Size maxSize=Size() );
/** @overload
- if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights`
+ This function allows you to retrieve the final stage decision certainty of classification.
+ For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter.
+ For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage.
+ This value can then be used to separate strong from weaker classifications.
+
+ A code sample on how to use it efficiently can be found below:
+ @code
+ Mat img;
+ vector<double> weights;
+ vector<int> levels;
+ vector<Rect> detections;
+ CascadeClassifier model("/path/to/your/model.xml");
+ model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true);
+ cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl;
+ @endcode
*/
CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image,
CV_OUT std::vector<Rect>& objects,