import cv2
import dlib
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
%matplotlib inline

paramsb = {
    'nthread': 8,
    'eta': .1,
    'gamma': .0,
    'max_depth': 4,
    'subsample': .7,
    'colsample_bylevel': .7,
    'tree_method': 'approx',
    'objective': 'multi:softprob',
    'seed': 0,
    'num_class': 2,
    'silent': 1
}

PREDICTOR_PATH = "../../../fapai/fap-ai/crawlers/grabber/shape_predictor_68_face_landmarks.dat"
RECOGNIZER_PATH = "../../../fapai/fap-ai/crawlers/grabber/dlib_face_recognition_resnet_model_v1.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
recognizer = dlib.face_recognition_model_v1(RECOGNIZER_PATH)

TEMPLATE = np.float32([
    (0.0792396913815, 0.339223741112), (0.0829219487236, 0.456955367943),
    (0.0967927109165, 0.575648016728), (0.122141515615, 0.691921601066),
    (0.168687863544, 0.800341263616), (0.239789390707, 0.895732504778),
    (0.325662452515, 0.977068762493), (0.422318282013, 1.04329000149),
    (0.531777802068, 1.06080371126), (0.641296298053, 1.03981924107),
    (0.738105872266, 0.972268833998), (0.824444363295, 0.889624082279),
    (0.894792677532, 0.792494155836), (0.939395486253, 0.681546643421),
    (0.96111933829, 0.562238253072), (0.970579841181, 0.441758925744),
    (0.971193274221, 0.322118743967), (0.163846223133, 0.249151738053),
    (0.21780354657, 0.204255863861), (0.291299351124, 0.192367318323),
    (0.367460241458, 0.203582210627), (0.4392945113, 0.233135599851),
    (0.586445962425, 0.228141644834), (0.660152671635, 0.195923841854),
    (0.737466449096, 0.182360984545), (0.813236546239, 0.192828009114),
    (0.8707571886, 0.235293377042), (0.51534533827, 0.31863546193),
    (0.516221448289, 0.396200446263), (0.517118861835, 0.473797687758),
    (0.51816430343, 0.553157797772), (0.433701156035, 0.604054457668),
    (0.475501237769, 0.62076344024), (0.520712933176, 0.634268222208),
    (0.565874114041, 0.618796581487), (0.607054002672, 0.60157671656),
    (0.252418718401, 0.331052263829), (0.298663015648, 0.302646354002),
    (0.355749724218, 0.303020650651), (0.403718978315, 0.33867711083),
    (0.352507175597, 0.349987615384), (0.296791759886, 0.350478978225),
    (0.631326076346, 0.334136672344), (0.679073381078, 0.29645404267),
    (0.73597236153, 0.294721285802), (0.782865376271, 0.321305281656),
    (0.740312274764, 0.341849376713), (0.68499850091, 0.343734332172),
    (0.353167761422, 0.746189164237), (0.414587777921, 0.719053835073),
    (0.477677654595, 0.706835892494), (0.522732900812, 0.717092275768),
    (0.569832064287, 0.705414478982), (0.635195811927, 0.71565572516),
    (0.69951672331, 0.739419187253), (0.639447159575, 0.805236879972),
    (0.576410514055, 0.835436670169), (0.525398405766, 0.841706377792),
    (0.47641545769, 0.837505914975), (0.41379548902, 0.810045601727),
    (0.380084785646, 0.749979603086), (0.477955996282, 0.74513234612),
    (0.523389793327, 0.748924302636), (0.571057789237, 0.74332894691),
    (0.672409137852, 0.744177032192), (0.572539621444, 0.776609286626),
    (0.5240106503, 0.783370783245), (0.477561227414, 0.778476346951)])

TPL_MIN, TPL_MAX = np.min(TEMPLATE, axis=0), np.max(TEMPLATE, axis=0)
MINMAX_TEMPLATE = (TEMPLATE - TPL_MIN) / (TPL_MAX - TPL_MIN)

