import cv2
from skimage import feature
import matplotlib.pyplot as plt
import numpy as np


class LBPImageEncoder:
    def __init__(self, numPoints, radius):
        self.numPoints = numPoints
        self.radius = radius

    def describe(self, image, eps=1e-7):
        lbp = feature.local_binary_pattern(image, self.numPoints, self.radius)
        hist, _ = np.histogram(lbp.ravel(), bins=np.arange(0, self.numPoints + 3), range=(0, self.numPoints + 2))
        hist = hist.astype("float")
        hist /= (hist.sum() + eps)
        return hist

    def face_detection(self, image):
        cascadePath = "haarcascade_frontalface_default.xml"
        detector = cv2.CascadeClassifier()
        detector.load(cascadePath)

        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        rects = detector.detectMultiScale(gray, scaleFactor=1.05, minNeighbors=10, minSize=(30, 30),flags=cv2.CASCADE_SCALE_IMAGE)
        return rects

    def preprocess_img(self, image):
        img = np.array(image)
        rects = self.face_detection(img)
        feature_vector = np.zeros((self.numPoints + 2) * 3)

        for (x, y, w, h) in rects:
            face = img[y:y + h, x:x + w]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)

            lbp = self.describe(face)
            feature_vector[(self.numPoints + 2) * 0: (self.numPoints + 2) * 1] += lbp

            # Process green channel
            lbp = self.describe(face[:, :, 1])
            feature_vector[(self.numPoints + 2) * 1: (self.numPoints + 2) * 2] += lbp

            # Process blue channel
            lbp = self.describe(face[:, :, 2])
            feature_vector[(self.numPoints + 2) * 2: (self.numPoints + 2) * 3] += lbp

        feature_vector /= len(rects)

        return feature_vector