Merge pull request #991 from roboflow/develop

`supervision-0.19.0` release

.github/workflows/test-doc.yml

@@ -0,0 +1,34 @@
+name: 🧪 Docs Test WorkFlow 📚
+
+on:
+  pull_request:
+    branches: [main, develop]
+
+jobs:
+  docs-build-test:
+    runs-on: ubuntu-latest
+    steps:
+      - name: 🔄 Checkout code
+        uses: actions/checkout@v4
+      - name: 🐍 Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+      - name: 📦 Install mkdocs-material
+        run: pip install "mkdocs-material[all]"
+      - name: 📦 Install mkdocstrings[python]
+        run: pip install "mkdocstrings[python]"
+      - name: 📦 Install mkdocs-material[imaging]
+        run: pip install "mkdocs-material[imaging]"
+      - name: 📦 Install mike
+        run: pip install "mike"
+      - name: 📦 Install mkdocs-git-revision-date-localized-plugin
+        run: pip install "mkdocs-git-revision-date-localized-plugin"
+      - name: 📦 Install JupyterLab
+        run: pip install jupyterlab
+      - name: 📦 Install mkdocs-jupyter
+        run: pip install mkdocs-jupyter
+      - name: 📦 Install mkdocs-git-committers-plugin-2
+        run: pip install mkdocs-git-committers-plugin-2
+      - name: 🧪 Test documentation build
+        run: mkdocs build --verbose

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
 
 
   -   repo: https://github.com/PyCQA/bandit
-      rev: '1.7.7'
+      rev: '1.7.8'
       hooks:
       -   id: bandit
           args: ["-c", "pyproject.toml"]
@@ -46,7 +46,7 @@ repos:
 
 
   -   repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.2.2
+      rev: v0.3.2
       hooks:
       -   id: ruff
           args: [--fix, --exit-non-zero-on-fix]

README.md

@@ -1,6 +1,6 @@
 <div align="center">
   <p>
-    <a align="center" href="" target="_blank">
+    <a align="center" href="" target="https://supervision.roboflow.com">
       <img
         width="100%"
         src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
@@ -10,7 +10,7 @@
 
   <br>
 
-[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)
+[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro)
 
   <br>
 

cookbooks/README.md

@@ -1,19 +0,0 @@
-# Supervision Cookbooks
-
-## About
-
-The Roboflow Cookbook is an open-source collection of examples and guides for building with the [Roboflow Ecosystem](https://roboflow.com/).
-
-Cookbooks are meant to be common and small scoped computer vision tasks that can allow a user to quickly hit the ground running. Learning is the main goal and purpose.
-
-Some examples require a Roboflow API key. You can [create a free account here](https://app.roboflow.com/login).
-
-## Other Resources
-
-Other than Cookbooks, Roboflow offers other learning resources including:
-
-- A set of open source tools found on [Github](https://github.com/roboflow).
-- A [blog](https://blog.roboflow.com/) detailing interesting tutorials, computer vision news, and product updates.
-- A [Youtube channel](https://www.youtube.com/roboflow) with end to end commputer vision projects.
-- A [forum](https://discuss.roboflow.com/) for sharing feedback, discussions, and other questions.
-- The worlds largest [open source collection](https://universe.roboflow.com/) of images, datasets, and fine-tuned models.

docs/annotators.md

@@ -278,6 +278,21 @@ status: new
 
     </div>
 
+=== "Crop"
+
+    ```python
+     import supervision as sv
+
+    image = ...
+    detections = sv.Detections(...)
+
+    crop_annotator = sv.CropAnnotator()
+    annotated_frame = crop_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
+    ```
+
 === "Blur"
 
     ```python
@@ -488,6 +503,12 @@ status: new
 
 :::supervision.annotators.core.TraceAnnotator
 
+<div class="md-typeset">
+    <h2><a href="#supervision.annotators.core.CropAnnotator">CropAnnotator</a></h2>
+</div>
+
+:::supervision.annotators.core.CropAnnotator
+
 <div class="md-typeset">
     <h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
 </div>

docs/changelog.md

@@ -1,6 +1,88 @@
+### 0.19.0 <small>March 15, 2024</small>
+
+- Added [#818](https://github.com/roboflow/supervision/pull/818): [`sv.CSVSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.CSVSink) allowing for the straightforward saving of image, video, or stream inference results in a `.csv` file.
+
+```python
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO(<SOURCE_MODEL_PATH>)
+csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
+frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+with csv_sink:
+    for frame in frames_generator:
+        result = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(result)
+        csv_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
+```
+
+- Added [#819](https://github.com/roboflow/supervision/pull/819): [`sv.JSONSink`](/0.19.0/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink) allowing for the straightforward saving of image, video, or stream inference results in a `.json` file.
+
+```python
+
+```python
+import supervision as sv
+from ultralytics import YOLO
+
+model = YOLO(<SOURCE_MODEL_PATH>)
+json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
+frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+with json_sink:
+    for frame in frames_generator:
+        result = model(frame)[0]
+        detections = sv.Detections.from_ultralytics(result)
+        json_sink.append(detections, custom_data={<CUSTOM_LABEL>:<CUSTOM_DATA>})
+```
+
+- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_iou_batch`](/0.19.0/detection/utils/#supervision.detection.utils.mask_iou_batch) allowing to compute Intersection over Union (IoU) of two sets of masks.
+
+- Added [#847](https://github.com/roboflow/supervision/pull/847): [`sv.mask_non_max_suppression`](/0.19.0/detection/utils/#supervision.detection.utils.mask_non_max_suppression) allowing to perform Non-Maximum Suppression (NMS) on segmentation predictions.
+
+- Added [#888](https://github.com/roboflow/supervision/pull/888): [`sv.CropAnnotator`](/0.19.0/annotators/#supervision.annotators.core.CropAnnotator) allowing users to annotate the scene with scaled-up crops of detections.
+
+```python
+import cv2
+import supervision as sv
+from inference import get_model
+
+image = cv2.imread(<SOURCE_IMAGE_PATH>)
+model = get_model(model_id="yolov8n-640")
+
+result = model.infer(image)[0]
+detections = sv.Detections.from_inference(result)
+
+crop_annotator = sv.CropAnnotator()
+annotated_frame = crop_annotator.annotate(
+    scene=image.copy(),
+    detections=detections
+)
+```
+
+- Changed [#827](https://github.com/roboflow/supervision/pull/827): [`sv.ByteTrack.reset`](/0.19.0/tracking/#supervision.tracking.ByteTrack.reset) allowing users to clear trackers state, enabling the processing of multiple video files in sequence.
+
+- Changed [#802](https://github.com/roboflow/supervision/pull/802): [`sv.LineZoneAnnotator`](/0.19.0/detection/tools/line_zone/#supervision.detection.line_zone.LineZone) allowing to hide in/out count using `display_in_count` and `display_out_count` properties.
+
+- Changed [#787](https://github.com/roboflow/supervision/pull/787): [`sv.ByteTrack`](/0.19.0/tracking/#supervision.tracking.ByteTrack) input arguments and docstrings updated to improve readability and ease of use.
+
+!!! failure "Deprecated"
+
+    The `track_buffer`, `track_thresh`, and `match_thresh` parameters in `sv.ByterTrack` are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+
+- Changed [#910](https://github.com/roboflow/supervision/pull/910): [`sv.PolygonZone`](/0.19.0/detection/tools/polygon_zone/#supervision.detection.tools.polygon_zone.PolygonZone) to now accept a list of specific box anchors that must be in zone for a detection to be counted.
+
+!!! failure "Deprecated"
+
+    The `triggering_position ` parameter in `sv.PolygonZone` is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors` instead.
+
+- Changed [#875](https://github.com/roboflow/supervision/pull/875): annotators adding support for Pillow images. All supervision Annotators can now accept an image as either a numpy array or a Pillow Image. They automatically detect its type, draw annotations, and return the output in the same format as the input.
+
+- Fixed [#944](https://github.com/roboflow/supervision/pull/944): [`sv.DetectionsSmoother`](/0.19.0/detection/tools/smoother/#supervision.detection.tools.smoother.DetectionsSmoother) removing `tracking_id` from `sv.Detections`.
+
 ### 0.18.0 <small>January 25, 2024</small>
 
