print(label)
if len(label2image[label]) >= 4:
continue
label2image[label].append(image)
for label, images in label2image.items():
for i, image in enumerate(images[:4]):
plt.subplot(2, 2, 1 + i)
plt.imshow(image)
plt.title(f'label {label}')
plt.axis('off')
plt.show()
image_root = '/home/martin/Desktop/data/darknet_data/openimgs_extra_v2'
model = CVAE.from_file('models/oi_cvae_5/oi_cvae_5.state',
'models/oi_cvae_5/oi_cvae_5.weights')
tb = CVAEToolBox(model)
images = ImageFeed(image_root, tb)
set_size = 100000
kmeans = Kmeans(49)
load = False
save = True
if load:
kmeans.cluster_centers_ = np.load('centroids.npy')
else:
batch = []
for _ in tqdm(range(set_size), leave=False, desc='loading '):
tensor, _ = next(images)
(64, 3, (2, 2)), # out: 128, 128, 64
(128, 3, (2, 2)), # out: 64, 64, 128
(256, 3, (2, 2)), # out: 32, 32, 256
(512, 3, (2, 2)), # out: 16, 3162, 512
(1024, 3, (2, 2)), # out: 8, 8, 1024
(1024, 3, (2, 2)), # out: 4, 4, 1024
],
'decode':
None, # Mirror enconding for reconstruction
'name':
'face_cvae_1'
}
model = CVAE(arch_def)
model.load_weights(weights_path)
else:
model = CVAE.from_file(arch_def_path, weights_path)
load_and_preprocess = get_load_and_preprocess_func(model.arch_def['input'])
image_paths = [
'/home/martin/Desktop/data/validation_set/Bakeriet_4-Action_sequence_00336.jpg',
'/home/martin/Desktop/data/validation_set/Stand_up_small_group_2.jpg'
]
toolbox = CVAEToolBox(model)
a = toolbox.to_tensor(image_paths[0], with_batch_dim=True, preprocess=True)
b = toolbox.to_tensor(image_paths[1], with_batch_dim=True, preprocess=True)
images = toolbox.interpolate_between_images(a, b, steps=12)
a_img, a_latent = toolbox.load_and_reconstruct_image(image_paths[0])