqrt(w * h) * factor);
@@ -90,21 +94,16 @@ static void crop_image(const Mat& src, Mat& dst, Rect box, int factor)
Rect roi(x1 + x1_pad, y1 + y1_pad, x2 - x2_pad - x1 - x1_pad, y2 - y2_pad - y1 - y1_pad);
Mat im_crop = src(roi);
copyMakeBorder(im_crop, dst, y1_pad, y2_pad, x1_pad, x2_pad, BORDER_CONSTANT);
return crop_sz;
}
void TrackerVitImpl::preprocess(const Mat& src, Mat& dst, Size size)
{
Mat mean = Mat(size, CV_32FC3, params.meanvalue);
Mat std = Mat(size, CV_32FC3, params.stdvalue);
mean = dnn::blobFromImage(mean, 1.0, Size(), Scalar(), false);
std = dnn::blobFromImage(std, 1.0, Size(), Scalar(), false);
Mat img;
resize(src, img, size);
dst = dnn::blobFromImage(img, 1.0, Size(), Scalar(), false);
dst /= 255;
dst = (dst - mean) / std;
dst = dnn::blobFromImageWithParams(img, i2bp);
}
static Mat hann1d(int sz, bool centered = true) {
@@ -141,22 +140,21 @@ static Mat hann2d(Size size, bool centered = true) {
return hanningWindow;
}
static Rect returnfromcrop(float x, float y, float w, float h, Rect res_Last)
static void updateLastRect(float cx, float cy, float w, float h, int crop_size, Rect &rect_last)
{
int cropwindowwh = 4 * cvFloor(sqrt(res_Last.width * res_Last.height));
int x0 = res_Last.x + (res_Last.width - cropwindowwh) / 2;
int y0 = res_Last.y + (res_Last.height - cropwindowwh) / 2;
Rect finalres;
finalres.x = cvFloor(x * cropwindowwh + x0);
finalres.y = cvFloor(y * cropwindowwh + y0);
finalres.width = cvFloor(w * cropwindowwh);
finalres.height = cvFloor(h * cropwindowwh);
return finalres;
int x0 = rect_last.x + (rect_last.width - crop_size) / 2;
int y0 = rect_last.y + (rect_last.height - crop_size) / 2;
float x1 = cx - w / 2, y1 = cy - h / 2;
rect_last.x = cvFloor(x1 * crop_size + x0);
rect_last.y = cvFloor(y1 * crop_size + y0);
rect_last.width = cvFloor(w * crop_size);
rect_last.height = cvFloor(h * crop_size);
}
void TrackerVitImpl::init(InputArray image_, const Rect &boundingBox_)
{
image = image_.getMat().clone();
Mat image = image_.getMat();
Mat crop;
crop_image(image, crop, boundingBox_, 2);
Mat blob;
@@ -169,9 +167,9 @@ void TrackerVitImpl::init(InputArray image_, const Rect &boundingBox_)
bool TrackerVitImpl::update(InputArray image_, Rect &boundingBoxRes)
{
image = image_.getMat().clone();
Mat image = image_.getMat();
Mat crop;
crop_image(image, crop, rect_last, 4);
int crop_size = crop_image(image, crop, rect_last, 4); // crop: [crop_size, crop_size]
Mat blob;
preprocess(crop, blob, searchSize);
net.setInput(blob, "search");
@@ -191,15 +189,18 @@ bool TrackerVitImpl::update(InputArray image_, Rect &boundingBoxRes)
minMaxLoc(conf_map, nullptr, &maxVal, nullptr, &maxLoc);
tracking_score = static_cast<float>(maxVal);
float cx = (maxLoc.x + offset_map.at<float>(0, maxLoc.y, maxLoc.x)) / 16;
float cy = (maxLoc.y + offset_map.at<float>(1, maxLoc.y, maxLoc.x)) / 16;
float w = size_map.at<float>(0, maxLoc.y, maxLoc.x);
float h = size_map.at<float>(1, maxLoc.y, maxLoc.x);
if (tracking_score >= params.tracking_score_threshold) {
float cx = (maxLoc.x + offset_map.at<float>(0, maxLoc.y, maxLoc.x)) / 16;
float cy = (maxLoc.y + offset_map.at<float>(1, maxLoc.y, maxLoc.x)) / 16;
float w = size_map.at<float>(0, maxLoc.y, maxLoc.x);
float h = size_map.at<float>(1, maxLoc.y, maxLoc.x);
Rect finalres = returnfromcrop(cx - w / 2, cy - h / 2, w, h, rect_last);
rect_last = finalres;
boundingBoxRes = finalres;
return true;
updateLastRect(cx, cy, w, h, crop_size, rect_last);
boundingBoxRes = rect_last;
return true;
} else {
return false;
}
}
float TrackerVitImpl::getTrackingScore()