Update project version to 0.17.0rc5

The project version in the `pyproject.toml` file has been updated to `0.17.0rc5` from `0.17.0rc4`.

.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."

docs/draw/utils.md

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

mkdocs.yml

@@ -79,6 +79,8 @@ theme:
   font:
     text: Roboto
     code: Roboto Mono
+  features:
+    - content.code.copy
 
 plugins:
   - mkdocstrings

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 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"
@@ -571,34 +571,34 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "cryptography"
-version = "41.0.4"
+version = "41.0.6"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
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+    {file = "cryptography-41.0.6.tar.gz", hash = "sha256:422e3e31d63743855e43e5a6fcc8b4acab860f560f9321b0ee6269cc7ed70cc3"},
 ]
 
 [package.dependencies]

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "supervision"
-version = "0.17.0rc4"
+version = "0.17.0rc5"
 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>"]

supervision/__init__.py

@@ -45,6 +45,8 @@ from supervision.detection.utils import (
 )
 from supervision.draw.color import Color, ColorPalette
 from supervision.draw.utils import (
+    calculate_dynamic_font_scale,
+    calculate_dynamic_line_thickness,
     draw_filled_rectangle,
     draw_image,
     draw_line,

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

@@ -324,7 +324,7 @@ 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)
             ```
         """

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_font_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