def rebuild_image(img_filename, dic_filename, output_filename, patch_size):
D = load_dic(dic_filename)
# Open image
img = Image.open(img_filename)
# Get patches from image
patches = img_preprocessing.get_patches(img, patch_size)
sparse_code(patches, D, atoms=3)
n_img = img_preprocessing.build_from_patches(patches, img.size)
n_img.save(output_filename)
def build_dic(img_filename, dic_filename, dic_img_filename, patch_size):
# Define parameters
dic_size = 100 # Size of dictionary (number of atoms)
regularization = 1.2 / dic_size # Regularization parameter of the Lasso optimization problem
batch_size = 5 # Size of batches
n_iter = 5000 # Number of iterations
# Open image
img = Image.open(img_filename)
# Get patches from image
patches = img_preprocessing.get_patches(img, patch_size)
# Learn dictionary
D = learn_dic(patches, dic_size, T=n_iter, batch_size=batch_size, lambd=regularization)
# Save dictionary to file
save_dic(D, dic_filename)
# Save dictionary as nice image
dicimg = show_dic(D, patch_size)
misc.imsave(dic_img_filename, dicimg)