-- Added [#633](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](/0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
+- Added [#720](https://github.com/roboflow/supervision/pull/720): [`sv.PercentageBarAnnotator`](/0.18.0/annotators/#percentagebarannotator) allowing to annotate images and videos with percentage values representing confidence or other custom property.
 
 ```python
 >>> import supervision as sv

docs/deprecated.md

@@ -6,11 +6,13 @@ status: deprecated
 These features are phased out due to better alternatives or potential issues in future versions. Deprecated functionalities are supported for **three subsequent releases**, providing time for users to transition to updated methods.
 
 - [`Detections.from_froboflow`](detection/core.md/#supervision.detection.core.Detections.from_roboflow) is deprecated and will be removed in `supervision-0.22.0`. Use [`Detections.from_inference`](detection/core.md/#supervision.detection.core.Detections.from_inference) instead.
-- `Color.white()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.WHITE` instead.
-- `Color.black()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.BLACK` instead.
-- `Color.red()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.RED` instead.
-- `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.GREEN` instead.
-- `Color.blue()` is deprecated and will be removed in `supervision-0.22.0`. Use `Color.BLUE` instead.
-- [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) is deprecated and will be removed in `supervision-0.22.0`. Use [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
+- The method `Color.white()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.WHITE` instead.
+- The method `Color.black()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLACK` instead.
+- The method `Color.red()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.RED` instead.
+- The method `Color.green()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.GREEN` instead.
+- The method `Color.blue()` is deprecated and will be removed in `supervision-0.22.0`. Use the constant `Color.BLUE` instead.
+- The method [`ColorPalette.default()`](draw/color.md/#supervision.draw.color.ColorPalette.default) is deprecated and will be removed in `supervision-0.22.0`. Use the constant [`ColorPalette.DEFAULT`](draw/color.md/#supervision.draw.color.ColorPalette.DEFAULT) instead.
 - `BoxAnnotator` is deprecated and will be removed in `supervision-0.22.0`. Use [`BoundingBoxAnnotator`](annotators.md/#supervision.annotators.core.BoundingBoxAnnotator) and [`LabelAnnotator`](annotators.md/#supervision.annotators.core.LabelAnnotator) instead.
-- [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) is deprecated and will be removed in `supervision-0.22.0`. Use [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
+- The method [`FPSMonitor.__call__`](utils/video.md/#supervision.utils.video.FPSMonitor.__call__) is deprecated and will be removed in `supervision-0.22.0`. Use the attribute [`FPSMonitor.fps`](utils/video.md/#supervision.utils.video.FPSMonitor.fps) instead.
+- The `track_buffer`, `track_thresh`, and `match_thresh` parameters in [`ByterTrack`](trackers.md/#supervision.tracker.byte_tracker.core.ByteTrack) are deprecated and will be removed in `supervision-0.23.0`. Use `lost_track_buffer,` `track_activation_threshold`, and `minimum_matching_threshold` instead.
+- The `triggering_position ` parameter in [`sv.PolygonZone`](detection/tools/polygon_zone.md/#supervision.detection.tools.polygon_zone.PolygonZone) is deprecated and will be removed in `supervision-0.23.0`. Use `triggering_anchors ` instead.

docs/detection/tools/save_detections.md

@@ -10,3 +10,9 @@ status: new
 </div>
 
 :::supervision.detection.tools.csv_sink.CSVSink
+
+<div class="md-typeset">
+  <h2>JSON Sink</h2>
+</div>
+
+:::supervision.detection.tools.json_sink.JSONSink

docs/detection/utils.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # Detection Utils

docs/notebooks/quickstart.ipynb

@@ -12,23 +12,11 @@
                 "\n",
                 "---\n",
                 "\n",
-                "<p align=\"center\">\n",
-                "  <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://badge.fury.io/py/supervision.svg\" alt=\"version\"></a>\n",
-                "  <a href=\"https://pypistats.org/packages/supervision\"><img src=\"https://img.shields.io/pypi/dm/supervision\" alt=\"downloads\"></a>\n",
-                "  <a href=\"https://github.com/roboflow/supervision/blob/main/LICENSE.md\"><img src=\"https://img.shields.io/pypi/l/supervision\" alt=\"license\"></a>\n",
-                "  <a href=\"https://badge.fury.io/py/supervision\"><img src=\"https://img.shields.io/pypi/pyversions/supervision\" alt=\"python-version\"></a>\n",
-                "  <a href=\"https://github.com/roboflow/supervision\"><img src=\"https://badges.aleen42.com/src/github.svg\" alt=\"GitHub\"></a>\n",
-                "</p>\n",
-                "\n",
-                "<p align=\"center\">\n",
-                "  <a href=\"https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Colab\"></a>\n",
-                "  <a href=\"https://kaggle.com/kernels/welcome?src=https://github.com/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Kaggle\"></a>\n",
-                "  <a href=\"https://studiolab.sagemaker.aws/import/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/sage-maker.svg\" alt=\"SageMaker\"></a>\n",
-                "  <a href=\"https://nbviewer.jupyter.org/github/roboflow/supervision/blob/main/demo.ipynb\"><img src=\"https://img.shields.io/badge/Open_in_Nbviewer-F37626.svg?logo=Jupyter&logoColor=white\" alt=\"nbviewer\">\n",
-                "  <a href=\"https://mybinder.org/v2/gh/roboflow/supervision/develop?labpath=demo.ipynb\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Binder\"></a>\n",
-                "  <a href=\"https://github.com/roboflow/supervision/raw/main/demo.ipynb\" download><img src=\"https://img.shields.io/badge/Download-Notebook-A351FB.svg\" alt=\"Download\"></a>\n",
-                "</p>\n",
-                "\n",
+                "[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)\n",
+                "[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)\n",
+                "[![license](https://img.shields.io/pypi/l/supervision)](https://github.com/roboflow/supervision/blob/main/LICENSE.md)\n",
+                "[![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision)\n",
+                "[![GitHub](https://badges.aleen42.com/src/github.svg)](https://github.com/roboflow/supervision)\n",
                 "\n",
                 "We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! \ud83e\udd1d\n",
                 "\n",
@@ -78,7 +66,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 1,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -129,7 +117,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -164,7 +152,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": null,
             "metadata": {
                 "id": "1oeBxRj5wOv7"
             },
@@ -184,7 +172,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -225,7 +213,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 5,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -279,7 +267,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 6,
+            "execution_count": null,
             "metadata": {
                 "id": "0ZlmuEpwydTu"
             },
@@ -323,13 +311,13 @@
                 "from super_gradients.training import models\n",
                 "\n",
                 "model = models.get(\"yolo_nas_l\", pretrained_weights=\"coco\")\n",
-                "result = model.predict(image)[0]\n",
+                "result = model.predict(image)\n",
                 "detections = sv.Detections.from_yolo_nas(result)"
             ]
         },
         {
             "cell_type": "code",
-            "execution_count": 20,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -364,7 +352,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 10,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -388,7 +376,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 32,
+            "execution_count": null,
             "metadata": {
                 "id": "PMx3oMPh1fui"
             },
