detectionmetrics.utils package

Submodules

detectionmetrics.utils.conversion module

detectionmetrics.utils.conversion.get_ontology_conversion_lut(old_ontology: dict, new_ontology: dict, ontology_translation: dict | None = None) → ndarray

Build a LUT that links old ontology and new ontology indices. If class names don’t match between the provided ontologies, user must provide an ontology translation dictionary with old and new class names as keys and values, respectively

Parameters:

• old_ontology (dict) – Origin ontology definition

• new_ontology (dict) – Target ontology definition

• ontology_translation (Optional[dict], optional) – Ontology translation dictionary, defaults to None

Returns:

numpy array associating old and new ontology indices

Return type:

np.ndarray

detectionmetrics.utils.conversion.hex_to_rgb(hex: str) → Tuple[int, ...]

Convert HEX color code to sRGB

Parameters:

hex (str) – HEX color code

Returns:

sRGB color value

Return type:

Tuple[int, …]

detectionmetrics.utils.conversion.label_to_rgb(label: Image, ontology: dict) → Image

Convert an image with raw label indices to RGB mask

Parameters:

• label (Image.Image) – Raw label indices as PIL image

• ontology (dict) – Ontology definition

Returns:

RGB mask

Return type:

Image.Image

detectionmetrics.utils.conversion.ontology_to_rgb_lut(ontology: dict) → ndarray

Given an ontology definition, build a LUT that links indices and RGB values

Parameters:

ontology (dict) – Ontology definition

Returns:

numpy array containing RGB values per index

Return type:

np.ndarray

detectionmetrics.utils.io module

detectionmetrics.utils.io.get_image_mode(fname: str) → str

Given an image retrieve its color mode using PIL

Parameters:

fname (str) – Input image

Returns:

PIL color image mode

Return type:

str

detectionmetrics.utils.io.read_json(fname: str) → dict

Read a JSON file

Parameters:

fname (str) – JSON filename

Returns:

Dictionary containing JSON file data

Return type:

dict

detectionmetrics.utils.io.read_txt(fname: str) → List[str]

Read a .txt file line by line

Parameters:

fname (str) – .txt filename

Returns:

List of lines found in the .txt file

Return type:

List[str]

detectionmetrics.utils.io.read_yaml(fname: str) → dict

Read a YAML file

Parameters:

fname (str) – YAML filename

Returns:

Dictionary containing YAML file data

Return type:

dict

detectionmetrics.utils.io.write_json(fname: str, data: dict)

Write a JSON file properly indented

Parameters:

• fname (str) – Target JSON filename

• data (dict) – Dictionary containing data to be dumped as a JSON file

detectionmetrics.utils.metrics module

class detectionmetrics.utils.metrics.Accuracy(n_classes: int)

Bases: Metric

Compute accuracy as:

\[\text{Accuracy} = \frac{1}{N}\sum_i^N 1(y_i = \hat{y}_i)\]

Accuracy per sample and class is accumulated and then the average per class is computed.

Parameters:

n_classes (int) – Number of classes to evaluate

compute() → Tuple[float, ndarray]

Get accuracy (global and per class)

Returns:

per class accuracy, and global accuracy

Return type:

Tuple[float, np.ndarray]

update(pred: ndarray, gt: ndarray)

Accumulate accuracy values for a new set of samples

Parameters:

• pred (np.ndarray) – label encoded prediction array (batch, width, height)

• gt (np.ndarray) – label encoded ground truth array (batch, width, height)

class detectionmetrics.utils.metrics.IoU(n_classes: int)

Bases: Metric

Compute Intersection over Union (IoU). IoU per sample and class is accumulated and then the average per class is computed.

Parameters:

n_classes (int) – Number of classes to evaluate

compute() → ndarray

Get IoU (global and per class)

Returns:

per class IoU, and global IoU

Return type:

Tuple[float, np.ndarray]

update(pred: ndarray, gt: ndarray)

Accumulate IoU values for a new set of samples

Parameters:

• pred (np.ndarray) – one-hot encoded prediction array (batch, class, width, height)

• gt (np.ndarray) – one-hot encoded ground truth array (batch, class, width, height)

class detectionmetrics.utils.metrics.Metric(n_classes: int)

Bases: ABC

Abstract class for metrics

Parameters:

n_classes (int) – Number of classes to evaluate

abstract compute() → ndarray

Get final values

Returns:

Array containing final values

Return type:

np.ndarray

abstract update(pred: ndarray, gt: ndarray)

Accumulate results for a new batch

Parameters:

• pred (np.ndarray) – Array containing prediction

• gt (np.ndarray) – Array containing ground truth

Module contents