Using Bob Databases

Bob database packages can be used to query databases for samples and associated metadata. The queries can be done both in Python or through a command-line-based application (see Development Guide).


A sample represents one atomic data point from the dataset. For example, this may mean:

  • An image of a person’s face in a face recognition database,

  • An audio track within a file (with multiple audio tracks) in a speech recognition database,

  • A row in a large file containing measurements of flower petals (width and length),

  • A phrase in a collection of documents stored in an SQL server.

Because sample storage varies from dataset to dataset, it is difficult to provide a one-fits-all set of tools to read out samples from any kind of support. Bob provides typical readout routines for samples stored in files (one-file-per-sample model), while other tools for reading subsets of data from files are provided by third-party libraries or within the Python standard library.

A programmatic interface to a database represents an abstraction of this concept and should allow usage of such samples independently of the storage model used by the raw dataset, allowing the user to, as transparently as possible, access information with minimal setup.

Currently, among all types of samples implemented through different db packages in the Bob eco-system, file-based samples are probably the most used. In a file-based storage, typically, one raw sample corresponds to data stored in a single file on the file system. Very often, samples are organized in subdirectories and share a common root installation directory.

Example Database

We’ll use a hypothetical image database to exemplify database usage and development in this guide. This database is small and its raw files are included with this package (tests/sample/data directory, on the root of the package).

This is its directory organization:

+-- dir1
|   +-- sample1-1.png
|   +-- sample1-1.txt
|   +-- sample1-2.png
|   +-- sample1-2.txt
+-- dir2
|   +-- sample2-1.png
|   +-- sample2-1.txt
|   +-- sample2-2.png
|   +-- sample2-2.txt
+-- dir3
    +-- sample3-1.png
    +-- sample3-1.txt
    +-- sample3-2.png
    +-- sample3-2.txt

Each sample in this database has the suffix .png and corresponds to an image. For each image, there is a matching file containing tag annotations (2 per image, one tag per line) indicating what the image file contains and the dominant color. The tag files have a .txt suffix.

The hypothetical task this database was primarily designed for is “dominant color recognition”: You get an image and you must tell which color is the dominant color in the image.

The authors of this database defined a usage protocol for the images in the database to allow different publications to be compared fairly. Images ending with -1.png should be used for training and/or validation, while images ending with -2.png should be used for testing (i.e., may not be used for adjusting system hyper-parameters).

Your task is to devise a programmatic python-API allowing users of this db package interface to easily select train or test images for modelling and testing their classifiers respectively.

The Python API

There are no firm project standards for the pythonic API of a Bob db package, though we advise package developers to follow guidelines (see Development Guide) which ensure homogeneity through different packages. Typically, interfaces provide a Database class, allowing the user to build an object that will be used to access raw data samples (and associated metadata) available in the dataset, possibly constrained to a number of selector parameters for sub-selecting samples.

The constructor of a database is pretty much database dependent, but it generally allows the user to set up installation-dependent parameters such as, for example, the location where raw data files may be stored, in case those are not shipped with the Bob db package. Examples in this guide try to abstract away from such specificities in order to provide a general understanding of the framework. When specific information is required about a db package interface, we recommend you read the specific API documentation for that package.

The usage of a db package API normally goes through 3 stages:

  1. Construct a Database object

  2. Use the Database.objects() method to query for samples

  3. Use the returned list of samples in your application.

Here is an example of a Bob db package interface in use:

>>> from bob.db.base.tests.sample import Database
>>> db = Database()
>>> for sample in db.objects():
...     print(sample)

In this example, the user imports the data package (line 1), instantiates the database (line 2) and then starts iterating over its objects (line 3). Each object returned by the objects() method represents one sample from the database.

Each sample in the database in turn provides a number of methods to access information about its raw or meta-data, allowing the user to create a continuous processing pipeline.

