wow-ai-cv 0.6.0

# clone repository and navigate to root directory
git clone https://github.com/roboflow/wow_ai_cv.git
cd wow_ai_cv

# setup python environment and activate it
python3 -m venv venv
source venv/bin/activate

# install
pip install -e ".[dev]"

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

  >>> import wow_ai_cv 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_cv as sv
  
  >>> box_annotator = sv.BoxAnnotator()
  >>> annotated_frame = box_annotator.annotate(
  ...     scene=IMAGE,
  ...     detections=detections
  ... )
  

• Load object detection / instance segmentation datasets in one of 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='...'
  ... )
  

• 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)
  

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

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

• Convert labels between suppoted 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 supported formats

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

• Save classification datasets in one of supported formats

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