Merge pull request #337 from roboflow/develop

`supervision-0.14.0` release

.github/workflows/publish-test.yml

@@ -1,9 +1,6 @@
 name: Supervision Test Releases to PyPi
 on:
   push:
-    branches:
-      - main
-      - develop
     tags:
       - '[0-9]+.[0-9]+[0-9]+.[0-9]+a[0-9]'
       - '[0-9]+.[0-9]+[0-9]+.[0-9]+b[0-9]'

.github/workflows/publish.yml

@@ -1,8 +1,13 @@
-name: Publish WorkFlow
-
+name: Supervision Releases to PyPi
 on:
-  release:
-    types: [created]
+  push:
+    tags:
+      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
+      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
+      - '[0-9]+.[0-9]+[0-9]+.[0-9]'
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
 
 jobs:
   build:
@@ -19,15 +24,6 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-      - name: 🦾 Install dependencies
-        run: |
-          python -m pip install --upgrade virtualenv
-          python -m pip install --upgrade pip
-          virtualenv venv
-          source venv/bin/activate
-          python -m pip install --upgrade pip
-          python -m pip install --upgrade poetry
-          poetry install
 
       - name:  🏗️ Build source and wheel distributions
         run: |
@@ -43,5 +39,5 @@ jobs:
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
           repository-url: https://test.pypi.org/legacy/
-          user: ${{ secrets.PYPI_USERNAME }}
+          user: ${{ secrets.PYPI_TEST_USERNAME }}
           password: ${{ secrets.PYPI_TEST_PASSWORD }}

.gitignore

@@ -54,6 +54,7 @@ coverage.xml
 *.py,cover
 .hypothesis/
 .pytest_cache/
+.ruff_cache/
 
 # Translations
 *.mo

docs/changelog.md

@@ -1,3 +1,38 @@
+### 0.14.0 <small>August 31, 2023</small>
+
+- Added [#282](https://github.com/roboflow/supervision/pull/282): support for SAHI inference technique with [`sv.InferenceSlicer`](https://supervision.roboflow.com/detection/tools/inference_slicer).
+
+```python
+>>> import cv2
+>>> import supervision as sv
+>>> from ultralytics import YOLO
+
+>>> image = cv2.imread(SOURCE_IMAGE_PATH)
+>>> model = YOLO(...)
+
+>>> def callback(image_slice: np.ndarray) -> sv.Detections:
+...     result = model(image_slice)[0]
+...     return sv.Detections.from_ultralytics(result)
+
+>>> slicer = sv.InferenceSlicer(callback = callback)
+
+>>> detections = slicer(image)
+```
+
+- Added [#297](https://github.com/roboflow/supervision/pull/297): [`Detections.from_deepsparse`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_deepsparse) to enable seamless integration with [DeepSparse](https://github.com/neuralmagic/deepsparse) framework.
+
+- Added [#281](https://github.com/roboflow/supervision/pull/281): [`sv.Classifications.from_ultralytics`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_ultralytics) to enable seamless integration with [Ultralytics](https://github.com/ultralytics/ultralytics) framework. This will enable you to use supervision with all [models](https://docs.ultralytics.com/models/) that Ultralytics supports.
+
+!!! warning
+
+    [sv.Detections.from_yolov8](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8) and [sv.Classifications.from_yolov8](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_yolov8) are now deprecated and will be removed with supervision-0.16.0 release.
+
+- Added [#341](https://github.com/roboflow/supervision/pull/341): First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision.
+
+- Changed [#296](https://github.com/roboflow/supervision/pull/296): [`sv.ClassificationDataset`](https://supervision.roboflow.com/dataset/core/#supervision.dataset.core.ClassificationDataset) and [`sv.DetectionDataset`](https://supervision.roboflow.com/dataset/core/#supervision.dataset.core.DetectionDataset) now use image path (not image name) as dataset keys.
+
+- Fixed [#300](https://github.com/roboflow/supervision/pull/300): [`Detections.from_roboflow`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_roboflow) to filter out polygons with less than 3 points.
+
 ### 0.13.0 <small>August 8, 2023</small>
 
