typo fix

impact_server.py

@@ -3,6 +3,11 @@ from aiohttp import web
 import server
 import folder_paths
 
+import impact_core as core
+import impact_pack
+from segment_anything import SamPredictor
+import numpy as np
+import nodes
 
 @server.PromptServer.instance.routes.post("/upload/temp")
 async def upload_image(request):
@@ -33,3 +38,54 @@ async def upload_image(request):
         return web.json_response({"name": filename})
     else:
         return web.Response(status=400)
+
+
+sam_predictor = None
+default_sam_model_name = os.path.join(impact_pack.model_path, "sams", "sam_vit_b_01ec64.pth")
+
+
+@server.PromptServer.instance.routes.post("/sam/prepare")
+async def load_sam_model(request):
+    data = await request.json()
+    
+    sam_model_name = os.path.join(impact_pack.model_path, "sams", data['sam_model_name'])
+    sam_predictor = SamPredictor(sam_model_name)
+    
+    image = nodes.LoadImage().load_image(data['img_path'])[0]
+    image = np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)
+
+    sam_predictor.set_image(image, "RGB")
+
+
+@server.PromptServer.instance.routes.post("/sam/release")
+async def unload_sam_model(request):
+    sam_predictor = None
+
+
+@server.PromptServer.instance.routes.post("/sam/detect")
+async def upload_image(request):
+    data = await request.json()
+    
+    positive_points = data['positive_points']
+    negative_points = data['negative_points']
+    threshold = data['threshold']
+
+    points = []
+    plabs = []
+
+    for p in positive_points:
+        points.append(p)
+        plabs.append(1)
+
+    for p in negative_points:
+        points.append(p)
+        plabs.append(0)
+        
+    detected_masks = core.sam_predict(sam_predictor, points, plabs, None, threshold)
+    
+    print(detected_masks)
+
+
+        
+
+