images_placeholder: images[batches[i - 1]:batches[i]],
phase_train_placeholder: False
}
# Use the facenet model to calcualte embeddings
embed = sess.run(embeddings, feed_dict=feed_dict)
embed_array.extend(embed.tolist())
np.save('embeddings.npy', embed_array)
if __name__ == '__main__':
sess = tf.Session()
#Models
pnet, rnet, onet = nets(sess, 'models/')
sentiment_model = sent.Transfer_learning()
yolo = YOLO()
Video = 0
if (Video == 1):
print('Still')
clip = VideoFileClip('Video/Video_1.mp4')
fps = clip.fps
print(fps)
crops, new_frames, crop_idcs = process_video(
clip.subclip(0, 10).iter_frames(), pnet, rnet, onet,
sentiment_model)
newer_frames = human_tracking(new_frames, yolo)
np.save('face_crops.npy', crops)
get_embeddings(crops)
clip = ImageSequenceClip(new_frames, fps=fps)
clip.write_videofile('Video_Output/newvideo.mp4', fps=fps)
