Merge pull request #627 from roboflow/develop

`supervision-0.17.0` release

.github/requirements-dev-min.txt

@@ -0,0 +1,27 @@
+attrs==23.1.0
+certifi==2023.7.22
+charset-normalizer==2.0.12
+cycler==0.12.1
+exceptiongroup==1.1.3
+fonttools==4.43.1
+idna==3.4
+iniconfig==2.0.0
+kiwisolver==1.4.5
+matplotlib==3.5.0
+numpy==1.21.2
+opencv-python==4.5.5.64
+packaging==23.2
+Pillow==10.1.0
+pluggy==1.3.0
+pyparsing==3.1.1
+pytest==7.2.0
+python-dateutil==2.8.2
+PyYAML==5.3
+requests==2.26.0
+scipy==1.9.0
+setuptools-scm==8.0.4
+six==1.16.0
+tomli==2.0.1
+tqdm==4.62.3
+typing_extensions==4.8.0
+urllib3==1.26.18

.github/workflows/docs.yml

@@ -22,4 +22,5 @@ jobs:
           python-version: 3.x
       - run: pip install mkdocs-material
       - run: pip install "mkdocstrings[python]"
+      - run: pip install "mkdocs-material[imaging]"
       - run: mkdocs gh-deploy --force

.github/workflows/test-min.yml

@@ -0,0 +1,32 @@
+name: Python 3.8 - Min Dep Test WorkFlow
+
+on:
+  pull_request:
+    branches: [main, develop]
+
+jobs:
+  build-min-dep-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8"]
+    steps:
+      - name: 🛎️ Checkout
+        uses: actions/checkout@v4
+      - name: 🐍 Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        # id based on python version
+        id: python-setup
+        with:
+          python-version: ${{ matrix.python-version }}
+          check-latest: true
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-dev-min.txt'
+
+      - name: 📦 Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r .github/requirements-dev-min.txt
+
+      - name: 🧪 Test
+        run: "python -m pytest ./test"

.github/workflows/test.yml

@@ -5,7 +5,7 @@ on:
     branches: [main, develop]
 
 jobs:
-  build:
+  build-dev-test:
     runs-on: ubuntu-latest
     strategy:
       matrix:

.github/workflows/welcome.yml

@@ -11,7 +11,7 @@ jobs:
     name: 👋 Welcome
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/first-interaction@v1.2.0
+      - uses: actions/first-interaction@v1.3.0
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
           issue-message: "Hello there, thank you for opening an Issue ! 🙏🏻 The team was notified and they will get back to you asap."

README.md

@@ -34,73 +34,62 @@ Pip install the supervision package in a
 [**3.11>=Python>=3.8**](https://www.python.org/) environment.
 
 ```bash
-pip install supervision[desktop]
+pip install supervision
 ```
 
 Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
 
 ## 🔥 quickstart
 
-### [detections processing](https://roboflow.github.io/supervision/detection/core/)
+### models
+
+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.
 
 ```python
+>>> import cv2
 >>> import supervision as sv
 >>> from ultralytics import YOLO
 
+>>> image = cv2.imread(...)
 >>> model = YOLO('yolov8s.pt')
->>> result = model(IMAGE)[0]
+>>> result = model(image)[0]
 >>> detections = sv.Detections.from_ultralytics(result)
 
 >>> len(detections)
 5
 ```
 
-<details close>
-<summary>👉 more detections utils</summary>
+### annotators
 
-- Easily switch inference pipeline between supported object detection/instance segmentation models
+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.
 
-    ```python
-    >>> import supervision as sv
-    >>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+```python
+>>> import cv2
+>>> import supervision as sv
 
-    >>> 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)
-    ```
+>>> image = cv2.imread(...)
+>>> detections = sv.Detections(...)
 
-- [Advanced filtering](https://roboflow.github.io/supervision/quickstart/detections/)
+>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
+>>> annotated_frame = bounding_box_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
 
-    ```python
-    >>> detections = detections[detections.class_id == 0]
-    >>> detections = detections[detections.confidence > 0.5]
-    >>> detections = detections[detections.area > 1000]
-    ```
+https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
 
-- Image annotation
+### datasets
 
-    ```python
-    >>> import supervision as sv
-
-    >>> box_annotator = sv.BoxAnnotator()
-    >>> annotated_frame = box_annotator.annotate(
-    ...     scene=IMAGE,
-    ...     detections=detections
-    ... )
-    ```
-
-</details>
-
-### [datasets processing](https://roboflow.github.io/supervision/dataset/core/)
+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.
 
 ```python
 >>> import supervision as sv
 
 >>> dataset = sv.DetectionDataset.from_yolo(
-...     images_directory_path='...',
-...     annotations_directory_path='...',
-...     data_yaml_path='...'
+...     images_directory_path=...,
+...     annotations_directory_path=...,
+...     data_yaml_path=...
 ... )
 
 >>> dataset.classes
@@ -113,38 +102,27 @@ Read more about desktop, headless, and local installation in our [guide](https:/
 <details close>
 <summary>👉 more dataset utils</summary>
 
-- Load object detection/instance segmentation datasets in one of the supported formats
+- load
 
     ```python
     >>> dataset = sv.DetectionDataset.from_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...,
+    ...     data_yaml_path=...
     ... )
 
     >>> dataset = sv.DetectionDataset.from_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...
     ... )
 
     >>> dataset = sv.DetectionDataset.from_coco(
-    ...     images_directory_path='...',
-    ...     annotations_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_path=...
     ... )
     ```
 
-- Loop over dataset entries
-
-    ```python
-    >>> 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
+- split
 
