Address some review feedback:
- separated args list in examples between inference and ultralytics - added depricated decorator to from_roboflow - use `with supress(...)` for parsing inference response models instead of checking instance type (no more pydantic dep) - a few other README changes
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@@ -10,17 +10,17 @@
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<br>
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[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect)
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[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect)
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<br>
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[](https://badge.fury.io/py/supervision)
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[](https://pypistats.org/packages/supervision)
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[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
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[](https://badge.fury.io/py/supervision)
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[](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
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[](https://huggingface.co/spaces/Roboflow/Annotators)
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[](https://discord.gg/GbfgXGJ8Bk)
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[](https://badge.fury.io/py/supervision)
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[](https://pypistats.org/packages/supervision)
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[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
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[](https://badge.fury.io/py/supervision)
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[](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
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[](https://huggingface.co/spaces/Roboflow/Annotators)
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[](https://discord.gg/GbfgXGJ8Bk)
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</div>
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@@ -69,7 +69,7 @@ Running with [Inference](https://github.com/roboflow/inference) can improve spee
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>>> image = cv2.imread(...)
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>>> model = get_roboflow_model(model_id="coco/6", api_key="YOUR_API_KEY")
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>>> result = model.infer(image)[0]
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>>> detections = sv.Detections.from_roboflow(result)
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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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@@ -120,88 +120,88 @@ 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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```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_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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```
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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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```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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>>> len(train_dataset), len(test_dataset), len(valid_dataset)
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(700, 150, 150)
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```
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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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```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_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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```
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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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```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_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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```
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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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```
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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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```
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</details>
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@@ -39,6 +39,10 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
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## 🛠️ script arguments
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### inference args
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- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
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- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/6`.
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- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
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configurations. This file defines the polygonal areas in the video where objects will
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be counted.
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@@ -50,15 +54,20 @@ https://github.com/roboflow/supervision/assets/26109316/f84db7b5-79e2-4142-a1da-
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- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
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for the model. Default is `0.7`.
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### inference args
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- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
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- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/6`.
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### ultralytics args
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- `--source_weights_path` (optional): The path to the YOLO model's weights file.
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Defaults to `"yolov8x.pt"` if not specified.
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- `--zone_configuration_path`: Specifies the path to the JSON file containing zone
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configurations. This file defines the polygonal areas in the video where objects will
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be counted.
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- `--source_video_path`: The path to the source video file that will be analyzed.
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- `--target_video_path` (optional): The path to save the output video with annotations.
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If not provided, the processed video will be displayed in real-time.
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- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
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to filter detections. Default is `0.3`.
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- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
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for the model. Default is `0.7`.
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## 🚩 zone configuration
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@@ -29,6 +29,10 @@ detection and Supervision for tracking and annotation.
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## 🛠️ script arguments
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### inference args
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- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
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- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/13`.
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- `--source_video_path`: Required. The path to the source video file to be processed.
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This is the video on which object detection and annotation will be performed.
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- `--target_video_path`: Required. The path where the processed video, with annotations
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@@ -40,16 +44,21 @@ detection and Supervision for tracking and annotation.
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for the model, defaulting to `0.7`. This parameter helps in differentiating between
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distinct objects, especially in crowded scenes.
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|
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### inference args
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|
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- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/13`.
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### ultralytics args
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- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
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file, which is essential for the object detection process. This file contains the data
|
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that the model uses to identify objects in the video.
|
||||
- `--source_video_path`: Required. The path to the source video file to be processed.
|
||||
This is the video on which object detection and annotation will be performed.
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- `--target_video_path`: Required. The path where the processed video, with annotations
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added, will be saved. This is your output video file.
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- `--confidence_threshold` (optional): Sets the confidence level at which the model
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identifies objects in the video. Default is `0.3`. A higher threshold makes the model
|
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more selective, while a lower threshold makes it more inclusive in identifying objects.
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- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
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for the model, defaulting to `0.7`. This parameter helps in differentiating between
|
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distinct objects, especially in crowded scenes.
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|
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## ⚙️ run
|
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|
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@@ -38,6 +38,10 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
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|
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## 🛠️ script arguments
|
||||
|
||||
### inference args
|
||||
|
||||
- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/13`.
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- `--source_video_path`: Required. The path to the source video file that will be
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analyzed. This is the input video on which traffic flow analysis will be performed.