Database sample objects often provide a load() allowing the pointed object to be loaded in memory:

>>> all_samples = list(db.objects())
>>> f = all_samples[0] #get only sample 0
>>> type(f)
<class 'bob.db.base.tests.sample.Sample'>

Each “sample” returned by bob.db.base.tests.sample.Database.objects() is actually an object of class bob.db.base.tests.sample.Sample, representing the abstraction of a single (raw) dataset file. File objects in this package also contain a path variable that point to their relative location with respect to a database root directory:

>>> f.path 

You may use the method bob.db.base.tests.sample.Sample.make_path() to construct paths which contain both a prefix directory and a suffix extension. For example, to build a full path to an installed image in the raw dataset, call this method without any parameters:

>>> f.make_path()

You may override the default directory and extensions that are attached to the return path. For example:

>>> f.make_path('/another/path', '.hdf5')

You may load the contents of the image file pointed by this database entry using the bob.db.base.tests.sample.Sample.load() method:

>>> import
>>> image = f.load()
>>> type(image) 
<... 'numpy.ndarray'>
>>> image.shape
(3, 128, 128)
>>> image.dtype


In data processing pipelines, it is typical to save the intermediate result of processing images to temporary files you’ll need to load later. In Bob, those files are normally HDF5 files (see Bob’s Core I/O Routines). You can easily create a processing pipeline re-using the database interface like this:

1>>> image = f.load()
2>>> processed = processor(image)
3>>>, '/path/to/processed', '.hdf5')
4# stores "processed" in an HDF5 file file named /path/to/processed/s1/9.hdf5

Line 1 loads the image. Line 2 processes the image and generates a processed version of the image (e.g. as a numpy.ndarray). Line 3 uses this db package interface to save the resulting file, respecting the original database structure. This is convenient because of two reasons:

  1. You can manually inspect the directory containing processed images and quickly find the processed version of any original image in the database;

  2. You can re-use bob.db.base.tests.sample.Sample.load() to reload the processed file and continue the pipelining indefinitely.

For example, suppose one would like to re-process the processed image above, it is possible to repeat the coding pattern above, now defining input and output directories:

>>> processed = f.load('/path/to/processed', '.hdf5')
>>> reprocessed = reprocessor(processed)
>>>, '/path/to/reprocessed', '.hdf5')


You may iterate over a subset of samples from the sample database using parameters to bob.db.base.tests.sample.Database.objects() (check its documentation for details). For example, to iterate over all the training images, one can write:

>>> training_images = []
>>> for sample in db.objects(group='train'):
...   training_images.append(sample.load())

Command-line Interface

The command-line interface allows users to check or export information encoded in Python API via the console. Its main purpose is to allow quick administrative and sanity verifications. The most important command-line option for the main database program is --help. If you pass it to the main program, it prints a list of all currently installed databases:

$ --help
usage: [-h] {samples,all} ...

This script drives all commands from the specific database subdrivers.

optional arguments:
  -h, --help     show this help message and exit

    samples      Samples dataset
    all          Drive commands to all (above) databases in one shot

  For a list of available databases:
  >>> --help

  For a list of actions on a database:
  >>> <database-name> --help

From the example above, one observes a single db package is installed on that environment, called samples (our example database). The entry all refers to a shortcut allowing the user to interact with all installed databases at once.

Each database interface implementation is free to set up any number of commands that may be required for command-line usage. To access the list of commands available for the samples use the --help command-line option again:

$ samples --help
usage: samples [-h] {version,files,dumplist,checkfiles} ...

optional arguments:
  -h, --help            show this help message and exit

    version             Outputs the database version
    files               Prints the current location of raw database files.
    dumplist            Dumps list of files based on your criteria
    checkfiles          Check if the files exist, based on your criteria

Each of the commands produces a different output and runs different routines. The version command, for example, prints the version of the database:

$ samples version
samples == 2.2.1b0

The command dumplist dumps a list of files that belong to the database:

$ samples dumplist --directory='' --extension=''

The interface provided by the samples db package also allows the user to filter down the printed list of files, to only print, for instance, files that belong to the train set using the command-line option --group:

$ samples dumplist --group=train --directory='' --extension=''

The command checkfiles runs a file search to make sure all files for the database (or a given group) are available on a base directory. This is useful, for example, to check the completeness of a pipeline after it was run. Suppose, for instance, that we ran through the samples database, a script to process all images and extract color histograms which we saved in a directory called histograms. Now, we would like to check if all files have been correctly processed. In this case, we can simply do:

$ samples checkfiles --directory='histograms' --extension='.hdf5'
Cannot find file "histograms/dir1/sample1-2.hdf5"
Cannot find file "histograms/dir2/sample2-1.hdf5"
2 files (out of 6) were not found at "histograms"

The example output shown above indicates that our earlier pipeline possibly missed two files.