Merge pull request #136 from roboflow/0.10.0-release

`0.10.0` release

CITATION.cff

@@ -2,7 +2,7 @@ cff-version: 1.2.0
 author: Roboflow
 message: If you use this software, please cite it as below.
 title: supervision
-version: 0.9.0
+version: 0.10.0
 date-released: 2023-01-19
 license: MIT
 repository-code: https://github.com/roboflow/supervision

README.md

@@ -186,6 +186,22 @@ pip install -e ".[dev]"
     ...     annotations_directory_path='...'
     ... )
     ```
+
+- Load classification datasets in one of supported formats
+
+    ```python
+    >>> cs = sv.ClassificationDataset.from_folder_structure(
+    ...     root_directory_path='...'
+    ... )
+    ```
+
+- Save classification datasets in one of supported formats
+
+    ```python
+    >>> cs.as_folder_structure(
+    ...     root_directory_path='...'
+    ... )
+    ```
 
 </details>
 

docs/changelog.md

@@ -1,56 +1,47 @@
-### 0.9.0 <small>June 7, 2023</small>
+### 0.10.0 <small>June 14, 2023</small>
 
-- Added [[#118](https://github.com/roboflow/supervision/pull/118)]: ability to select [`sv.Detections`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.
+- Added [[#125](https://github.com/roboflow/supervision/pull/125)]: ability to load and save [`sv.ClassificationDataset`](https://roboflow.github.io/supervision/dataset/core/#classificationdataset) in a folder structure format.
 
 ```python
 >>> import supervision as sv
 
->>> detections = sv.Detections(...)
->>> len(detections[0])
-1
->>> len(detections[[0, 1]])
-2
->>> len(detections[0:2])
-2
+>>> cs = sv.ClassificationDataset.from_folder_structure(
+...     root_directory_path='...'
+... )
+
+>>> cs.as_folder_structure(
+...     root_directory_path='...'
+... )
 ```
 
-- Added [[#101](https://github.com/roboflow/supervision/pull/101)]: ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
+- Added [[#125](https://github.com/roboflow/supervision/pull/125)]: support for [`sv.ClassificationDataset.split`](https://roboflow.github.io/supervision/dataset/core/#supervision.dataset.core.ClassificationDataset.split) allowing to divide `sv.ClassificationDataset` into two parts.
 
-```python
->>> import cv2
->>> from ultralytics import YOLO
->>> import supervision as sv
+- Added [[#110](https://github.com/roboflow/supervision/pull/110)]: ability to extract masks from Roboflow API results using [`sv.Detections.from_roboflow`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_roboflow). 
 
->>> image = cv2.imread(...)
->>> image.shape
-(640, 640, 3)
+- Added [[commit hash](https://github.com/roboflow/supervision/commit/d000292eb2f2342544e0947b65528082e60fb8d6)]: Supervision Quickstart [notebook](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) where you can learn more about Detection, Dataset and Video APIs.
 
->>> model = YOLO('yolov8s-seg.pt')
->>> result = model(image)[0]
->>> detections = sv.Detections.from_yolov8(result)
->>> detections.mask.shape
-(2, 640, 640)
-```
+- Changed [[#135](https://github.com/roboflow/supervision/pull/135)]: `sv.get_video_frames_generator` documentation to better describe actual behavior.  
 
-- Added [[#122](https://github.com/roboflow/supervision/pull/122)]: ability to crop image using [`sv.crop`](https://roboflow.github.io/supervision/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
+### 0.9.0 <small>June 7, 2023</small>
+
+- Added [[#118](https://github.com/roboflow/supervision/pull/118)]: ability to select [`sv.Detections`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.__getitem__) by index, list of indexes or slice. Here is an example illustrating the new selection methods.
 
 ```python
->>> import cv2
 >>> import supervision as sv
 
->>> image = cv2.imread(...)
 >>> detections = sv.Detections(...)
->>> len(detections)
+>>> len(detections[0])
+1
+>>> len(detections[[0, 1]])
 2
->>> crops = [
-...     sv.crop(image=image, xyxy=xyxy) 
-...     for xyxy 
-...     in detections.xyxy
-... ]
->>> len(crops)
+>>> len(detections[0:2])
 2
 ```
 
+- Added [[#101](https://github.com/roboflow/supervision/pull/101)]: ability to extract masks from YOLOv8 result using [`sv.Detections.from_yolov8`](https://roboflow.github.io/supervision/detection/core/#supervision.detection.core.Detections.from_yolov8). Here is an example illustrating how to extract boolean masks from the result of the YOLOv8 model inference.
+
+- Added [[#122](https://github.com/roboflow/supervision/pull/122)]: ability to crop image using [`sv.crop`](https://roboflow.github.io/supervision/utils/image/#crop). Here is an example showing how to get a separate crop for each detection in `sv.Detections`.
+
 - Added [[#120](https://github.com/roboflow/supervision/pull/120)]: ability to conveniently save multiple images into directory using [`sv.ImageSink`](https://roboflow.github.io/supervision/utils/image/#imagesink). Here is an example showing how to save every tenth video frame as a separate image.
 
 ```python

setup.py

@@ -64,6 +64,6 @@ def get_version():
         'Operating System :: Unix',
         'Operating System :: MacOS'
     ],
-    keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLOv5, YOLOv8, Roboflow",
+    keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLOv5, YOLOv8, SAM, Roboflow",
     python_requires='>=3.7',
 )

supervision/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.9.0"
+__version__ = "0.10.0"
 
 from supervision.classification.core import Classifications
 from supervision.dataset.core import (

supervision/dataset/core.py

@@ -396,7 +396,7 @@ def split(
         )
         return train_dataset, test_dataset
 
-    def as_multiclass_folder_structure(self, root_directory_path: str) -> None:
+    def as_folder_structure(self, root_directory_path: str) -> None:
         """
         Saves the dataset as a multi-class folder structure.
 
@@ -421,9 +421,7 @@ def as_multiclass_folder_structure(self, root_directory_path: str) -> None:
             cv2.imwrite(image_path, image)
 
     @classmethod
-    def from_multiclass_folder_structure(
-        cls, root_directory_path: str
-    ) -> ClassificationDataset:
+    def from_folder_structure(cls, root_directory_path: str) -> ClassificationDataset:
         """
         Load data from a multiclass folder structure into a ClassificationDataset.
 
@@ -446,7 +444,7 @@ def from_multiclass_folder_structure(
             >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
             >>> dataset = project.version(PROJECT_VERSION).download("folder")
 
-            >>> cd = sv.ClassificationDataset.from_multiclass_folder_structure(
+            >>> cd = sv.ClassificationDataset.from_folder_structure(
             ...     root_directory_path=f"{dataset.location}/train"
             ... )
             ```