     ```python
     >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
@@ -154,7 +132,7 @@ Read more about desktop, headless, and local installation in our [guide](https:/
     (700, 150, 150)
     ```
 
-- Merge multiple datasets
+- merge
 
     ```python
     >>> ds_1 = sv.DetectionDataset(...)
@@ -176,103 +154,39 @@ Read more about desktop, headless, and local installation in our [guide](https:/
     ['cat', 'dog', 'person']
     ```
 
-- Save object detection/instance segmentation datasets in one of the supported formats
+- save
 
     ```python
     >>> dataset.as_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...,
+    ...     data_yaml_path=...
     ... )
 
     >>> dataset.as_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...
     ... )
 
     >>> dataset.as_coco(
-    ...     images_directory_path='...',
-    ...     annotations_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_path=...
     ... )
     ```
 
-- Convert labels between supported formats
+- convert
 
     ```python
     >>> sv.DetectionDataset.from_yolo(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...',
-    ...     data_yaml_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...,
+    ...     data_yaml_path=...
     ... ).as_pascal_voc(
-    ...     images_directory_path='...',
-    ...     annotations_directory_path='...'
+    ...     images_directory_path=...,
+    ...     annotations_directory_path=...
     ... )
     ```
 
-- Load classification datasets in one of the supported formats
-
-    ```python
-    >>> cs = sv.ClassificationDataset.from_folder_structure(
-    ...     root_directory_path='...'
-    ... )
-    ```
-
-- Save classification datasets in one of the supported formats
-
-    ```python
-    >>> cs.as_folder_structure(
-    ...     root_directory_path='...'
-    ... )
-    ```
-
-</details>
-
-### [model evaluation](https://roboflow.github.io/supervision/metrics/detection/)
-
-```python
->>> import supervision as sv
-
->>> dataset = sv.DetectionDataset.from_yolo(...)
-
->>> def callback(image: np.ndarray) -> sv.Detections:
-...     ...
-
->>> confusion_matrix = sv.ConfusionMatrix.benchmark(
-...     dataset = dataset,
-...     callback = callback
-... )
-
->>> confusion_matrix.matrix
-array([
-    [0., 0., 0., 0.],
-    [0., 1., 0., 1.],
-    [0., 1., 1., 0.],
-    [1., 1., 0., 0.]
-])
-```
-
-<details close>
-<summary>👉 more metrics</summary>
-
-- Mean average precision (mAP) for object detection tasks.
-
-    ```python
-    >>> import supervision as sv
-
-    >>> dataset = sv.DetectionDataset.from_yolo(...)
-
-    >>> def callback(image: np.ndarray) -> sv.Detections:
-    ...     ...
-
-    >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
-    ...     dataset = dataset,
-    ...     callback = callback
-    ... )
-
-    >>> mean_average_precision.map50_95
-    0.433
-    ```
-
 </details>
 
 ## 🎬 tutorials

docs/annotators.md

@@ -40,7 +40,7 @@
 
     </div>
 
-=== "BoxMask"
+=== "Color"
 
     ```python
     >>> import supervision as sv
@@ -48,8 +48,8 @@
     >>> image = ...
     >>> detections = sv.Detections(...)
 
-    >>> box_mask_annotator = sv.BoxMaskAnnotator()
-    >>> annotated_frame = box_mask_annotator.annotate(
+    >>> color_annotator = sv.ColorAnnotator()
+    >>> annotated_frame = color_annotator.annotate(
     ...     scene=image.copy(),
     ...     detections=detections
     ... )
@@ -103,6 +103,27 @@
 
     </div>
 
+=== "Triangle"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> triangle_annotator = sv.TriangleAnnotator()
+    >>> annotated_frame = triangle_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![triangle-annotator-example](https://media.roboflow.com/supervision-annotator-examples/triangle-annotator-example.png){ align=center width="800" }
+
+    </div>
+
 === "Ellipse"
 
