Python tables.UInt8Atom方法代碼示例

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def create_carray(h5f, chrom, data_type):
    if data_type == "uint8":
        atom = tables.UInt8Atom(dflt=0)
    elif data_type == "uint16":
        atom = tables.UInt16Atom(dflt=0)
    else:
        raise NotImplementedError("unsupported datatype %s" % data_type)

    zlib_filter = tables.Filters(complevel=1, complib="zlib")

    # create CArray for this chromosome
    shape = [chrom.length]
    carray = h5f.create_carray(h5f.root, chrom.name,
                              atom, shape, filters=zlib_filter)

    return carray 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def fetch_svhn_extra(source_paths, target_path):
    extra_path = source_paths[0]

    print('Converting {} to HDF5 (compressed)...'.format(extra_path))
    f_out = tables.open_file(target_path, mode='w')
    g_out = f_out.create_group(f_out.root, 'svhn', 'SVHN data')
    filters = tables.Filters(complevel=9, complib='blosc')
    X_u8_arr = f_out.create_earray(
        g_out, 'extra_X_u8', tables.UInt8Atom(), (0, 3, 32, 32),
        filters=filters)
    y_arr = f_out.create_earray(
        g_out, 'extra_y', tables.Int32Atom(), (0,), filters=filters)

    # Load in the extra data Matlab file
    _insert_svhn_matlab_to_h5(X_u8_arr, y_arr, extra_path)

    f_out.close()

    return target_path 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def setUp(self):
        num_rows = 500
        filters = tables.Filters(complib='blosc', complevel=5)
        h5file = tables.open_file(
            'tmp.h5', mode='w', title='Test', filters=filters)
        group = h5file.create_group("/", 'Data')
        atom = tables.UInt8Atom()
        y = h5file.create_carray(group, 'y', atom=atom, title='Data targets',
                                 shape=(num_rows, 1), filters=filters)
        for i in range(num_rows):
            y[i] = i
        h5file.flush()
        h5file.close()
        self.dataset = PytablesDataset('tmp.h5', ('y',), 20, 500)
        self.dataset_default = PytablesDataset('tmp.h5', ('y',)) 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def createImgGroup(fid, name, tot_frames, im_height, im_width, is_expandable=True):
    parentnode, _, name = name.rpartition('/')
    parentnode += '/'

    if is_expandable:
        img_dataset = fid.create_earray(
                        parentnode,
                        name,
                        atom=tables.UInt8Atom(),
                        shape =(0,
                             im_height,
                             im_width),
                        chunkshape=(1,
                             im_height,
                             im_width),
                        expectedrows=tot_frames,
                        filters=TABLE_FILTERS
                        )
    else:
        img_dataset = fid.create_carray(
                        parentnode,
                        name,
                        atom=tables.UInt8Atom(),
                        shape =(tot_frames,
                             im_height,
                             im_width),
                        filters=TABLE_FILTERS
                        )

    img_dataset._v_attrs["CLASS"] = np.string_("IMAGE")
    img_dataset._v_attrs["IMAGE_SUBCLASS"] = np.string_("IMAGE_GRAYSCALE")
    img_dataset._v_attrs["IMAGE_WHITE_IS_ZERO"] = np.array(0, dtype="uint8")
    img_dataset._v_attrs["DISPLAY_ORIGIN"] = np.string_("UL")  # not rotated
    img_dataset._v_attrs["IMAGE_VERSION"] = np.string_("1.2")