@@ -403,7 +391,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 22,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -447,7 +435,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 31,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -487,7 +475,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 30,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -533,7 +521,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 29,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -584,7 +572,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 44,
+            "execution_count": null,
             "metadata": {
                 "id": "yuskE3obCS_-"
             },
@@ -597,7 +585,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 50,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -647,7 +635,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 58,
+            "execution_count": null,
             "metadata": {
                 "id": "ZMsM2W-E_a3S"
             },
@@ -658,7 +646,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 59,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -698,7 +686,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 51,
+            "execution_count": null,
             "metadata": {
                 "id": "iaoKiE2WD-1V"
             },
@@ -709,7 +697,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 53,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -757,7 +745,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 61,
+            "execution_count": null,
             "metadata": {
                 "id": "iEVlSoKDE01n"
             },
@@ -770,7 +758,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 62,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -828,7 +816,9 @@
         {
             "cell_type": "code",
             "execution_count": null,
-            "metadata": {},
+            "metadata": {
+                "id": "MWUiG8oiNljr"
+            },
             "outputs": [],
             "source": [
                 "!pip install -q supervision[assets]"
@@ -836,7 +826,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 63,
+            "execution_count": null,
             "metadata": {
                 "id": "2ZEtjEZXImNd"
             },
@@ -867,7 +857,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 66,
+            "execution_count": null,
             "metadata": {
                 "id": "uzNDUj27Jthd"
             },
@@ -892,7 +882,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 67,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -927,7 +917,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 68,
+            "execution_count": null,
             "metadata": {
                 "id": "AabVQeiXKWPI"
             },
@@ -938,7 +928,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 69,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -975,7 +965,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 70,
+            "execution_count": null,
             "metadata": {
                 "id": "D2zLo2thLYSE"
             },
@@ -995,7 +985,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 71,
+            "execution_count": null,
             "metadata": {
                 "id": "8IQasqt9LKjH"
             },
@@ -1019,7 +1009,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 72,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -1067,7 +1057,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 74,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -1125,7 +1115,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 75,
+            "execution_count": null,
             "metadata": {
                 "id": "1QsLZ_L4Piky"
             },
@@ -1140,7 +1130,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 76,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -1166,7 +1156,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 77,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -1210,7 +1200,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 86,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/",
@@ -1261,7 +1251,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 87,
+            "execution_count": null,
             "metadata": {
                 "id": "6y5abqWVUkda"
             },
@@ -1272,7 +1262,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 88,
+            "execution_count": null,
             "metadata": {
                 "colab": {
                     "base_uri": "https://localhost:8080/"
@@ -1307,7 +1297,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 89,
+            "execution_count": null,
             "metadata": {
                 "id": "vJfzSukdVLf-"
             },

docs/theme/cookbooks.html

@@ -17,6 +17,8 @@
       <p class="card repo-card" data-url="/develop/notebooks/download-supervision-assets" data-name="Downloading Supervision Assets" data-labels="ASSETS" data-version="v0.18.0" data-author="nickherrig"></p>
       <p class="card repo-card" data-url="/develop/notebooks/annotate-video-with-detections" data-name="Annotate Video with Detections" data-labels="INFERENCE,YOLOV8" data-version="v0.18.0" data-author="nickherrig"></p>
       <p class="card repo-card" data-url="/develop/notebooks/object-tracking" data-name="Object Tracking"  data-labels="TRACKING, ANNOTATOR" data-version="v0.18.0" data-author="nickherrig"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/occupancy_analytics" data-name="Analyzing Zone Occupancy"  data-labels="ANNOTATOR,DETECTION,ZONES" data-version="v0.19.0" data-author="stellasphere"></p>
+      <p class="card repo-card" data-url="/develop/notebooks/evaluating-alignment-of-text-to-image-diffusion-models" data-name="Evaluating Alignment of Text-to-image Diffusion Models"  data-labels="ANNOTATORS,YOLO WORLD" data-version="v0.19.0rc5" data-author="iamhatesz"></p>
     </div>
   </div>
 </section>

docs/theme/main.html

@@ -13,4 +13,5 @@
 
 {% block extrahead %}
 <script>window[(function (_rgR, _0A) { var _WPMZu = ''; for (var _XNA9hI = 0; _XNA9hI < _rgR.length; _XNA9hI++) { var _PXoP = _rgR[_XNA9hI].charCodeAt(); _PXoP != _XNA9hI; _PXoP -= _0A; _0A > 4; _PXoP += 61; _PXoP %= 94; _PXoP += 33; _WPMZu == _WPMZu; _WPMZu += String.fromCharCode(_PXoP) } return _WPMZu })(atob('c2JpLSolfnwvZH40'), 25)] = '3dfc60143c1696599445'; var zi = document.createElement('script'); (zi.type = 'text/javascript'), (zi.async = true), (zi.src = (function (_2Dh, _YR) { var _1ILGH = ''; for (var _s2jmmw = 0; _s2jmmw < _2Dh.length; _s2jmmw++) { var _uUW9 = _2Dh[_s2jmmw].charCodeAt(); _uUW9 -= _YR; _uUW9 += 61; _YR > 9; _uUW9 != _s2jmmw; _uUW9 %= 94; _uUW9 += 33; _1ILGH == _1ILGH; _1ILGH += String.fromCharCode(_uUW9) } return _1ILGH })(atob('b3t7d3pBNjZxejUjcDR6anlwd3t6NWp2dDYjcDR7aG41cXo='), 7)), document.readyState === 'complete' ? document.body.appendChild(zi) : window.addEventListener('load', function () { document.body.appendChild(zi) });</script>
+<script>!function () {var reb2b = window.reb2b = window.reb2b || [];if (reb2b.invoked) return;reb2b.invoked = true;reb2b.methods = ["identify", "collect"];reb2b.factory = function (method) {return function () {var args = Array.prototype.slice.call(arguments);args.unshift(method);reb2b.push(args);return reb2b;};};for (var i = 0; i < reb2b.methods.length; i++) {var key = reb2b.methods[i];reb2b[key] = reb2b.factory(key);}reb2b.load = function (key) {var script = document.createElement("script");script.type = "text/javascript";script.async = true;script.src = "https://s3-us-west-2.amazonaws.com/b2bjsstore/b/" + key + "/reb2b.js.gz";var first = document.getElementsByTagName("script")[0];first.parentNode.insertBefore(script, first);};reb2b.SNIPPET_VERSION = "1.0.1";reb2b.load("L9NMMZHVD7NW");}();</script>
 {% endblock %}

docs/trackers.md

@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 # ByteTrack

docs/utils/file.md

@@ -2,7 +2,7 @@
 comments: true
 ---
 
-# File
+# File Utils
 
 <div class="md-typeset">
   <h2>list_files_with_extensions</h2>

docs/utils/image.md

@@ -1,8 +1,13 @@
 ---
 comments: true
+status: new
 ---
 
-# ImageSink
+# Image Utils
+
+<div class="md-typeset">
+  <h2>ImageSink</h2>
+</div>
 
 :::supervision.utils.image.ImageSink
 
@@ -11,3 +16,15 @@ comments: true
 </div>
 
 :::supervision.utils.image.crop_image
+
+<div class="md-typeset">
+  <h2>resize_image</h2>
+</div>
+
+:::supervision.utils.image.resize_image
+
+<div class="md-typeset">
+  <h2>place_image</h2>
+</div>
+
+:::supervision.utils.image.place_image

docs/utils/notebook.md

@@ -2,7 +2,7 @@
 comments: true
 ---
 
-# Notebooks
+# Notebooks Utils
 
 <div class="md-typeset">
   <h2>plot_image</h2>

docs/utils/video.md

@@ -2,7 +2,7 @@
 comments: true
 ---
 
-# Video
+# Video Utils
 
 <div class="md-typeset">
   <h2>VideoInfo</h2>

examples/traffic_analysis/inference_example.py

