def main_train(self):
with tf.Graph().as_default():
with tf.Session() as sess:
# img_data = facenet.get_dataset_all(self.datadir)
# path, label = facenet.get_image_paths_and_labels(img_data)
labs, imgs = aug.Make_Data(self.datadir)
total_imgs_len = len(imgs) * len(imgs[0])
print('Classes: %d' % len(imgs))
print('Images: %d' % total_imgs_len)
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 = 1000
# image_size = 160
nrof_images = total_imgs_len
nrof_batches_per_epoch = int(
math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
imgs = np.array(imgs).reshape(total_imgs_len, 200, 200, 3)
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]
imgs_batch = imgs[start_index:end_index]
# images = facenet.load_data(paths_batch, False, False, image_size)
# feed_dict = {images_placeholder: images, phase_train_placeholder: False}
feed_dict = {
images_placeholder: imgs_batch,
pahse_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
import imgaug as ia
import cv2
import augmentation as aug
label_faces, aug_faces = aug.Make_Data("avengers")
for i in range(len(aug_faces)):
ia.imshow(ia.draw_grid(aug_faces[i], cols=8))
# cv2.imshow("img", aug_faces[0][0])
# cv2.waitKey(0)
# cv2.destroyAllWindows()
import imgaug as ia
import cv2
import augmentation as aug
name_faces, aug_faces = aug.Make_Data("avengers")
for i in range(len(aug_faces)):
ia.imshow(ia.draw_grid(name_faces[i], cols = 8))
# cv2.imshow("img", aug_faces[0][0])
# cv2.waitKey(0)
# cv2.destroyAllWindows()