import os
import numpy as np
import pickle as pkl
import yaml
from collections import defaultdict
import tensorflow.compat.v1 as tf
import facenet
from PIL import Image

infomation = defaultdict(dict)
cfg = yaml.load(open('config.yaml', 'r'), Loader=yaml.FullLoader)
MODEL_DIR = cfg['PATH']['MODEL_DIR']
CLASSIFIER_DIR = cfg['PATH']['CLASSIFIER_DIR']
NPY_DIR = cfg['PATH']['NPY_DIR']
TRAIN_IMG_DIR = cfg['PATH']['TRAIN_IMG_DIR']

def get_elements_dir(x):
    path = x
    return path

def load_essentail_components():
    model_dir = get_elements_dir(MODEL_DIR)
    classifier_filename = get_elements_dir(CLASSIFIER_DIR)
    npy = get_elements_dir(NPY_DIR)
    train_img = get_elements_dir(TRAIN_IMG_DIR)
    return model_dir, classifier_filename, npy, train_img

def gpu_session():
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
    sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
    return sess

def configure_mtcnn(sess, npy, train_img):
    pnet, rnet, onet = detect_face.create_mtcnn(sess, npy)
    minsize = 30  # minimum size of face
    threshold = [0.7, 0.8, 0.8]  # three steps's threshold
    factor = 0.709  # scale factor
    margin = 44
    batch_size = 100  # 1000
    image_size = 182
    input_image_size = 160
    HumanNames = os.listdir(train_img)
    HumanNames.sort()

def recognize(image):
    model_dir, classifier_filename, npy, train_img = load_essentail_components()

    with tf.Graph().as_default():
        sess = gpu_session()
        with sess.as_default():
            configure_mtcnn(sess, npy, train_img)
            print('Loading Model ...')
            facenet.load_model(model=model_dir)
            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
            embedding_size = embeddings.get_shape()[1]
            classifier_filename_exp = os.path.expanduser(classifier_filename)
            with open(classifier_filename_exp, 'rb') as infile:
                (model, class_names) = pickle.load(infile, encoding='latin1')

                if image.ndim == 2:
                    image = facenet.to_rgb(image)
                bounding_boxes, _ = detect_face.detect_face(image, minsize, pnet, rnet, onet, threshold, factor)
                faceNum = bounding_boxes.shape[0]