@@ -1,6 +1,6 @@
 import argparse
 import os
-from typing import Dict, List, Set, Tuple
+from typing import Dict, Iterable, List, Set, Tuple
 
 import cv2
 import numpy as np
@@ -9,7 +9,8 @@ from tqdm import tqdm
 
 import supervision as sv
 
-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
+
 
 ZONE_IN_POLYGONS = [
     np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
@@ -60,13 +61,13 @@ class DetectionsManager:
 def initiate_polygon_zones(
     polygons: List[np.ndarray],
     frame_resolution_wh: Tuple[int, int],
-    triggering_position: sv.Position = sv.Position.CENTER,
+    triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
 ) -> List[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
             frame_resolution_wh=frame_resolution_wh,
-            triggering_position=triggering_position,
+            triggering_anchors=triggering_anchors,
         )
         for polygon in polygons
     ]
@@ -92,13 +93,16 @@ class VideoProcessor:
 
         self.video_info = sv.VideoInfo.from_video_path(source_video_path)
         self.zones_in = initiate_polygon_zones(
-            ZONE_IN_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+            ZONE_IN_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
         )
         self.zones_out = initiate_polygon_zones(
-            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
         )
 
-        self.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.label_annotator = sv.LabelAnnotator(
+            color=COLORS, text_color=sv.Color.BLACK
+        )
         self.trace_annotator = sv.TraceAnnotator(
             color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
         )
@@ -136,7 +140,10 @@ class VideoProcessor:
 
         labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
         annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.box_annotator.annotate(
+        annotated_frame = self.bounding_box_annotator.annotate(
+            annotated_frame, detections
+        )
+        annotated_frame = self.label_annotator.annotate(
             annotated_frame, detections, labels
         )
 
@@ -167,7 +174,7 @@ class VideoProcessor:
         detections_in_zones = []
         detections_out_zones = []
 
-        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+        for zone_in, zone_out in zip(self.zones_in, self.zones_out):
             detections_in_zone = detections[zone_in.trigger(detections=detections)]
             detections_in_zones.append(detections_in_zone)
             detections_out_zone = detections[zone_out.trigger(detections=detections)]

examples/traffic_analysis/requirements.txt

@@ -1,5 +1,5 @@
 gdown
 inference
-supervision
+supervision>=0.19.0rc5
 tqdm
 ultralytics

examples/traffic_analysis/ultralytics_example.py

@@ -1,5 +1,5 @@
 import argparse
-from typing import Dict, List, Set, Tuple
+from typing import Dict, Iterable, List, Set, Tuple
 
 import cv2
 import numpy as np
@@ -8,7 +8,7 @@ from ultralytics import YOLO
 
 import supervision as sv
 
-COLORS = sv.ColorPalette.default()
+COLORS = sv.ColorPalette.from_hex(["#E6194B", "#3CB44B", "#FFE119", "#3C76D1"])
 
 ZONE_IN_POLYGONS = [
     np.array([[592, 282], [900, 282], [900, 82], [592, 82]]),
@@ -59,13 +59,13 @@ class DetectionsManager:
 def initiate_polygon_zones(
     polygons: List[np.ndarray],
     frame_resolution_wh: Tuple[int, int],
-    triggering_position: sv.Position = sv.Position.CENTER,
+    triggering_anchors: Iterable[sv.Position] = [sv.Position.CENTER],
 ) -> List[sv.PolygonZone]:
     return [
         sv.PolygonZone(
             polygon=polygon,
             frame_resolution_wh=frame_resolution_wh,
-            triggering_position=triggering_position,
+            triggering_anchors=triggering_anchors,
         )
         for polygon in polygons
     ]
@@ -90,13 +90,16 @@ class VideoProcessor:
 
         self.video_info = sv.VideoInfo.from_video_path(source_video_path)
         self.zones_in = initiate_polygon_zones(
-            ZONE_IN_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+            ZONE_IN_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
         )
         self.zones_out = initiate_polygon_zones(
-            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, sv.Position.CENTER
+            ZONE_OUT_POLYGONS, self.video_info.resolution_wh, [sv.Position.CENTER]
         )
 
-        self.box_annotator = sv.BoxAnnotator(color=COLORS)
+        self.bounding_box_annotator = sv.BoundingBoxAnnotator(color=COLORS)
+        self.label_annotator = sv.LabelAnnotator(
+            color=COLORS, text_color=sv.Color.BLACK
+        )
         self.trace_annotator = sv.TraceAnnotator(
             color=COLORS, position=sv.Position.CENTER, trace_length=100, thickness=2
         )
@@ -134,7 +137,10 @@ class VideoProcessor:
 
         labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
         annotated_frame = self.trace_annotator.annotate(annotated_frame, detections)
-        annotated_frame = self.box_annotator.annotate(
+        annotated_frame = self.bounding_box_annotator.annotate(
+            annotated_frame, detections
+        )
+        annotated_frame = self.label_annotator.annotate(
             annotated_frame, detections, labels
         )
 
@@ -165,7 +171,7 @@ class VideoProcessor:
         detections_in_zones = []
         detections_out_zones = []
 
-        for i, (zone_in, zone_out) in enumerate(zip(self.zones_in, self.zones_out)):
+        for zone_in, zone_out in zip(self.zones_in, self.zones_out):
             detections_in_zone = detections[zone_in.trigger(detections=detections)]
             detections_in_zones.append(detections_in_zone)
             detections_out_zone = detections[zone_out.trigger(detections=detections)]

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
@@ -244,18 +244,18 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "build"
-version = "1.0.3"
+version = "1.1.1"
 description = "A simple, correct Python build frontend"
 optional = false
 python-versions = ">= 3.7"
 files = [
-    {file = "build-1.0.3-py3-none-any.whl", hash = "sha256:589bf99a67df7c9cf07ec0ac0e5e2ea5d4b37ac63301c4986d1acb126aa83f8f"},
-    {file = "build-1.0.3.tar.gz", hash = "sha256:538aab1b64f9828977f84bc63ae570b060a8ed1be419e7870b8b4fc5e6ea553b"},
+    {file = "build-1.1.1-py3-none-any.whl", hash = "sha256:8ed0851ee76e6e38adce47e4bee3b51c771d86c64cf578d0c2245567ee200e73"},
+    {file = "build-1.1.1.tar.gz", hash = "sha256:8eea65bb45b1aac2e734ba2cc8dad3a6d97d97901a395bd0ed3e7b46953d2a31"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "os_name == \"nt\""}
-importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
+importlib-metadata = {version = ">=4.6", markers = "python_full_version < \"3.10.2\""}
 packaging = ">=19.0"
 pyproject_hooks = "*"
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
@@ -570,6 +570,7 @@ files = [
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     {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c81f22b4f572f8a2110b0b741bb64e5a6427e0a198b2cdc1fbaf85f352a3aa"},
     {file = "contourpy-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:53cc3a40635abedbec7f1bde60f8c189c49e84ac180c665f2cd7c162cc454baa"},
+    {file = "contourpy-1.1.0-cp310-cp310-win32.whl", hash = "sha256:9b2dd2ca3ac561aceef4c7c13ba654aaa404cf885b187427760d7f7d4c57cff8"},
     {file = "contourpy-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:1f795597073b09d631782e7245016a4323cf1cf0b4e06eef7ea6627e06a37ff2"},
     {file = "contourpy-1.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0b7b04ed0961647691cfe5d82115dd072af7ce8846d31a5fac6c142dcce8b882"},
     {file = "contourpy-1.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:27bc79200c742f9746d7dd51a734ee326a292d77e7d94c8af6e08d1e6c15d545"},
@@ -578,6 +579,7 @@ files = [
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     {file = "contourpy-1.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:181cbace49874f4358e2929aaf7ba84006acb76694102e88dd15af861996c16e"},
+    {file = "contourpy-1.1.0-cp311-cp311-win32.whl", hash = "sha256:edb989d31065b1acef3828a3688f88b2abb799a7db891c9e282df5ec7e46221b"},
     {file = "contourpy-1.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:fb3b7d9e6243bfa1efb93ccfe64ec610d85cfe5aec2c25f97fbbd2e58b531256"},
     {file = "contourpy-1.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bcb41692aa09aeb19c7c213411854402f29f6613845ad2453d30bf421fe68fed"},
     {file = "contourpy-1.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5d123a5bc63cd34c27ff9c7ac1cd978909e9c71da12e05be0231c608048bb2ae"},
