# 模型训练脚本
import numpy as np
from sklearn import decomposition
from sklearn.ensemble import RandomForestRegressor
from sklearn.externals import joblib


# 特征和对应的分数路径
features_path = './data/features_ALL.txt'
ratings_path = './data/ratings.txt'

# 载入数据
# 共500组数据
# 其中前480组数据作为训练集，后20组数据作为测试集
features = np.loadtxt(features_path, delimiter=',')
features_train = features[0: -20]
features_test = features[-20: ]
ratings = np.loadtxt(ratings_path, delimiter=',')
ratings_train = ratings[0: -20]
ratings_test = ratings[-20: ]

# 训练模型
# 这里用PCA算法对特征进行了压缩和降维。
# 降维之后特征变成了20维，也就是说特征一共有500行，每行是一个人的特征向量，每个特征向量有20个元素。
# 用随机森林训练模型
pca = decomposition.PCA(n_components=20)
pca.fit(features_train)
features_train = pca.transform(features_train)
features_test = pca.transform(features_test)
regr = RandomForestRegressor(n_estimators=50, max_depth=None, min_samples_split=10, random_state=0)
regr = regr.fit(features_train, ratings_train)
joblib.dump(regr, './model/face_rating.pkl', compress=1)

# 训练完之后提示训练结束
print('Generate Model Successfully!')

# 人脸关键点提取脚本
import cv2
import dlib
import numpy


# 模型路径
PREDICTOR_PATH = './model/shape_predictor_68_face_landmarks.dat'
# 使用dlib自带的frontal_face_detector作为人脸提取器
detector = dlib.get_frontal_face_detector()
# 使用官方提供的模型构建特征提取器
predictor = dlib.shape_predictor(PREDICTOR_PATH)
face_img = cv2.imread("test_img/test.jpg")
# 使用detector进行人脸检测，rects为返回的结果
rects = detector(face_img, 1)
# 如果检测到人脸
if len(rects) >= 1:
    print("{} faces detected".format(len(rects)))
else:
    print('No faces detected')
    exit()
with open('./results/landmarks.txt', 'w') as f:
    f.truncate()
    for faces in range(len(rects)):
        # 使用predictor进行人脸关键点识别
        landmarks = numpy.matrix([[p.x, p.y] for p in predictor(face_img, rects[faces]).parts()])
        face_img = face_img.copy()
        # 使用enumerate函数遍历序列中的元素以及它们的下标
        for idx, point in enumerate(landmarks):
            pos = (point[0, 0], point[0, 1])
            f.write(str(point[0, 0]))
            f.write(',')
            f.write(str(point[0, 1]))
            f.write(',')
        f.write('
')
    f.close()
# 成功后提示
print('Get landmarks successfully')

# 特征生成脚本
# 具体原理请参见参考论文
import math
import numpy
import itertools


def facialRatio(points):
    x1 = points[0]
    y1 = points[1]
    x2 = points[2]
    y2 = points[3]
    x3 = points[4]
    y3 = points[5]
    x4 = points[6]
    y4 = points[7]
    dist1 = math.sqrt((x1-x2)**2 + (y1-y2)**2)
    dist2 = math.sqrt((x3-x4)**2 + (y3-y4)**2)
    ratio = dist1/dist2
    return ratio


def generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates):
    size = allLandmarkCoordinates.shape
    if len(size) > 1:
        allFeatures = numpy.zeros((size[0], len(pointIndices1)))
        for x in range(0, size[0]):
            landmarkCoordinates = allLandmarkCoordinates[x, :]
            ratios = []
            for i in range(0, len(pointIndices1)):
                x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
                y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
                x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
                y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
                x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
                y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
                x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
                y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
                points = [x1, y1, x2, y2, x3, y3, x4, y4]
                ratios.append(facialRatio(points))
            allFeatures[x, :] = numpy.asarray(ratios)
    else:
        allFeatures = numpy.zeros((1, len(pointIndices1)))
        landmarkCoordinates = allLandmarkCoordinates
        ratios = []
        for i in range(0, len(pointIndices1)):
            x1 = landmarkCoordinates[2*(pointIndices1[i]-1)]
            y1 = landmarkCoordinates[2*pointIndices1[i] - 1]
            x2 = landmarkCoordinates[2*(pointIndices2[i]-1)]
            y2 = landmarkCoordinates[2*pointIndices2[i] - 1]
            x3 = landmarkCoordinates[2*(pointIndices3[i]-1)]
            y3 = landmarkCoordinates[2*pointIndices3[i] - 1]
            x4 = landmarkCoordinates[2*(pointIndices4[i]-1)]
            y4 = landmarkCoordinates[2*pointIndices4[i] - 1]
            points = [x1, y1, x2, y2, x3, y3, x4, y4]
            ratios.append(facialRatio(points))
        allFeatures[0, :] = numpy.asarray(ratios)
    return allFeatures


def generateAllFeatures(allLandmarkCoordinates):
    a = [18, 22, 23, 27, 37, 40, 43, 46, 28, 32, 34, 36, 5, 9, 13, 49, 55, 52, 58]
    combinations = itertools.combinations(a, 4)
    i = 0
    pointIndices1 = []
    pointIndices2 = []
    pointIndices3 = []
    pointIndices4 = []
    for combination in combinations:
        pointIndices1.append(combination[0])
        pointIndices2.append(combination[1])
        pointIndices3.append(combination[2])
        pointIndices4.append(combination[3])
        i = i+1
        pointIndices1.append(combination[0])
        pointIndices2.append(combination[2])
        pointIndices3.append(combination[1])
        pointIndices4.append(combination[3])
        i = i+1
        pointIndices1.append(combination[0])
        pointIndices2.append(combination[3])
        pointIndices3.append(combination[1])
        pointIndices4.append(combination[2])
        i = i+1
    return generateFeatures(pointIndices1, pointIndices2, pointIndices3, pointIndices4, allLandmarkCoordinates)


landmarks = numpy.loadtxt("./results/landmarks.txt", delimiter=',', usecols=range(136))
featuresALL = generateAllFeatures(landmarks)
numpy.savetxt("./results/my_features.txt", featuresALL, delimiter=',', fmt = '%.04f')
print("Generate Feature Successfully!")