"""An example of how to use your own dataset to train a classifier that recognizes people.
"""
# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

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

import tensorflow as tf
import numpy as np
import argparse
import facenet
import os
import sys
import math
import pickle
from sklearn.svm import SVC

def main(args):
  
    with tf.Graph().as_default():
      
        with tf.compat.v1.Session() as sess:
            
            np.random.seed(seed=args.seed)
            
            if args.use_split_dataset:
                dataset_tmp = facenet.get_dataset(args.data_dir)
                train_set, test_set = split_dataset(dataset_tmp, args.min_nrof_images_per_class, args.nrof_train_images_per_class)
                if (args.mode=='TRAIN'):
                    dataset = train_set
                elif (args.mode=='CLASSIFY'):
                    dataset = test_set
            else:
                dataset = facenet.get_dataset(args.data_dir)

            # Check that there are at least one training image per class
            for cls in dataset:
                assert(len(cls.image_paths)>0, 'There must be at least one image for each class in the dataset')            

                 
            paths, labels = facenet.get_image_paths_and_labels(dataset)
            
            print('Number of classes: %d' % len(dataset))
            print('Number of images: %d' % len(paths))
            
            # Load the model
            print('Loading feature extraction model')
            facenet.load_model(args.model)
            
            # Get input and output tensors
            images_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("input:0")
            embeddings = tf.compat.v1.get_default_graph().get_tensor_by_name("embeddings:0")
            phase_train_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("phase_train:0")
            embedding_size = embeddings.get_shape()[1]
            
            # Run forward pass to calculate embeddings
            print('Calculating features for images')
            nrof_images = len(paths)
            nrof_batches_per_epoch = int(math.ceil(1.0*nrof_images / args.batch_size))
            emb_array = np.zeros((nrof_images, embedding_size))
            pathName = []
            for i in range(nrof_batches_per_epoch):
                start_index = i*args.batch_size
                end_index = min((i+1)*args.batch_size, nrof_images)
                paths_batch = paths[start_index:end_index]
                for j in range(len(paths_batch)):
                    pathName.append(paths_batch[j])
                images = facenet.load_data(paths_batch, False, False, args.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_filename_exp = os.path.expanduser(args.classifier_filename)

            if (args.mode=='TRAIN'):
                # Train classifier
                print('Training classifier')
                model = SVC(kernel='linear', probability=True)
                model.fit(emb_array, labels)
            
                # Create a list of class names
                class_names = [ cls.name.replace('_', ' ') for cls in dataset]

                # Saving classifier model
                with open(classifier_filename_exp, 'wb') as outfile:
                    pickle.dump((model, class_names), outfile)
                print('Saved classifier model to file "%s"' % classifier_filename_exp)
                
            elif (args.mode=='CLASSIFY'):
                # Classify images
                print('Testing classifier')
                with open(classifier_filename_exp, 'rb') as infile:
                    (model, class_names) = pickle.load(infile, encoding="latin1")

                print('Loaded classifier model from file "%s"' % classifier_filename_exp)

                predictions = model.predict_proba(emb_array)
                #print (predictions)
                best_class_indices = np.argmax(predictions, axis=1)
                best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
                scoreDic = {}
                for i in range(len(predictions[1])):
                    #print(class_names[i],predictions[1][i])
                    scoreDic[class_names[i]] = predictions[1][i]

                sorted_d = sorted(scoreDic.items(), key=lambda x: x[1])
                #print(sorted_d) 
                # equivalent version
                # sorted_d = sorted(d.items(), key=lambda (k,v): v)
                print (emb_array)
                #file1 = open('classify_result2.txt','w')
                resultDic = {}
                print (predictions.shape)
                print(sorted(range(len(predictions[1])), key=lambda i: predictions[1][i], reverse=True)[:5])
                top5 = sorted(range(len(predictions[1])), key=lambda i: predictions[1][i], reverse=True)[:5]
                for i in top5:
                    print(class_names[i])
                    print(predictions[1][i])
                for i in range(len(best_class_indices)):
                    print('%s\t%s\t%.3f' % (pathName[i], class_names[best_class_indices[i]], best_class_probabilities[i]))
                    #print class_names
                    #print 
                    #file1.write('%s\t%s\t%.3f' % (pathName[i], class_names[best_class_indices[i]], best_class_probabilities[i])+'\n')
                    resultDic[pathName[i]] = (class_names[best_class_indices[i]],best_class_probabilities[i],emb_array[i])
                    #file1.write(pathName[i]+'\t'+class_names[best_class_indices[i]]+'\t'+best_class_probabilities[i]+'\n')
                    
                accuracy = np.mean(np.equal(best_class_indices, labels))
                print('Accuracy: %.3f' % accuracy)

                pickle.dump( resultDic, open( "results/"+args.output+".p", "wb" ) )
            
def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_per_class):
    train_set = []
    test_set = []
    for cls in dataset:
        paths = cls.image_paths
        # Remove classes with less than min_nrof_images_per_class
        if len(paths)>=min_nrof_images_per_class:
            np.random.shuffle(paths)
            train_set.append(facenet.ImageClass(cls.name, paths[:nrof_train_images_per_class]))
            test_set.append(facenet.ImageClass(cls.name, paths[nrof_train_images_per_class:]))
    return train_set, test_set

            
def parse_arguments(argv):
    parser = argparse.ArgumentParser()
    
    parser.add_argument('mode', type=str, choices=['TRAIN', 'CLASSIFY'],
        help='Indicates if a new classifier should be trained or a classification ' + 
        'model should be used for classification', default='CLASSIFY')
    parser.add_argument('data_dir', type=str,
        help='Path to the data directory containing aligned LFW face patches.')
    parser.add_argument('model', type=str, 
        help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
    parser.add_argument('classifier_filename', 
        help='Classifier model file name as a pickle (.pkl) file. ' + 
        'For training this is the output and for classification this is an input.')
    parser.add_argument('output', 
        help='Classifier model file name as a pickle (.pkl) file. ' + 
        'For training this is the output and for classification this is an input.')
    parser.add_argument('--use_split_dataset', 
        help='Indicates that the dataset specified by data_dir should be split into a training and test set. ' +  
        'Otherwise a separate test set can be specified using the test_data_dir option.', action='store_true')
    parser.add_argument('--test_data_dir', type=str,
        help='Path to the test data directory containing aligned images used for testing.')
    parser.add_argument('--batch_size', type=int,
        help='Number of images to process in a batch.', default=90)
    parser.add_argument('--image_size', type=int,
        help='Image size (height, width) in pixels.', default=160)
    parser.add_argument('--seed', type=int,
        help='Random seed.', default=666)
    parser.add_argument('--min_nrof_images_per_class', type=int,
        help='Only include classes with at least this number of images in the dataset', default=20)
    parser.add_argument('--nrof_train_images_per_class', type=int,
        help='Use this number of images from each class for training and the rest for testing', default=10)
    
    return parser.parse_args(argv)

if __name__ == '__main__':
    main(parse_arguments(sys.argv[1:]))
