frigate plus submission fix

still show frigate+ section if snapshot has already been submitted and run optimistic update, local state was being overridden

docs/docs/configuration/hardware_acceleration_enrichments.md

@@ -5,7 +5,7 @@ title: Enrichments
 
 # Enrichments
 
-Some of Frigate's enrichments can use a discrete GPU / NPU for accelerated processing.
+Some of Frigate's enrichments can use a discrete GPU or integrated GPU for accelerated processing.
 
 ## Requirements
 
@@ -18,8 +18,10 @@ Object detection and enrichments (like Semantic Search, Face Recognition, and Li
 - **Intel**
 
   - OpenVINO will automatically be detected and used for enrichments in the default Frigate image.
+  - **Note:** Intel NPUs have limited model support for enrichments. GPU is recommended for enrichments when available.
 
 - **Nvidia**
+
   - Nvidia GPUs will automatically be detected and used for enrichments in the `-tensorrt` Frigate image.
   - Jetson devices will automatically be detected and used for enrichments in the `-tensorrt-jp6` Frigate image.
 

docs/docs/configuration/object_detectors.md

@@ -261,6 +261,8 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
 
 :::tip
 
+**NPU + GPU Systems:** If you have both NPU and GPU available (Intel Core Ultra processors), use NPU for object detection and GPU for enrichments (semantic search, face recognition, etc.) for best performance and compatibility.
+
 When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
 
 ```yaml
@@ -283,7 +285,7 @@ detectors:
 | [RF-DETR](#rf-detr)                   | ✅  | ✅  | Requires XE iGPU or Arc                                      |
 | [YOLO-NAS](#yolo-nas)                 | ✅  | ✅  |                                                              |
 | [MobileNet v2](#ssdlite-mobilenet-v2) | ✅  | ✅  | Fast and lightweight model, less accurate than larger models |
-| [YOLOX](#yolox)                       | ✅  |  ?  |                                                              |
+| [YOLOX](#yolox)                       | ✅  | ?   |                                                              |
 | [D-FINE](#d-fine)                     | ❌  | ❌  |                                                              |
 
 #### SSDLite MobileNet v2

docs/docs/configuration/semantic_search.md

@@ -78,7 +78,7 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 
 ### GPU Acceleration
 
-The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU / NPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
+The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.
 
 ```yaml
 semantic_search:
@@ -90,7 +90,7 @@ semantic_search:
 
 :::info
 
-If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU / NPU will be detected and used automatically.
+If the correct build is used for your GPU / NPU and the `large` model is configured, then the GPU will be detected and used automatically.
 Specify the `device` option to target a specific GPU in a multi-GPU system (see [onnxruntime's provider options](https://onnxruntime.ai/docs/execution-providers/)).
 If you do not specify a device, the first available GPU will be used.
 

frigate/data_processing/real_time/custom_classification.py

@@ -418,8 +418,8 @@ class CustomObjectClassificationProcessor(RealTimeProcessorApi):
             obj_data["box"][2],
             obj_data["box"][3],
             max(
-                obj_data["box"][1] - obj_data["box"][0],
-                obj_data["box"][3] - obj_data["box"][2],
+                obj_data["box"][2] - obj_data["box"][0],
+                obj_data["box"][3] - obj_data["box"][1],
             ),
             1.0,
         )

frigate/detectors/detection_runners.py

@@ -3,6 +3,7 @@
 import logging
 import os
 import platform
+import threading
 from abc import ABC, abstractmethod
 from typing import Any
 
@@ -161,12 +162,12 @@ class CudaGraphRunner(BaseModelRunner):
     """
 
     @staticmethod
-    def is_complex_model(model_type: str) -> bool:
+    def is_model_supported(model_type: str) -> bool:
         # Import here to avoid circular imports
         from frigate.detectors.detector_config import ModelTypeEnum
         from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-        return model_type in [
+        return model_type not in [
             ModelTypeEnum.yolonas.value,
             EnrichmentModelTypeEnum.paddleocr.value,
             EnrichmentModelTypeEnum.jina_v1.value,
@@ -239,9 +240,30 @@ class OpenVINOModelRunner(BaseModelRunner):
             EnrichmentModelTypeEnum.jina_v2.value,
         ]
 
