def get_numbers_example():
"""Builds plot with number calculation example"""
image_paths_train = glob.glob(pathname='images_diff/train/*.*',
recursive=True)
image_paths_test = glob.glob(pathname='images_diff/test/*.*',
recursive=True)
config = Config()
config.image_size = (28, 28)
config.num_iter = 70
config.threshold = 100
model = get_trained_model(image_paths=image_paths_train,
image_size=config.image_size,
asynchronous=config.asynchronous)
dataset_test = Dataset(list_of_paths=image_paths_test,
image_size=config.image_size,
add_noise=False)
flatten_images_test = dataset_test.get_all_flatten_images()
dataset_original = Dataset(list_of_paths=image_paths_train,
image_size=config.image_size,
add_noise=False)
flatten_images_original = dataset_original.get_all_flatten_images()
predictions = model.predict(data=flatten_images_test,
num_iter=config.num_iter,
threshold=config.threshold)
fig, axs = plt.subplots(len(predictions), 3)
axs[0][0].set_title(f'Original image')
axs[0][1].set_title(f'Test image')
axs[0][2].set_title(f'Pred image')
for idx, (curr_original_image, curr_test_image, curr_pred) in enumerate(
zip(flatten_images_original, flatten_images_test, predictions)):
curr_original_image = np.reshape(curr_original_image,
newshape=config.image_size)
curr_test_image = np.reshape(curr_test_image,
newshape=config.image_size)
curr_pred = np.reshape(curr_pred, newshape=config.image_size)
axs[idx][0].imshow(curr_original_image)
axs[idx][1].imshow(curr_test_image)
axs[idx][2].imshow(curr_pred)
fig.suptitle(f'Result of number predictions')
plt.show()
def get_image_num_iters_plot():
"""Creates num_iters for images plot"""
config = Config()
num_iter_list = [1, 5, 10, 20, 50, 100, 1000]
image_paths = glob.glob(pathname='images_same/*.*', recursive=True)
fig, axs = plt.subplots(len(image_paths), len(num_iter_list) + 2)
[curr_ax.set_axis_off() for curr_ax in axs.ravel()]
model = get_trained_model(image_paths=image_paths,
image_size=config.image_size,
asynchronous=config.asynchronous)
dataset_original = Dataset(list_of_paths=image_paths,
image_size=config.image_size,
add_noise=False)
flatten_images = dataset_original.get_all_flatten_images()
dataset_original.add_noise = True
flatten_images_noise = dataset_original.get_all_flatten_images()
axs[0][0].set_title(f'Original image')
axs[0][1].set_title(f'Noise image')
for idx_columns, curr_iter_value in enumerate(num_iter_list, start=2):
axs[0][idx_columns].set_title(curr_iter_value)
for idx, (curr_flatten_image, curr_flatten_image_noise) in enumerate(
zip(flatten_images, flatten_images_noise)):
original_image = np.reshape(curr_flatten_image,
newshape=config.image_size)
noise_image = np.reshape(curr_flatten_image_noise,
newshape=config.image_size)
axs[idx][0].imshow(original_image)
axs[idx][1].imshow(noise_image)
for image_idx, curr_num_iter in enumerate(num_iter_list, start=2):
prediction = model.predict(data=[curr_flatten_image_noise],
num_iter=curr_num_iter,
threshold=config.threshold)[0]
pred_image = np.reshape(prediction, newshape=config.image_size)
axs[idx][image_idx].imshow(pred_image)
fig.suptitle(f'Num of model iterations for different models')
plt.show()
def get_weights_plot():
"""Creates weights plot"""
image_paths_train = glob.glob(pathname='images_diff/train/*.*',
recursive=True)
config = Config()
config.image_size = (64, 64)
model = get_trained_model(image_paths=image_paths_train,
image_size=config.image_size,
asynchronous=config.asynchronous)
plt.title(f'Weight heatmap.')
sns.heatmap(model.weights)
plt.show()
'prediction_image': predictions
}
return data
if __name__ == '__main__':
use_numbers_dataset = False
if use_numbers_dataset:
image_paths_train = glob.glob(pathname='images_diff/train/*.*', recursive=True)
image_paths_test = glob.glob(pathname='images_diff/test/*.*', recursive=True)
model = get_trained_model(image_paths=image_paths_train,
image_size=Config.image_size,
asynchronous=Config.asynchronous)
data = evaluation(model=model,
image_paths=image_paths_test,
image_size=Config.image_size,
num_iter=Config.num_iter,
threshold=Config.threshold)
plot_images_compare(data=data)
else:
image_paths = glob.glob(pathname='images_same/*.*', recursive=True)
model = get_trained_model(image_paths=image_paths,
image_size=Config.image_size,
asynchronous=False)