import cv2, glob, random, math, numpy as np, dlib, itertools
from sklearn.svm import SVC
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn import linear_model 
from sklearn.neighbors import KNeighborsClassifier 

emotions = ["Angry", "Disgust", "Fear", "Happy", "Neutral", "Sad", "Surprise"]
#  "Happy", "Neutral", "Sad"] #Emotion list
# hist 均衡化
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("./shape_predictor_68_face_landmarks.dat") 

clf = SVC(kernel='linear', probability=True, tol=1e-3)  #SVM

# clf = RandomForestClassifier(min_samples_leaf=20)   #随机森林

# knn = KNeighborsClassifier(n_neighbors=7)      #KNN
# clf = linear_model.LogisticRegression(C=1e5)    #逻辑回归


# n_estimators = 10
# clf = OneVsRestClassifier(BaggingClassifier(SVC(kernel='linear', probability=True, class_weight='balanced'), max_samples=1.0 / n_estimators, n_estimators=n_estimators),n_jobs=-1)


def get_files(emotion):
    print("./data/%s/*" % emotion)
    files = glob.glob("./data/%s/*" % emotion)
    print(len(files))
   
    random.shuffle(files)
    training = files[:int(len(files)*0.8)]  # get first 80% of file list
    prediction = files[-int(len(files)*0.2):]  # get last 20% of file list
   
    return training, prediction


def get_landmarks(image):
    detections = detector(image, 1)
    for k,d in enumerate(detections):  # For all detected face instances individually
        shape = predictor(image, d)  # Draw Facial Landmarks with the predictor class
        xlist = []
        ylist = []
        for i in range(1, 68):  # Store X and Y coordinates in two lists
            xlist.append(float(shape.part(i).x))
            ylist.append(float(shape.part(i).y))
            
        xmean = np.mean(xlist)  # Get the mean of both axes to determine centre of gravity
        ymean = np.mean(ylist)
        xcentral = [(x-xmean) for x in xlist]  # get distance between each point and the central point in both axes
        ycentral = [(y-ymean) for y in ylist]

        if xlist[26] == xlist[29]:
            # If x-coordinates of the set are the same, the angle is 0, catch to prevent 'divide by 0' error in function
            anglenose = 0
        else:
            anglenose = int(math.atan((ylist[26]-ylist[29])/(xlist[26]-xlist[29]))*180/math.pi)

        if anglenose < 0:
            anglenose += 90
        else:
            anglenose -= 90

        landmarks_vectorised = []
        for x, y, w, z in zip(xcentral, ycentral, xlist, ylist):
            landmarks_vectorised.append(x)
            landmarks_vectorised.append(y)
            meannp = np.asarray((ymean,xmean))
            coornp = np.asarray((z,w))
            dist = np.linalg.norm(coornp-meannp)
            anglerelative = (math.atan((z-ymean)/(w-xmean))*180/math.pi) - anglenose
            landmarks_vectorised.append(dist)
            landmarks_vectorised.append(anglerelative)

    if len(detections) < 1: 
        landmarks_vectorised = "error"
    return landmarks_vectorised

def make_sets():
    training_data = []
    training_labels = []
    prediction_data = []
    prediction_labels = []
    for emotion in emotions:
        training, prediction = get_files(emotion)
        for item in training:
            image = cv2.imread(item)  # open image
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # convert to grayscale
            clahe_image = clahe.apply(gray)
            landmarks_vectorised = get_landmarks(clahe_image)
            if landmarks_vectorised == "error":
                pass
            else:
                training_data.append(landmarks_vectorised)  # append image array to training data list
                training_labels.append(emotions.index(emotion))
    
        for item in prediction:
            image = cv2.imread(item)
            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
            clahe_image = clahe.apply(gray)
            landmarks_vectorised = get_landmarks(clahe_image)
            if landmarks_vectorised == "error":
                pass
            else:
                prediction_data.append(landmarks_vectorised)
                prediction_labels.append(emotions.index(emotion))

    return training_data, training_labels, prediction_data, prediction_labels   


accur_lin = []  # 10 random set generation
for i in range(0,10):
    print("Making sets %s" % i)  # Make sets by random sampling 80/20%
    training_data, training_labels, prediction_data, prediction_labels = make_sets()

    npar_train = np.array(training_data)  # numpy array
    npar_trainlabs = np.array(training_labels)
    
    print("training model %s" % i)  # train model
    clf.fit(npar_train, training_labels)

    print("getting accuracies %s" % i)
    npar_pred = np.array(prediction_data)
    pred_lin = clf.score(npar_pred, prediction_labels)
    print ("Model: ", pred_lin)
    accur_lin.append(pred_lin)  # Store accuracy in a list

print("Mean value accuracy in Model: %.3f" %np.mean(accur_lin))