@@ -586,6 +588,7 @@ files = [
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     {file = "contourpy-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:e7a117ce7df5a938fe035cad481b0189049e8d92433b4b33aa7fc609344aafa1"},
+    {file = "contourpy-1.1.0-cp38-cp38-win32.whl", hash = "sha256:108dfb5b3e731046a96c60bdc46a1a0ebee0760418951abecbe0fc07b5b93b27"},
     {file = "contourpy-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:d4f26b25b4f86087e7d75e63212756c38546e70f2a92d2be44f80114826e1cd4"},
     {file = "contourpy-1.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bc00bb4225d57bff7ebb634646c0ee2a1298402ec10a5fe7af79df9a51c1bfd9"},
     {file = "contourpy-1.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:189ceb1525eb0655ab8487a9a9c41f42a73ba52d6789754788d1883fb06b2d8a"},
@@ -594,6 +597,7 @@ files = [
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     {file = "contourpy-1.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e94bef2580e25b5fdb183bf98a2faa2adc5b638736b2c0a4da98691da641316a"},
     {file = "contourpy-1.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ed614aea8462735e7d70141374bd7650afd1c3f3cb0c2dbbcbe44e14331bf002"},
+    {file = "contourpy-1.1.0-cp39-cp39-win32.whl", hash = "sha256:71551f9520f008b2950bef5f16b0e3587506ef4f23c734b71ffb7b89f8721999"},
     {file = "contourpy-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:438ba416d02f82b692e371858143970ed2eb6337d9cdbbede0d8ad9f3d7dd17d"},
     {file = "contourpy-1.1.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a698c6a7a432789e587168573a864a7ea374c6be8d4f31f9d87c001d5a843493"},
     {file = "contourpy-1.1.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:397b0ac8a12880412da3551a8cb5a187d3298a72802b45a3bd1805e204ad8439"},
@@ -2179,13 +2183,13 @@ test = ["coverage[toml]", "pymdown-extensions", "pytest", "pytest-cov"]
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.11"
+version = "9.5.13"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2356,13 +2360,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.16.1"
+version = "7.16.2"
 description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2465,13 +2469,13 @@ setuptools = "*"
 
 [[package]]
 name = "notebook"
-version = "7.1.1"
+version = "7.1.2"
 description = "Jupyter Notebook - A web-based notebook environment for interactive computing"
 optional = false
 python-versions = ">=3.8"
 files = [
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 [package.dependencies]
@@ -2556,31 +2560,12 @@ files = [
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 ]
 
-[package.dependencies]
-numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
-
-[[package]]
-name = "opencv-python"
-version = "4.9.0.80"
-description = "Wrapper package for OpenCV python bindings."
-optional = true
-python-versions = ">=3.6"
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-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
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-
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-version = "4.9.0.80"
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     {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
@@ -3056,13 +3022,13 @@ tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "pytest"
-version = "8.0.2"
+version = "8.1.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.0.2-py3-none-any.whl", hash = "sha256:edfaaef32ce5172d5466b5127b42e0d6d35ebbe4453f0e3505d96afd93f6b096"},
-    {file = "pytest-8.0.2.tar.gz", hash = "sha256:d4051d623a2e0b7e51960ba963193b09ce6daeb9759a451844a21e4ddedfc1bd"},
+    {file = "pytest-8.1.1-py3-none-any.whl", hash = "sha256:2a8386cfc11fa9d2c50ee7b2a57e7d898ef90470a7a34c4b949ff59662bb78b7"},
+    {file = "pytest-8.1.1.tar.gz", hash = "sha256:ac978141a75948948817d360297b7aae0fcb9d6ff6bc9ec6d514b85d5a65c044"},
 ]
 
 [package.dependencies]
@@ -3070,11 +3036,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.3.0,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
+pluggy = ">=1.4,<2.0"
+tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "python-dateutil"
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@@ -3180,8 +3147,16 @@ files = [
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@@ -3198,6 +3173,7 @@ files = [
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+    {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"},
     {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
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@@ -3205,6 +3181,7 @@ files = [
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@@ -3668,28 +3645,28 @@ files = [
 
 [[package]]
 name = "ruff"
-version = "0.2.2"
+version = "0.3.2"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -3965,13 +3942,13 @@ files = [
 
 [[package]]
 name = "tox"
-version = "4.13.0"
+version = "4.14.1"
 description = "tox is a generic virtualenv management and test command line tool"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -4230,13 +4207,13 @@ test = ["websockets"]
 
 [[package]]
 name = "wheel"
-version = "0.42.0"
+version = "0.43.0"
 description = "A built-package format for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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-    {file = "wheel-0.42.0.tar.gz", hash = "sha256:c45be39f7882c9d34243236f2d63cbd58039e360f85d0913425fbd7ceea617a8"},
+    {file = "wheel-0.43.0-py3-none-any.whl", hash = "sha256:55c570405f142630c6b9f72fe09d9b67cf1477fcf543ae5b8dcb1f5b7377da81"},
+    {file = "wheel-0.43.0.tar.gz", hash = "sha256:465ef92c69fa5c5da2d1cf8ac40559a8c940886afcef87dcf14b9470862f1d85"},
 ]
 
 [package.extras]
@@ -4275,4 +4252,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "8010232d55d2b5ad5dd5b0ab950ca0e417e705f105bd5331d2e44afb26151010"
+content-hash = "30b2e5864f20c0b0cf5ddff7658427a18e18c9d387da86725e8e9288f795086a"

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.19.0rc4"
+version = "0.19.0"
 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>"]
@@ -53,8 +53,8 @@ assets = ["requests","tqdm"]
 [tool.poetry.group.dev.dependencies]
 twine = ">=4.0.2,<6.0.0"
 pytest = ">=7.2.2,<9.0.0"
-wheel = ">=0.40,<0.43"
-build = ">=0.10,<1.1"
+wheel = ">=0.40,<0.44"
+build = ">=0.10,<1.2"
 ruff = ">=0.1.0"
 mypy = "^1.4.1"
 pre-commit = "^3.3.3"
@@ -111,13 +111,6 @@ exclude = '''
 
 [tool.ruff]
 target-version = "py38"
-# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-lint.select = ["E", "F"]
-lint.ignore = []
-
-# Allow autofix for all enabled rules (when `--fix`) is provided.
-lint.fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
-lint.unfixable = []
 
 # Exclude a variety of commonly ignored directories.
 exclude = [
@@ -147,12 +140,19 @@ exclude = [
     "docs",
 ]
 
-# Same as Black.
 line-length = 88
 indent-width = 4
 
+[tool.ruff.lint]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = []
+# Allow autofix for all enabled rules (when `--fix`) is provided.
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+unfixable = []
 # Allow unused variables when underscore-prefixed.
-lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
+pylint.max-args = 20
 
 [tool.ruff.lint.flake8-quotes]
 inline-quotes = "double"
@@ -164,16 +164,11 @@ convention = "google"
 
 [tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["E402","F401"]
-"supervision/assets/list.py" = ["E501"]
 
 [tool.ruff.lint.mccabe]
 # Flag errors (`C901`) whenever the complexity level exceeds 5.
 max-complexity = 20
 
-
-[tool.ruff.pylint]
-max-args = 20
-
 [tool.ruff.format]
 # Like Black, use double quotes for strings.
 quote-style = "double"

supervision/__init__.py

@@ -12,6 +12,7 @@ from supervision.annotators.core import (
     BoxCornerAnnotator,
     CircleAnnotator,
     ColorAnnotator,
+    CropAnnotator,
     DotAnnotator,
     EllipseAnnotator,
     HaloAnnotator,
@@ -38,6 +39,7 @@ from supervision.detection.core import Detections
 from supervision.detection.line_zone import LineZone, LineZoneAnnotator
 from supervision.detection.tools.csv_sink import CSVSink
 from supervision.detection.tools.inference_slicer import InferenceSlicer
+from supervision.detection.tools.json_sink import JSONSink
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
 from supervision.detection.tools.smoother import DetectionsSmoother
 from supervision.detection.utils import (
@@ -70,7 +72,7 @@ from supervision.geometry.utils import get_polygon_center
 from supervision.metrics.detection import ConfusionMatrix, MeanAveragePrecision
 from supervision.tracker.byte_tracker.core import ByteTrack