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- `--target_video_path` (optional): The path to save the output video with annotations.
|
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@@ -50,21 +54,40 @@ https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-
|
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for the model. Default is 0.7. This value is used to manage object detection accuracy,
|
||||
particularly in distinguishing between different objects.
|
||||
|
||||
### inference args
|
||||
|
||||
- `--roboflow_api_key`: Your [Roboflow API key](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key)
|
||||
- `--model_id` (optional): Specifies the Roboflow model id (dataset/version) to use for inference. See [COCO models on Roboflow Universe](https://universe.roboflow.com/microsoft/coco/dataset/13). Default is `coco/13`.
|
||||
|
||||
### ultralytics args
|
||||
|
||||
- `--source_weights_path`: Required. Specifies the path to the YOLO model's weights
|
||||
file, which is essential for the object detection process. This file contains the data
|
||||
that the model uses to identify objects in the video.
|
||||
- `--source_video_path`: Required. The path to the source video file that will be
|
||||
analyzed. This is the input video on which traffic flow analysis will be performed.
|
||||
- `--target_video_path` (optional): The path to save the output video with annotations.
|
||||
If not specified, the processed video will be displayed in real-time without being
|
||||
saved.
|
||||
- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
|
||||
to filter detections. Default is `0.3`. This determines how confident the model should
|
||||
be to recognize an object in the video.
|
||||
- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
|
||||
for the model. Default is 0.7. This value is used to manage object detection accuracy,
|
||||
particularly in distinguishing between different objects.
|
||||
|
||||
## ⚙️ run
|
||||
|
||||
### inference
|
||||
|
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```bash
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||||
python script.py \
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python inference_example.py \
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--source_video_path data/traffic_analysis.mov \
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--confidence_threshold 0.3 \
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--iou_threshold 0.5 \
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--target_video_path data/traffic_analysis_result.mov \
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--roboflow_api_key <ROBOFLOW API KEY>
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||||
```
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|
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### ultralytics
|
||||
|
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```bash
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||||
python ultralytics_example.py \
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--source_weights_path data/traffic_analysis.pt \
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--source_video_path data/traffic_analysis.mov \
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--confidence_threshold 0.3 \
|
||||
|
||||
Generated
+1
-151
@@ -1,19 +1,5 @@
|
||||
# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "annotated-types"
|
||||
version = "0.6.0"
|
||||
description = "Reusable constraint types to use with typing.Annotated"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
|
||||
{file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
version = "4.1.0"
|
||||
@@ -2607,142 +2593,6 @@ files = [
|
||||
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.5.2"
|
||||
description = "Data validation using Python type hints"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pydantic-2.5.2-py3-none-any.whl", hash = "sha256:80c50fb8e3dcecfddae1adbcc00ec5822918490c99ab31f6cf6140ca1c1429f0"},
|
||||
{file = "pydantic-2.5.2.tar.gz", hash = "sha256:ff177ba64c6faf73d7afa2e8cad38fd456c0dbe01c9954e71038001cd15a6edd"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
annotated-types = ">=0.4.0"
|
||||
pydantic-core = "2.14.5"
|
||||
typing-extensions = ">=4.6.1"
|
||||
|
||||
[package.extras]
|
||||
email = ["email-validator (>=2.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "pydantic-core"
|
||||
version = "2.14.5"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:7e88f5696153dc516ba6e79f82cc4747e87027205f0e02390c21f7cb3bd8abfd"},
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4641e8ad4efb697f38a9b64ca0523b557c7931c5f84e0fd377a9a3b05121f0de"},
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:774de879d212db5ce02dfbf5b0da9a0ea386aeba12b0b95674a4ce0593df3d07"},
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ebb4e035e28f49b6f1a7032920bb9a0c064aedbbabe52c543343d39341a5b2a3"},
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b53e9ad053cd064f7e473a5f29b37fc4cc9dc6d35f341e6afc0155ea257fc911"},
|
||||
{file = "pydantic_core-2.14.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8aa1768c151cf562a9992462239dfc356b3d1037cc5a3ac829bb7f3bda7cc1f9"},