     ```python
@@ -229,6 +250,27 @@
 
     </div>
 
+=== "Pixelate"
+
+    ```python
+    >>> import supervision as sv
+
+    >>> image = ...
+    >>> detections = sv.Detections(...)
+
+    >>> pixelate_annotator = sv.PixelateAnnotator()
+    >>> annotated_frame = pixelate_annotator.annotate(
+    ...     scene=image.copy(),
+    ...     detections=detections
+    ... )
+    ```
+
+    <div class="result" markdown>
+
+    ![pixelate-annotator-example](https://media.roboflow.com/supervision-annotator-examples/pixelate-annotator-example-10.png){ align=center width="800" }
+
+    </div>
+
 === "Trace"
 
     ```python
@@ -297,9 +339,9 @@
 
 :::supervision.annotators.core.BoxCornerAnnotator
 
-## BoxMaskAnnotator
+## ColorAnnotator
 
-:::supervision.annotators.core.BoxMaskAnnotator
+:::supervision.annotators.core.ColorAnnotator
 
 ## CircleAnnotator
 
@@ -309,6 +351,10 @@
 
 :::supervision.annotators.core.DotAnnotator
 
+## TriangleAnnotator
+
+:::supervision.annotators.core.TriangleAnnotator
+
 ## EllipseAnnotator
 
 :::supervision.annotators.core.EllipseAnnotator
@@ -337,6 +383,10 @@
 
 :::supervision.annotators.core.BlurAnnotator
 
+## PixelateAnnotator
+
+:::supervision.annotators.core.PixelateAnnotator
+
 ## TraceAnnotator
 
 :::supervision.annotators.core.TraceAnnotator

docs/changelog.md

@@ -1,3 +1,56 @@
+### 0.17.0 <small>December 06, 2023</small>
+
+- Added [#633](https://github.com/roboflow/supervision/pull/633): [`sv.PixelateAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.PixelateAnnotator) allowing to pixelate objects on images and videos.
+
+- Added [#652](https://github.com/roboflow/supervision/pull/652): [`sv.TriangleAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TriangleAnnotator) allowing to annotate images and videos with triangle markers.
+
+- Added [#602](https://github.com/roboflow/supervision/pull/602): [`sv.PolygonAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.PolygonAnnotator) allowing to annotate images and videos with segmentation mask outline.
+
+```python
+>>> import supervision as sv
+
+>>> image = ...
+>>> detections = sv.Detections(...)
+
+>>> polygon_annotator = sv.PolygonAnnotator()
+>>> annotated_frame = polygon_annotator.annotate(
+...     scene=image.copy(),
+...     detections=detections
+... )
+```
+
+- Added [#476](https://github.com/roboflow/supervision/pull/476): [`sv.assets`](https://supervision.roboflow.com/assets/) allowing download of video files that you can use in your demos.
+
+```python
+>>> from supervision.assets import download_assets, VideoAssets
+>>> download_assets(VideoAssets.VEHICLES)
+"vehicles.mp4"
+```
+
+- Added [#605](https://github.com/roboflow/supervision/pull/605): [`Position.CENTER_OF_MASS`](https://supervision.roboflow.com/geometry/core/#position) allowing to place labels in center of mass of segmentation masks.
+
+- Added [#651](https://github.com/roboflow/supervision/pull/651): [`sv.scale_boxes`](https://supervision.roboflow.com/detection/utils/#supervision.detection.utils.scale_boxes) allowing to scale [`sv.Detections.xyxy`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections) values.
+
+- Added [#637](https://github.com/roboflow/supervision/pull/637): [`sv.calculate_dynamic_text_scale`](https://supervision.roboflow.com/draw/utils/#supervision.draw.utils.calculate_dynamic_text_scale) and [`sv.calculate_dynamic_line_thickness`](https://supervision.roboflow.com/draw/utils/#supervision.draw.utils.calculate_dynamic_line_thickness) allowing text scale and line thickness to match image resolution.
+
+- Added [#620](https://github.com/roboflow/supervision/pull/620): [`sv.Color.as_hex`](https://supervision.roboflow.com/draw/color/#supervision.draw.color.Color.as_hex) allowing to extract color value in HEX format.
+
+- Added [#572](https://github.com/roboflow/supervision/pull/572): [`sv.Classifications.from_timm`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_timm) allowing to load classification result from [timm](https://huggingface.co/docs/hub/timm) models.
+
+- Added [#478](https://github.com/roboflow/supervision/pull/478): [`sv.Classifications.from_clip`](https://supervision.roboflow.com/classification/core/#supervision.classification.core.Classifications.from_clip) allowing to load classification result from [clip](https://github.com/openai/clip) model.
+
+- Added [#571](https://github.com/roboflow/supervision/pull/571): [`sv.Detections.from_azure_analyze_image`](https://supervision.roboflow.com/detection/core/#supervision.detection.core.Detections.from_azure_analyze_image) allowing to load detection results from [Azure Image Analysis](https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40).
+
+- Changed [#646](https://github.com/roboflow/supervision/pull/646): `sv.BoxMaskAnnotator` renaming it to [`sv.ColorAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.ColorAnnotator).
+
+- Changed [#606](https://github.com/roboflow/supervision/pull/606): [`sv.MaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.MaskAnnotator) to make it **5x faster**.
+
+- Fixed [#584](https://github.com/roboflow/supervision/pull/584): [`sv.DetectionDataset.from_yolo`](https://supervision.roboflow.com/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to ignore empty lines in annotation files.
+
+- Fixed [#555](https://github.com/roboflow/supervision/pull/555): [`sv.BlurAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BlurAnnotator) to trim negative coordinates before bluring detections.
+
+- Fixed [#511](https://github.com/roboflow/supervision/pull/511): [`sv.TraceAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.TraceAnnotator) to respect trace position.
+
 ### 0.16.0 <small>October 19, 2023</small>
 
