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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import numpy as np |
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import facenet |
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import os |
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import math |
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import pickle |
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from sklearn.svm import SVC |
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import sys |
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import tensorflow.compat.v1 as tf |
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class training: |
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def __init__(self, datadir, modeldir,classifier_filename): |
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self.datadir = datadir |
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self.modeldir = modeldir |
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self.classifier_filename = classifier_filename |
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def main_train(self): |
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with tf.Graph().as_default(): |
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with tf.Session() as sess: |
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img_data = facenet.get_dataset(self.datadir) |
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path, label = facenet.get_image_paths_and_labels(img_data) |
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print('Classes: %d' % len(img_data)) |
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print('Images: %d' % len(path)) |
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facenet.load_model(self.modeldir) |
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images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0") |
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embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0") |
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phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0") |
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embedding_size = embeddings.get_shape()[1] |
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print('Extracting features of images for model') |
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batch_size = 256 |
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image_size = 160 |
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nrof_images = len(path) |
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nrof_batches_per_epoch = int(math.ceil(1.0 * nrof_images / batch_size)) |
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emb_array = np.zeros((nrof_images, embedding_size)) |
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for i in range(nrof_batches_per_epoch): |
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start_index = i * batch_size |
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end_index = min((i + 1) * batch_size, nrof_images) |
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paths_batch = path[start_index:end_index] |
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images = facenet.load_data(paths_batch, False, False, image_size) |
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feed_dict = {images_placeholder: images, phase_train_placeholder: False} |
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emb_array[start_index:end_index, :] = sess.run(embeddings, feed_dict=feed_dict) |
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classifier_file_name = os.path.expanduser(self.classifier_filename) |
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print('Training Started') |
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model = SVC(kernel='linear', probability=True) |
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model.fit(emb_array, label) |
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class_names = [cls.name.replace('_', ' ') for cls in img_data] |
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with open(classifier_file_name, 'wb') as outfile: |
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pickle.dump((model, class_names), outfile) |
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return classifier_file_name |
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