Update module

samgeo/efficient_sam.py

@@ -80,18 +80,19 @@ def __init__(
             if device == "cuda":
                 torch.cuda.empty_cache()
 
-        # self.checkpoint = checkpoint
-        # self.model_type = model_type
-        # self.device = device
-        # self.sam_kwargs = sam_kwargs  # Optional arguments for fine-tuning the SAM model
-        # self.source = None  # Store the input image path
-        # self.image = None  # Store the input image as a numpy array
-        # # Store the masks as a list of dictionaries. Each mask is a dictionary
-        # # containing segmentation, area, bbox, predicted_iou, point_coords, stability_score, and crop_box
-        # self.masks = None
-        # self.objects = None  # Store the mask objects as a numpy array
-        # # Store the annotations (objects with random color) as a numpy array.
-        # self.annotations = None
+        self.checkpoint = checkpoint
+        self.model_type = model_type
+        self.model = model
+        self.device = device
+        self.sam_kwargs = sam_kwargs  # Optional arguments for fine-tuning the SAM model
+        self.source = None  # Store the input image path
+        self.image = None  # Store the input image as a numpy array
+        # Store the masks as a list of dictionaries. Each mask is a dictionary
+        # containing segmentation, area, bbox, predicted_iou, point_coords, stability_score, and crop_box
+        self.masks = None
+        self.objects = None  # Store the mask objects as a numpy array
+        # Store the annotations (objects with random color) as a numpy array.
+        self.annotations = None
 
         # # Store the predicted masks, iou_predictions, and low_res_masks
         # self.prediction = None
@@ -111,50 +112,6 @@ def __init__(
         #     # Segment selected objects using input prompts
         #     self.predictor = SamPredictor(self.sam, **sam_kwargs)
 
-    def __call__(
-        self,
-        image,
-        foreground=True,
-        erosion_kernel=(3, 3),
-        mask_multiplier=255,
-        **kwargs,
-    ):
-        """Generate masks for the input tile. This function originates from the segment-anything-eo repository.
-            See https://bit.ly/41pwiHw
-
-        Args:
-            image (np.ndarray): The input image as a numpy array.
-            foreground (bool, optional): Whether to generate the foreground mask. Defaults to True.
-            erosion_kernel (tuple, optional): The erosion kernel for filtering object masks and extract borders. Defaults to (3, 3).
-            mask_multiplier (int, optional): The mask multiplier for the output mask, which is usually a binary mask [0, 1].
-                You can use this parameter to scale the mask to a larger range, for example [0, 255]. Defaults to 255.
-        """
-        h, w, _ = image.shape
-
-        masks = self.mask_generator.generate(image)
-
-        if foreground:  # Extract foreground objects only
-            resulting_mask = np.zeros((h, w), dtype=np.uint8)
-        else:
-            resulting_mask = np.ones((h, w), dtype=np.uint8)
-        resulting_borders = np.zeros((h, w), dtype=np.uint8)
-
-        for m in masks:
-            mask = (m["segmentation"] > 0).astype(np.uint8)
-            resulting_mask += mask
-
-            # Apply erosion to the mask
-            if erosion_kernel is not None:
-                mask_erode = cv2.erode(mask, erosion_kernel, iterations=1)
-                mask_erode = (mask_erode > 0).astype(np.uint8)
-                edge_mask = mask - mask_erode
-                resulting_borders += edge_mask
-
-        resulting_mask = (resulting_mask > 0).astype(np.uint8)
-        resulting_borders = (resulting_borders > 0).astype(np.uint8)
-        resulting_mask_with_borders = resulting_mask - resulting_borders
-        return resulting_mask_with_borders * mask_multiplier
-
     def generate(
         self,
         source,