Function load_voc_dataset

tensorlayer/files/utils.py:1352–1674

Pascal VOC 2007/2012 Dataset. It has 20 objects: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor and additional 3 classes : head, hand, foot for person. Parameters --------

(path='data', dataset='2012', contain_classes_in_person=False)

def load_voc_dataset(path='data', dataset='2012', contain_classes_in_person=False):
    """Pascal VOC 2007/2012 Dataset.

    It has 20 objects:
    aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
    and additional 3 classes : head, hand, foot for person.

    Parameters
    -----------
    path : str
        The path that the data is downloaded to, defaults is ``data/VOC``.
    dataset : str
        The VOC dataset version, `2012`, `2007`, `2007test` or `2012test`. We usually train model on `2007+2012` and test it on `2007test`.
    contain_classes_in_person : boolean
        Whether include head, hand and foot annotation, default is False.

    Returns
    ---------
    imgs_file_list : list of str
        Full paths of all images.
    imgs_semseg_file_list : list of str
        Full paths of all maps for semantic segmentation. Note that not all images have this map!
    imgs_insseg_file_list : list of str
        Full paths of all maps for instance segmentation. Note that not all images have this map!
    imgs_ann_file_list : list of str
        Full paths of all annotations for bounding box and object class, all images have this annotations.
    classes : list of str
        Classes in order.
    classes_in_person : list of str
        Classes in person.
    classes_dict : dictionary
        Class label to integer.
    n_objs_list : list of int
        Number of objects in all images in ``imgs_file_list`` in order.
    objs_info_list : list of str
        Darknet format for the annotation of all images in ``imgs_file_list`` in order. ``[class_id x_centre y_centre width height]`` in ratio format.
    objs_info_dicts : dictionary
        The annotation of all images in ``imgs_file_list``, ``{imgs_file_list : dictionary for annotation}``,
        format from `TensorFlow/Models/object-detection <https://github.com/tensorflow/models/blob/master/object_detection/create_pascal_tf_record.py>`__.

    Examples
    ----------
    >>> imgs_file_list, imgs_semseg_file_list, imgs_insseg_file_list, imgs_ann_file_list,
    >>>     classes, classes_in_person, classes_dict,
    >>>     n_objs_list, objs_info_list, objs_info_dicts = tl.files.load_voc_dataset(dataset="2012", contain_classes_in_person=False)
    >>> idx = 26
    >>> print(classes)
    ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']
    >>> print(classes_dict)
    {'sheep': 16, 'horse': 12, 'bicycle': 1, 'bottle': 4, 'cow': 9, 'sofa': 17, 'car': 6, 'dog': 11, 'cat': 7, 'person': 14, 'train': 18, 'diningtable': 10, 'aeroplane': 0, 'bus': 5, 'pottedplant': 15, 'tvmonitor': 19, 'chair': 8, 'bird': 2, 'boat': 3, 'motorbike': 13}
    >>> print(imgs_file_list[idx])
    data/VOC/VOC2012/JPEGImages/2007_000423.jpg
    >>> print(n_objs_list[idx])
    2
    >>> print(imgs_ann_file_list[idx])
    data/VOC/VOC2012/Annotations/2007_000423.xml
    >>> print(objs_info_list[idx])
    14 0.173 0.461333333333 0.142 0.496