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anomalib.data.utils

Helper utilities for data.

Subpackages

• anomalib.data.utils.generators
  • anomalib.data.utils.generators.perlin

Submodules

• anomalib.data.utils.download
• anomalib.data.utils.image
• anomalib.data.utils.split

Package Contents

Classes

DownloadProgressBar

Create progress bar for urlretrieve. Subclasses tqdm.

Functions

hash_check(file_path, expected_hash)	Raise assert error if hash does not match the calculated hash of the file.
random_2d_perlin(→ Union[numpy.ndarray, torch.Tensor])	Returns a random 2d perlin noise array.
generate_output_image_filename(→ pathlib.Path)	Generate an output filename to save the inference image.
get_image_filenames(→ List[pathlib.Path])	Get image filenames.
read_image(→ numpy.ndarray)	Read image from disk in RGB format.

class anomalib.data.utils.DownloadProgressBar(iterable: Optional[Iterable] = None, desc: Optional[str] = None, total: Optional[Union[int, float]] = None, leave: Optional[bool] = True, file: Optional[Union[io.TextIOWrapper, io.StringIO]] = None, ncols: Optional[int] = None, mininterval: Optional[float] = 0.1, maxinterval: Optional[float] = 10.0, miniters: Optional[Union[int, float]] = None, use_ascii: Optional[Union[bool, str]] = None, disable: Optional[bool] = False, unit: Optional[str] = 'it', unit_scale: Optional[Union[bool, int, float]] = False, dynamic_ncols: Optional[bool] = False, smoothing: Optional[float] = 0.3, bar_format: Optional[str] = None, initial: Optional[Union[int, float]] = 0, position: Optional[int] = None, postfix: Optional[Dict] = None, unit_divisor: Optional[float] = 1000, write_bytes: Optional[bool] = None, lock_args: Optional[tuple] = None, nrows: Optional[int] = None, colour: Optional[str] = None, delay: Optional[float] = 0, gui: Optional[bool] = False, **kwargs)

Bases: tqdm.tqdm

Create progress bar for urlretrieve. Subclasses tqdm.

For information about the parameters in constructor, refer to tqdm’s documentation.

Parameters

• iterable (Optional[Iterable]) – Iterable to decorate with a progressbar. Leave blank to manually manage the updates.

• desc (Optional[str]) – Prefix for the progressbar.

• total (Optional[Union[int, float]]) – The number of expected iterations. If unspecified, len(iterable) is used if possible. If float(“inf”) or as a last resort, only basic progress statistics are displayed (no ETA, no progressbar). If gui is True and this parameter needs subsequent updating, specify an initial arbitrary large positive number, e.g. 9e9.

• leave (Optional[bool]) – upon termination of iteration. If None, will leave only if position is 0.

• file (Optional[Union[io.TextIOWrapper, io.StringIO]]) – Specifies where to output the progress messages (default: sys.stderr). Uses file.write(str) and file.flush() methods. For encoding, see write_bytes.

• ncols (Optional[int]) – The width of the entire output message. If specified, dynamically resizes the progressbar to stay within this bound. If unspecified, attempts to use environment width. The fallback is a meter width of 10 and no limit for the counter and statistics. If 0, will not print any meter (only stats).

• mininterval (Optional[float]) – Minimum progress display update interval [default: 0.1] seconds.

• maxinterval (Optional[float]) – Maximum progress display update interval [default: 10] seconds. Automatically adjusts miniters to correspond to mininterval after long display update lag. Only works if dynamic_miniters or monitor thread is enabled.

• miniters (Optional[Union[int, float]]) – Minimum progress display update interval, in iterations. If 0 and dynamic_miniters, will automatically adjust to equal mininterval (more CPU efficient, good for tight loops). If > 0, will skip display of specified number of iterations. Tweak this and mininterval to get very efficient loops. If your progress is erratic with both fast and slow iterations (network, skipping items, etc) you should set miniters=1.

• use_ascii (Optional[Union[bool, str]]) – If unspecified or False, use unicode (smooth blocks) to fill the meter. The fallback is to use ASCII characters ” 123456789#”.

• disable (Optional[bool]) – Whether to disable the entire progressbar wrapper [default: False]. If set to None, disable on non-TTY.

• unit (Optional[str]) – String that will be used to define the unit of each iteration [default: it].

• unit_scale (Union[bool, int, float]) – If 1 or True, the number of iterations will be reduced/scaled automatically and a metric prefix following the International System of Units standard will be added (kilo, mega, etc.) [default: False]. If any other non-zero number, will scale total and n.

• dynamic_ncols (Optional[bool]) – If set, constantly alters ncols and nrows to the environment (allowing for window resizes) [default: False].

• smoothing (Optional[float]) – Exponential moving average smoothing factor for speed estimates (ignored in GUI mode). Ranges from 0 (average speed) to 1 (current/instantaneous speed) [default: 0.3].

• bar_format (Optional[str]) – Specify a custom bar string formatting. May impact performance. [default: ‘{l_bar}{bar}{r_bar}’], where l_bar=’{desc}: {percentage:3.0f}%|’ and r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘ ‘{rate_fmt}{postfix}]’ Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt, percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, rate, rate_fmt, rate_noinv, rate_noinv_fmt, rate_inv, rate_inv_fmt, postfix, unit_divisor, remaining, remaining_s, eta. Note that a trailing “: ” is automatically removed after {desc} if the latter is empty.

