def get_new_embeddings(input_images, latentgan_model: LatentGAN,
confignet_model: ConfigNet):
'''Samples new embeddings from either:
- the LatentGAN if no input images were provided
- by embedding the input images into the latent space using the real encoder
'''
if input_images is None:
embeddings = latentgan_model.generate_latents(1, truncation=0.7)
rotations = np.zeros((1, 3), dtype=np.float32)
orig_images = confignet_model.generate_images(embeddings, rotations)
else:
sample_indices = np.random.randint(0, len(input_images), 1)
orig_images = np.array([input_images[x] for x in sample_indices])
embeddings, rotations = confignet_model.encode_images(orig_images)
return embeddings, rotations, orig_images
def get_new_embeddings(args, input_images, latentgan_model: LatentGAN, confignet_model: ConfigNet):
'''Samples new embeddings from either:
- the LatentGAN if no input images were provided
- by embedding the input images into the latent space using the real encoder
'''
if input_images is None:
n_samples = args.n_rows * args.n_cols
embeddings = latentgan_model.generate_latents(n_samples, truncation=0.7)
rotations = np.zeros((n_samples, 3), dtype=np.float32)
orig_images = confignet_model.generate_images(embeddings, rotations)
else:
# special case for one image so the demo is faster and nicer
if len(input_images) == 1:
args.n_rows = 1
args.n_cols = 1
n_samples = args.n_rows * args.n_cols
sample_indices = np.random.randint(0, len(input_images), n_samples)
orig_images = np.array([input_images[x] for x in sample_indices])
embeddings, rotations = confignet_model.encode_images(orig_images)
return embeddings, rotations, orig_images