+    @staticmethod
+    def is_model_npu_supported(model_type: str) -> bool:
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+
+        return model_type not in [
+            EnrichmentModelTypeEnum.paddleocr.value,
+            EnrichmentModelTypeEnum.jina_v1.value,
+            EnrichmentModelTypeEnum.jina_v2.value,
+            EnrichmentModelTypeEnum.arcface.value,
+        ]
+
     def __init__(self, model_path: str, device: str, model_type: str, **kwargs):
         self.model_path = model_path
         self.device = device
+
+        if device == "NPU" and not OpenVINOModelRunner.is_model_npu_supported(
+            model_type
+        ):
+            logger.warning(
+                f"OpenVINO model {model_type} is not supported on NPU, using GPU instead"
+            )
+            device = "GPU"
+
         self.complex_model = OpenVINOModelRunner.is_complex_model(model_type)
 
         if not os.path.isfile(model_path):
@@ -269,6 +291,10 @@ class OpenVINOModelRunner(BaseModelRunner):
         self.infer_request = self.compiled_model.create_infer_request()
         self.input_tensor: ov.Tensor | None = None
 
+        # Thread lock to prevent concurrent inference (needed for JinaV2 which shares
+        # one runner between text and vision embeddings called from different threads)
+        self._inference_lock = threading.Lock()
+
         if not self.complex_model:
             try:
                 input_shape = self.compiled_model.inputs[0].get_shape()
@@ -312,67 +338,70 @@ class OpenVINOModelRunner(BaseModelRunner):
         Returns:
             List of output tensors
         """
-        # Handle single input case for backward compatibility
-        if (
-            len(inputs) == 1
-            and len(self.compiled_model.inputs) == 1
-            and self.input_tensor is not None
-        ):
-            # Single input case - use the pre-allocated tensor for efficiency
-            input_data = list(inputs.values())[0]
-            np.copyto(self.input_tensor.data, input_data)
-            self.infer_request.infer(self.input_tensor)
-        else:
-            if self.complex_model:
-                try:
-                    # This ensures the model starts with a clean state for each sequence
-                    # Important for RNN models like PaddleOCR recognition
-                    self.infer_request.reset_state()
-                except Exception:
-                    # this will raise an exception for models with AUTO set as the device
-                    pass
+        # Lock prevents concurrent access to infer_request
+        # Needed for JinaV2: genai thread (text) + embeddings thread (vision)
+        with self._inference_lock:
+            # Handle single input case for backward compatibility
+            if (
+                len(inputs) == 1
+                and len(self.compiled_model.inputs) == 1
+                and self.input_tensor is not None
+            ):
+                # Single input case - use the pre-allocated tensor for efficiency
+                input_data = list(inputs.values())[0]
+                np.copyto(self.input_tensor.data, input_data)
+                self.infer_request.infer(self.input_tensor)
+            else:
+                if self.complex_model:
+                    try:
+                        # This ensures the model starts with a clean state for each sequence
+                        # Important for RNN models like PaddleOCR recognition
+                        self.infer_request.reset_state()
+                    except Exception:
+                        # this will raise an exception for models with AUTO set as the device
+                        pass
 
-            # Multiple inputs case - set each input by name
-            for input_name, input_data in inputs.items():
-                # Find the input by name and its index
-                input_port = None
-                input_index = None
-                for idx, port in enumerate(self.compiled_model.inputs):
-                    if port.get_any_name() == input_name:
-                        input_port = port
-                        input_index = idx
-                        break
+                # Multiple inputs case - set each input by name
+                for input_name, input_data in inputs.items():
+                    # Find the input by name and its index
+                    input_port = None
+                    input_index = None
+                    for idx, port in enumerate(self.compiled_model.inputs):
+                        if port.get_any_name() == input_name:
+                            input_port = port
+                            input_index = idx
+                            break
 
-                if input_port is None:
-                    raise ValueError(f"Input '{input_name}' not found in model")
+                    if input_port is None:
+                        raise ValueError(f"Input '{input_name}' not found in model")
 
