openvino: update

plugins/openvino/.vscode/settings.json

@@ -11,7 +11,7 @@
     // local checkout
     "scrypted.debugHost": "127.0.0.1",
     "scrypted.serverRoot": "/Users/koush/.scrypted",
-    // "scrypted.debugHost": "koushik-windows",
+    // "scrypted.debugHost": "koushik-winvm",
     // "scrypted.serverRoot": "C:\\Users\\koush\\.scrypted",
 
     "scrypted.pythonRemoteRoot": "${config:scrypted.serverRoot}/volume/plugin.zip",

plugins/openvino/package.json

@@ -41,5 +41,5 @@
    "devDependencies": {
       "@scrypted/sdk": "file:../../sdk"
    },
-   "version": "0.1.58"
+   "version": "0.1.60"
 }

plugins/openvino/src/ov/__init__.py

@@ -18,6 +18,8 @@
     "Default",
     "scrypted_yolov9c_320",
     "scrypted_yolov9c",
+    "scrypted_yolov8n_320",
+    "scrypted_yolov8n",
     "ssd_mobilenet_v1_coco",
     "ssdlite_mobilenet_v2",
     "yolo-v3-tiny-tf",
@@ -97,15 +99,15 @@ def __init__(self, nativeId: str | None = None):
                 self.storage.setItem("model", "Default")
             model = "scrypted_yolov9c_320"
         self.yolo = "yolo" in model
-        self.yolov9 = "yolov9" in model
+        self.scrypted_yolo = "scrypted_yolo" in model
         self.scrypted_model = "scrypted" in model
         self.sigmoid = model == "yolo-v4-tiny-tf"
 
         print(f"model/mode/precision: {model}/{mode}/{precision}")
 
         ovmodel = "best" if self.scrypted_model else model
 
-        model_version = "v4"
+        model_version = "v5"
         xmlFile = self.downloadFile(
             f"https://raw.githubusercontent.com/koush/openvino-models/main/{model}/{precision}/{ovmodel}.xml",
             f"{model_version}/{precision}/{ovmodel}.xml",
@@ -219,7 +221,7 @@ async def predict():
             infer_request = self.compiled_model.create_infer_request()
             # the input_tensor can be created with the shared_memory=True parameter,
             # but that seems to cause issues on some platforms.
-            if self.yolov9:
+            if self.scrypted_yolo:
                 im = np.stack([input])
                 im = im.transpose((0, 3, 1, 2))  # BHWC to BCHW, (n, 3, h, w)
                 im = im.astype(np.float32) / 255.0
@@ -239,7 +241,7 @@ async def predict():
 
             objs = []
 
-            if self.yolov9:
+            if self.scrypted_yolo:
                 objs = yolo.parse_yolov9(infer_request.output_tensors[0].data[0])
                 return objs