visualization_ex.py 文件源码

def whitened_rgb_atoms():

    #a small dataset of images
    imgs = get_images(colored=True)

    #alternatively we could use the lfw dataset
    """
    lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4,color=True)
    faces = lfw_people.data
    n_imgs,h,w,n_channels = lfw_people.images.shape
    imgs = []
    for i in range(n_imgs):
        img = lfw_people.images[i,:,:,:].reshape((h,w,n_channels))
        imgs.append(img)
    """

    patch_shape = (8,8)
    n_atoms = 100
    n_plot_atoms = 100
    n_nonzero_coefs = 1

    print 'Extracting reference patches...'
    X = extract_patches(imgs, patch_size=patch_shape[0],scale=False,n_patches=int(5e5),mem="low")
    print "number of patches:",X.shape[1]

    wn = preproc("whitening")
    from lyssa.feature_extract.preproc import local_contrast_normalization
    #apply lcn and then whiten the patches
    X = wn(local_contrast_normalization(X))
    #learn the dictionary using Batch Orthognal Matching Pursuit and KSVD
    se = sparse_encoder(algorithm='bomp',params={'n_nonzero_coefs':n_nonzero_coefs},n_jobs=8)
    kc = ksvd_coder(n_atoms=n_atoms,sparse_coder=se,init_dict = "data",
                    max_iter=10,verbose=True,approx=False,n_jobs=8)

    kc.fit(X)
    D = kc.D
    for i in range(n_atoms):
        D[:, i] = (D[:, i] - D[:, i].min()) / float((D[:, i].max() - D[:, i].min()))
    #plot the learned dictionary
    plt.figure(figsize=(4.2, 4))
    for i in range(n_plot_atoms):
        plt.subplot(10, 10, i + 1)
        plt.imshow(D[:, i].reshape((patch_shape[0], patch_shape[1], 3)))
        plt.xticks(())
        plt.yticks(())
    plt.show()