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@ -294,7 +294,21 @@ public: |
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Size maxSize=Size() ); |
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/** @overload
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if `outputRejectLevels` is `true` returns `rejectLevels` and `levelWeights` |
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This function allows you to retrieve the final stage decision certainty of classification. |
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For this, one needs to set `outputRejectLevels` on true and provide the `rejectLevels` and `levelWeights` parameter. |
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For each resulting detection, `levelWeights` will then contain the certainty of classification at the final stage. |
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This value can then be used to separate strong from weaker classifications. |
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A code sample on how to use it efficiently can be found below: |
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@code |
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Mat img; |
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vector<double> weights; |
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vector<int> levels; |
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vector<Rect> detections; |
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CascadeClassifier model("/path/to/your/model.xml"); |
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model.detectMultiScale(img, detections, levels, weights, 1.1, 3, 0, Size(), Size(), true); |
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cerr << "Detection " << detections[0] << " with weight " << weights[0] << endl; |
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@endcode |
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*/ |
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CV_WRAP_AS(detectMultiScale3) void detectMultiScale( InputArray image, |
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CV_OUT std::vector<Rect>& objects, |
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