docs: 📝 readme >>>/... removed
Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
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Onuralp SEZER
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README.md
178
README.md
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@ -46,17 +46,17 @@ Read more about desktop, headless, and local installation in our [guide](https:/
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Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection.
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```python
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>>> import cv2
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>>> import supervision as sv
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>>> from ultralytics import YOLO
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import cv2
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import supervision as sv
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from ultralytics import YOLO
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>>> image = cv2.imread(...)
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>>> model = YOLO('yolov8s.pt')
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>>> result = model(image)[0]
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>>> detections = sv.Detections.from_ultralytics(result)
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image = cv2.imread(...)
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model = YOLO('yolov8s.pt')
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result = model(image)[0]
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detections = sv.Detections.from_ultralytics(result)
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>>> len(detections)
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5
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len(detections)
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# 5
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```
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<details>
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@ -67,17 +67,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
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Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
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```python
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>>> import cv2
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>>> import supervision as sv
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>>> from inference.models.utils import get_roboflow_model
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import cv2
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import supervision as sv
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from inference.models.utils import get_roboflow_model
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>>> image = cv2.imread(...)
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>>> model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
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>>> result = model.infer(image)[0]
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>>> detections = sv.Detections.from_inference(result)
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image = cv2.imread(...)
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model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
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result = model.infer(image)[0]
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detections = sv.Detections.from_inference(result)
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>>> len(detections)
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>>> 5
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len(detections)
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# 5
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```
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@ -88,17 +88,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
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Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/annotators/), allowing you to compose the perfect visualization for your use case.
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```python
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>>> import cv2
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>>> import supervision as sv
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import cv2
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import supervision as sv
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>>> image = cv2.imread(...)
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>>> detections = sv.Detections(...)
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image = cv2.imread(...)
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detections = sv.Detections(...)
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>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
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>>> annotated_frame = bounding_box_annotator.annotate(
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... scene=image.copy(),
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... detections=detections
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... )
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bounding_box_annotator = sv.BoundingBoxAnnotator()
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annotated_frame = bounding_box_annotator.annotate(
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scene=image.copy(),
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detections=detections
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)
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```
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https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
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@ -108,19 +108,19 @@ https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-
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Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.
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```python
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>>> import supervision as sv
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import supervision as sv
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>>> dataset = sv.DetectionDataset.from_yolo(
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... images_directory_path=...,
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... annotations_directory_path=...,
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... data_yaml_path=...
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... )
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dataset = sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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>>> dataset.classes
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dataset.classes
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['dog', 'person']
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>>> len(dataset)
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1000
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len(dataset)
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# 1000
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```
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<details close>
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@ -129,86 +129,86 @@ Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/
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- load
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```python
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>>> dataset = sv.DetectionDataset.from_yolo(
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... images_directory_path=...,
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... annotations_directory_path=...,
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... data_yaml_path=...
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... )
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dataset = sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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>>> dataset = sv.DetectionDataset.from_pascal_voc(
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... images_directory_path=...,
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... annotations_directory_path=...
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... )
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dataset = sv.DetectionDataset.from_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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>>> dataset = sv.DetectionDataset.from_coco(
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... images_directory_path=...,
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... annotations_path=...
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... )
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dataset = sv.DetectionDataset.from_coco(
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images_directory_path=...,
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annotations_path=...
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)
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```
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- split
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```python
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>>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
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>>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
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train_dataset, test_dataset = dataset.split(split_ratio=0.7)
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test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
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>>> len(train_dataset), len(test_dataset), len(valid_dataset)
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(700, 150, 150)
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len(train_dataset), len(test_dataset), len(valid_dataset)
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# (700, 150, 150)
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```
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- merge
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```python
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>>> ds_1 = sv.DetectionDataset(...)
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>>> len(ds_1)
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100
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>>> ds_1.classes
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['dog', 'person']
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ds_1 = sv.DetectionDataset(...)
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len(ds_1)
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# 100
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ds_1.classes
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# ['dog', 'person']
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>>> ds_2 = sv.DetectionDataset(...)
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>>> len(ds_2)
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200
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>>> ds_2.classes
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['cat']
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ds_2 = sv.DetectionDataset(...)
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len(ds_2)
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# 200
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ds_2.classes
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# ['cat']
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>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
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>>> len(ds_merged)
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300
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>>> ds_merged.classes
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['cat', 'dog', 'person']
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ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
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len(ds_merged)
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# 300
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ds_merged.classes
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# ['cat', 'dog', 'person']
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```
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- save
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```python
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>>> dataset.as_yolo(
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... images_directory_path=...,
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... annotations_directory_path=...,
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... data_yaml_path=...
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... )
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dataset.as_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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>>> dataset.as_pascal_voc(
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... images_directory_path=...,
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... annotations_directory_path=...
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... )
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dataset.as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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>>> dataset.as_coco(
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... images_directory_path=...,
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... annotations_path=...
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... )
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dataset.as_coco(
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images_directory_path=...,
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annotations_path=...
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)
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```
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- convert
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```python
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>>> sv.DetectionDataset.from_yolo(
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... images_directory_path=...,
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... annotations_directory_path=...,
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... data_yaml_path=...
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... ).as_pascal_voc(
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... images_directory_path=...,
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... annotations_directory_path=...
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... )
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sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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).as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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```
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</details>
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