@techstark/opencv-js/dist/src/types/opencv/CascadeClassifier.d.ts

OpenCV JavaScript version for node.js or browser

import type { bool, double, FileNode, InputArray, int, Mat, Ptr, Size } from "./_types";
export declare class CascadeClassifier extends Mat {
    cc: Ptr;
    constructor();
    /**
     * @param filename Name of the file from which the classifier is loaded.
     */
    constructor(filename: String);
    /**
     *   The function is parallelized with the TBB library.
     *
     * (Python) A face detection example using cascade classifiers can be found at
     * opencv_source_code/samples/python/facedetect.py
     *
     * @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 maxSize ==
     * minSize model is evaluated on single scale.
     */
    detectMultiScale(image: InputArray, objects: any, scaleFactor?: double, minNeighbors?: int, flags?: int, minSize?: Size, maxSize?: Size): InputArray;
    /**
     *   This is an overloaded member function, provided for convenience. It differs from the above
     * function only in what argument(s) it accepts.
     *
     * @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 maxSize ==
     * minSize model is evaluated on single scale.
     */
    detectMultiScale(image: InputArray, objects: any, numDetections: any, scaleFactor?: double, minNeighbors?: int, flags?: int, minSize?: Size, maxSize?: Size): InputArray;
    /**
     *   This is an overloaded member function, provided for convenience. It differs from the above
     * function only in what argument(s) it accepts. 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:
     *
     *   ```cpp
     *   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;
     *   ```
     */
    detectMultiScale(image: InputArray, objects: any, rejectLevels: any, levelWeights: any, scaleFactor?: double, minNeighbors?: int, flags?: int, minSize?: Size, maxSize?: Size, outputRejectLevels?: bool): InputArray;
    empty(): bool;
    getFeatureType(): int;
    getMaskGenerator(): Ptr;
    getOldCascade(): any;
    getOriginalWindowSize(): Size;
    isOldFormatCascade(): bool;
    /**
     * @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.
     */
    load(filename: String): String;
    /**
     *   The file may contain a new cascade classifier (trained traincascade application) only.
     */
    read(node: FileNode): FileNode;
    setMaskGenerator(maskGenerator: Ptr): Ptr;
    static convert(oldcascade: String, newcascade: String): String;
}