ulralytics 8.0.199 *.npy image loading exception handling (#5683)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: snyk-bot <snyk-bot@snyk.io>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/models/sam/model.py

@@ -1,5 +1,18 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-"""SAM model interface."""
+"""
+SAM model interface.
+
+This module provides an interface to the Segment Anything Model (SAM) from Ultralytics, designed for real-time image
+segmentation tasks. The SAM model allows for promptable segmentation with unparalleled versatility in image analysis,
+and has been trained on the SA-1B dataset. It features zero-shot performance capabilities, enabling it to adapt to new
+image distributions and tasks without prior knowledge.
+
+Key Features:
+    - Promptable segmentation
+    - Real-time performance
+    - Zero-shot transfer capabilities
+    - Trained on SA-1B dataset
+"""
 
 from pathlib import Path
 
@@ -11,40 +24,94 @@ from .predict import Predictor
 
 
 class SAM(Model):
-    """SAM model interface."""
+    """
+    SAM (Segment Anything Model) interface class.
+
+    SAM is designed for promptable real-time image segmentation. It can be used with a variety of prompts such as
+    bounding boxes, points, or labels. The model has capabilities for zero-shot performance and is trained on the SA-1B
+    dataset.
+    """
 
     def __init__(self, model='sam_b.pt') -> None:
-        """Initializes the SAM model instance with the specified pre-trained model file."""
+        """
+        Initializes the SAM model with a pre-trained model file.
+
+        Args:
+            model (str): Path to the pre-trained SAM model file. File should have a .pt or .pth extension.
+
+        Raises:
+            NotImplementedError: If the model file extension is not .pt or .pth.
+        """
         if model and Path(model).suffix not in ('.pt', '.pth'):
             raise NotImplementedError('SAM prediction requires pre-trained *.pt or *.pth model.')
         super().__init__(model=model, task='segment')
 
     def _load(self, weights: str, task=None):
-        """Loads the provided weights into the SAM model."""
+        """
+        Loads the specified weights into the SAM model.
+
+        Args:
+            weights (str): Path to the weights file.
+            task (str, optional): Task name. Defaults to None.
+        """
         self.model = build_sam(weights)
 
     def predict(self, source, stream=False, bboxes=None, points=None, labels=None, **kwargs):
-        """Predicts and returns segmentation masks for given image or video source."""
+        """
+        Performs segmentation prediction on the given image or video source.
+
+        Args:
+            source: Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
+            stream (bool, optional): If True, enables real-time streaming. Defaults to False.
+            bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None.
+            points (list, optional): List of points for prompted segmentation. Defaults to None.
+            labels (list, optional): List of labels for prompted segmentation. Defaults to None.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            The segmentation masks.
+        """
         overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024)
         kwargs.update(overrides)
         prompts = dict(bboxes=bboxes, points=points, labels=labels)
         return super().predict(source, stream, prompts=prompts, **kwargs)
 
     def __call__(self, source=None, stream=False, bboxes=None, points=None, labels=None, **kwargs):
-        """Calls the 'predict' function with given arguments to perform object detection."""
+        """
+        Alias for the 'predict' method.
+
+        Args:
+            source: Path to the image or video file, or a PIL.Image object, or a numpy.ndarray object.
+            stream (bool, optional): If True, enables real-time streaming. Defaults to False.
+            bboxes (list, optional): List of bounding box coordinates for prompted segmentation. Defaults to None.
+            points (list, optional): List of points for prompted segmentation. Defaults to None.
+            labels (list, optional): List of labels for prompted segmentation. Defaults to None.
+            **kwargs: Additional keyword arguments.
+
+        Returns:
+            The segmentation masks.
+        """
         return self.predict(source, stream, bboxes, points, labels, **kwargs)
 
     def info(self, detailed=False, verbose=True):
         """
-        Logs model info.
+        Logs information about the SAM model.
 
         Args:
-            detailed (bool): Show detailed information about model.
-            verbose (bool): Controls verbosity.
+            detailed (bool, optional): If True, displays detailed information about the model. Defaults to False.
+            verbose (bool, optional): If True, displays information on the console. Defaults to True.
+
+        Returns:
+            (tuple): A tuple containing the model's information.
         """
         return model_info(self.model, detailed=detailed, verbose=verbose)
 
     @property
     def task_map(self):
-        """Returns a dictionary mapping the 'segment' task to its corresponding 'Predictor'."""
+        """
+        Provides a mapping from the 'segment' task to its corresponding 'Predictor'.
+
+        Returns:
+            dict: A dictionary mapping the 'segment' task to its corresponding 'Predictor'.
+        """
         return {'segment': {'predictor': Predictor}}