 - Added [#236](https://github.com/roboflow/supervision/pull/236): support for mean average precision (mAP) for object detection models with [`sv.MeanAveragePrecision`](https://roboflow.github.io/supervision/metrics/detection/#meanaverageprecision).

examples/tracking/README.md

@@ -0,0 +1,20 @@
+## 👋 hello
+
+This script provides functionality for processing videos using YOLOv8 for object
+detection and Supervision for tracking and annotation.
+
+## 💻 install
+
+```bash
+pip install -r requirements.txt
+```
+
+## ⚙️ parameters
+
+| parameter                | required | description                                                                       |
+|:-------------------------|:--------:|:----------------------------------------------------------------------------------|
+| `--source_weights_path`  |    ✓     | Path to the source weights file for YOLOv8.                                       |
+| `--source_video_path`    |    ✓     | Path to the source video file to be processed.                                    |
+| `--target_video_path`    |    ✓     | Path to the target video file (output).                                           |
+| `--confidence_threshold` |    ✗     | Confidence threshold for YOLO model detection. Default is 0.3.                    |
+| `--iou_threshold`        |    ✗     | IOU (Intersection over Union) threshold for YOLO model detection. Default is 0.7. |

examples/tracking/requirements.txt

@@ -0,0 +1,3 @@
+supervision
+tqdm
+ultralytics

examples/tracking/script.py

@@ -0,0 +1,83 @@
+import argparse
+
+from tqdm import tqdm
+from ultralytics import YOLO
+
+import supervision as sv
+
+
+def process_video(
+    source_weights_path: str,
+    source_video_path: str,
+    target_video_path: str,
+    confidence_threshold: float = 0.3,
+    iou_threshold: float = 0.7,
+) -> None:
+    model = YOLO(source_weights_path)
+
+    tracker = sv.ByteTrack()
+    box_annotator = sv.BoxAnnotator()
+    frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
+    video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)
+
+    with sv.VideoSink(target_path=target_video_path, video_info=video_info) as sink:
+        for frame in tqdm(frame_generator, total=video_info.total_frames):
+            results = model(
+                frame, verbose=False, conf=confidence_threshold, iou=iou_threshold
+            )[0]
+            detections = sv.Detections.from_ultralytics(results)
+            detections = tracker.update_with_detections(detections)
+
+            labels = [
+                f"#{tracker_id} {model.model.names[class_id]}"
+                for _, _, _, class_id, tracker_id in detections
+            ]
+
+            annotated_frame = box_annotator.annotate(
+                scene=frame.copy(), detections=detections, labels=labels
+            )
+
+            sink.write_frame(frame=annotated_frame)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Video Processing with YOLO and ByteTrack"
+    )
+    parser.add_argument(
+        "--source_weights_path",
+        required=True,
+        help="Path to the source weights file",
+        type=str,
+    )
+    parser.add_argument(
+        "--source_video_path",
+        required=True,
+        help="Path to the source video file",
+        type=str,
+    )
+    parser.add_argument(
+        "--target_video_path",
+        required=True,
+        help="Path to the target video file (output)",
+        type=str,
+    )
+    parser.add_argument(
+        "--confidence_threshold",
+        default=0.3,
+        help="Confidence threshold for the model",
+        type=float,
+    )
+    parser.add_argument(
+        "--iou_threshold", default=0.7, help="IOU threshold for the model", type=float
+    )
+
+    args = parser.parse_args()
+
+    process_video(
+        source_weights_path=args.source_weights_path,
+        source_video_path=args.source_video_path,
+        target_video_path=args.target_video_path,
+        confidence_threshold=args.confidence_threshold,
+        iou_threshold=args.iou_threshold,
+    )

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.14.0b1"
+version = "0.14.0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
 maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]