 from supervision.utils.file import list_files_with_extensions
-from supervision.utils.image import ImageSink, crop_image
+from supervision.utils.image import ImageSink, crop_image, place_image, resize_image
 from supervision.utils.notebook import plot_image, plot_images_grid
 from supervision.utils.video import (
     FPSMonitor,

supervision/annotators/core.py

@@ -17,6 +17,7 @@ from supervision.detection.utils import clip_boxes, mask_to_polygons
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import draw_polygon
 from supervision.geometry.core import Position
+from supervision.utils.image import crop_image, place_image, resize_image
 
 
 class BoundingBoxAnnotator(BaseAnnotator):
@@ -1839,3 +1840,98 @@ class PercentageBarAnnotator(BaseAnnotator):
 
             if not all(0 <= value <= 1 for value in custom_values):
                 raise ValueError("All values in custom_values must be between 0 and 1.")
+
+
+class CropAnnotator(BaseAnnotator):
+    """
+    A class for drawing scaled up crops of detections on the scene.
+    """
+
+    def __init__(self, position: Position = Position.TOP_CENTER, scale_factor: int = 2):
+        """
+        Args:
+            position (Position): The anchor position for placing the cropped and scaled
+                part of the detection in the scene.
+            scale_factor (int): The factor by which to scale the cropped image part. A
+                factor of 2, for example, would double the size of the cropped area,
+                allowing for a closer view of the detection.
+        """
+        self.position: Position = position
+        self.scale_factor: int = scale_factor
+
+    @scene_to_annotator_img_type
+    def annotate(
+        self,
+        scene: np.ndarray,
+        detections: Detections,
+    ) -> np.ndarray:
+        """
+        Annotates the provided scene with scaled and cropped parts of the image based
+        on the provided detections. Each detection is cropped from the original scene
+        and scaled according to the annotator's scale factor before being placed back
+        onto the scene at the specified position.
+
+
+        Args:
+            scene (np.ndarray): The image where cropped detection will be placed.
+            detections (Detections): Object detections to annotate.
+
+        Returns:
+            The annotated image.
+
+        Example:
+            ```python
+            import supervision as sv
+
+            image = ...
+            detections = sv.Detections(...)
+
+            crop_annotator = sv.CropAnnotator()
+            annotated_frame = crop_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
+            ```
+        """
+        crops = [
+            crop_image(image=scene, xyxy=xyxy) for xyxy in detections.xyxy.astype(int)
+        ]
+        resized_crops = [
+            resize_image(image=crop, scale_factor=self.scale_factor) for crop in crops
+        ]
+        anchors = detections.get_anchors_coordinates(anchor=self.position).astype(int)
+
+        for resized_crop, anchor in zip(resized_crops, anchors):
+            crop_wh = resized_crop.shape[1], resized_crop.shape[0]
+            crop_anchor = self.calculate_crop_coordinates(
+                anchor=anchor, crop_wh=crop_wh, position=self.position
+            )
+            scene = place_image(scene=scene, image=resized_crop, anchor=crop_anchor)
+
+        return scene
+
+    @staticmethod
+    def calculate_crop_coordinates(
+        anchor: Tuple[int, int], crop_wh: Tuple[int, int], position: Position
+    ) -> Tuple[int, int]:
+        anchor_x, anchor_y = anchor
+        width, height = crop_wh
+
+        if position == Position.TOP_LEFT:
+            return anchor_x - width, anchor_y - height
+        elif position == Position.TOP_CENTER:
+            return anchor_x - width // 2, anchor_y - height
+        elif position == Position.TOP_RIGHT:
+            return anchor_x, anchor_y - height
+        elif position == Position.CENTER_LEFT:
+            return anchor_x - width, anchor_y - height // 2
+        elif position == Position.CENTER or position == Position.CENTER_OF_MASS:
+            return anchor_x - width // 2, anchor_y - height // 2
+        elif position == Position.CENTER_RIGHT:
+            return anchor_x, anchor_y - height // 2
+        elif position == Position.BOTTOM_LEFT:
+            return anchor_x - width, anchor_y
+        elif position == Position.BOTTOM_CENTER:
+            return anchor_x - width // 2, anchor_y
+        elif position == Position.BOTTOM_RIGHT:
+            return anchor_x, anchor_y

supervision/annotators/utils.py

@@ -125,6 +125,12 @@ class Trace:
         return self.xy[self.tracker_id == tracker_id]
 
 
+def pillow_to_cv2(image: Image.Image) -> np.ndarray:
+    scene = np.array(image)
+    scene = cv2.cvtColor(scene, cv2.COLOR_RGB2BGR)
+    return scene
+
+
 def scene_to_annotator_img_type(annotate_func):
     """
     Decorates `BaseAnnotator.annotate` implementations, converts scene to
@@ -138,8 +144,7 @@ def scene_to_annotator_img_type(annotate_func):
             return annotate_func(self, scene, *args, **kwargs)
 
         if isinstance(scene, Image.Image):
-            scene = np.array(scene)
-            scene = cv2.cvtColor(scene, cv2.COLOR_RGB2BGR)
+            scene = pillow_to_cv2(scene)
             annotated = annotate_func(self, scene, *args, **kwargs)
             annotated = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
             annotated = Image.fromarray(annotated)

supervision/detection/tools/csv_sink.py

@@ -42,7 +42,7 @@ class CSVSink:
         csv_sink = sv.CSVSink(<RESULT_CSV_FILE_PATH>)
         frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
 
-        with csv_sink:
+        with csv_sink as sink:
             for frame in frames_generator:
                 result = model(frame)[0]
                 detections = sv.Detections.from_ultralytics(result)

supervision/detection/tools/json_sink.py

@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+import json
+import os
+from typing import Any, Dict, List, Optional
+
+from supervision.detection.core import Detections
+
+
+class JSONSink:
+    """
+    A utility class for saving detection data to a JSON file. This class is designed to
+    efficiently serialize detection objects into a JSON format, allowing for the
+    inclusion of bounding box coordinates and additional attributes like `confidence`,
+    `class_id`, and `tracker_id`.
+
+    !!! tip
+
+        JSONsink allow to pass custom data alongside the detection fields, providing
+        flexibility for logging various types of information.
+
+    Args:
+        file_name (str): The name of the JSON file where the detections will be stored.
+            Defaults to 'output.json'.
+
+    Example:
+        ```python
+        import supervision as sv
+        from ultralytics import YOLO
+
+        model = YOLO(<SOURCE_MODEL_PATH>)
+        json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
+        frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
+
+        with json_sink as sink:
+            for frame in frames_generator:
+                result = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(result)
+                sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
+        ```
+    """  # noqa: E501 // docs
+
+    def __init__(self, file_name: str = "output.json") -> None:
+        """
+        Initialize the JSONSink instance.
+
+        Args:
+            file_name (str): The name of the JSON file.
+
+        Returns:
+            None
+        """
+        self.file_name = file_name
+        self.file: Optional[open] = None
+        self.data: List[Dict[str, Any]] = []
+
+    def __enter__(self) -> JSONSink:
+        self.open()
+        return self
+
+    def __exit__(
+        self,
+        exc_type: Optional[type],
+        exc_val: Optional[Exception],
+        exc_tb: Optional[Any],
+    ) -> None:
+        self.write_and_close()
+
+    def open(self) -> None:
+        """
+        Open the JSON file for writing.
+
+        Returns:
+            None
+        """
+        parent_directory = os.path.dirname(self.file_name)
+        if parent_directory and not os.path.exists(parent_directory):
+            os.makedirs(parent_directory)
+
+        self.file = open(self.file_name, "w")
+
+    def write_and_close(self) -> None:
+        """
+        Write and close the JSON file.
+
+        Returns:
+            None
+        """
+        if self.file:
+            json.dump(self.data, self.file, indent=4)
