polimorfo.utils package

Submodules

polimorfo.utils.datautils module

polimorfo.utils.datautils.download_file(name: str, url: str, file_hash=None, extract: bool = True, cache_dir: str = '~/.polimorfo', cache_subdir: str = 'datasets') → List[pathlib.Path]

Downloads a file frolsm a URL if it not already saved Arguments:

name {str} – the name of the file (e.g. ) url {str} – the url of the file or the idx of the file in

case files are download from google drive

Keyword Arguments:

file_hash {str} – the hash of the file downloads (default: {None}) extract {bool} – try to extract the file (default: {True}) cache_dir {str} – the default folder where the file is saved

(default: {carambola.utils.datautils.CACHE_DIR})

cache_subdir {str} – the subdir where the file is downloaded

(default: {carambola.utils.datautils.CACHE_SUBDIR})

polimorfo.utils.datautils.download_from_gdrive(uri: str, dst_path: str) → pathlib.Path

download a file/folder from google drive Given a url https://drive.google.com/file/d/1EcUzQPNQXQGiHES9gU7oh-886wbBH3VF/view?usp=sharing the idx -> 1EcUzQPNQXQGiHES9gU7oh-886wbBH3VF Arguments:

uri {str} – the id of the file to download or the full google drive url dst_path {str} – the path to save the file

Returns:

Path – the path where the file is saved

polimorfo.utils.datautils.download_url(url: str, dst_path: str) → Tuple[pathlib.Path, int]

download a url to the destination folder Arguments:

url {str} – [description] dst_path {str} – [description]

Returns:

Tuple[Path, int] – [description]

polimorfo.utils.datautils.extract_archive(file_path: str, dst_path: str = '', archive_format='auto') → List[pathlib.Path]

Extract the archive if it match tar, tar.gz, tar.bz or zip format Arguments:

file_path {str} – the path to the archive

Keyword Arguments:

dst_path {str} – the path to extract the folder (default: {None}

the directory where the archive is placed)

archive_format {str} – The format of the archive (default: {‘auto’})

Options are: ‘auto’, ‘tar’, ‘zip’

Returns:

List[Path] – the paths where the file is saved

polimorfo.utils.datautils.validate_file(fpath, file_hash, algorithm='auto', chunk_size=65535)

Validates a file against a sha256 or md5 hash. Arguments:

fpath: path to the file being validated file_hash: The expected hash string of the file.

The sha256 and md5 hash algorithms are both supported.

algorithm: Hash algorithm, one of ‘auto’, ‘sha256’, or ‘md5’.

The default ‘auto’ detects the hash algorithm in use.

chunk_size: Bytes to read at a time, important for large files.

Returns:

Whether the file is valid

polimorfo.utils.imageutils module

polimorfo.utils.imageutils.download_image(url_path)

download an image and save to the destination path

Arguments:

url {str} – the url of the image path {Path} – the base path where the images shold be saved

Keyword Arguments:

timeout {int} – the timeout in seconds (default: {1})

polimorfo.utils.imageutils.download_images(urls_filepath: List[str], timeout: int)

downalod the images from the given list

Args:

urls_filepath (List[str]): the list of the urls timeout (int): the timeout to retrieve the image

polimorfo.utils.maskutils module

polimorfo.utils.visualizeutils module

class polimorfo.utils.visualizeutils.BoxType

Bases: enum.Enum

An enumeration.

xywh = 2

xyxy = 1

polimorfo.utils.visualizeutils.change_color_brightness(color: Tuple, brightness_factor: float)

Depending on the brightness_factor, gives a lighter or darker color i.e. a color with less or more saturation than the original color. Args:

color: color of the polygon. Refer to matplotlib.colors for a full list of

formats that are accepted.

brightness_factor (float): a value in [-1.0, 1.0] range. A lightness factor of

0 will correspond to no change, a factor in [-1.0, 0) range will result in a darker color and a factor in (0, 1.0] range will result in a lighter color.

