import os
import pickle
import time

import cv2
import face_recognition


def build_data():
    """ Build the face_enc file with data to recognize from """

    knownEncodings = []
    knownNames = []

    members = os.listdir('../profile_pictures')

    #get paths of each file in folder named Images
    #Images here contains my data(folders of various persons)
    for member in members:
        if '.DS_Store' in member:
            continue
        imagePaths = []
        for path in os.listdir(f'../profile_pictures/{member}'):
            if '.jpg' in path:
                imagePaths.append(f'../profile_pictures/{member}/{path}')

        # loop over the image paths
        for imagePath in imagePaths:
            print(imagePath)
            # load the input image and convert it from BGR (OpenCV ordering)
            # to dlib ordering (RGB)
            image = cv2.imread(imagePath)
            rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            #Use Face_recognition to locate faces
            boxes = face_recognition.face_locations(rgb, number_of_times_to_upsample = 2) #,model='hog'
            # compute the facial embedding for the face
            encodings = face_recognition.face_encodings(image, boxes)
            # loop over the encodings
            for encoding in encodings:
                knownEncodings.append(encoding)
                knownNames.append(member)
    #save emcodings along with their names in dictionary data
    data = {"encodings": knownEncodings, "names": knownNames}
    #use pickle to save data into a file for later use
    with open("face_enc", "wb") as f:
        f.write(pickle.dumps(data))
        f.close()

def identify_face(imagePath):
    #find path of xml file containing haarcascade file
    cascPathface = os.path.dirname(
    cv2.__file__) + "/data/haarcascade_frontalface_alt2.xml"
    # load the harcaascade in the cascade classifier
    faceCascade = cv2.CascadeClassifier(cascPathface)
    # load the known faces and embeddings saved in last file
    data = pickle.loads(open('face_enc', "rb").read())
    #Find path to the image you want to detect face and pass it here
    image = cv2.imread(imagePath)
    rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    #convert image to Greyscale for haarcascade
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(gray,
                                        scaleFactor=1.1,
                                        minNeighbors=5,
                                        minSize=(60, 60),
                                        flags=cv2.CASCADE_SCALE_IMAGE)

    # the facial embeddings for face in input
    encodings = face_recognition.face_encodings(rgb)
    names = []
    # loop over the facial embeddings incase
    # we have multiple embeddings for multiple fcaes
    for encoding in encodings:
        #Compare encodings with encodings in data["encodings"]
        #Matches contain array with boolean values and True for the embeddings it matches closely
        #and False for rest
        matches = face_recognition.compare_faces(data["encodings"],
        encoding)
        #set name =unknown if no encoding matches
        name = "Unknown"
        # check to see if we have found a match
        if True in matches:
            #Find positions at which we get True and store them
            matchedIdxs = [i for (i, b) in enumerate(matches) if b]
            counts = {}
            # loop over the matched indexes and maintain a count for
            # each recognized face face
            for i in matchedIdxs:
                #Check the names at respective indexes we stored in matchedIdxs
                name = data["names"][i]
                #increase count for the name we got
                counts[name] = counts.get(name, 0) + 1
                #set name which has highest count
                name = max(counts, key=counts.get)
                print(counts)

            # update the list of names
            names.append(name)
            # loop over the recognized faces
            for ((x, y, w, h), name) in zip(faces, names):
                # rescale the face coordinates
                # draw the predicted face name on the image
                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
                cv2.putText(image, name, (x, y), cv2.FONT_HERSHEY_SIMPLEX,
                0.75, (0, 255, 0), 2)
        cv2.imshow("Frame", image)
        cv2.waitKey(0)


identify_face('/Users/Lasse/Datorgemensamt/Programmeringsprojekt/Facebook/fb-scraper/profile_pictures/millington.jiang/4138068259557849.jpg')