INNER_EYES_AND_BOTTOM_LIP = [39, 42, 57]
OUTER_EYES_AND_NOSE = [36, 45, 33]

IM_SIZE = 224
FULL_RECT = dlib.rectangle(
        left=0,
        top=0,
        right=IM_SIZE,
        bottom=IM_SIZE,
)

def getAllFaceBoundingBoxes(rgbImg):
    assert rgbImg is not None
    try:
        return detector(rgbImg, 1)
    except Exception as e:
        print("Warning: {}".format(e))
        return []


def findLandmarks(rgbImg, bb):
    assert rgbImg is not None
    assert bb is not None

    points = predictor(rgbImg, bb)
    return list(map(lambda p: (p.x, p.y), points.parts()))


def getLargestFaceBoundingBox(rgbImg, skipMulti=False):
    assert rgbImg is not None

    faces = getAllFaceBoundingBoxes(rgbImg)
    if (not skipMulti and len(faces) > 0) or len(faces) == 1:
        return max(faces, key=lambda rect: rect.width() * rect.height())
    else:
        return None

# def scale_dlib_rect(rect, scale=1):

#     def resize(x, y, w, h, scale):
#         return (max(x - int(w * (scale - 1) / 2), 0),
#                 max(y - int(h * (scale - 1) / 2), 0),
#                 int(w * scale),
#                 int(h * scale))

#     x, y, w, h = rect.left(), rect.top(), rect.right()-rect.left(), rect.bottom()-rect.top()
#     print (x)
#     x, y, w, h = resize(x, y, w, h, scale)
#     print (x)

#     dlib_rectangle = dlib.rectangle(
#         left=x,
#         top=y,
#         right=x+w,
#         bottom=y+h,
#     )
#     return rect
#     #return dlib_rectangle


def align(rgbImg, imgDim=IM_SIZE, bb=None,
          landmarks=None, landmarkIndices=INNER_EYES_AND_BOTTOM_LIP,
          one_face=False):
    assert imgDim is not None
    assert rgbImg is not None
    assert landmarkIndices is not None
    bbs = getAllFaceBoundingBoxes(rgbImg)
    #bbs = [scale_dlib_rect(rect) for rect in bbs]

    all_landmarks = [findLandmarks(rgbImg, bb) for bb in bbs]

    all_npLandmarks = [np.float32(landmarks) for landmarks in all_landmarks]
    npLandmarkIndices = np.array(landmarkIndices)

    Hs = [cv2.getAffineTransform(npLandmarks[npLandmarkIndices],
                               imgDim * MINMAX_TEMPLATE[npLandmarkIndices]) for npLandmarks in all_npLandmarks]

    thumbnails = [cv2.warpAffine(rgbImg, H, (imgDim, imgDim)) for H in Hs]

    return thumbnails

def get_embs_from_aligned_img(crops_arr):
    fts = [recognizer.compute_face_descriptor(crop, predictor(crop, FULL_RECT))
           for crop in crops_arr]
    return fts

def classify(img):
    names = ['jewish' , 'african', 'european' , 'indus', 'arabs' , 'caucasian' , 'latino', 'asian']
    classifys = []
    for i in range(8):
        #print('~/xgb_'+'{}'.format(i) + '.gbm')
        classifys.append(xgb.Booster(paramsb, model_file='/home/libfun/lineage/ai.hack17/xgbb_'+'{}'.format(i) + '.gbm'))
    if len(align(img)) == 1:
        race = defaultdict()
        embs2 = get_embs_from_aligned_img(align(img))
        predic = []
        for i in range(8):
            race[names[i]] = classifys[i].predict(xgb.DMatrix(np.array(embs2[0]).reshape(1, -1)))[0, :][1]
            #predic.append(classifys[i].predict(xgb.DMatrix(np.array(embs2[0]).reshape(1, -1)))[0, :][1])
            #print(names[i] + " - " + "{:.0f}".format(classifys[i].predict(xgb.DMatrix(np.array(embs2[0]).reshape(1, -1)))[0, :][1]*100))
        return race
    return None