 - Added [#422](https://github.com/roboflow/supervision/pull/422): [`sv.BoxMaskAnnotator`](https://supervision.roboflow.com/annotators/#supervision.annotators.core.BoxMaskAnnotator) allowing to annotate images and videos with mox masks.

docs/detection/utils.md

@@ -25,3 +25,11 @@
 ## filter_polygons_by_area
 
 :::supervision.detection.utils.filter_polygons_by_area
+
+## move_boxes
+
+:::supervision.detection.utils.move_boxes
+
+## scale_boxes
+
+:::supervision.detection.utils.scale_boxes

docs/draw/utils.md

@@ -21,3 +21,11 @@
 ## draw_image
 
 :::supervision.draw.utils.draw_image
+
+## calculate_dynamic_font_scale
+
+:::supervision.draw.utils.calculate_dynamic_text_scale
+
+## calculate_dynamic_line_thickness
+
+:::supervision.draw.utils.calculate_dynamic_line_thickness

mkdocs.yml

@@ -1,7 +1,7 @@
 site_name: Supervision
 site_url: https://supervision.roboflow.com/
 site_author: Roboflow
-site_description: A set of easy-to-use utils that will come in handy in any Computer Vision project
+site_description: A set of easy-to-use utilities that will come in handy in any computer vision project.
 repo_name: roboflow/supervision
 repo_url: https://github.com/roboflow/supervision
 edit_uri: https://github.com/roboflow/supervision/tree/main/docs
@@ -60,8 +60,8 @@ nav:
 
 theme:
   name: 'material'
-  logo: https://media.roboflow.com/open-source/supervision/supervision-lenny.png
-  favicon: https://media.roboflow.com/open-source/supervision/supervision-lenny.png
+  logo: assets/supervision-lenny.png
+  favicon: assets/supervision-lenny.png
   palette:
     # Palette for light mode
     - scheme: default
@@ -79,10 +79,15 @@ theme:
   font:
     text: Roboto
     code: Roboto Mono
+  features:
+    - content.code.copy
 
 plugins:
   - mkdocstrings
   - search
+  - social:
+      cards_layout_dir: docs/layouts
+      cards_layout: custom
 
 markdown_extensions:
   - admonition

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.17.0rc4"
+version = "0.17.0"
 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>"]
@@ -36,15 +36,15 @@ classifiers=[
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.12.0"
-numpy = "^1.20.0"
-matplotlib = "^3.7.1"
-pyyaml = "^6.0"
-pillow = ">=9.4,<11.0"
-opencv-python = { version = "^4.8.0.74", optional = true }
-opencv-python-headless = "^4.8.0.74"
-scipy = "^1.9.0"
-requests = { version = "^2.31.0", optional = true }
-tqdm = { version = "^4.66.1", optional = true }
+numpy = ">=1.21.2"
+matplotlib = ">=3.5.0"
+pyyaml = ">=5.3,<=6.0.1"
+pillow = ">=9.4"
+opencv-python = { version = ">=4.5.5.64,<=4.8.1.78", optional = true }
+opencv-python-headless = ">=4.5.5.64,<=4.8.1.78"
+scipy = ">=1.9.0,<=2.0.0"
+requests = { version = ">=2.26.0,<=2.31.0", optional = true }
+tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
 
 
 [tool.poetry.extras]
@@ -54,10 +54,10 @@ assets = ["requests","tqdm"]
 [tool.poetry.group.dev.dependencies]
 twine = "^4.0.2"
 pytest = "^7.2.2"
-wheel = ">=0.40,<0.42"
+wheel = ">=0.40,<0.43"
 notebook = ">=6.5.3,<8.0.0"
 build = ">=0.10,<1.1"
-ruff = ">=0.0.280,<0.1.7"
+ruff = ">=0.0.280,<0.1.8"
 isort = "^5.12.0"
 black = "^23.7.0"
 mypy = "^1.4.1"