    return img_dataset 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def create_data_file(out_file, n_channels, n_samples, image_shape):
    hdf5_file = tables.open_file(out_file, mode='w')
    filters = tables.Filters(complevel=5, complib='blosc')
    data_shape = tuple([0, n_channels] + list(image_shape))
    truth_shape = tuple([0, 1] + list(image_shape))
    data_storage = hdf5_file.create_earray(hdf5_file.root, 'data', tables.Float32Atom(), shape=data_shape,
                                           filters=filters, expectedrows=n_samples)
    truth_storage = hdf5_file.create_earray(hdf5_file.root, 'truth', tables.UInt8Atom(), shape=truth_shape,
                                            filters=filters, expectedrows=n_samples)
    affine_storage = hdf5_file.create_earray(hdf5_file.root, 'affine', tables.Float32Atom(), shape=(0, 4, 4),
                                             filters=filters, expectedrows=n_samples)
    return hdf5_file, data_storage, truth_storage, affine_storage 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def create_data_file(out_file, n_channels, n_samples, image_shape):
    hdf5_file = tables.open_file(out_file, mode='w')
    filters = tables.Filters(complevel=5, complib='blosc')
    data_shape = tuple([0, n_channels] + list(image_shape))
    truth_shape = tuple([0, 1])
    data_storage = hdf5_file.create_earray(hdf5_file.root, 'data', tables.Float32Atom(), shape=data_shape,
                                           filters=filters, expectedrows=n_samples)
    truth_storage = hdf5_file.create_earray(hdf5_file.root, 'truth', tables.UInt8Atom(), shape=truth_shape,
                                            filters=filters, expectedrows=n_samples)
    return hdf5_file, data_storage, truth_storage 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def _save_ndarray(handler, group, name, x, filters=None):
    if np.issubdtype(x.dtype, np.unicode_):
        # Convert unicode strings to pure byte arrays
        strtype = b'unicode'
        itemsize = x.itemsize // 4
        atom = tables.UInt8Atom()
        x = x.view(dtype=np.uint8)
    elif np.issubdtype(x.dtype, np.string_):
        strtype = b'ascii'
        itemsize = x.itemsize
        atom = tables.StringAtom(itemsize)
    elif x.dtype == np.object:
        # Not supported by HDF5, force pickling
        _save_pickled(handler, group, x, name=name)
        return
    else:
        atom = tables.Atom.from_dtype(x.dtype)
        strtype = None
        itemsize = None

    if x.ndim > 0 and np.min(x.shape) == 0:
        sh = np.array(x.shape)
        atom0 = tables.Atom.from_dtype(np.dtype(np.int64))
        node = handler.create_array(group, name, atom=atom0,
                                    shape=(sh.size,))
        node._v_attrs.zeroarray_dtype = np.dtype(x.dtype).str.encode('ascii')
        node[:] = sh
        return

    if x.ndim == 0 and len(x.shape) == 0:
        # This is a numpy array scalar. We will store it as a regular scalar
        # instead, which means it will be unpacked as a numpy scalar (not numpy
        # array scalar)
        setattr(group._v_attrs, name, x[()])
        return

    # For small arrays, compression actually leads to larger files, so we are
    # settings a threshold here. The threshold has been set through
    # experimentation.
    if filters is not None and x.size > 300:
        node = handler.create_carray(group, name, atom=atom,
                                     shape=x.shape,
                                     chunkshape=None,
                                     filters=filters)
    else:
        node = handler.create_array(group, name, atom=atom,
                                    shape=x.shape)
    if strtype is not None:
        node._v_attrs.strtype = strtype
        node._v_attrs.itemsize = itemsize
    node[:] = x 

# 需要導入模塊: import tables [as 別名]
# 或者: from tables import UInt8Atom [as 別名]
def prepare():
    import os
    import sys
    import numpy as np
    import tables
    import tqdm
    import domain_datasets
    import cv2

    synsigns_path = domain_datasets.get_data_dir('syn_signs')
    data_path = os.path.join(synsigns_path, 'synthetic_data')

    labels_path = os.path.join(data_path, 'train_labelling.txt')

    if not os.path.exists(labels_path):
        print('Labels path {} does not exist'.format(labels_path))
        sys.exit(0)

    # Open the file that lists the image files along with their ground truth class
    lines = [line.strip() for line in open(labels_path, 'r').readlines()]
    lines = [line for line in lines if line != '']

    output_path = os.path.join(synsigns_path, 'syn_signs.h5')
    print('Creating {}...'.format(output_path))
    f_out = tables.open_file(output_path, mode='w')
    g_out = f_out.create_group(f_out.root, 'syn_signs', 'Syn-Signs data')
    filters = tables.Filters(complevel=9, complib='blosc')
    X_u8_arr = f_out.create_earray(
        g_out, 'X_u8', tables.UInt8Atom(), (0, 3, 40, 40), expectedrows=len(lines),
        filters=filters)

    y = []
    for line in tqdm.tqdm(lines):
        image_filename, gt, _ = line.split()
        image_path = os.path.join(data_path, image_filename)

        if not os.path.exists(image_path):
            print('Could not find image file {} mentioned in annotations'.format(image_path))
            return
        image_data = cv2.imread(image_path)[:, :, ::-1]

        X_u8_arr.append(image_data.transpose(2, 0, 1)[None, ...])
        y.append(int(gt))

    y = np.array(y, dtype=np.int32)
    f_out.create_array(g_out, 'y', y)

    print('X.shape={}'.format(X_u8_arr.shape))
    print('y.shape={}'.format(y.shape))

    f_out.close()