//
// This file is auto-generated. Please don't modify it!
//
package org.opencv.objdetect;
import org.opencv.core.Mat;
import org.opencv.core.MatOfDouble;
import org.opencv.core.MatOfInt;
import org.opencv.core.MatOfRect;
import org.opencv.core.Size;
// C++: class CascadeClassifier
/**
* Cascade classifier class for object detection.
*/
public class CascadeClassifier {
protected final long nativeObj;
protected CascadeClassifier(long addr) { nativeObj = addr; }
public long getNativeObjAddr() { return nativeObj; }
// internal usage only
public static CascadeClassifier __fromPtr__(long addr) { return new CascadeClassifier(addr); }
//
// C++: cv::CascadeClassifier::CascadeClassifier()
//
public CascadeClassifier() {
nativeObj = CascadeClassifier_0();
}
//
// C++: cv::CascadeClassifier::CascadeClassifier(String filename)
//
/**
* Loads a classifier from a file.
*
* @param filename Name of the file from which the classifier is loaded.
*/
public CascadeClassifier(String filename) {
nativeObj = CascadeClassifier_1(filename);
}
//
// C++: bool cv::CascadeClassifier::empty()
//
/**
* Checks whether the classifier has been loaded.
* @return automatically generated
*/
public boolean empty() {
return empty_0(nativeObj);
}
//
// C++: bool cv::CascadeClassifier::load(String filename)
//
/**
* Loads a classifier from a file.
*
* @param filename Name of the file from which the classifier is loaded. The file may contain an old
* HAAR classifier trained by the haartraining application or a new cascade classifier trained by the
* traincascade application.
* @return automatically generated
*/
public boolean load(String filename) {
return load_0(nativeObj, filename);
}
//
// C++: bool cv::CascadeClassifier::read(FileNode node)
//
// Unknown type 'FileNode' (I), skipping the function
//
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
//
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
* @param minSize Minimum possible object size. Objects smaller than that are ignored.
* @param maxSize Maximum possible object size. Objects larger than that are ignored. If {@code maxSize == minSize} model is evaluated on single scale.
*/
public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize) {
Mat objects_mat = objects;
detectMultiScale_0(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height);
}
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
* @param minSize Minimum possible object size. Objects smaller than that are ignored.
*/
public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags, Size minSize) {
Mat objects_mat = objects;
detectMultiScale_1(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height);
}
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors, int flags) {
Mat objects_mat = objects;
detectMultiScale_2(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor, minNeighbors, flags);
}
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor, int minNeighbors) {
Mat objects_mat = objects;
detectMultiScale_3(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor, minNeighbors);
}
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale(Mat image, MatOfRect objects, double scaleFactor) {
Mat objects_mat = objects;
detectMultiScale_4(nativeObj, image.nativeObj, objects_mat.nativeObj, scaleFactor);
}
/**
* Detects objects of different sizes in the input image. The detected objects are returned as a list
* of rectangles.
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale(Mat image, MatOfRect objects) {
Mat objects_mat = objects;
detectMultiScale_5(nativeObj, image.nativeObj, objects_mat.nativeObj);
}
//
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& numDetections, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
//
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
* @param minSize Minimum possible object size. Objects smaller than that are ignored.
* @param maxSize Maximum possible object size. Objects larger than that are ignored. If {@code maxSize == minSize} model is evaluated on single scale.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_0(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height);
}
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
* @param minSize Minimum possible object size. Objects smaller than that are ignored.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags, Size minSize) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_1(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height);
}
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* @param flags Parameter with the same meaning for an old cascade as in the function
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors, int flags) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_2(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors, flags);
}
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* @param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor, int minNeighbors) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_3(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor, minNeighbors);
}
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* @param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections, double scaleFactor) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_4(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj, scaleFactor);
}
/**
*
* @param image Matrix of the type CV_8U containing an image where objects are detected.
* @param objects Vector of rectangles where each rectangle contains the detected object, the
* rectangles may be partially outside the original image.
* @param numDetections Vector of detection numbers for the corresponding objects. An object's number
* of detections is the number of neighboring positively classified rectangles that were joined
* together to form the object.
* to retain it.
* cvHaarDetectObjects. It is not used for a new cascade.
*/
public void detectMultiScale2(Mat image, MatOfRect objects, MatOfInt numDetections) {
Mat objects_mat = objects;
Mat numDetections_mat = numDetections;
detectMultiScale2_5(nativeObj, image.nativeObj, objects_mat.nativeObj, numDetections_mat.nativeObj);
}
//
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& rejectLevels, vector_double& levelWeights, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size(), bool outputRejectLevels = false)
//
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
* @param minNeighbors automatically generated
* @param flags automatically generated
* @param minSize automatically generated
* @param maxSize automatically generated
* @param outputRejectLevels automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize, boolean outputRejectLevels) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_0(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height, outputRejectLevels);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
* @param minNeighbors automatically generated
* @param flags automatically generated
* @param minSize automatically generated
* @param maxSize automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_1(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height, maxSize.width, maxSize.height);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
* @param minNeighbors automatically generated
* @param flags automatically generated
* @param minSize automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags, Size minSize) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_2(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags, minSize.width, minSize.height);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
* @param minNeighbors automatically generated
* @param flags automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors, int flags) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_3(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors, flags);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
* @param minNeighbors automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor, int minNeighbors) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_4(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor, minNeighbors);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
* @param scaleFactor automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights, double scaleFactor) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_5(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj, scaleFactor);
}
/**
*
* This function allows you to retrieve the final stage decision certainty of classification.
