Add docformatter to pre-commit (#5279)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

ultralytics/models/fastsam/model.py

@@ -22,7 +22,7 @@ class FastSAM(Model):
     """
 
     def __init__(self, model='FastSAM-x.pt'):
-        """Call the __init__ method of the parent class (YOLO) with the updated default model"""
+        """Call the __init__ method of the parent class (YOLO) with the updated default model."""
         if str(model) == 'FastSAM.pt':
             model = 'FastSAM-x.pt'
         assert Path(model).suffix not in ('.yaml', '.yml'), 'FastSAM models only support pre-trained models.'
@@ -30,4 +30,5 @@ class FastSAM(Model):
 
     @property
     def task_map(self):
+        """Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
         return {'segment': {'predictor': FastSAMPredictor, 'validator': FastSAMValidator}}

ultralytics/models/fastsam/predict.py

@@ -11,10 +11,12 @@ from ultralytics.utils import DEFAULT_CFG, ops
 class FastSAMPredictor(DetectionPredictor):
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes FastSAMPredictor class by inheriting from DetectionPredictor and setting task to 'segment'."""
         super().__init__(cfg, overrides, _callbacks)
         self.args.task = 'segment'
 
     def postprocess(self, preds, img, orig_imgs):
+        """Postprocesses the predictions, applies non-max suppression, scales the boxes, and returns the results."""
         p = ops.non_max_suppression(
             preds[0],
             self.args.conf,

ultralytics/models/fastsam/prompt.py

@@ -15,6 +15,7 @@ from ultralytics.utils import TQDM
 class FastSAMPrompt:
 
     def __init__(self, source, results, device='cuda') -> None:
+        """Initializes FastSAMPrompt with given source, results and device, and assigns clip for linear assignment."""
         self.device = device
         self.results = results
         self.source = source
@@ -30,6 +31,7 @@ class FastSAMPrompt:
 
     @staticmethod
     def _segment_image(image, bbox):
+        """Segments the given image according to the provided bounding box coordinates."""
         image_array = np.array(image)
         segmented_image_array = np.zeros_like(image_array)
         x1, y1, x2, y2 = bbox
@@ -45,6 +47,9 @@ class FastSAMPrompt:
 
     @staticmethod
     def _format_results(result, filter=0):
+        """Formats detection results into list of annotations each containing ID, segmentation, bounding box, score and
+        area.
+        """
         annotations = []
         n = len(result.masks.data) if result.masks is not None else 0
         for i in range(n):
@@ -61,6 +66,9 @@ class FastSAMPrompt:
 
     @staticmethod
     def _get_bbox_from_mask(mask):
+        """Applies morphological transformations to the mask, displays it, and if with_contours is True, draws
+        contours.
+        """
         mask = mask.astype(np.uint8)
         contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
         x1, y1, w, h = cv2.boundingRect(contours[0])
@@ -195,6 +203,7 @@ class FastSAMPrompt:
 
     @torch.no_grad()
     def retrieve(self, model, preprocess, elements, search_text: str, device) -> int:
+        """Processes images and text with a model, calculates similarity, and returns softmax score."""
         preprocessed_images = [preprocess(image).to(device) for image in elements]
         tokenized_text = self.clip.tokenize([search_text]).to(device)
         stacked_images = torch.stack(preprocessed_images)
@@ -206,6 +215,7 @@ class FastSAMPrompt:
         return probs[:, 0].softmax(dim=0)
 
     def _crop_image(self, format_results):
+        """Crops an image based on provided annotation format and returns cropped images and related data."""
         if os.path.isdir(self.source):
             raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
         image = Image.fromarray(cv2.cvtColor(self.results[0].orig_img, cv2.COLOR_BGR2RGB))
@@ -229,6 +239,7 @@ class FastSAMPrompt:
         return cropped_boxes, cropped_images, not_crop, filter_id, annotations
 
     def box_prompt(self, bbox):
+        """Modifies the bounding box properties and calculates IoU between masks and bounding box."""
         if self.results[0].masks is not None:
             assert (bbox[2] != 0 and bbox[3] != 0)
             if os.path.isdir(self.source):
@@ -261,7 +272,8 @@ class FastSAMPrompt:
             self.results[0].masks.data = torch.tensor(np.array([masks[max_iou_index].cpu().numpy()]))
         return self.results
 
-    def point_prompt(self, points, pointlabel):  # numpy 处理
+    def point_prompt(self, points, pointlabel):  # numpy
+        """Adjusts points on detected masks based on user input and returns the modified results."""
         if self.results[0].masks is not None:
             if os.path.isdir(self.source):
                 raise ValueError(f"'{self.source}' is a directory, not a valid source for this function.")
@@ -284,6 +296,7 @@ class FastSAMPrompt:
         return self.results
 
     def text_prompt(self, text):
+        """Processes a text prompt, applies it to existing results and returns the updated results."""
         if self.results[0].masks is not None:
             format_results = self._format_results(self.results[0], 0)
             cropped_boxes, cropped_images, not_crop, filter_id, annotations = self._crop_image(format_results)
@@ -296,4 +309,5 @@ class FastSAMPrompt:
         return self.results
 
     def everything_prompt(self):
+        """Returns the processed results from the previous methods in the class."""
         return self.results