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Merge pull request #29220 from omrope79:doc_optimizations_v4

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[FOLLOW UP] : Documentation optimizations for the new Sphinx structure #29220

### Pull Request Readiness Checklist

This PR serves as a follow-up to the new documentation system introduced in [#29206](https://github.com/opencv/opencv/pull/29206)
Co-authored by: @abhishek-gola @kirtijindal14 @Akansha-977 @Prasadayus @varun-jaiswal17

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:
omrope79
2026-06-05 16:48:27 +05:30
committed by GitHub
parent c9e7878a1d
commit 04aee009aa
43 changed files with 3468 additions and 650 deletions
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samples/dnn/object_detection.cpp
+59 -31
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@@ -77,7 +77,7 @@ string modelName, framework;
static void preprocess(const Mat& frame, Net& net, Size inpSize);
static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing);
static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing);
static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
@@ -334,7 +334,7 @@ int main(int argc, char** argv)
classIds.clear();
confidences.clear();
boxes.clear();
postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
postprocess(frame, outs, net, classIds, confidences, boxes, postprocessing);
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
@@ -383,7 +383,7 @@ int main(int argc, char** argv)
confidences.clear();
boxes.clear();
postprocess(frame, outs, net, backend, classIds, confidences, boxes, postprocessing);
postprocess(frame, outs, net, classIds, confidences, boxes, postprocessing);
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
@@ -404,10 +404,7 @@ void preprocess(const Mat& frame, Net& net, Size inpSize)
// Prepare the blob from the image
Mat inp;
if(framework == "weights"){ // checks whether model is darknet
blobFromImage(frame, inp, scale, size, meanv, swapRB, false, CV_32F);
}
else{
{
//![preprocess_call]
Image2BlobParams imgParams(
Scalar::all(scale),
@@ -513,7 +510,7 @@ void yoloPostProcessing(
}
}
void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing)
void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string postprocessing)
{
static vector<int> outLayers = net.getUnconnectedOutLayers();
if (postprocessing == "ssd")
@@ -552,35 +549,66 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
}
}
}
else if (postprocessing == "darknet")
else if (postprocessing == "yolov4")
{
for (size_t i = 0; i < outs.size(); ++i)
// boxes[b,N,1,4]+confs[b,N,classes] (normalized) or boxes[b,N,4]+scores[b,N]+classIdx[b,N] (model-px)
bool isBoxConfsFormat = (outs.size() == 2 && outs[0].dims == 4 && outs[0].size[outs[0].dims - 1] == 4);
bool isBoxScoresIdxFormat = (outs.size() == 3 && outs[0].dims == 3 && outs[0].size[2] == 4);
if (isBoxScoresIdxFormat)
{
// Network produces output blob with a shape NxC where N is a number of
// detected objects and C is a number of classes + 4 where the first 4
// numbers are [center_x, center_y, width, height]
float* data = (float*)outs[i].data;
for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
int N = outs[0].size[1];
const float* boxesPtr = outs[0].ptr<float>(0);
const float* scoresPtr = outs[1].ptr<float>(0);
const float* classIdxPtr = outs[2].ptr<float>(0);
for (int j = 0; j < N; ++j)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;
double confidence;
minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
if (confidence > confThreshold)
float score = scoresPtr[j];
if (score > confThreshold)
{
int centerX = (int)(data[0] * frame.cols);
int centerY = (int)(data[1] * frame.rows);
int width = (int)(data[2] * frame.cols);
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint.x);
confidences.push_back((float)confidence);
boxes.push_back(Rect(left, top, width, height));
float x1 = boxesPtr[j * 4 + 0];
float y1 = boxesPtr[j * 4 + 1];
float x2 = boxesPtr[j * 4 + 2];
float y2 = boxesPtr[j * 4 + 3];
boxes.push_back(Rect((int)x1, (int)y1, (int)(x2 - x1), (int)(y2 - y1)));
confidences.push_back(score);
classIds.push_back((int)classIdxPtr[j]);
}
}
}
else if (isBoxConfsFormat)
{
Mat boxesMat = outs[0];
Mat confsMat = outs[1];
int numBoxes = (int)(boxesMat.total() / 4);
boxesMat = boxesMat.reshape(1, numBoxes);
confsMat = confsMat.reshape(1, numBoxes);
for (int j = 0; j < numBoxes; ++j)
{
Point maxLoc;
double confidence;
minMaxLoc(confsMat.row(j), 0, &confidence, 0, &maxLoc);
if (confidence > confThreshold)
{
const float* box = boxesMat.ptr<float>(j);
boxes.push_back(Rect((int)(box[0] * inpWidth), (int)(box[1] * inpHeight),
(int)((box[2] - box[0]) * inpWidth), (int)((box[3] - box[1]) * inpHeight)));
confidences.push_back((float)confidence);
classIds.push_back(maxLoc.x);
}
}
}
else
{
cout << "Unsupported YOLO ONNX output format" << endl;
exit(-1);
}
Image2BlobParams paramNet;
paramNet.scalefactor = Scalar::all(scale);
paramNet.size = Size(inpWidth, inpHeight);
paramNet.mean = meanv;
paramNet.swapRB = swapRB;
paramNet.paddingmode = paddingMode;
paramNet.blobRectsToImageRects(boxes, boxes, frame.size());
}
else if (postprocessing == "yolov8" || postprocessing == "yolov5")
{
@@ -620,7 +648,7 @@ void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vec
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for other backends we need NMS in sample
// or NMS is required if the number of outputs > 1
if (outLayers.size() > 1 || (postprocessing == "darknet" && backend != DNN_BACKEND_OPENCV))
if (outLayers.size() > 1)
{
map<int, vector<size_t> > class2indices;
for (size_t i = 0; i < classIds.size(); i++)