コード例 #1
0
ファイル: transform.py プロジェクト: dracz/vision
def test_pca_input(sh, m):
    """
    Generate input data from labeled faces data
    :param sh: The shape of the image tiles to sample
    :param m: The number of examples to sample
    :return: 2d ndarray where columns are examples
    """
    return sample_patches(load_lfwc(), patch_shape=sh, n_samples=m, flatten=True).T
コード例 #2
0
def get_input(input_pattern=lfwc_pattern,
              n_samples=5000,
              patch_shape=(12, 12),
              n_images=-1,
              epsilon=0.1,
              norm_axis=0):
    """
    Get input data from lfwc patches
    :param input_pattern: The pattern to glob for image files
    :param n_images: The number of images to draw samples from
    :param n_samples: The number of samples to extract
    :param patch_shape: The shape of the patches to sample
    :param epsilon: Regularization for whitening, or None to disable whitening
    :param norm_axis: Whether to mean normalize across each feature (0) or each patch (1)
    :return: m x n ndarray of flattened images
    """

    paths = glob.glob(input_pattern)
    random.shuffle(paths)
    paths = paths[:n_images]

    print("Sampling {} {}x{} patches from {} images...".format(
        n_samples, patch_shape[0], patch_shape[1], len(paths)))

    # read images into a m x img_row x img_col ndarray
    imgs = np.asarray([cv2.imread(path, 0) for path in paths],
                      dtype=theano.config.floatX)
    imgs /= 255.  # scale to [0,1]

    if input_pattern == lfwc_pattern and patch_shape == (
            64, 64) and n_samples == lfwc_count:
        patches = load_matrix_2d(paths)
        epsilon = 0  # disable whitening... too slow

    else:
        # sample patches into a 3d array
        patches = sample_patches(imgs,
                                 patch_shape=patch_shape,
                                 n_samples=n_samples)

        # flatten each image to 1d
        patches = patches.reshape(patches.shape[0],
                                  patch_shape[0] * patch_shape[1])

    if norm_axis == 0:
        # subtract per feature (pixel) means across the data set (m x n)
        normed = patches - patches.mean(axis=0)
    else:
        # remove mean val of each patch
        normed = patches - patches.mean(axis=1)[:, np.newaxis]

    if epsilon is None:
        return normed

    return whiten_zca(normed, epsilon=epsilon)
コード例 #3
0
ファイル: transform.py プロジェクト: dracz/vision
def test_pca_input(sh, m):
    """
    Generate input data from labeled faces data
    :param sh: The shape of the image tiles to sample
    :param m: The number of examples to sample
    :return: 2d ndarray where columns are examples
    """
    return sample_patches(load_lfwc(),
                          patch_shape=sh,
                          n_samples=m,
                          flatten=True).T
コード例 #4
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def main(cmd, args):
    """process the command"""
    data_set = "lfw" if len(args) > 0 and args[0] == "lfw" else "lfwc"

    if cmd == "show":
        if len(args) > 0 and args[0] == "lfw":
            show_images(lfw_paths())
        else:
            show_images(lfwc_paths())
        return

    n_samples = 10000
    sh = (20, 20)
    out_shape = (8, 8)
    data = load_lfw() if data_set == "lfw" else load_lfwc()
    out_dir = "./img/"
    shape_range = range(8, 21, 2)

    if cmd == "sample":
        patches = sample_patches(data, patch_shape=sh, n_samples=n_samples)
        tile_images(patches, patch_shape=sh, output_shape=out_shape, show=True)

    elif cmd == "sweep":
        shapes = [(i, i) for i in shape_range]
        for sh in shapes:
            patches = sample_patches(data, n_samples=n_samples, patch_shape=sh)
            img = tile_images(patches, sh, output_shape=out_shape, show=True)
            fn = os.path.join(out_dir,
                              "{}_patches_{}.png".format(data_set, sh))
            print("saving {}...".format(fn))
            img.save(fn)

    elif cmd == "alfw":
        print(load_alfw())

    else:
        print("unexpected cmd")
コード例 #5
0
ファイル: faces.py プロジェクト: dracz/vision
def main(cmd, args):
    """process the command"""
    data_set = "lfw" if len(args) > 0 and args[0] == "lfw" else "lfwc"

    if cmd == "show":
        if len(args) > 0 and args[0] == "lfw":
            show_images(lfw_paths())
        else:
            show_images(lfwc_paths())
        return

    n_samples = 10000
    sh = (20, 20)
    out_shape = (8, 8)
    data = load_lfw() if data_set == "lfw" else load_lfwc()
    out_dir = "./img/"
    shape_range = range(8, 21, 2)

    if cmd == "sample":
        patches = sample_patches(data, patch_shape=sh, n_samples=n_samples)
        tile_images(patches, patch_shape=sh, output_shape=out_shape, show=True)

    elif cmd == "sweep":
        shapes = [(i, i) for i in shape_range]
        for sh in shapes:
            patches = sample_patches(data, n_samples=n_samples, patch_shape=sh)
            img = tile_images(patches, sh, output_shape=out_shape, show=True)
            fn = os.path.join(out_dir, "{}_patches_{}.png".format(data_set, sh))
            print("saving {}...".format(fn))
            img.save(fn)

    elif cmd == "alfw":
        print(load_alfw())

    else:
        print("unexpected cmd")
コード例 #6
0
ファイル: face2vec.py プロジェクト: dracz/vision
def get_input(input_pattern=lfwc_pattern,
              n_samples=5000, patch_shape=(12, 12),
              n_images=-1, epsilon=0.1, norm_axis=0):
    """
    Get input data from lfwc patches
    :param input_pattern: The pattern to glob for image files
    :param n_images: The number of images to draw samples from
    :param n_samples: The number of samples to extract
    :param patch_shape: The shape of the patches to sample
    :param epsilon: Regularization for whitening, or None to disable whitening
    :param norm_axis: Whether to mean normalize across each feature (0) or each patch (1)
    :return: m x n ndarray of flattened images
    """

    paths = glob.glob(input_pattern)
    random.shuffle(paths)
    paths = paths[:n_images]

    print("Sampling {} {}x{} patches from {} images..."
          .format(n_samples, patch_shape[0], patch_shape[1], len(paths)))

    # read images into a m x img_row x img_col ndarray
    imgs = np.asarray([cv2.imread(path, 0) for path in paths], dtype=theano.config.floatX)
    imgs /= 255.  # scale to [0,1]

    if input_pattern == lfwc_pattern and patch_shape == (64,64) and n_samples == lfwc_count:
        patches = load_matrix_2d(paths)
        epsilon = 0 # disable whitening... too slow

    else:
        # sample patches into a 3d array
        patches = sample_patches(imgs, patch_shape=patch_shape, n_samples=n_samples)

        # flatten each image to 1d
        patches = patches.reshape(patches.shape[0], patch_shape[0]*patch_shape[1])

    if norm_axis == 0:
        # subtract per feature (pixel) means across the data set (m x n)
        normed = patches - patches.mean(axis=0)
    else:
        # remove mean val of each patch
        normed = patches - patches.mean(axis=1)[:, np.newaxis]

    if epsilon is None:
        return normed

    return whiten_zca(normed, epsilon=epsilon)