supervision/__init__.py

@@ -10,16 +10,18 @@ from supervision.annotators.core import (
     BlurAnnotator,
     BoundingBoxAnnotator,
     BoxCornerAnnotator,
-    BoxMaskAnnotator,
     CircleAnnotator,
+    ColorAnnotator,
     DotAnnotator,
     EllipseAnnotator,
     HaloAnnotator,
     HeatMapAnnotator,
     LabelAnnotator,
     MaskAnnotator,
+    PixelateAnnotator,
     PolygonAnnotator,
     TraceAnnotator,
+    TriangleAnnotator,
 )
 from supervision.annotators.utils import ColorLookup
 from supervision.classification.core import Classifications
@@ -39,12 +41,16 @@ from supervision.detection.utils import (
     filter_polygons_by_area,
     mask_to_polygons,
     mask_to_xyxy,
+    move_boxes,
     non_max_suppression,
     polygon_to_mask,
     polygon_to_xyxy,
+    scale_boxes,
 )
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import (
+    calculate_dynamic_line_thickness,
+    calculate_dynamic_text_scale,
     draw_filled_rectangle,
     draw_image,
     draw_line,

supervision/annotators/core.py

@@ -260,7 +260,7 @@ class PolygonAnnotator(BaseAnnotator):
         return scene
 
 
-class BoxMaskAnnotator(BaseAnnotator):
+class ColorAnnotator(BaseAnnotator):
     """
     A class for drawing box masks on an image using provided detections.
     """
@@ -308,8 +308,8 @@ class BoxMaskAnnotator(BaseAnnotator):
             >>> image = ...
             >>> detections = sv.Detections(...)
 
-            >>> box_mask_annotator = sv.BoxMaskAnnotator()
-            >>> annotated_frame = box_mask_annotator.annotate(
+            >>> color_annotator = sv.ColorAnnotator()
+            >>> annotated_frame = color_annotator.annotate(
             ...     scene=image.copy(),
             ...     detections=detections
             ... )
@@ -1030,8 +1030,7 @@ class TraceAnnotator:
                 Options are `INDEX`, `CLASS`, `TRACK`.
         """
         self.color: Union[Color, ColorPalette] = color
-        self.position = position
-        self.trace = Trace(max_size=trace_length)
+        self.trace = Trace(max_size=trace_length, anchor=position)
         self.thickness = thickness
         self.color_lookup: ColorLookup = color_lookup
 
@@ -1197,3 +1196,159 @@ class HeatMapAnnotator:
             mask
         ]
         return scene
+
+
+class PixelateAnnotator(BaseAnnotator):
+    """
+    A class for pixelating regions in an image using provided detections.
+    """
+
+    def __init__(self, pixel_size: int = 20):
+        """
+        Args:
+            pixel_size (int): The size of the pixelation.
+        """
+        self.pixel_size: int = pixel_size
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene by pixelating regions based on the provided
+            detections.
+
+        Args:
+            scene (np.ndarray): The image where pixelating will be applied.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> pixelate_annotator = sv.PixelateAnnotator()
+            >>> annotated_frame = pixelate_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![pixelate-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/pixelate-annotator-example-10.png)
+        """
+        image_height, image_width = scene.shape[:2]
+        clipped_xyxy = clip_boxes(
+            xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
+        ).astype(int)
+
+        for x1, y1, x2, y2 in clipped_xyxy:
+            roi = scene[y1:y2, x1:x2]
+            scaled_up_roi = cv2.resize(
+                src=roi, dsize=None, fx=1 / self.pixel_size, fy=1 / self.pixel_size
+            )
+            scaled_down_roi = cv2.resize(
+                src=scaled_up_roi,
+                dsize=(roi.shape[1], roi.shape[0]),
+                interpolation=cv2.INTER_NEAREST,
+            )
+
+            scene[y1:y2, x1:x2] = scaled_down_roi
+
+        return scene
+
+
+class TriangleAnnotator(BaseAnnotator):
+    """
+    A class for drawing triangle markers on an image at specific coordinates based on
+    provided detections.
+    """
+
+    def __init__(
+        self,
+        color: Union[Color, ColorPalette] = ColorPalette.default(),
+        base: int = 10,
+        height: int = 10,
+        position: Position = Position.TOP_CENTER,
+        color_lookup: ColorLookup = ColorLookup.CLASS,
+    ):
+        """
+        Args:
+            color (Union[Color, ColorPalette]): The color or color palette to use for
+                annotating detections.
+            base (int): The base width of the triangle.
+            height (int): The height of the triangle.
+            position (Position): The anchor position for placing the triangle.
+            color_lookup (ColorLookup): Strategy for mapping colors to annotations.
+                Options are `INDEX`, `CLASS`, `TRACK`.
+        """
+        self.color: Union[Color, ColorPalette] = color
+        self.base: int = base
+        self.height: int = height
+        self.position: Position = position
+        self.color_lookup: ColorLookup = color_lookup
+
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+        custom_color_lookup: Optional[np.ndarray] = None,
+    ) -> np.ndarray:
+        """
+        Annotates the given scene with triangles based on the provided detections.
+
+        Args:
+            scene (np.ndarray): The image where triangles will be drawn.
+            detections (Detections): Object detections to annotate.
+            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
+                Allows to override the default color mapping strategy.
+
+        Returns:
+            np.ndarray: The annotated image.
+
+        Example:
+            ```python
+            >>> import supervision as sv
+
+            >>> image = ...
+            >>> detections = sv.Detections(...)
+
+            >>> triangle_annotator = sv.TriangleAnnotator()
+            >>> annotated_frame = triangle_annotator.annotate(
+            ...     scene=image.copy(),
+            ...     detections=detections
+            ... )
+            ```
+
+        ![triangle-annotator-example](https://media.roboflow.com/
+        supervision-annotator-examples/triangle-annotator-example.png)
+        """
+        xy = detections.get_anchors_coordinates(anchor=self.position)
+        for detection_idx in range(len(detections)):
+            color = resolve_color(
+                color=self.color,
+                detections=detections,
+                detection_idx=detection_idx,
+                color_lookup=self.color_lookup
+                if custom_color_lookup is None
+                else custom_color_lookup,
+            )
+            tip_x, tip_y = int(xy[detection_idx, 0]), int(xy[detection_idx, 1])
+            vertices = np.array(
+                [
+                    [tip_x - self.base // 2, tip_y - self.height],
+                    [tip_x + self.base // 2, tip_y - self.height],
+                    [tip_x, tip_y],
+                ],
+                np.int32,
+            )
+
+            cv2.fillPoly(scene, [vertices], color.as_bgr())
+
+        return scene

