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digiKam Developer Documentation
Professional Photo Management with the Power of Open Source
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Public Member Functions | |
DNNFaceDetectorBase (float scale, const cv::Scalar &val, const cv::Size &inputImgSize) | |
virtual void | detectFaces (const cv::Mat &inputImage, const cv::Size &paddedSize, std::vector< cv::Rect > &detectedBboxes)=0 |
cv::Size | nnInputSizeRequired () const |
virtual void | setFaceDetectionSize (FaceScanSettings::FaceDetectionSize faceSize) |
Protected Member Functions | |
void | correctBbox (cv::Rect &bbox, const cv::Size &paddedSize) const |
void | selectBbox (const cv::Size &paddedSize, float confidence, int left, int right, int top, int bottom, std::vector< float > &goodConfidences, std::vector< cv::Rect > &goodBoxes, std::vector< float > &doubtConfidences, std::vector< cv::Rect > &doubtBoxes) const |
Protected Attributes | |
cv::Size | inputImageSize = cv::Size(300, 300) |
cv::Scalar | meanValToSubtract = cv::Scalar(0.0, 0.0, 0.0) |
DNNModelBase * | model = nullptr |
float | scaleFactor = 1.0F |
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protected |
Classify bounding boxes detected. Good bounding boxes are defined as boxes that reside within the non-padded zone or those that are out only for min of (10% of padded range, 10% of bbox dim).
Bad bounding boxes are defined as boxes that have at maximum 25% of each dimension out of non-padded zone.