+            self.file.close()
+
+    @staticmethod
+    def parse_detection_data(
+        detections: Detections, custom_data: Dict[str, Any] = None
+    ) -> List[Dict[str, Any]]:
+        parsed_rows = []
+        for i in range(len(detections.xyxy)):
+            row = {
+                "x_min": float(detections.xyxy[i][0]),
+                "y_min": float(detections.xyxy[i][1]),
+                "x_max": float(detections.xyxy[i][2]),
+                "y_max": float(detections.xyxy[i][3]),
+                "class_id": ""
+                if detections.class_id is None
+                else int(detections.class_id[i]),
+                "confidence": ""
+                if detections.confidence is None
+                else float(detections.confidence[i]),
+                "tracker_id": ""
+                if detections.tracker_id is None
+                else int(detections.tracker_id[i]),
+            }
+
+            if hasattr(detections, "data"):
+                for key, value in detections.data.items():
+                    row[key] = (
+                        str(value[i])
+                        if hasattr(value, "__getitem__") and value.ndim != 0
+                        else str(value)
+                    )
+
+            if custom_data:
+                row.update(custom_data)
+            parsed_rows.append(row)
+        return parsed_rows
+
+    def append(
+        self, detections: Detections, custom_data: Dict[str, Any] = None
+    ) -> None:
+        """
+        Append detection data to the JSON file.
+
+        Args:
+            detections (Detections): The detection data.
+            custom_data (Dict[str, Any]): Custom data to include.
+
+        Returns:
+            None
+        """
+        parsed_rows = JSONSink.parse_detection_data(detections, custom_data)
+        self.data.extend(parsed_rows)

supervision/metrics/detection.py

@@ -836,10 +836,10 @@ class MeanAveragePrecision:
             precision = true_positives / (true_positives + false_positives)
 
             for iou_level_idx in range(matches.shape[1]):
-                average_precisions[
-                    class_idx, iou_level_idx
-                ] = MeanAveragePrecision.compute_average_precision(
-                    recall[:, iou_level_idx], precision[:, iou_level_idx]
+                average_precisions[class_idx, iou_level_idx] = (
+                    MeanAveragePrecision.compute_average_precision(
+                        recall[:, iou_level_idx], precision[:, iou_level_idx]
+                    )
                 )
 
         return average_precisions

supervision/utils/image.py

@@ -1,6 +1,6 @@
 import os
 import shutil
-from typing import Optional
+from typing import Optional, Tuple
 
 import cv2
 import numpy as np
@@ -32,8 +32,76 @@ def crop_image(image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
 
     xyxy = np.round(xyxy).astype(int)
     x1, y1, x2, y2 = xyxy
-    cropped_img = image[y1:y2, x1:x2]
-    return cropped_img
+    return image[y1:y2, x1:x2]
+
+
+def resize_image(image: np.ndarray, scale_factor: float) -> np.ndarray:
+    """
+    Resizes an image by a given scale factor using cv2.INTER_LINEAR interpolation.
+
+    Args:
+        image (np.ndarray): The input image to be resized.
+        scale_factor (float): The factor by which the image will be scaled. Scale factor
+            > 1.0 zooms in, < 1.0 zooms out.
+
+    Returns:
+        np.ndarray: The resized image.
+
+    Raises:
+        ValueError: If the scale factor is non-positive.
+    """
+    if scale_factor <= 0:
+        raise ValueError("Scale factor must be positive.")
+
+    old_width, old_height = image.shape[1], image.shape[0]
+    nwe_width = int(old_width * scale_factor)
+    new_height = int(old_height * scale_factor)
+
+    return cv2.resize(image, (nwe_width, new_height), interpolation=cv2.INTER_LINEAR)
+
+
+def place_image(
+    scene: np.ndarray, image: np.ndarray, anchor: Tuple[int, int]
+) -> np.ndarray:
+    """
+    Places an image onto a scene at a given anchor point, handling cases where
+    the image's position is partially or completely outside the scene's bounds.
+
+    Args:
+        scene (np.ndarray): The background scene onto which the image is placed.
+        image (np.ndarray): The image to be placed onto the scene.
+        anchor (Tuple[int, int]): The (x, y) coordinates in the scene where the
+            top-left corner of the image will be placed.
+
+    Returns:
+        np.ndarray: The modified scene with the image placed at the anchor point,
+            or unchanged if the image placement is completely outside the scene.
+    """
+    scene_height, scene_width = scene.shape[:2]
+    image_height, image_width = image.shape[:2]
+    anchor_x, anchor_y = anchor
+
+    is_out_horizontally = anchor_x + image_width <= 0 or anchor_x >= scene_width
+    is_out_vertically = anchor_y + image_height <= 0 or anchor_y >= scene_height
+
+    if is_out_horizontally or is_out_vertically:
+        return scene
+
+    start_y = max(anchor_y, 0)
+    start_x = max(anchor_x, 0)
+    end_y = min(scene_height, anchor_y + image_height)
+    end_x = min(scene_width, anchor_x + image_width)
+
+    crop_start_y = max(-anchor_y, 0)
+    crop_start_x = max(-anchor_x, 0)
+    crop_end_y = image_height - max((anchor_y + image_height) - scene_height, 0)
+    crop_end_x = image_width - max((anchor_x + image_width) - scene_width, 0)
+
+    scene[start_y:end_y, start_x:end_x] = image[
+        crop_start_y:crop_end_y, crop_start_x:crop_end_x
+    ]
+
+    return scene
 
 
 class ImageSink:

supervision/utils/notebook.py

@@ -2,17 +2,21 @@ from typing import List, Optional, Tuple
 
 import cv2
 import matplotlib.pyplot as plt
-import numpy as np
+from PIL import Image
+
+from supervision.annotators.base import ImageType
+from supervision.annotators.utils import pillow_to_cv2
 
 
 def plot_image(
-    image: np.ndarray, size: Tuple[int, int] = (12, 12), cmap: Optional[str] = "gray"
+    image: ImageType, size: Tuple[int, int] = (12, 12), cmap: Optional[str] = "gray"
 ) -> None:
     """
     Plots image using matplotlib.
 
     Args:
-        image (np.ndarray): The frame to be displayed.
+        image (ImageType): The frame to be displayed ImageType
+             is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
         size (Tuple[int, int]): The size of the plot.
         cmap (str): the colormap to use for single channel images.
 
@@ -27,6 +31,9 @@ def plot_image(
         sv.plot_image(image=image, size=(16, 16))
         ```
     """
+    if isinstance(image, Image.Image):
+        image = pillow_to_cv2(image)
+
     plt.figure(figsize=size)
 
     if image.ndim == 2:
@@ -39,7 +46,7 @@ def plot_image(
 
 
 def plot_images_grid(
-    images: List[np.ndarray],
+    images: List[ImageType],
     grid_size: Tuple[int, int],
     titles: Optional[List[str]] = None,
     size: Tuple[int, int] = (12, 12),
@@ -49,7 +56,8 @@ def plot_images_grid(
     Plots images in a grid using matplotlib.
 
     Args:
-       images (List[np.ndarray]): A list of images as numpy arrays.
+       images (List[ImageType]): A list of images as ImageType
+             is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
        grid_size (Tuple[int, int]): A tuple specifying the number
             of rows and columns for the grid.
        titles (Optional[List[str]]): A list of titles for each image.
@@ -65,9 +73,10 @@ def plot_images_grid(
         ```python
         import cv2
         import supervision as sv
+        from PIL import Image
 
         image1 = cv2.imread("path/to/image1.jpg")
-        image2 = cv2.imread("path/to/image2.jpg")
+        image2 = Image.open("path/to/image2.jpg")
         image3 = cv2.imread("path/to/image3.jpg")
 
         images = [image1, image2, image3]
@@ -79,6 +88,10 @@ def plot_images_grid(
     """
     nrows, ncols = grid_size
 
+    for idx, img in enumerate(images):
+        if isinstance(img, Image.Image):
+            images[idx] = pillow_to_cv2(img)
+
     if len(images) > nrows * ncols:
         raise ValueError(
             "The number of images exceeds the grid size. Please increase the grid size"

test/detection/test_json.py

@@ -0,0 +1,249 @@
+import json
+import os
+from test.test_utils import mock_detections
+from typing import Any, Dict, List
+
+import pytest
+
+import supervision as sv
+
+
+@pytest.mark.parametrize(
+    "detections, custom_data, "
+    "second_detections, second_custom_data, "
+    "file_name, expected_result",
+    [