Returns:

modified_color (tuple[double]): a tuple containing the RGB values of the

modified color. Each value in the tuple is in the [0.0, 1.0] range.

polimorfo.utils.visualizeutils.create_text_labels(classes: List[int], scores: List[float], idx_class_dict: Dict[int, str])

Args:

classes (list[int] or None): scores (list[float] or None): idx_class_dict (Dict[int, str] or None):

Returns:

list[str]

polimorfo.utils.visualizeutils.draw_instances(img: Union[PIL.Image.Image, numpy.ndarray], boxes: Union[numpy.ndarray, List[T]], labels: Union[numpy.ndarray, List[T]], scores: Union[numpy.ndarray, List[T]], masks: Union[numpy.ndarray, List[T]], idx_class_dict: Dict[int, str], title: str = '', figsize: Tuple = (16, 8), show_boxes: bool = False, show_masks: bool = True, min_score: float = 0.5, min_area: int = 0, colors: List[T] = None, ax: matplotlib.axes._axes.Axes = None, box_type: polimorfo.utils.visualizeutils.BoxType = <BoxType.xyxy: 1>, only_class_idxs: List[int] = None)

draw the instances from a object detector or an instance segmentation model

Args:

img (np.ndarray): an image with shape (width, height, channels) boxes (np.ndarray): an array of shape (nboxes, 4) labels (np.ndarray): an array of shape (nlabels,) scores (np.ndarray): an array of shape (nscores,) masks (np.ndarray): an array of shape [nmasks, 1, width, height ] idx_class_dict (Dict[int, str]): a dictionary that maps class id to class name title (str, optional): [description]. Defaults to ‘’. figsize (Tuple, optional): [description]. Defaults to (16, 8). show_boxes (bool, optional): [description]. Defaults to False. show_masks (bool, optional): [description]. Defaults to True. min_score (float, optional): [description]. Defaults to 0.5. colors (List, optional): [description]. Defaults to None. ax (plt.Axes, optional): [description]. Defaults to None. box_type (BoxType, optional): [description]. Defaults to BoxType.xyxy. only_class_idxs (List[int], optional): [description]. Defaults to None.

Returns:

[type]: [description]

polimorfo.utils.visualizeutils.draw_segmentation(img: Union[numpy.ndarray, PIL.Image.Image], logits_or_mask: numpy.ndarray, idx_name_dict: Dict[int, str], min_conf: float, colors: List[T] = None, title: str = '', ax: matplotlib.axes._axes.Axes = None, figsize: Tuple[int, int] = (16, 8))

draw the result from a segmentation model

Args:

img (Union[np.ndarray, Image.Image]): an PIL image or a numpy array logits_or_mask (np.ndarray): it accepts:

• the logits coming from the model with shape (n_classes, H, W), or
• the mask coming from true annotations with shape (H,W) and containing pixel classification

idx_name_dict (Dict[int, str]): min_conf (float): the min confidence of the mask given as output colors (List, optional): the colors to diplay categories. Defaults to None. title (str, optional): [description]. Defaults to ‘’. ax (plt.Axes, optional): [description]. Defaults to None. figsize (Tuple[int, int], optional): [description]. Defaults to (16, 8).

Returns:

[plt.Axes]: the ax of the given plot

polimorfo.utils.visualizeutils.draw_text(ax: matplotlib.axes._axes.Axes, text: str, position: Tuple, font_size: float, color: str = 'g', horizontal_alignment: str = 'center', rotation: int = 0)

Args:

text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image. font_size (int, optional): font of the text. If not provided, a font size

proportional to the image width is calculated and used.

color: color of the text. Refer to matplotlib.colors for full list

of formats that are accepted.

horizontal_alignment (str): see matplotlib.text.Text rotation: rotation angle in degrees CCW

Returns:

output (VisImage): image object with text drawn.

polimorfo.utils.visualizeutils.generate_colormap(nelems: int, scaled: bool = False, bright: bool = True)

Module contents