Example

The following example shows how to cluster patches and reconstruct an image using these clusters.

import patchdistances as pd

# Load image as Float32 numpy array. It can either be gray-scale or rgb.
img = load_image()

// Initialize the distance class.
// Alternatively: similarity_inv_dist.
aid = pd.affine_inv_dist()

// Greedy-k-center clustering of the image patches using the distance.
labels, indices_of_labels = aid.greedy_k_center(
    image=img,
    patch_shape=(7,7),
    clusters=20,
    first_patch_idx=0,
    solver=pd.solver_t.procrustes,
    func_family=pd.func_t.superlevelsets,
    use_imed=False,
    interpolation=pd.inter_t.bicubic,
)

// Reconstruct the image using the previously clustered patches.
// Alternatively: reconstruct_w_translation.
reconstructed_image, nearest_neighbor_labeling = aid.reconstruct(
    image=img,
    labels=labels,
    rec_type=pd.rec_t.Median,
    solver=pd.solver_t.procrustes,
    func_family=pd.func_t.superlevelsets,
    use_imed=False,
    interpolation=pd.inter_t.bicubic,
)