package id.co.gtc.erhacam;

import Config.SomeCodes;
import lombok.NonNull;
import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_objdetect.CascadeClassifier;
import org.tinylog.Logger;

import java.util.ArrayList;
import java.util.List;

public class Detectors {
    public CascadeClassifier frontalfaceDetector;
    private CascadeClassifier eyeDetector;
    private CascadeClassifier profilefaceDetector;

    private  double scaleFactor = 1.05; // revisi 09/05/2025, dari nilai 1.05
    private final int minNeighbors = 3; // revisi 09/05/2025, dari nilai 3
    private final  int flags = 0;
    private Size FaceminSize;
    private Size FacemaxSize;

    public Detectors(){
        LoadFrontalFaceDetector();
        LoadEyeDetector();

        LoadProfileFaceDetector();
    }

    private void LoadFrontalFaceDetector(){
        // revisi 09/05/2025, dari filename = SomeCodes.ExtractResource("/haarcascade_frontalface_default.xml");
        String filename = SomeCodes.ExtractResource("/haarcascade_frontalface_alt.xml");
        if (filename!=null) {
            try{
                frontalfaceDetector = new CascadeClassifier(filename);
            } catch (Exception e){
                Logger.error("Exception on loading FaceDetector : " + e.getMessage());
            }
        } else Logger.error("Unable to extract face detector file");
    }

    private void LoadProfileFaceDetector(){
        String filename = SomeCodes.ExtractResource("/haarcascade_profileface.xml");
        if (filename!=null) {
            try{
                profilefaceDetector = new CascadeClassifier(filename);
            } catch (Exception e){
                Logger.error("Exception on loading ProfileFaceDetector : " + e.getMessage());
            }
        } else Logger.error("Unable to extract profile face detector file");
    }

    private void LoadEyeDetector(){
        String filename = SomeCodes.ExtractResource("/haarcascade_eye.xml");
        if (filename!=null) {
            try{

                eyeDetector = new CascadeClassifier(filename);
            } catch (Exception e){
                Logger.error("Exception on loading EyeDetector : " + e.getMessage());
            }
        } else Logger.error("Unable to extract eye detector file");
    }

    /**
     * Detect if there is a frontal face, containing 2 eyes
     * @param graymat Mat in Gray Scale
     * @return List of Rect if face detected, otherwise empty list
     */
    public @NonNull List<DetectorResult> HaveFrontalFace(Mat graymat){
        List<DetectorResult> result = new ArrayList<>();
        //System.out.println("Detecting frontal from "+ graymat.size().width() + "x" + graymat.size().height());
        RectVector faces = DetectFrontalFace(graymat);
        if (faces!=null && faces.size()>0){
            //System.out.println("faces size = " + faces.size());
            for(Rect face : faces.get()){
                RectVector eyes = DetectEye(graymat, face.width());
                DetectorResult dr = new DetectorResult();
                dr.setFace(face);
                //System.out.println("eyes size = " + eyes.size());
                if (eyes.size()>=2){
                    for(Rect eye : eyes.get()){
                        if (SomeCodes.IsInsideRect(eye, face)) {
                            dr.AddEye(eye);
                        }
                    }
                }
                result.add(dr);
            }
        } //else System.out.println("faces size = 0");
        return result;
    }

    public @NonNull List<DetectorResult> HaveLeft45Face(Mat graymat){
        List<DetectorResult> result = new ArrayList<>();
        RectVector faces = DetectProfileFace(graymat);
        if (faces!=null && faces.size()>0){

            for(Rect face : faces.get()){
                RectVector eyes = DetectEye(graymat, face.width());
                DetectorResult dr = new DetectorResult();
                dr.setFace(face);
                if (eyes.size()>0){
                    for(Rect eye : eyes.get()){
                        if (SomeCodes.IsInsideRect(eye, face)) dr.AddEye(eye);
                    }
                }
                result.add(dr);
            }
        }
        return result;
    }




    public void setScaleFactor(double value){
        if (scaleFactor!=value) scaleFactor = value;
    }

    public void setFaceMinSize(int value){
        if (FaceminSize!=null){
            if (FaceminSize.width()!=value || FaceminSize.height()!=value) {
                FaceminSize = new Size(value, value);
                Logger.info("FaceMinSize changed to : " + FaceminSize.width());
            }
        } else {
            FaceminSize = new Size(value, value);
            Logger.info("FaceMinSize created with value : " + FaceminSize.width());
        }

    }

    public void setFaceMaxSize(int value){
        if (FacemaxSize!=null){
            if (FacemaxSize.width()!=value || FacemaxSize.height()!=value) {
                FacemaxSize = new Size(value, value);
                Logger.info("FaceMaxSize changed to : " + FacemaxSize.width());
            }
        } else {
            FacemaxSize = new Size(value, value);
            Logger.info("FaceMaxSize created with value : " + FacemaxSize.width());
        }

    }


    public RectVector DetectProfileFace(Mat graymat){
        return Detect(graymat, profilefaceDetector, scaleFactor, minNeighbors, flags, FaceminSize, FacemaxSize);
    }

    /**
     * Detect Face from Mat
     * @param graymat Mat in Gray Scale
     * @return RectVector if face detected, otherwise null
     */
    public RectVector DetectFrontalFace(Mat graymat){
        return Detect(graymat, frontalfaceDetector, scaleFactor, minNeighbors, flags, FaceminSize, FacemaxSize);
    }

    /**
     * Detect Eye from Mat
     * If Eye detected, it will return RectVector with size is number of eyes detected
     * @param graymat Mat in Gray Scale
     * @return RectVector if eye detected, otherwise null
     */
    public RectVector DetectEye(Mat graymat, int facewidth){
        //return Detect(graymat, eyeDetector);
        int minwidth = (int)(facewidth*0.2);
        int maxwidth = (int)(facewidth*0.4);
        Size minsize = new Size(minwidth, minwidth);
        Size maxsize = new Size(maxwidth, maxwidth);
        //System.out.println("Detecting Eye with minsize = " + minsize.width() + "x" + minsize.height() + ", maxsize = " + maxsize.width() + "x" + maxsize.height());
        return Detect(graymat, eyeDetector, scaleFactor, minNeighbors, flags, minsize, maxsize);
    }

    @SuppressWarnings("SameParameterValue")
    private RectVector Detect(Mat graymat, CascadeClassifier detector, double scaleFactor, int minNeighbors, int flags, Size minSize, Size maxSize){
        if (detector!=null && !detector.empty()){
            if (graymat!=null && graymat.channels()==1 && !graymat.empty()){
                if (minSize!=null && maxSize!=null){
                    if (minSize.width()< maxSize.width() && minSize.height() < maxSize.height()){
                        if (graymat.cols()> minSize.width() && graymat.rows() > minSize.height()) {
                            try {
                                RectVector detected = new RectVector();
                                // try defaulting minSize and maxSize
                                detector.detectMultiScale(graymat, detected, scaleFactor, minNeighbors, flags, minSize, new Size());

                                return detected;
                            } catch (Exception e) {
                                System.out.println("Detectors Detect Error, Message : " + e.getMessage());
                            }
                        } else System.out.println("graymat is smaller than minSize");

                    } else System.out.println("minSize is larger than maxSize");
                } else System.out.println("minSize or maxSize is null");
            } else System.out.println("graymat is null, not 1 channel, or empty");

        } else System.out.println("detector empty");
        return null;
    }



}