• initial (Optional[Union[int, float]]) – The initial counter value. Useful when restarting a progress bar [default: 0]. If using float, consider specifying {n:.3f} or similar in bar_format, or specifying unit_scale.

• position (Optional[int]) – Specify the line offset to print this bar (starting from 0) Automatic if unspecified. Useful to manage multiple bars at once (eg, from threads).

• postfix (Optional[Dict]) – Specify additional stats to display at the end of the bar. Calls set_postfix(**postfix) if possible (dict).

• unit_divisor (Optional[float]) – [default: 1000], ignored unless unit_scale is True.

• write_bytes (Optional[bool]) – If (default: None) and file is unspecified, bytes will be written in Python 2. If True will also write bytes. In all other cases will default to unicode.

• lock_args (Optional[tuple]) – Passed to refresh for intermediate output (initialisation, iterating, and updating). nrows (Optional[int]): The screen height. If specified, hides nested bars outside this bound. If unspecified, attempts to use environment height. The fallback is 20.

• colour (Optional[str]) – Bar colour (e.g. ‘green’, ‘#00ff00’).

• delay (Optional[float]) – Don’t display until [default: 0] seconds have elapsed.

• gui (Optional[bool]) – WARNING: internal parameter - do not use. Use tqdm.gui.tqdm(…) instead. If set, will attempt to use matplotlib animations for a graphical output [default: False].

Example

>>> with DownloadProgressBar(unit='B', unit_scale=True, miniters=1, desc=url.split('/')[-1]) as p_bar:
>>>         urllib.request.urlretrieve(url, filename=output_path, reporthook=p_bar.update_to)

update_to(chunk_number: int = 1, max_chunk_size: int = 1, total_size=None)

Progress bar hook for tqdm.

Based on https://stackoverflow.com/a/53877507 The implementor does not have to bother about passing parameters to this as it gets them from urlretrieve. However the context needs a few parameters. Refer to the example.

Parameters

• chunk_number (int, optional) – The current chunk being processed. Defaults to 1.

• max_chunk_size (int, optional) – Maximum size of each chunk. Defaults to 1.

• total_size ([type], optional) – Total download size. Defaults to None.

anomalib.data.utils.hash_check(file_path: pathlib.Path, expected_hash: str)

Raise assert error if hash does not match the calculated hash of the file.

Parameters

• file_path (Path) – Path to file.

• expected_hash (str) – Expected hash of the file.

anomalib.data.utils.random_2d_perlin(shape: Tuple, res: Tuple[Union[int, torch.Tensor], Union[int, torch.Tensor]], fade=lambda t: ...) → Union[numpy.ndarray, torch.Tensor]

Returns a random 2d perlin noise array.

Parameters

• shape (Tuple) – Shape of the 2d map.

• res (Tuple[Union[int, Tensor]]) – Tuple of scales for perlin noise for height and width dimension.

• fade (_type_, optional) – Function used for fading the resulting 2d map. Defaults to equation 6*t**5-15*t**4+10*t**3.

Returns

Random 2d-array/tensor generated using perlin noise.

Return type

Union[np.ndarray, Tensor]

anomalib.data.utils.generate_output_image_filename(input_path: Union[str, pathlib.Path], output_path: Union[str, pathlib.Path]) → pathlib.Path

Generate an output filename to save the inference image.

This function generates an output filaname by checking the input and output filenames. Input path is the input to infer, and output path is the path to save the output predictions specified by the user.

The function expects input_path to always be a file, not a directory. output_path could be a filename or directory. If it is a filename, the function checks if the specified filename exists on the file system. If yes, the function calls duplicate_filename to duplicate the filename to avoid overwriting the existing file. If output_path is a directory, this function adds the parent and filenames of input_path to output_path.

Parameters

• input_path (Union[str, Path]) – Path to the input image to infer.

• output_path (Union[str, Path]) – Path to output to save the predictions. Could be a filename or a directory.

Examples

>>> input_path = Path("datasets/MVTec/bottle/test/broken_large/000.png")
>>> output_path = Path("datasets/MVTec/bottle/test/broken_large/000.png")
>>> generate_output_image_filename(input_path, output_path)
PosixPath('datasets/MVTec/bottle/test/broken_large/000_1.png')

>>> input_path = Path("datasets/MVTec/bottle/test/broken_large/000.png")
>>> output_path = Path("results/images")
>>> generate_output_image_filename(input_path, output_path)
PosixPath('results/images/broken_large/000.png')

Raises

ValueError – When the input_path is not a file.

Returns

The output filename to save the output predictions from the inferencer.

Return type

Path

anomalib.data.utils.get_image_filenames(path: Union[str, pathlib.Path]) → List[pathlib.Path]

Get image filenames.

Parameters

path (Union[str, Path]) – Path to image or image-folder.

Returns

List of image filenames

Return type

List[Path]

anomalib.data.utils.read_image(path: Union[str, pathlib.Path]) → numpy.ndarray

Read image from disk in RGB format.

Parameters

path (str, Path) – path to the image file

Example

>>> image = read_image("test_image.jpg")

Returns

image as numpy array