IkDatasetIO#
- class ikomia.dnn.datasetio.IkDatasetIO(format='other')#
Define task input or output containing deep learning dataset structure. Derived from
CDatasetIO
.Instances can be added as input or output of a
CWorkflowTask
or derived object. Such input or output is required for deep learning training task. Dataset structure is composed of a global dict with 2 main entries ‘images’ and ‘metadata’.It follows the specifications below (mandatory fields may vary depending on the training goal).
images (list[dict]): image information and corresponding annotations.
filename (str): full path of the image file.
height, width (int): size of the image.
image_id (int): unique image identifier.
annotations (list[dict]): each dict corresponds to annotations of one instance in this image.
bbox (list[float]): x, y, width, height of the bounding box.
category_id (int): integer representing the category label.
segmentation_poly (list[list[float]]): list of polygons, one for each connected component.
keypoints (list[float]).
iscrowd (boolean): whether the instance is labelled as a crowd region (COCO).
segmentation_masks_np (numpy array [N, H, W]).
instance_seg_masks_file: full path of the ground truth instance segmentation image file.
semantic_seg_masks_file: full path of the ground truth semantic segmentation image file.
metadata (dict): key-value mapping that contains information that’s shared among the entire dataset.
category_names (dict(id, name)).
category_colors (list[tuple(r,g,b)]).
keypoint_names (list[str]).
keypoint_connection_rules (list[tuple(str, str, (r,g,b))]): each tuple specifies a pair of connected keypoints and the color to use for the line between them.
Import
from ikomia.dnn.datasetio import IkDatasetIO
Methods
__init__
([format])Constructor
Clear whole dataset structure
Return the list of categories (ie instance classes) in the dataset.
Return the number of categories (ie instance classes) in the dataset.
get_graphics_annotations
(img_path)Return a list of Ikomia graphics items corresponding to the annotations of a given image (bounding box, polygons).
Return the list of all images path contained in the dataset.
get_mask_path
(img_path)Return the file path of the segmentation mask for the given image.
get_source_format
(arg1)Get the source format of the dataset.
Check whether the dataset structure contains data.
load
(path)Load JSON as dataset structure.
save
(path)Save dataset structure as JSON.
Details
- __init__(format='other')#
Constructor
- Parameters:
format (str) – dataset source format.
- clear_data()#
Clear whole dataset structure
- get_categories()#
Return the list of categories (ie instance classes) in the dataset.
- Returns:
categories (dict-like structure)
- Return type:
MapIntStr
list
- get_category_count()#
Return the number of categories (ie instance classes) in the dataset.
- Returns:
int
- get_graphics_annotations(img_path)#
Return a list of Ikomia graphics items corresponding to the annotations of a given image (bounding box, polygons).
- Parameters:
img_path (str) – path of the image from which we want annotations
- Returns:
graphics items
- Return type:
CGraphicsItem
list
- get_image_paths()#
Return the list of all images path contained in the dataset.
- Returns:
path list
- Return type:
str[]
- get_mask_path(img_path)#
Return the file path of the segmentation mask for the given image.
- Parameters:
img_path (str) – path of the image from which segmentation mask is requested
- Returns:
file path of the segmentation mask
- Return type:
str
- get_source_format((CDatasetIO)arg1) str : #
Get the source format of the dataset.
- Returns:
source format string identifier (lowercase)
- Return type:
str
- is_data_available()#
Check whether the dataset structure contains data.
- Returns:
True or False
- Return type:
boolean
- load(path)#
Load JSON as dataset structure.
- Parameters:
path – file path where dataset is saved
- save(path)#
Save dataset structure as JSON.
- Parameters:
path – file path where dataset is saved