Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import cv2
+from segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+from PIL import Image
+import os
+import urllib.request
+
+# Modell-URL & Speicherpfad
+MODEL_URL = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth"
+MODEL_PATH = "sam_vit_b_01ec64.pth"
+
+# Falls Modell nicht vorhanden, herunterladen
+if not os.path.exists(MODEL_PATH):
+    print("Modell wird heruntergeladen...")
+    urllib.request.urlretrieve(MODEL_URL, MODEL_PATH)
+    print("Modell erfolgreich heruntergeladen.")
+
+# Modell laden
+model_type = "vit_b"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+sam = sam_model_registry[model_type](checkpoint=MODEL_PATH)
+sam.to(device=device)
+mask_generator = SamAutomaticMaskGenerator(sam)
+
+def segment_all_objects(image):
+    image_np = np.array(image)
+    masks = mask_generator.generate(image_np)
+
+    overlay = image_np.copy()
+    for i, mask in enumerate(masks):
+        m = mask["segmentation"]
+        color = np.random.randint(0, 255, size=(3,))
+        overlay[m] = overlay[m] * 0.3 + color * 0.7
+
+        # Objekt-Label anzeigen
+        y, x = np.where(m)
+        if len(x) > 0 and len(y) > 0:
+            cx, cy = int(np.mean(x)), int(np.mean(y))
+            cv2.putText(overlay, f"Obj {i+1}", (cx, cy),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+
+    return Image.fromarray(overlay.astype(np.uint8))
+
+demo = gr.Interface(
+    fn=segment_all_objects,
+    inputs=gr.Image(type="pil", label="Bild hochladen"),
+    outputs=gr.Image(type="pil", label="Segmentiertes Ergebnis"),
+    title="FishBoost SAM2 Segmentierung",
+    description="Automatische Segmentierung aller Objekte im Bild mit farbiger Darstellung (Meta SAM-Modell)."
+)
+
+demo.launch()