cc31d7f56d6cd8569a5f7b47c254d89a85e8691b,skimage/transform/pyramids.py,,pyramid_laplacian,#Any#Any#Any#Any#Any#Any#Any#Any#,215

Before Change

        // automatically determine sigma which covers > 99% of distribution
        sigma = 2 * downscale / 6.0

    layer = 0
    current_shape = image.shape

    smoothed_image = _smooth(image, sigma, mode, cval, multichannel)
    yield image - smoothed_image

    // build downsampled images until max_layer is reached or downscale process
    // does not change image size
    while layer != max_layer:
        layer += 1

        out_shape = tuple(
            [math.ceil(d / float(downscale)) for d in current_shape])

        if multichannel:
            out_shape = out_shape[:-1]

        resized_image = resize(smoothed_image, out_shape,
                               order=order, mode=mode, cval=cval)
        smoothed_image = _smooth(resized_image, sigma, mode, cval,
                                 multichannel)

        prev_shape = np.asarray(current_shape)
        current_shape = np.asarray(resized_image.shape)

        // no change to previous pyramid layer
        if np.all(current_shape == prev_shape):
            break

        yield resized_image - smoothed_image

After Change

    // build downsampled images until max_layer is reached or downscale process
    // does not change image size
    if max_layer == -1:
        max_layer = int(np.ceil(math.log(np.max(current_shape), downscale)))

    for layer in range(max_layer):

        out_shape = tuple(
            [math.ceil(d / float(downscale)) for d in current_shape])

        if multichannel:
            out_shape = out_shape[:-1]

        resized_image = resize(smoothed_image, out_shape,
                               order=order, mode=mode, cval=cval)
        smoothed_image = _smooth(resized_image, sigma, mode, cval,
                                 multichannel)
        current_shape = np.asarray(resized_image.shape)

        yield resized_image - smoothed_image

Instances