supervision/classification/core.py

@@ -39,15 +39,58 @@ class Classifications:
         _validate_class_ids(self.class_id, n)
         _validate_confidence(self.confidence, n)
 
+    def __len__(self) -> int:
+        """
+        Returns the number of classifications.
+        """
+        return len(self.class_id)
+
+    @classmethod
+    def from_clip(cls, clip_results) -> Classifications:
+        """
+        Creates a Classifications instance from a
+        [clip](https://github.com/openai/clip) inference result.
+
+        Args:
+            clip_results (np.ndarray): The inference result from clip model.
+
+        Returns:
+            Classifications: A new Classifications object.
+
+        Example:
+            ```python
+            >>> from PIL import Image
+            >>> import clip
+            >>> import supervision as sv
+
+            >>> model, preprocess = clip.load('ViT-B/32')
+
+            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+            >>> image = preprocess(image).unsqueeze(0)
+
+            >>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
+            >>> output, _ = model(image, text)
+            >>> classifications = sv.Classifications.from_clip(output)
+            ```
+        """
+
+        confidence = clip_results.softmax(dim=-1).cpu().detach().numpy()[0]
+
+        if len(confidence) == 0:
+            return cls(class_id=np.array([]), confidence=np.array([]))
+
+        class_ids = np.arange(len(confidence))
+        return cls(class_id=class_ids, confidence=confidence)
+
     @classmethod
     def from_ultralytics(cls, ultralytics_results) -> Classifications:
         """
         Creates a Classifications instance from a
-        (https://github.com/ultralytics/ultralytics) inference result.
+        [ultralytics](https://github.com/ultralytics/ultralytics) inference result.
 
         Args:
             ultralytics_results (ultralytics.engine.results.Results):
-                The output Results instance from ultralytics model
+                The inference result from ultralytics model.
 
         Returns:
             Classifications: A new Classifications object.
@@ -60,15 +103,57 @@ class Classifications:
 
             >>> image = cv2.imread(SOURCE_IMAGE_PATH)
             >>> model = YOLO('yolov8n-cls.pt')
-            >>> model = YOLO('yolov8s-cls.pt')
 
-            >>> result = model(image)[0]
-            >>> classifications = sv.Classifications.from_ultralytics(result)
+            >>> output = model(image)[0]
+            >>> classifications = sv.Classifications.from_ultralytics(output)
             ```
         """
         confidence = ultralytics_results.probs.data.cpu().numpy()
         return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
 
+    @classmethod
+    def from_timm(cls, timm_results) -> Classifications:
+        """
+        Creates a Classifications instance from a
+        [timm](https://huggingface.co/docs/hub/timm) inference result.
+
+        Args:
+            timm_results: The inference result from timm model.
+
+        Returns:
+            Classifications: A new Classifications object.
+
+        Example:
+            ```python
+            >>> from PIL import Image
+            >>> import timm
+            >>> from timm.data import resolve_data_config, create_transform
+            >>> import supervision as sv
+
+            >>> model = timm.create_model(
+            ...     model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
+            ...     pretrained=True
+            ... ).eval()
+
+            >>> config = resolve_data_config({}, model=model)
+            >>> transform = create_transform(**config)
+
+            >>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
+            >>> x = transform(image).unsqueeze(0)
+
+            >>> output = model(x)
+
+            >>> classifications = sv.Classifications.from_timm(output)
+            ```
+        """
+        confidence = timm_results.cpu().detach().numpy()[0]
+
+        if len(confidence) == 0:
+            return cls(class_id=np.array([]), confidence=np.array([]))
+
+        class_id = np.arange(len(confidence))
+        return cls(class_id=class_id, confidence=confidence)
+
     def get_top_k(self, k: int) -> Tuple[np.ndarray, np.ndarray]:
         """
         Retrieve the top k class IDs and confidences,

