train_validation_split.py 文件源码

def _label_statistics(image_paths):
    '''

    Calculates label statistics (number of picked pixels for each class)

    Parameters
    ----------
    image_paths : list
        List of absolute paths for picked images

    Returns
    -------
    array: numpy array
        Number of selected pixels per class


    '''
    ds = KittiDataset()

    def _rgb_2_label(rgb):
        return ds.color2label[tuple(rgb)].trainId

    total_counts = np.zeros(ds.num_classes())
    for img in image_paths:
        rgb = skimage.data.load(img)
        labels = np.apply_along_axis(_rgb_2_label, 2, rgb)
        indices, counts = np.unique(labels, return_counts=True)
        if indices[-1] >= ds.num_classes():
            indices = indices[0:-1]
            counts = counts[0:-1]
        total_counts[indices] += counts
    return total_counts