Package org.opencv.objdetect

Class CascadeClassifier

  • public class CascadeClassifier
extends java.lang.Object
    Cascade classifier class for object detection.

    • Field Detail

      • nativeObj

        protected final long nativeObj

    • Constructor Detail

      • CascadeClassifier

        protected CascadeClassifier​(long addr)

      • CascadeClassifier

        public CascadeClassifier()

      • CascadeClassifier

        public CascadeClassifier​(java.lang.String filename)
        Loads a classifier from a file.
        Parameters:
        filename - Name of the file from which the classifier is loaded.

    • Method Detail

      • getNativeObjAddr

        public long getNativeObjAddr()

      • empty

        public boolean empty()
        Checks whether the classifier has been loaded.
        Returns:
        automatically generated

      • load

        public boolean load​(java.lang.String filename)
        Loads a classifier from a file.
        Parameters:
        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.
        Returns:
        automatically generated

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects,
                             double scaleFactor,
                             int minNeighbors,
                             int flags,
                             Size minSize,
                             Size maxSize)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
        minSize - Minimum possible object size. Objects smaller than that are ignored.
        maxSize - Maximum possible object size. Objects larger than that are ignored. If maxSize == minSize model is evaluated on single scale. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects,
                             double scaleFactor,
                             int minNeighbors,
                             int flags,
                             Size minSize)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
        minSize - Minimum possible object size. Objects smaller than that are ignored. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects,
                             double scaleFactor,
                             int minNeighbors,
                             int flags)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects,
                             double scaleFactor,
                             int minNeighbors)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it. cvHaarDetectObjects. It is not used for a new cascade. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects,
                             double scaleFactor)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale

        public void detectMultiScale​(Mat image,
                             MatOfRect objects)
        Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        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. The function is parallelized with the TBB library. Note:
        • (Python) A face detection example using cascade classifiers can be found at opencv_source_code/samples/python/facedetect.py

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections,
                              double scaleFactor,
                              int minNeighbors,
                              int flags,
                              Size minSize,
                              Size maxSize)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
        minSize - Minimum possible object size. Objects smaller than that are ignored.
        maxSize - Maximum possible object size. Objects larger than that are ignored. If maxSize == minSize model is evaluated on single scale.

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections,
                              double scaleFactor,
                              int minNeighbors,
                              int flags,
                              Size minSize)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.
        minSize - Minimum possible object size. Objects smaller than that are ignored.

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections,
                              double scaleFactor,
                              int minNeighbors,
                              int flags)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it.
        flags - Parameter with the same meaning for an old cascade as in the function cvHaarDetectObjects. It is not used for a new cascade.

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections,
                              double scaleFactor,
                              int minNeighbors)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.
        scaleFactor - Parameter specifying how much the image size is reduced at each image scale.
        minNeighbors - Parameter specifying how many neighbors each candidate rectangle should have to retain it. cvHaarDetectObjects. It is not used for a new cascade.

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections,
                              double scaleFactor)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.
        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.

      • detectMultiScale2

        public void detectMultiScale2​(Mat image,
                              MatOfRect objects,
                              MatOfInt numDetections)
        Parameters:
        image - Matrix of the type CV_8U containing an image where objects are detected.
        objects - Vector of rectangles where each rectangle contains the detected object, the rectangles may be partially outside the original image.
        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.

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor,
                              int minNeighbors,
                              int flags,
                              Size minSize,
                              Size maxSize,
                              boolean outputRejectLevels)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated
        minNeighbors - automatically generated
        flags - automatically generated
        minSize - automatically generated
        maxSize - automatically generated
        outputRejectLevels - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor,
                              int minNeighbors,
                              int flags,
                              Size minSize,
                              Size maxSize)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated
        minNeighbors - automatically generated
        flags - automatically generated
        minSize - automatically generated
        maxSize - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor,
                              int minNeighbors,
                              int flags,
                              Size minSize)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated
        minNeighbors - automatically generated
        flags - automatically generated
        minSize - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor,
                              int minNeighbors,
                              int flags)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated
        minNeighbors - automatically generated
        flags - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor,
                              int minNeighbors)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated
        minNeighbors - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble levelWeights,
                              double scaleFactor)
        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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated
        scaleFactor - automatically generated

      • detectMultiScale3

        public void detectMultiScale3​(Mat image,
                              MatOfRect objects,
                              MatOfInt rejectLevels,
                              MatOfDouble 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: 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;
        Parameters:
        image - automatically generated
        objects - automatically generated
        rejectLevels - automatically generated
        levelWeights - automatically generated

      • isOldFormatCascade

        public boolean isOldFormatCascade()

      • getOriginalWindowSize

        public Size getOriginalWindowSize()

      • getFeatureType

        public int getFeatureType()

      • convert

        public static boolean convert​(java.lang.String oldcascade,
                              java.lang.String newcascade)

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class java.lang.Object
        Throws:
        java.lang.Throwable