-                # Create tensor with the correct element type
-                input_element_type = input_port.get_element_type()
+                    # Create tensor with the correct element type
+                    input_element_type = input_port.get_element_type()
 
-                # Ensure input data matches the expected dtype to prevent type mismatches
-                # that can occur with models like Jina-CLIP v2 running on OpenVINO
-                expected_dtype = input_element_type.to_dtype()
-                if input_data.dtype != expected_dtype:
-                    logger.debug(
-                        f"Converting input '{input_name}' from {input_data.dtype} to {expected_dtype}"
-                    )
-                    input_data = input_data.astype(expected_dtype)
+                    # Ensure input data matches the expected dtype to prevent type mismatches
+                    # that can occur with models like Jina-CLIP v2 running on OpenVINO
+                    expected_dtype = input_element_type.to_dtype()
+                    if input_data.dtype != expected_dtype:
+                        logger.debug(
+                            f"Converting input '{input_name}' from {input_data.dtype} to {expected_dtype}"
+                        )
+                        input_data = input_data.astype(expected_dtype)
 
-                input_tensor = ov.Tensor(input_element_type, input_data.shape)
-                np.copyto(input_tensor.data, input_data)
+                    input_tensor = ov.Tensor(input_element_type, input_data.shape)
+                    np.copyto(input_tensor.data, input_data)
 
-                # Set the input tensor for the specific port index
-                self.infer_request.set_input_tensor(input_index, input_tensor)
+                    # Set the input tensor for the specific port index
+                    self.infer_request.set_input_tensor(input_index, input_tensor)
 
-            # Run inference
-            self.infer_request.infer()
+                # Run inference
+                self.infer_request.infer()
 
-        # Get all output tensors
-        outputs = []
-        for i in range(len(self.compiled_model.outputs)):
-            outputs.append(self.infer_request.get_output_tensor(i).data)
+            # Get all output tensors
+            outputs = []
+            for i in range(len(self.compiled_model.outputs)):
+                outputs.append(self.infer_request.get_output_tensor(i).data)
 
-        return outputs
+            return outputs
 
 
 class RKNNModelRunner(BaseModelRunner):
@@ -500,7 +529,7 @@ def get_optimized_runner(
             return OpenVINOModelRunner(model_path, device, model_type, **kwargs)
 
     if (
-        not CudaGraphRunner.is_complex_model(model_type)
+        not CudaGraphRunner.is_model_supported(model_type)
         and providers[0] == "CUDAExecutionProvider"
     ):
         options[0] = {

web/src/components/overlay/detail/DetailActionsMenu.tsx

@@ -55,29 +55,32 @@ export default function DetailActionsMenu({
       </DropdownMenuTrigger>
       <DropdownMenuPortal>
         <DropdownMenuContent align="end">
-          <DropdownMenuItem>
-            <a
-              className="w-full"
-              href={`${baseUrl}api/events/${search.id}/snapshot.jpg?bbox=1`}
-              download={`${search.camera}_${search.label}.jpg`}
-            >
-              <div className="flex cursor-pointer items-center gap-2">
-                <span>{t("itemMenu.downloadSnapshot.label")}</span>
-              </div>
-            </a>
-          </DropdownMenuItem>
-
-          <DropdownMenuItem>
-            <a
-              className="w-full"
-              href={`${baseUrl}api/${search.camera}/${clipTimeRange}/clip.mp4`}
-              download
-            >
-              <div className="flex cursor-pointer items-center gap-2">
-                <span>{t("itemMenu.downloadVideo.label")}</span>
-              </div>
-            </a>
-          </DropdownMenuItem>
+          {search.has_snapshot && (
+            <DropdownMenuItem>
+              <a
+                className="w-full"
+                href={`${baseUrl}api/events/${search.id}/snapshot.jpg?bbox=1`}
+                download={`${search.camera}_${search.label}.jpg`}
+              >
+                <div className="flex cursor-pointer items-center gap-2">
+                  <span>{t("itemMenu.downloadSnapshot.label")}</span>
+                </div>
+              </a>
+            </DropdownMenuItem>
+          )}
+          {search.has_clip && (
+            <DropdownMenuItem>
+              <a
+                className="w-full"
+                href={`${baseUrl}api/${search.camera}/${clipTimeRange}/clip.mp4`}
+                download
+              >
+                <div className="flex cursor-pointer items-center gap-2">
+                  <span>{t("itemMenu.downloadVideo.label")}</span>
+                </div>
+              </a>
+            </DropdownMenuItem>
+          )}
 