supervision/detection/core.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import astuple, dataclass
-from typing import Any, Iterator, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -64,6 +64,7 @@ def _validate_tracker_id(tracker_id: Any, n: int) -> None:
 class Detections:
     """
     Data class containing information about the detections in a video frame.
+
     Attributes:
         xyxy (np.ndarray): An array of shape `(n, 4)` containing
             the bounding boxes coordinates in format `[x1, y1, x2, y2]`
@@ -324,7 +325,7 @@ class Detections:
 
             >>> inferencer = DetInferencer(model_name, checkpoint, device)
             >>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
-            ...                           return_datasample=True)["predictions"][0]
+            ...                           return_datasamples=True)["predictions"][0]
             >>> detections = sv.Detections.from_mmdet(mmdet_result)
             ```
         """
@@ -461,7 +462,7 @@ class Detections:
             >>> from segment_anything import (
             ...     sam_model_registry,
             ...     SamAutomaticMaskGenerator
-            ...     )
+            ... )
 
             >>> sam_model_reg = sam_model_registry[MODEL_TYPE]
             >>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
@@ -484,6 +485,93 @@ class Detections:
         xyxy = xywh_to_xyxy(boxes_xywh=xywh)
         return cls(xyxy=xyxy, mask=mask)
 
+    @classmethod
+    def from_azure_analyze_image(
+        cls, azure_result: dict, class_map: Optional[Dict[int, str]] = None
+    ) -> Detections:
+        """
+        Creates a Detections instance from [Azure Image Analysis 4.0](
+        https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/
+        concept-object-detection-40).
+
+        Args:
+            azure_result (dict): The result from Azure Image Analysis. It should
+                contain detected objects and their bounding box coordinates.
+            class_map (Optional[Dict[int, str]]): A mapping ofclass IDs (int) to class
+                names (str). If None, a new mapping is created dynamically.
+
+        Returns:
+            Detections: A new Detections object.
+
+        Example:
+            ```python
+            >>> import requests
+            >>> import supervision as sv
+
+            >>> image = open(input, "rb").read()
+
+            >>> endpoint = "https://.cognitiveservices.azure.com/"
+            >>> subscription_key = "..."
+
+            >>> headers = {
+            ...    "Content-Type": "application/octet-stream",
+            ...    "Ocp-Apim-Subscription-Key": subscription_key
+            ... }
+
+            >>> response = requests.post(endpoint,
+            ...     headers=self.headers,
+            ...     data=image
+            ... ).json()
+
+            >>> detections = sv.Detections.from_azure_analyze_image(response)
+            ```
+        """
+        if "error" in azure_result:
+            raise ValueError(
+                f'Azure API returned an error {azure_result["error"]["message"]}'
+            )
+
+        xyxy, confidences, class_ids = [], [], []
+
+        is_dynamic_mapping = class_map is None
+        if is_dynamic_mapping:
+            class_map = {}
+
+        class_map = {value: key for key, value in class_map.items()}
+
+        for detection in azure_result["objectsResult"]["values"]:
+            bbox = detection["boundingBox"]
+
+            tags = detection["tags"]
+
+            x0 = bbox["x"]
+            y0 = bbox["y"]
+            x1 = x0 + bbox["w"]
+            y1 = y0 + bbox["h"]
+
+            for tag in tags:
+                confidence = tag["confidence"]
+                class_name = tag["name"]
+                class_id = class_map.get(class_name, None)
+
+                if is_dynamic_mapping and class_id is None:
+                    class_id = len(class_map)
+                    class_map[class_name] = class_id
+
+                if class_id is not None:
+                    xyxy.append([x0, y0, x1, y1])
+                    confidences.append(confidence)
+                    class_ids.append(class_id)
+
+        if len(xyxy) == 0:
+            return Detections.empty()
+
+        return cls(
+            xyxy=np.array(xyxy),
+            class_id=np.array(class_ids),
+            confidence=np.array(confidences),
+        )
+
     @classmethod
     def from_paddledet(cls, paddledet_result) -> Detections:
         """

supervision/detection/utils.py

@@ -383,18 +383,65 @@ def process_roboflow_result(
 
 def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
     """
-    Args:
+    Parameters:
         xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
             coordinates in format `[x1, y1, x2, y2]`
         offset (np.array): An array of shape `(2,)` containing offset values in format
             is `[dx, dy]`.
 
