示例#1
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def test_cwh_to_chw():

    x = np.array([[[ 1, 2, 3],
                   [ 4, 5, 6]
                   ],
                  [[ 7, 8, 9],
                   [10, 11, 12]
                   ],
                  [[13, 14, 15],
                   [16, 17, 18],
                   ],
                  [[19, 20, 21],
                   [22, 23, 24]
                   ]
                  ])

    assert_equals(x.shape[0], 4)  # c
    assert_equals(x.shape[1], 2)  # w
    assert_equals(x.shape[2], 3)  # h

    y = mu.cwh_to_chw(x)

    assert_equals(y.shape[0], 4)
    assert_equals(y.shape[1], 3)
    assert_equals(y.shape[2], 2)

    assert_equals(x[3][1][2], y[3][2][1])

    for i in range(4):
        for j in range(2):
            for k in range(3):
                assert_equals(x[i][j][k], y[i][k][j])
示例#2
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def nyudv2_to_lmdb(path_mat, dst_prefix, dir_dst, val_list=None):

    val_list = val_list or []
    if not os.path.isfile(path_mat):
        raise IOError("Path is not a regular file (%s)" % path_mat)

    _, ext = os.path.splitext(path_mat)

    if ext != '.mat' and ext != '.h5' and ext != '.hdf5':
        raise IOError("Invalid file type, expecting mat/h5/hdf5 file (%s)" %
                      path_mat)

    try:
        data = io.loadmat(path_mat)
    except (ValueError, NotImplementedError):
        data = h5py.File(
            path_mat)  # support version >= 7.3 matfile HDF5 format
        pass

    lmdb_info = []
    train_idx = None

    for typ in [
            NYUDV2DataType.IMAGES, NYUDV2DataType.LABELS, NYUDV2DataType.DEPTHS
    ]:

        if typ == NYUDV2DataType.IMAGES:

            dat = [mu.cwh_to_chw(x).astype(np.float) for x in data[typ]]

        elif typ == NYUDV2DataType.LABELS:

            dat = np.expand_dims(data[typ], axis=1).astype(int)
            dat = big_arr_to_arrs(dat)

        elif typ == NYUDV2DataType.DEPTHS:

            dat = np.expand_dims(data[typ], axis=1).astype(np.float)
            dat = big_arr_to_arrs(dat)

        else:
            raise ValueError("unknown NYUDV2DataType")

        if train_idx is None:
            train_idx, val_idx = get_train_val_split_from_idx(
                len(dat), val_list)
            shuffle(train_idx)
            print(train_idx)

    #     # len(ndarray) same as ndarray.shape[0]
    #     if  len(labels) != len(imgs):
    #         raise ValueError("No. of images != no. of labels. (%d) != (%d)",
    #                          len(imgs), len(labels))
    #
    #     if  len(labels) != len(depths):
    #         raise ValueError("No. of depths != no. of labels. (%d) != (%d)",
    #                          len(depths), len(labels))

        print typ, len(dat), dat[0].shape

        fpath_lmdb = os.path.join(dir_dst,
                                  '%s%s_train_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in train_idx], fpath_lmdb)

        lmdb_info.append((len(train_idx), fpath_lmdb))

        fpath_lmdb = os.path.join(dir_dst, '%s%s_val_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in val_idx], fpath_lmdb)

        lmdb_info.append((len(val_idx), fpath_lmdb))

    return lmdb_info
示例#3
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def big_arr_to_arrs(a):
    """
    Turn NxCxWxH array into list of CxHxW for Caffe
    """
    return [mu.cwh_to_chw(x) for x in a]
示例#4
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def nyudv2_to_lmdb(path_mat,
                   dst_prefix,
                   dir_dst,
                   val_list=None):

    val_list = val_list or []
    if not os.path.isfile(path_mat):
        raise IOError("Path is not a regular file (%s)" % path_mat)

    _, ext = os.path.splitext(path_mat)

    if ext != '.mat' and ext != '.h5'  and ext != '.hdf5' :
        raise IOError("Invalid file type, expecting mat/h5/hdf5 file (%s)" % path_mat)

    try:
        data = io.loadmat(path_mat)
    except (ValueError, NotImplementedError):
        data = h5py.File(path_mat)  # support version >= 7.3 matfile HDF5 format
        pass

    lmdb_info = []
    train_idx = None

    for typ in [NYUDV2DataType.IMAGES,
                NYUDV2DataType.LABELS,
                NYUDV2DataType.DEPTHS]:

        if typ == NYUDV2DataType.IMAGES:

            dat = [mu.cwh_to_chw(x).astype(np.float) for x in data[typ]]

        elif typ == NYUDV2DataType.LABELS:

            dat = np.expand_dims(data[typ], axis=1).astype(int)
            dat = big_arr_to_arrs(dat)

        elif typ == NYUDV2DataType.DEPTHS:

            dat = np.expand_dims(data[typ], axis=1).astype(np.float)
            dat = big_arr_to_arrs(dat)

        else:
            raise ValueError("unknown NYUDV2DataType")


        if train_idx is None:
            train_idx, val_idx = get_train_val_split_from_idx(len(dat), val_list)
            shuffle(train_idx)
            print(train_idx)

    #     # len(ndarray) same as ndarray.shape[0]
    #     if  len(labels) != len(imgs):
    #         raise ValueError("No. of images != no. of labels. (%d) != (%d)",
    #                          len(imgs), len(labels))
    #
    #     if  len(labels) != len(depths):
    #         raise ValueError("No. of depths != no. of labels. (%d) != (%d)",
    #                          len(depths), len(labels))

        print typ, len(dat), dat[0].shape

        fpath_lmdb = os.path.join(dir_dst, '%s%s_train_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in train_idx], fpath_lmdb)

        lmdb_info.append((len(train_idx), fpath_lmdb))

        fpath_lmdb = os.path.join(dir_dst, '%s%s_val_lmdb' % (dst_prefix, typ))
        to_lmdb.arrays_to_lmdb([dat[i] for i in val_idx], fpath_lmdb)

        lmdb_info.append((len(val_idx), fpath_lmdb))

    return lmdb_info
示例#5
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def big_arr_to_arrs(a):
    """
    Turn NxCxWxH array into list of CxHxW for Caffe
    """
    return [mu.cwh_to_chw(x) for x in a]