           {config?.semantic_search.enabled &&
             setSimilarity != undefined &&

web/src/components/overlay/detail/SearchDetailDialog.tsx

@@ -306,7 +306,7 @@ function DialogContentComponent({
   if (page === "tracking_details") {
     return (
       <TrackingDetails
-        className={cn("size-full", !isDesktop && "flex flex-col gap-4")}
+        className={cn(isDesktop ? "size-full" : "flex flex-col gap-4")}
         event={search as unknown as Event}
         tabs={
           isDesktop ? (
@@ -584,7 +584,7 @@ export default function SearchDetailDialog({
             "scrollbar-container overflow-y-auto",
             isDesktop &&
               "max-h-[95dvh] sm:max-w-xl md:max-w-4xl lg:max-w-[70%]",
-            isMobile && "px-4",
+            isMobile && "flex h-full flex-col px-4",
           )}
           onInteractOutside={(e) => {
             if (isPopoverOpen) {
@@ -596,7 +596,7 @@ export default function SearchDetailDialog({
             }
           }}
         >
-          <Header>
+          <Header className={cn(!isDesktop && "top-0 z-[60] mb-0")}>
             <Title>{t("trackedObjectDetails")}</Title>
             <Description className="sr-only">
               {t("trackedObjectDetails")}
@@ -1078,12 +1078,31 @@ function ObjectDetailsTab({
           });
 
       setState("submitted");
-      setSearch({
-        ...search,
-        plus_id: "new_upload",
-      });
+      mutate(
+        (key) =>
+          typeof key === "string" &&
+          (key.includes("events") ||
+            key.includes("events/search") ||
+            key.includes("events/explore")),
+        (currentData: SearchResult[][] | SearchResult[] | undefined) => {
+          if (!currentData) return currentData;
+          // optimistic update
+          return currentData
+            .flat()
+            .map((event) =>
+              event.id === search.id
+                ? { ...event, plus_id: "new_upload" }
+                : event,
+            );
+        },
+        {
+          optimisticData: true,
+          rollbackOnError: true,
+          revalidate: false,
+        },
+      );
     },
-    [search, setSearch],
+    [search, mutate],
   );
 
   const popoverContainerRef = useRef<HTMLDivElement | null>(null);
@@ -1243,8 +1262,8 @@ function ObjectDetailsTab({
       </div>
 
       {search.data.type === "object" &&
-        !search.plus_id &&
-        config?.plus?.enabled && (
+        config?.plus?.enabled &&
+        search.has_snapshot && (
           <div
             className={cn(
               "my-2 flex w-full flex-col justify-between gap-1.5",

web/src/components/overlay/detail/TrackingDetails.tsx

@@ -352,7 +352,8 @@ export function TrackingDetails({
       className={cn(
         isDesktop
           ? "flex size-full justify-evenly gap-4 overflow-hidden"
-          : "flex size-full flex-col gap-2",
+          : "flex flex-col gap-2",
+        !isDesktop && cameraAspect === "tall" && "size-full",
         className,
       )}
     >
@@ -719,9 +720,13 @@ function LifecycleIconRow({
                             backgroundColor: `rgb(${color})`,
                           }}
                         />
-                        <span className="smart-capitalize">
-                          {item.data?.zones_friendly_names?.[zidx] ??
-                            zone.replaceAll("_", " ")}
+                        <span
+                          className={cn(
+                            item.data?.zones_friendly_names?.[zidx] === zone &&
+                              "smart-capitalize",
+                          )}
+                        >
+                          {item.data?.zones_friendly_names?.[zidx]}
                         </span>
                       </Badge>
                     );

