Annotator

The Annotator component provides a GUI which allows the user to create or modify his data set with categories labels and/or meta-annotations.

AnnotatorClassification

The AnnotatorClassification class can be used to annotate data sets for classification tasks.

Parameters

dataset, optional

DatasetClassification

Dataset to be modified with annotations. If not specified, a new one is created.
(default is None)

task_type

TaskType, optional

Problem task_type. If the dataset is not specified, it is a mandatory parameter.
(default is None)

observations

list, optional

List of observations path. If the dataset is not specified, it is a mandatory parameter.
(default is None)

classes_to_annotate

list, optional

List of categories to be annotated. If the dataset is not specified, it is a mandatory parameter.
(default is None)

output_path

str, optional

The path where the annotated data set will be saved. If the dataset is not specified, it is a mandatory parameter.
(default is None)

ds_name

str, optional

Name of the data set. If the dataset is not specified, it is a mandatory parameter.
(default is None)

properties_and_values

dict, optional

Meta-annotations and corresponding values used to annotate the data set. If not specified, only the categeories will be annotated.
(default is None)Example: {'property_name': (property_type, ['value1', 'value2', '...', 'valuen'])}
The meta-annotations can be of the following types: MetaPropertiesType.UNIQUE, MetaPropertiesType.TEXT, MetaPropertiesType.CONTINUE

show_name

bool, optional

Indicates whether to show the name of the file.
(default is True)

show_axis

bool, optional

Indicates whether to show the axis of the plot.
(default is False)

fig_size

pair, optional

Indicates the size of the figure visualized during the annotation process.
(default is (10, 10))

buttons_vertical

bool, optional

Indicates whether to display the buttons vertically.
(default is False)

custom_display_function

function, optional

User custom display visualization.
(default is None)

validate_function

function, optional

Annotation process validation function.
(default is None)

show_reset

bool, optional

Indicates whether to show the 'reset annotation' button.
(default is True)

is_image

bool, optional

Indicates whether the observations represent images. If False, the 'custom_display_function' parameter is mandatory.
(default is True)

Example

Annotate a new data set with only categories labels

from odin.annotator import AnnotatorClassification

task_type = TaskType.CLASSIFICATION_MULTI_LABEL
images = ["path/image1.jpg", "path/image2.jpg", "...", "path/imagen.jpg"]
classes_to_annotate = ["category_a", "category_b", "...", "category_n"]
dataset_name = "my_dataset_name"
output_path = "new/dataset/output/path/"

my_annotator = AnnotatorClassification(task_type=task_type,
                                       observations=images,
                                       classes_to_annotate=classes,
                                       output_path=output_path,
                                       ds_name=dataset_name)
my_annotator.start_classification()

Annotate an existing data set with only meta-annotations

from odin.classes import DatasetClassification
from odin.annotator import AnnotatorClassification

dataset_gt = "path/to/gt/file.json"
images_path = "path/to/images"
classification_type = TaskType.CLASSIFICATION_MULTI_LABEL

my_dataset = DatasetClassification(dataset_gt,
                                   classification_type,
                                   observations_abs_path=images_path,
                                   for_analysis=False)

meta_annotations = {"property_name": (MetaPropertiesType.UNIQUE, ["value1", "value2"])}

def validate_function(img_record):
    return 'property_name' in img_record

my_annotator = AnnotatorClassification(dataset=my_dataset,
                                       properties_and_values=meta_annotations,
                                       validate_function=validate_function)

my_annotator.start_classification()

Annotate a new data set with categories labels and meta-annotations

from odin.annotator import AnnotatorClassification

task_type = TaskType.CLASSIFICATION_MULTI_LABEL
images = ["path/image1.jpg", "path/image2.jpg", "...", "path/imagen.jpg"]
classes_to_annotate = ["category_a", "category_b", "...", "category_n"]
dataset_name = "my_dataset_name"
output_path = "new/dataset/output/path/"
meta_annotations = {"property_name": (MetaPropertiesType.UNIQUE, ["value1", "value2"])}

my_annotator = AnnotatorClassification(task_type=task_type,
                                       observations=images,
                                       classes_to_annotate=classes,
                                       output_path=output_path,
                                       ds_name=dataset_name,
                                       properties_and_values=meta_annotations)
my_annotator.start_classification()

AnnotatorLocalization

The AnnotatorLocalization class can be used to annotate data sets for localization tasks, such as object detection (it is possible to draw and annotate bounding boxes) and instance segmentation (it is possible to draw and annotate segmentation masks).