     Returns:
-        (np.ndarray) repositioned bounding boxes
+        np.ndarray: Repositioned bounding boxes.
+
+    Example:
+        ```python
+        >>> import numpy as np
+        >>> import supervision as sv
+
+        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        >>> offset = np.array([5, 5])
+        >>> sv.move_boxes(boxes, offset)
+        ... array([
+        ...     [15, 15, 25, 25],
+        ...     [35, 35, 45, 45]
+        ... ])
+        ```
     """
     return xyxy + np.hstack([offset, offset])
 
 
+def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
+    """
+    Scale the dimensions of bounding boxes.
+
+    Parameters:
+        xyxy (np.ndarray): An array of shape `(n, 4)` containing the bounding boxes
+            coordinates in format `[x1, y1, x2, y2]`
+        factor (float): A float value representing the factor by which the box
+            dimensions are scaled. A factor greater than 1 enlarges the boxes, while a
+            factor less than 1 shrinks them.
+
+    Returns:
+        np.ndarray: Scaled bounding boxes.
+
+    Example:
+        ```python
+        >>> import numpy as np
+        >>> import supervision as sv
+
+        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        >>> factor = 1.5
+        >>> sv.scale_boxes(boxes, factor)
+        ... array([
+        ...     [ 7.5,  7.5, 22.5, 22.5],
+        ...     [27.5, 27.5, 42.5, 42.5]
+        ... ])
+        ```
+    """
+    centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2
+    new_sizes = (xyxy[:, 2:] - xyxy[:, :2]) * factor
+    return np.concatenate((centers - new_sizes / 2, centers + new_sizes / 2), axis=1)
+
+
 def calculate_masks_centroids(masks: np.ndarray) -> np.ndarray:
     """
     Calculate the centroids of binary masks in a tensor.

supervision/draw/utils.py

@@ -1,5 +1,5 @@
 import os
-from typing import Optional, Union
+from typing import Optional, Tuple, Union
 
 import cv2
 import numpy as np
@@ -231,3 +231,41 @@ def draw_image(
     scene[rect.y : rect.y + rect.height, rect.x : rect.x + rect.width] = blended_roi
 
     return scene
+
+
+def calculate_dynamic_text_scale(resolution_wh: Tuple[int, int]) -> float:
+    """
+    Calculate a dynamic font scale based on the resolution of an image.
+
+    Parameters:
+         resolution_wh (Tuple[int, int]): A tuple representing the width and height
+                 of the image.
+
+    Returns:
+         float: The calculated font scale factor.
+    """
+    return min(resolution_wh) * 1e-3
+
+
+def calculate_dynamic_line_thickness(resolution_wh: Tuple[int, int]) -> int:
+    """
+    Calculate a dynamic line thickness based on the resolution of an image.
+
+    Parameters:
+        resolution_wh (Tuple[int, int]): A tuple representing the width and height
+                of the image.
+
+    Returns:
+        int: The calculated line thickness in pixels.
+    """
+    min_dimension = min(resolution_wh)
+    if min_dimension < 480:
+        return 2
+    if min_dimension < 720:
+        return 2
+    if min_dimension < 1080:
+        return 2
+    if min_dimension < 2160:
+        return 4
+    else:
+        return 4

test/detection/test_utils.py

@@ -11,6 +11,7 @@ from supervision.detection.utils import (
     move_boxes,
     non_max_suppression,
     process_roboflow_result,
+    scale_boxes,
 )
 
 TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
@@ -504,6 +505,52 @@ def test_move_boxes(
         assert np.array_equal(result, expected_result)
 
 
+@pytest.mark.parametrize(
+    "xyxy, factor, expected_result, exception",
+    [
+        (
+            np.empty(shape=(0, 4)),
+            2.0,
+            np.empty(shape=(0, 4)),
+            DoesNotRaise(),
+        ),  # empty xyxy array
+        (
+            np.array([[0, 0, 10, 10]]),
+            1.0,
+            np.array([[0, 0, 10, 10]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 1.0
+        (
+            np.array([[0, 0, 10, 10]]),
+            2.0,
+            np.array([[-5, -5, 15, 15]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 2.0
+        (
+            np.array([[0, 0, 10, 10]]),
+            0.5,
+            np.array([[2.5, 2.5, 7.5, 7.5]]),
+            DoesNotRaise(),
+        ),  # single box with factor equal to 0.5
+        (
+            np.array([[0, 0, 10, 10], [10, 10, 30, 30]]),
+            2.0,
+            np.array([[-5, -5, 15, 15], [0, 0, 40, 40]]),
+            DoesNotRaise(),
+        ),  # two boxes with factor equal to 2.0
+    ],
+)
+def test_scale_boxes(
+    xyxy: np.ndarray,
+    factor: float,
+    expected_result: np.ndarray,
+    exception: Exception,
+) -> None:
+    with exception:
+        result = scale_boxes(xyxy=xyxy, factor=factor)
+        assert np.array_equal(result, expected_result)
+
+
 @pytest.mark.parametrize(
     "masks, expected_result, exception",
     [