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{file = "pydantic_core-2.14.5-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:439c9afe34638ace43a49bf72d201e0ffc1a800295bed8420c2a9ca8d5e3dbb3"},
|
||||
{file = "pydantic_core-2.14.5-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ba39688799094c75ea8a16a6b544eb57b5b0f3328697084f3f2790892510d144"},
|
||||
{file = "pydantic_core-2.14.5-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ccd4d5702bb90b84df13bd491be8d900b92016c5a455b7e14630ad7449eb03f8"},
|
||||
{file = "pydantic_core-2.14.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:81982d78a45d1e5396819bbb4ece1fadfe5f079335dd28c4ab3427cd95389944"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:7f8210297b04e53bc3da35db08b7302a6a1f4889c79173af69b72ec9754796b8"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:8c8a8812fe6f43a3a5b054af6ac2d7b8605c7bcab2804a8a7d68b53f3cd86e00"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:206ed23aecd67c71daf5c02c3cd19c0501b01ef3cbf7782db9e4e051426b3d0d"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c2027d05c8aebe61d898d4cffd774840a9cb82ed356ba47a90d99ad768f39789"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:40180930807ce806aa71eda5a5a5447abb6b6a3c0b4b3b1b1962651906484d68"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:615a0a4bff11c45eb3c1996ceed5bdaa2f7b432425253a7c2eed33bb86d80abc"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f5e412d717366e0677ef767eac93566582518fe8be923361a5c204c1a62eaafe"},
|
||||
{file = "pydantic_core-2.14.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:513b07e99c0a267b1d954243845d8a833758a6726a3b5d8948306e3fe14675e3"},
|
||||
{file = "pydantic_core-2.14.5.tar.gz", hash = "sha256:6d30226dfc816dd0fdf120cae611dd2215117e4f9b124af8c60ab9093b6e8e71"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
|
||||
|
||||
[[package]]
|
||||
name = "pygments"
|
||||
version = "2.17.2"
|
||||
@@ -3957,4 +3807,4 @@ desktop = ["opencv-python"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.8"
|
||||
content-hash = "dc4ee13e5429bc04e42a2afc9a31582a075f06db332b2d4005fa5b7e95a1d0df"
|
||||
content-hash = "5ecf358004c976e2dfbaaee0e8a962f49a55b0015c7a8e3ecb9eba324136e2c0"
|
||||
@@ -42,7 +42,6 @@ opencv-python-headless = ">=4.5.5.64,<=4.8.1.78"
|
||||
requests = { version = ">=2.26.0,<=2.31.0", optional = true }
|
||||
tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
|
||||
defusedxml = "^0.7.1"
|
||||
pydantic = ">=1.10"
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
|
||||
@@ -1,10 +1,10 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from contextlib import suppress
|
||||
from dataclasses import astuple, dataclass
|
||||
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
from pydantic import BaseModel
|
||||
|
||||
from supervision.detection.utils import (
|
||||
calculate_masks_centroids,
|
||||
@@ -14,6 +14,7 @@ from supervision.detection.utils import (
|
||||
xywh_to_xyxy,
|
||||
)
|
||||
from supervision.geometry.core import Position
|
||||
from supervision.utils.internal import deprecated
|
||||
|
||||
|
||||
def _validate_xyxy(xyxy: Any, n: int) -> None:
|
||||
@@ -439,14 +440,14 @@ class Detections:
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_roboflow(cls, roboflow_result: Union[dict, type[BaseModel]]) -> Detections:
|
||||
def from_inference(cls, roboflow_result: Union[dict, Any]) -> Detections:
|
||||
"""
|
||||
Create a Detections object from the [Roboflow](https://roboflow.com/)
|
||||
API inference result or the [Inference](https://inference.roboflow.com/) package results.
|
||||
|
||||
Args:
|
||||
roboflow_result (dict): The result from the
|
||||
Roboflow API containing predictions.
|
||||
roboflow_result (dict, any): The result from the
|
||||
Roboflow API or Inference package containing predictions
|
||||
|
||||
Returns:
|
||||
(Detections): A Detections object containing the bounding boxes, class IDs,
|
||||
@@ -474,8 +475,8 @@ class Detections:
|
||||
>>> detections = sv.Detections.from_roboflow(roboflow_result)
|
||||
```
|
||||
"""
|
||||
if isinstance(roboflow_result, BaseModel):
|
||||
roboflow_result: BaseModel = roboflow_result.dict(
|
||||
with suppress(AttributeError):
|
||||
roboflow_result = roboflow_result.dict(
|
||||
exclude_none=True, by_alias=True
|
||||
)
|
||||
xyxy, confidence, class_id, masks, trackers = process_roboflow_result(
|
||||
@@ -494,8 +495,9 @@ class Detections:
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_inference(
|
||||
cls, roboflow_result: Union[dict, type[BaseModel]]
|
||||
@deprecated("Use `from_inference` instead.")
|
||||
def from_roboflow(
|
||||
cls, roboflow_result: Union[dict, Any]
|
||||
) -> Detections:
|
||||
"""
|
||||
Create a Detections object from the [Roboflow](https://roboflow.com/)
|
||||
@@ -531,7 +533,7 @@ class Detections:
|
||||
>>> detections = sv.Detections.from_roboflow(roboflow_result)
|
||||
```
|
||||
"""
|
||||
return cls.from_roboflow(roboflow_result)
|
||||
return cls.from_inference(roboflow_result)
|
||||
|
||||
@classmethod
|
||||
def from_sam(cls, sam_result: List[dict]) -> Detections:
|
||||
|
||||
Reference in new issue
Block a user