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Merge branch 4.x

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This commit is contained in:
Alexander Smorkalov
2024-01-15 17:23:10 +03:00
parent 788c7252dc dad8af6b17
commit c739117a7c
531 changed files with 20900 additions and 12934 deletions
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modules/objdetect/include/opencv2/objdetect/aruco_detector.hpp
+17 -3
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@@ -33,7 +33,7 @@ struct CV_EXPORTS_W_SIMPLE DetectorParameters {
polygonalApproxAccuracyRate = 0.03;
minCornerDistanceRate = 0.05;
minDistanceToBorder = 3;
minMarkerDistanceRate = 0.05;
minMarkerDistanceRate = 0.125;
cornerRefinementMethod = (int)CORNER_REFINE_NONE;
cornerRefinementWinSize = 5;
relativeCornerRefinmentWinSize = 0.3f;
@@ -100,12 +100,26 @@ struct CV_EXPORTS_W_SIMPLE DetectorParameters {
/// minimum distance of any corner to the image border for detected markers (in pixels) (default 3)
CV_PROP_RW int minDistanceToBorder;
/** @brief minimum mean distance beetween two marker corners to be considered imilar, so that the smaller one is removed.
/** @brief minimum average distance between the corners of the two markers to be grouped (default 0.125).
*
* The rate is relative to the smaller perimeter of the two markers (default 0.05).
* The rate is relative to the smaller perimeter of the two markers.
* Two markers are grouped if average distance between the corners of the two markers is less than
* min(MarkerPerimeter1, MarkerPerimeter2)*minMarkerDistanceRate.
*
* default value is 0.125 because 0.125*MarkerPerimeter = (MarkerPerimeter / 4) * 0.5 = half the side of the marker.
*
* @note default value was changed from 0.05 after 4.8.1 release, because the filtering algorithm has been changed.
* Now a few candidates from the same group can be added to the list of candidates if they are far from each other.
* @sa minGroupDistance.
*/
CV_PROP_RW double minMarkerDistanceRate;
/** @brief minimum average distance between the corners of the two markers in group to add them to the list of candidates
*
* The average distance between the corners of the two markers is calculated relative to its module size (default 0.21).
*/
CV_PROP_RW float minGroupDistance = 0.21f;
/** @brief default value CORNER_REFINE_NONE */
CV_PROP_RW int cornerRefinementMethod;
modules/objdetect/include/opencv2/objdetect/face.hpp
+24 -1
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@@ -71,7 +71,7 @@ public:
*/
CV_WRAP virtual int detect(InputArray image, OutputArray faces) = 0;
/** @brief Creates an instance of this class with given parameters
/** @brief Creates an instance of face detector class with given parameters
*
* @param model the path to the requested model
* @param config the path to the config file for compability, which is not requested for ONNX models
@@ -90,6 +90,29 @@ public:
int top_k = 5000,
int backend_id = 0,
int target_id = 0);
/** @overload
*
* @param framework Name of origin framework
* @param bufferModel A buffer with a content of binary file with weights
* @param bufferConfig A buffer with a content of text file contains network configuration
* @param input_size the size of the input image
* @param score_threshold the threshold to filter out bounding boxes of score smaller than the given value
* @param nms_threshold the threshold to suppress bounding boxes of IoU bigger than the given value
* @param top_k keep top K bboxes before NMS
* @param backend_id the id of backend
* @param target_id the id of target device
*/
CV_WRAP static Ptr<FaceDetectorYN> create(const String& framework,
const std::vector<uchar>& bufferModel,
const std::vector<uchar>& bufferConfig,
const Size& input_size,
float score_threshold = 0.9f,
float nms_threshold = 0.3f,
int top_k = 5000,
int backend_id = 0,
int target_id = 0);
};
/** @brief DNN-based face recognizer