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pipeline.py

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+import torch
+from transformers import SamModel, SamProcessor
+from PIL import Image
+import numpy as np
+import cv2 as cv
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = SamModel.from_pretrained("facebook/sam-vit-base").to(device)
+processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+"""
+Segmentor Module that takes in an image and input points to generate segmentation masks.
+"""
+
+class Segmentor:
+    def __init__(self, model, processor, device):
+        self.model = model
+        self.processor = processor
+        self.device = device
+
+    def segment(self, image_input, input_points):
+        if isinstance(image_input, str):
+            image = Image.open(image_input).convert("RGB")
+        elif isinstance(image_input, np.ndarray):
+            # OpenCV uses BGR, PIL uses RGB
+            image = Image.fromarray(cv.cvtColor(image_input, cv.COLOR_BGR2RGB))
+        elif isinstance(image_input, Image.Image):
+            image = image_input.convert("RGB")
+        else:
+            raise ValueError("image_input must be a path, numpy array, or PIL Image")
+
+        points = [[[ [int(x), int(y)] for (x, y) in input_points ]]]
+        labels = [[[1] * len(input_points)]]
+
+        inputs = self.processor(
+            images=image,
+            input_points=points,
+            input_labels=labels,
+            return_tensors="pt"
+        ).to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        pred_masks = outputs.pred_masks
+        iou_scores = outputs.iou_scores
+
+        # Convert to original image size
+        processed = self.processor.post_process_masks(
+            masks=pred_masks,
+            reshaped_input_sizes=inputs["reshaped_input_sizes"],
+            original_sizes=inputs["original_sizes"]
+        )
+
+        # processed is a list per batch; we have batch=1
+        masks = processed[0]  # shape: [point_batch, num_masks, H, W] or similar
+        scores = iou_scores.cpu().numpy()
+
+        # Normalize to a flat list of 2D uint8 masks
+        flat_masks = []
+        flat_scores = []
+        masks_np = masks.cpu().numpy() if hasattr(masks, "cpu") else np.array(masks)
+        
+        for i, mask_group in enumerate(np.array(masks_np)):
+            score_group = scores[0][i]
+            for j, m in enumerate(np.array(mask_group)):
+                m2d = np.squeeze(m)               # remove singleton dims → HxW
+                m2d = (m2d > 0).astype(np.uint8)  # ensure binary 0/1
+                flat_masks.append(m2d)
+                flat_scores.append(score_group[j])
+        return flat_masks, flat_scores
+
+# Example usage
+if __name__ == "__main__":
+    segmentor = Segmentor(model, processor, device)
+    image_path = "redbull.jpg"
+
+    # get input from user input using cv2
+    input_points = []
+
+    def mouse_callback(event, x, y, flags, param):
+        if event == cv.EVENT_LBUTTONDOWN:
+            input_points.append([x, y])
+            print(f"Point added: ({x}, {y})")
+
+    cv.namedWindow("Input Image")
+    cv.setMouseCallback("Input Image", mouse_callback)
+    img = cv.imread(image_path)
+    
+    while True:
+        cv.imshow("Input Image", img)
+        if cv.waitKey(1) & 0xFF == ord('q'):
+            break
+    cv.destroyAllWindows()
+    cv.waitKey(1)
+
+    if len(input_points) == 0:
+        print("No input points provided. Exiting.")
+    else:
+        masks, scores = segmentor.segment(image_path, input_points)
+        
+        print(f"Generated {len(masks)} candidate masks.")
+        
+        # Display candidates
+        for i, (mask, score) in enumerate(zip(masks, scores)):
+            masked_preview = cv.bitwise_and(img, img, mask=mask)
+            cv.imshow(f"Candidate {i} (Score: {score:.4f})", masked_preview)
+            print(f"Candidate {i}: Score {score:.4f}")
+
+        print("Check the open windows for candidate masks.")
+        cv.waitKey(100) # Give time for windows to draw
+
+        try:
+            selected_idx = int(input("Enter the index of the desired mask: "))
+            if 0 <= selected_idx < len(masks):
+                selected_mask = masks[selected_idx]
+                masked_img = cv.bitwise_and(img, img, mask=selected_mask)
+                cv.imwrite("masked_image.png", masked_img)
+                print(f"Saved masked_image.png using candidate {selected_idx}")
+            else:
+                print("Invalid index selected.")
+        except ValueError:
+            print("Invalid input. Please enter a number.")
+            
+        cv.destroyAllWindows()
+
+