from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from scipy import misc
import sys
import os
import argparse
#import tensorflow as tf
import numpy as np
import mxnet as mx
import random
import cv2
import sklearn
from sklearn.decomposition import PCA
from time import sleep
from easydict import EasyDict as edict
from mtcnn_detector import MtcnnDetector
sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src', 'common'))
import face_image
import face_preprocess


def do_flip(data):
    for idx in range(data.shape[0]):
        data[idx, :, :] = np.fliplr(data[idx, :, :])


def get_model(ctx, image_size, model_str, layer):
    _vec = model_str.split(',')
    assert len(_vec) == 2
    prefix = _vec[0]
    epoch = int(_vec[1])
    print('loading', prefix, epoch)
    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)
    all_layers = sym.get_internals()
    sym = all_layers[layer + '_output']
    model = mx.mod.Module(symbol=sym, context=ctx, label_names=None)
    #model.bind(data_shapes=[('data', (args.batch_size, 3, image_size[0], image_size[1]))], label_shapes=[('softmax_label', (args.batch_size,))])
    model.bind(data_shapes=[('data', (1, 3, image_size[0], image_size[1]))])
    model.set_params(arg_params, aux_params)
    return model


class FaceModel:
    def __init__(self, args):
        self.args = args
        ctx = mx.gpu(args.gpu)
        _vec = args.image_size.split(',')
        assert len(_vec) == 2
        image_size = (int(_vec[0]), int(_vec[1]))
        self.model = None
        self.ga_model = None
        if len(args.model) > 0:
            self.model = get_model(ctx, image_size, args.model, 'fc1')
        if len(args.ga_model) > 0:
            self.ga_model = get_model(ctx, image_size, args.ga_model, 'fc1')

        self.threshold = args.threshold
        self.det_minsize = 50
        self.det_threshold = [0.6, 0.7, 0.8]
        #self.det_factor = 0.9
        self.image_size = image_size
        mtcnn_path = os.path.join(os.path.dirname(__file__), 'mtcnn-model')
        if args.det == 0:
            detector = MtcnnDetector(model_folder=mtcnn_path,
                                     ctx=ctx,
                                     num_worker=1,
                                     accurate_landmark=True,
                                     threshold=self.det_threshold)
        else:
            detector = MtcnnDetector(model_folder=mtcnn_path,
                                     ctx=ctx,
                                     num_worker=1,
                                     accurate_landmark=True,
                                     threshold=[0.0, 0.0, 0.2])
        self.detector = detector

    def get_input(self, face_img):
        ret = self.detector.detect_face(face_img, det_type=self.args.det)
        if ret is None:
            return None
        bbox, points = ret
        if bbox.shape[0] == 0:
            return None
        bbox = bbox[0, 0:4]
        points = points[0, :].reshape((2, 5)).T
        #print(bbox)
        #print(points)
        nimg = face_preprocess.preprocess(face_img,
                                          bbox,
                                          points,
                                          image_size='112,112')
        nimg = cv2.cvtColor(nimg, cv2.COLOR_BGR2RGB)
        aligned = np.transpose(nimg, (2, 0, 1))
        return aligned

    def get_feature(self, aligned):
        input_blob = np.expand_dims(aligned, axis=0)
        data = mx.nd.array(input_blob)
        db = mx.io.DataBatch(data=(data, ))
        self.model.forward(db, is_train=False)
        embedding = self.model.get_outputs()[0].asnumpy()
        embedding = sklearn.preprocessing.normalize(embedding).flatten()
        return embedding

    def get_ga(self, aligned):
        input_blob = np.expand_dims(aligned, axis=0)
        data = mx.nd.array(input_blob)
        db = mx.io.DataBatch(data=(data, ))
        self.ga_model.forward(db, is_train=False)
        ret = self.ga_model.get_outputs()[0].asnumpy()
        g = ret[:, 0:2].flatten()
        gender = np.argmax(g)
        a = ret[:, 2:202].reshape((100, 2))
        a = np.argmax(a, axis=1)
        age = int(sum(a))

        return gender, age