* For this, one needs to set {@code outputRejectLevels} on true and provide the {@code rejectLevels} and {@code levelWeights} parameter.
* For each resulting detection, {@code 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;
* </code>
* @param image automatically generated
* @param objects automatically generated
* @param rejectLevels automatically generated
* @param levelWeights automatically generated
*/
public void detectMultiScale3(Mat image, MatOfRect objects, MatOfInt rejectLevels, MatOfDouble levelWeights) {
Mat objects_mat = objects;
Mat rejectLevels_mat = rejectLevels;
Mat levelWeights_mat = levelWeights;
detectMultiScale3_6(nativeObj, image.nativeObj, objects_mat.nativeObj, rejectLevels_mat.nativeObj, levelWeights_mat.nativeObj);
}
//
// C++: bool cv::CascadeClassifier::isOldFormatCascade()
//
public boolean isOldFormatCascade() {
return isOldFormatCascade_0(nativeObj);
}
//
// C++: Size cv::CascadeClassifier::getOriginalWindowSize()
//
public Size getOriginalWindowSize() {
return new Size(getOriginalWindowSize_0(nativeObj));
}
//
// C++: int cv::CascadeClassifier::getFeatureType()
//
public int getFeatureType() {
return getFeatureType_0(nativeObj);
}
//
// C++: static bool cv::CascadeClassifier::convert(String oldcascade, String newcascade)
//
public static boolean convert(String oldcascade, String newcascade) {
return convert_0(oldcascade, newcascade);
}
@Override
protected void finalize() throws Throwable {
delete(nativeObj);
}
// C++: cv::CascadeClassifier::CascadeClassifier()
private static native long CascadeClassifier_0();
// C++: cv::CascadeClassifier::CascadeClassifier(String filename)
private static native long CascadeClassifier_1(String filename);
// C++: bool cv::CascadeClassifier::empty()
private static native boolean empty_0(long nativeObj);
// C++: bool cv::CascadeClassifier::load(String filename)
private static native boolean load_0(long nativeObj, String filename);
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
private static native void detectMultiScale_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height);
private static native void detectMultiScale_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height);
private static native void detectMultiScale_2(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor, int minNeighbors, int flags);
private static native void detectMultiScale_3(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor, int minNeighbors);
private static native void detectMultiScale_4(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, double scaleFactor);
private static native void detectMultiScale_5(long nativeObj, long image_nativeObj, long objects_mat_nativeObj);
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& numDetections, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size())
private static native void detectMultiScale2_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height);
private static native void detectMultiScale2_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height);
private static native void detectMultiScale2_2(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor, int minNeighbors, int flags);
private static native void detectMultiScale2_3(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor, int minNeighbors);
private static native void detectMultiScale2_4(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj, double scaleFactor);
private static native void detectMultiScale2_5(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long numDetections_mat_nativeObj);
// C++: void cv::CascadeClassifier::detectMultiScale(Mat image, vector_Rect& objects, vector_int& rejectLevels, vector_double& levelWeights, double scaleFactor = 1.1, int minNeighbors = 3, int flags = 0, Size minSize = Size(), Size maxSize = Size(), bool outputRejectLevels = false)
private static native void detectMultiScale3_0(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height, boolean outputRejectLevels);
private static native void detectMultiScale3_1(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height, double maxSize_width, double maxSize_height);
private static native void detectMultiScale3_2(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors, int flags, double minSize_width, double minSize_height);
private static native void detectMultiScale3_3(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors, int flags);
private static native void detectMultiScale3_4(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor, int minNeighbors);
private static native void detectMultiScale3_5(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj, double scaleFactor);
private static native void detectMultiScale3_6(long nativeObj, long image_nativeObj, long objects_mat_nativeObj, long rejectLevels_mat_nativeObj, long levelWeights_mat_nativeObj);
// C++: bool cv::CascadeClassifier::isOldFormatCascade()
private static native boolean isOldFormatCascade_0(long nativeObj);
// C++: Size cv::CascadeClassifier::getOriginalWindowSize()
private static native double[] getOriginalWindowSize_0(long nativeObj);
// C++: int cv::CascadeClassifier::getFeatureType()
private static native int getFeatureType_0(long nativeObj);
// C++: static bool cv::CascadeClassifier::convert(String oldcascade, String newcascade)
private static native boolean convert_0(String oldcascade, String newcascade);
// native support for java finalize()
private static native void delete(long nativeObj);
}