web/src/components/settings/ZoneEditPane.tsx

@@ -576,6 +576,7 @@ export default function ZoneEditPane({
             control={form.control}
             nameField="friendly_name"
             idField="name"
+            idVisible={(polygon && polygon.name.length > 0) ?? false}
             nameLabel={t("masksAndZones.zones.name.title")}
             nameDescription={t("masksAndZones.zones.name.tips")}
             placeholderName={t("masksAndZones.zones.name.inputPlaceHolder")}

web/src/utils/stringUtil.ts

@@ -21,20 +21,30 @@ export const capitalizeAll = (text: string): string => {
  * @returns A valid camera identifier (lowercase, alphanumeric, max 8 chars)
  */
 export function generateFixedHash(name: string, prefix: string = "id"): string {
-  // Safely encode Unicode as UTF-8 bytes
+  // Use the full UTF-8 bytes of the name and compute an FNV-1a 32-bit hash.
+  // This is deterministic, fast, works with Unicode and avoids collisions from
+  // simple truncation of base64 output.
   const utf8Bytes = new TextEncoder().encode(name);
 
-  // Convert to base64 manually
-  let binary = "";
-  for (const byte of utf8Bytes) {
-    binary += String.fromCharCode(byte);
+  // FNV-1a 32-bit hash algorithm
+  let hash = 0x811c9dc5; // FNV offset basis
+  for (let i = 0; i < utf8Bytes.length; i++) {
+    hash ^= utf8Bytes[i];
+    // Multiply by FNV prime (0x01000193) with 32-bit overflow
+    hash = (hash >>> 0) * 0x01000193;
+    // Ensure 32-bit unsigned integer
+    hash >>>= 0;
   }
-  const base64 = btoa(binary);
 
-  // Strip out non-alphanumeric characters and truncate
-  const cleanHash = base64.replace(/[^a-zA-Z0-9]/g, "").substring(0, 8);
+  // Convert to an 8-character lowercase hex string
+  const hashHex = (hash >>> 0).toString(16).padStart(8, "0").toLowerCase();
 
-  return `${prefix}_${cleanHash.toLowerCase()}`;
+  // Ensure the first character is a letter to avoid an identifier that's purely
+  // numeric (isValidId forbids all-digit IDs). If it starts with a digit,
+  // replace with 'a'. This is extremely unlikely but a simple safeguard.
+  const safeHash = /^[0-9]/.test(hashHex[0]) ? `a${hashHex.slice(1)}` : hashHex;
+
+  return `${prefix}_${safeHash}`;
 }
 
 /**

web/src/views/settings/CameraSettingsView.tsx

@@ -98,12 +98,12 @@ export default function CameraSettingsView({
       return Object.entries(cameraConfig.zones).map(([name, zoneData]) => ({
         camera: cameraConfig.name,
         name,
-        friendly_name: getZoneName(name, cameraConfig.name),
+        friendly_name: cameraConfig.zones[name].friendly_name,
         objects: zoneData.objects,
         color: zoneData.color,
       }));
     }
-  }, [cameraConfig, getZoneName]);
+  }, [cameraConfig]);
 
   const alertsLabels = useMemo(() => {
     return cameraConfig?.review.alerts.labels
@@ -533,8 +533,14 @@ export default function CameraSettingsView({
                                             }}
                                           />
                                         </FormControl>
-                                        <FormLabel className="font-normal smart-capitalize">
-                                          {zone.friendly_name}
+                                        <FormLabel
+                                          className={cn(
+                                            "font-normal",
+                                            !zone.friendly_name &&
+                                              "smart-capitalize",
+                                          )}
+                                        >
+                                          {zone.friendly_name || zone.name}
                                         </FormLabel>
                                       </FormItem>
                                     )}
@@ -632,8 +638,14 @@ export default function CameraSettingsView({
                                               }}
                                             />
                                           </FormControl>
-                                          <FormLabel className="font-normal smart-capitalize">
-                                            {zone.friendly_name}
+                                          <FormLabel
+                                            className={cn(
+                                              "font-normal",
+                                              !zone.friendly_name &&
+                                                "smart-capitalize",
+                                            )}
+                                          >
+                                            {zone.friendly_name || zone.name}
                                           </FormLabel>
                                         </FormItem>
                                       )}