wow-ai-vision 0.0.4

A set of easy-to-use utils that will come in handy in any Computer Vision project

👋 hello

We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝

💻 install

Pip install the wow-ai-vision package in a 3.11>=Python>=3.8 environment.

pip install wow-ai-vision[desktop]

Read more about desktop, headless, and local installation in our guide.

🔥 quickstart

detections processing

>>> import wow-ai-vision as sv
>>> from ultralytics import YOLO

>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_ultralytics(result)

>>> len(detections)
5

👉 more detections utils

• 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
  ... )
  

datasets processing

>>> import wow-ai-vision as sv

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

>>> dataset.classes
['dog', 'person']

>>> len(dataset)
1000

👉 more dataset utils

• 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='...'
  ... )
  

model evaluation

>>> import wow-ai-vision as sv

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

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

>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> confusion_matrix.matrix
array([
    [0., 0., 0., 0.],
    [0., 1., 0., 1.],
    [0., 1., 1., 0.],
    [1., 1., 0., 0.]
])

👉 more metrics

• 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
  

🎬 tutorials

💜 built with wow-ai-vision

📚 documentation

🏆 contribution