Parameters

dataset, optional

DatasetLocalization

Dataset to be modified with annotations. If not specified, a new one is created.
(default is None)

task_type

TaskType, optional

Problem task_type. If the dataset is not specified, it is a mandatory parameter.
(default is None)

images

list, optional

List of images path. If the dataset is not specified, it is a mandatory parameter.
(default is None)

classes_to_annotate

list, optional

List of categories to be annotated. If the dataset is not specified, it is a mandatory parameter.
(default is None)

output_path

str, optional

The path where the annotated data set will be saved. If the dataset is not specified, it is a mandatory parameter.
(default is None)

ds_name

str, optional

Name of the data set. If the dataset is not specified, it is a mandatory parameter.
(default is None)

properties_and_values

dict, optional

Meta-annotations and corresponding values used to annotate the data set. If not specified, only the categeories will be annotated..
(default is None)Example: {'property_name': (property_type, ['value1', 'value2', '...', 'valuen'])}
The meta-annotations can be of the following types: MetaPropertiesType.UNIQUE, MetaPropertiesType.TEXT, MetaPropertiesType.CONTINUE

show_name

bool, optional

Indicates whether to show the name of the file.
(default is True)

show_axis

bool, optional

Indicates whether to show the axis of the plot.
(default is False)

fig_size

pair, optional

Indicates the size of the figure visualized during the annotation process.
(default is (10, 10))

buttons_vertical

bool, optional

Indicates whether to display the buttons vertically.
(default is False)

custom_display_function

function, optional

User custom display visualization.
(default is None)

validate_function

function, optional

Annotation process validation function.
(default is None)

show_reset

bool, optional

Indicates whether to show the 'reset annotation' button.
(default is True)

Example

Annotate a new data set with only categories labels

from odin.annotator import AnnotatorLocalization

task_type = TaskType.OBJECT_DETECTION
images = ["path/image1.jpg", "path/image2.jpg", "...", "path/imagen.jpg"]
classes_to_annotate = ["category_a", "category_b", "...", "category_n"]
dataset_name = "my_dataset_name"
output_path = "new/dataset/output/path/"

my_annotator = AnnotatorLocalization(task_type=task_type,
                                     images=images,
                                     classes_to_annotate=classes,
                                     output_path=output_path,
                                     ds_name=dataset_name)
my_annotator.start_annotation()

Annotate an existing data set with only meta-annotations

from odin.classes import DatasetLocalization
from odin.annotator import AnnotatorLocalization

dataset_gt = "path/to/gt/file.json"
images_path = "path/to/images"
classification_type = TaskType.OBJECT_DETECTION

my_dataset = DatasetLocalization(dataset_gt,
                                 classification_type,
                                 images_abs_path=images_path,
                                 for_analysis=False)

meta_annotations = {"property_name": (MetaPropertiesType.UNIQUE, ["value1", "value2"])}

def validate_function(img_record):
    return 'property_name' in img_record

my_annotator = AnnotatorLocalization(dataset=my_dataset,
                                     properties_and_values=meta_annotations,
                                     validate_function=validate_function)

my_annotator.start_annotation()

Annotate a new data set with categories labels and meta-annotations

from odin.annotator import AnnotatorLocalization

task_type = TaskType.INSTANCE_SEGMENTATION
images = ["path/image1.jpg", "path/image2.jpg", "...", "path/imagen.jpg"]
classes_to_annotate = ["category_a", "category_b", "...", "category_n"]
dataset_name = "my_dataset_name"
output_path = "new/dataset/output/path/"
meta_annotations = {"property_name": (MetaPropertiesType.UNIQUE, ["value1", "value2"])}

my_annotator = AnnotatorLocalization(task_type=task_type,
                                     images=images,
                                     classes_to_annotate=classes,
                                     output_path=output_path,
                                     ds_name=dataset_name,
                                     properties_and_values=meta_annotations)
my_annotator.start_annotation()