from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import facenet
import os
import math
import pickle
from sklearn.svm import SVC
import sys
import tensorflow.compat.v1 as tf

class training:
    def __init__(self, datadir, modeldir,classifier_filename):
        self.datadir = datadir
        self.modeldir = modeldir
        self.classifier_filename = classifier_filename

    def main_train(self):
        with tf.Graph().as_default():
            with tf.Session() as sess:
                img_data = facenet.get_dataset(self.datadir)
                path, label = facenet.get_image_paths_and_labels(img_data)
                print('Classes: %d' % len(img_data))
                print('Images: %d' % len(path))

                facenet.load_model(self.modeldir)
                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]

                print('Extracting features of images for model')
                batch_size = 256
                image_size = 160 #160
                nrof_images = len(path)
                nrof_batches_per_epoch = int(math.ceil(1.0 * nrof_images / batch_size))
                emb_array = np.zeros((nrof_images, embedding_size))
                for i in range(nrof_batches_per_epoch):
                    start_index = i * batch_size
                    end_index = min((i + 1) * batch_size, nrof_images)
                    paths_batch = path[start_index:end_index]
                    images = facenet.load_data(paths_batch, False, False, image_size)
                    feed_dict = {images_placeholder: images, phase_train_placeholder: False}
                    emb_array[start_index:end_index, :] = sess.run(embeddings, feed_dict=feed_dict)

                classifier_file_name = os.path.expanduser(self.classifier_filename)

                # Training Started
                print('Training Started')
                model = SVC(kernel='linear', probability=True)
                model.fit(emb_array, label)

                class_names = [cls.name.replace('_', ' ') for cls in img_data]

                # Saving model
                with open(classifier_file_name, 'wb') as outfile:
                    pickle.dump((model, class_names), outfile)
                return classifier_file_name