wow-ai-vision 0.0.4

Easily switch inference pipeline between supported object detection/instance segmentation models

>>> import wow-ai-vision as sv
>>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator

>>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
>>> mask_generator = SamAutomaticMaskGenerator(sam)
>>> sam_result = mask_generator.generate(IMAGE)
>>> detections = sv.Detections.from_sam(sam_result=sam_result)

Advanced filtering

>>> detections = detections[detections.class_id == 0]
>>> detections = detections[detections.confidence > 0.5]
>>> detections = detections[detections.area > 1000]

Image annotation

>>> import wow-ai-vision as sv

>>> box_annotator = sv.BoxAnnotator()
>>> annotated_frame = box_annotator.annotate(
...     scene=IMAGE,
...     detections=detections
... )

Load object detection/instance segmentation datasets in one of the supported formats

>>> dataset = sv.DetectionDataset.from_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... )

>>> dataset = sv.DetectionDataset.from_pascal_voc(
...     images_directory_path='...',
...     annotations_directory_path='...'
... )

>>> dataset = sv.DetectionDataset.from_coco(
...     images_directory_path='...',
...     annotations_path='...'
... )

Loop over dataset entries

>>> for name, image, labels in dataset:
...     print(labels.xyxy)

array([[404.      , 719.      , 538.      , 884.5     ],
       [155.      , 497.      , 404.      , 833.5     ],
       [ 20.154999, 347.825   , 416.125   , 915.895   ]], dtype=float32)

Split dataset for training, testing, and validation

>>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
>>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)

>>> len(train_dataset), len(test_dataset), len(valid_dataset)
(700, 150, 150)

Merge multiple datasets

>>> ds_1 = sv.DetectionDataset(...)
>>> len(ds_1)
100
>>> ds_1.classes
['dog', 'person']

>>> ds_2 = sv.DetectionDataset(...)
>>> len(ds_2)
200
>>> ds_2.classes
['cat']

>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
>>> len(ds_merged)
300
>>> ds_merged.classes
['cat', 'dog', 'person']

Save object detection/instance segmentation datasets in one of the supported formats

>>> dataset.as_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... )

>>> dataset.as_pascal_voc(
...     images_directory_path='...',
...     annotations_directory_path='...'
... )

>>> dataset.as_coco(
...     images_directory_path='...',
...     annotations_path='...'
... )

Convert labels between supported formats

>>> sv.DetectionDataset.from_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... ).as_pascal_voc(
...     images_directory_path='...',
...     annotations_directory_path='...'
... )

Load classification datasets in one of the supported formats

>>> cs = sv.ClassificationDataset.from_folder_structure(
...     root_directory_path='...'
... )

Save classification datasets in one of the supported formats

>>> cs.as_folder_structure(
...     root_directory_path='...'
... )

Mean average precision (mAP) for object detection tasks.

>>> import wow-ai-vision as sv

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> def callback(image: np.ndarray) -> sv.Detections:
...     ...

>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> mean_average_precision.map50_95
0.433