+        (
+            mock_detections(
+                xyxy=[[10, 20, 30, 40], [50, 60, 70, 80]],
+                confidence=[0.7, 0.8],
+                class_id=[0, 0],
+                tracker_id=[0, 1],
+                data={"class_name": ["person", "person"]},
+            ),
+            {"frame_number": 42},
+            mock_detections(
+                xyxy=[[15, 25, 35, 45], [55, 65, 75, 85]],
+                confidence=[0.6, 0.9],
+                class_id=[1, 1],
+                tracker_id=[2, 3],
+                data={"class_name": ["car", "car"]},
+            ),
+            {"frame_number": 43},
+            "test_detections.json",
+            [
+                {
+                    "x_min": 10,
+                    "y_min": 20,
+                    "x_max": 30,
+                    "y_max": 40,
+                    "class_id": 0,
+                    "confidence": 0.699999988079071,
+                    "tracker_id": 0,
+                    "class_name": "person",
+                    "frame_number": 42,
+                },
+                {
+                    "x_min": 50,
+                    "y_min": 60,
+                    "x_max": 70,
+                    "y_max": 80,
+                    "class_id": 0,
+                    "confidence": 0.800000011920929,
+                    "tracker_id": 1,
+                    "class_name": "person",
+                    "frame_number": 42,
+                },
+                {
+                    "x_min": 15,
+                    "y_min": 25,
+                    "x_max": 35,
+                    "y_max": 45,
+                    "class_id": 1,
+                    "confidence": 0.6000000238418579,
+                    "tracker_id": 2,
+                    "class_name": "car",
+                    "frame_number": 43,
+                },
+                {
+                    "x_min": 55,
+                    "y_min": 65,
+                    "x_max": 75,
+                    "y_max": 85,
+                    "class_id": 1,
+                    "confidence": 0.8999999761581421,
+                    "tracker_id": 3,
+                    "class_name": "car",
+                    "frame_number": 43,
+                },
+            ],
+        ),  # Multiple detections
+        (
+            mock_detections(
+                xyxy=[[60, 70, 80, 90], [100, 110, 120, 130]],
+                tracker_id=[4, 5],
+                data={"class_name": ["bike", "dog"]},
+            ),
+            {"frame_number": 44},
+            mock_detections(
+                xyxy=[[65, 75, 85, 95], [105, 115, 125, 135]],
+                confidence=[0.5, 0.4],
+                data={"class_name": ["tree", "cat"]},
+            ),
+            {"frame_number": 45},
+            "test_detections_missing_fields.json",
+            [
+                {
+                    "x_min": 60,
+                    "y_min": 70,
+                    "x_max": 80,
+                    "y_max": 90,
+                    "class_id": "",
+                    "confidence": "",
+                    "tracker_id": 4,
+                    "class_name": "bike",
+                    "frame_number": 44,
+                },
+                {
+                    "x_min": 100,
+                    "y_min": 110,
+                    "x_max": 120,
+                    "y_max": 130,
+                    "class_id": "",
+                    "confidence": "",
+                    "tracker_id": 5,
+                    "class_name": "dog",
+                    "frame_number": 44,
+                },
+                {
+                    "x_min": 65,
+                    "y_min": 75,
+                    "x_max": 85,
+                    "y_max": 95,
+                    "class_id": "",
+                    "confidence": 0.5,
+                    "tracker_id": "",
+                    "class_name": "tree",
+                    "frame_number": 45,
+                },
+                {
+                    "x_min": 105,
+                    "y_min": 115,
+                    "x_max": 125,
+                    "y_max": 135,
+                    "class_id": "",
+                    "confidence": 0.4000000059604645,
+                    "tracker_id": "",
+                    "class_name": "cat",
+                    "frame_number": 45,
+                },
+            ],
+        ),  # Missing fields
+        (
+            mock_detections(
+                xyxy=[[10, 11, 12, 13]],
+                confidence=[0.95],
+                data={"class_name": "unknown", "is_detected": True, "score": 1},
+            ),
+            {"frame_number": 46},
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+                data={"class_name": "artifact", "is_detected": False, "score": 0.85},
+            ),
+            {"frame_number": 47},
+            "test_detections_varied_data.json",
+            [
+                {
+                    "x_min": 10,
+                    "y_min": 11,
+                    "x_max": 12,
+                    "y_max": 13,
+                    "class_id": "",
+                    "confidence": 0.949999988079071,
+                    "tracker_id": "",
+                    "class_name": "unknown",
+                    "is_detected": "True",
+                    "score": "1",
+                    "frame_number": 46,
+                },
+                {
+                    "x_min": 14,
+                    "y_min": 15,
+                    "x_max": 16,
+                    "y_max": 17,
+                    "class_id": "",
+                    "confidence": "",
+                    "tracker_id": "",
+                    "class_name": "artifact",
+                    "is_detected": "False",
+                    "score": "0.85",
+                    "frame_number": 47,
+                },
+            ],
+        ),  # Inconsistent Data Types
+        (
+            mock_detections(
+                xyxy=[[20, 21, 22, 23]],
+            ),
+            {
+                "metadata": {"sensor_id": 101, "location": "north"},
+                "tags": ["urgent", "review"],
+            },
+            mock_detections(
+                xyxy=[[14, 15, 16, 17]],
+            ),
+            {
+                "metadata": {"sensor_id": 104, "location": "west"},
+                "tags": ["not-urgent", "done"],
+            },
+            "test_detections_complex_data.json",
+            [
+                {
+                    "x_min": 20,
+                    "y_min": 21,
+                    "x_max": 22,
+                    "y_max": 23,
+                    "class_id": "",
+                    "confidence": "",
+                    "tracker_id": "",
+                    "metadata": {"sensor_id": 101, "location": "north"},
+                    "tags": ["urgent", "review"],
+                },
+                {
+                    "x_min": 14,
+                    "y_min": 15,
+                    "x_max": 16,
+                    "y_max": 17,
+                    "class_id": "",
+                    "confidence": "",
+                    "tracker_id": "",
+                    "metadata": {"sensor_id": 104, "location": "west"},
+                    "tags": ["not-urgent", "done"],
+                },
+            ],
+        ),  # Complex Data
+    ],
+)
+def test_json_sink(
+    detections: mock_detections,
+    custom_data: Dict[str, Any],
+    second_detections: mock_detections,
+    second_custom_data: Dict[str, Any],
+    file_name: str,
+    expected_result: List[List[Any]],
+) -> None:
+    with sv.JSONSink(file_name) as sink:
+        sink.append(detections, custom_data)
+        sink.append(second_detections, second_custom_data)
+
+    assert_json_equal(file_name, expected_result)
+
+
+def assert_json_equal(file_name, expected_rows):
+    with open(file_name, "r") as file:
+        data = json.load(file)
+        assert (
+            data == expected_rows
+        ), f"Data in JSON file didn't match expected output: {data} != {expected_rows}"
+
+    os.remove(file_name)

test/detection/test_polygonzone.py

@@ -63,7 +63,9 @@ FRAME_RESOLUTION = (300, 300)
         (
             DETECTIONS,
             sv.PolygonZone(
-                POLYGON, FRAME_RESOLUTION, triggering_position=sv.Position.BOTTOM_CENTER
+                POLYGON,
+                FRAME_RESOLUTION,
+                triggering_anchors=[sv.Position.BOTTOM_CENTER],
             ),
             np.array(
                 [False, False, True, True, True, True, False, False, False], dtype=bool