Merge c84bfd3ace into 99a363c047

navigating between tracked objects in Explore would sometimes prevent the object track from appearing

frigate/api/classification.py

@@ -595,9 +595,13 @@ def get_classification_dataset(name: str):
             "last_training_image_count": 0,
             "current_image_count": current_image_count,
             "new_images_count": current_image_count,
+            "dataset_changed": current_image_count > 0,
         }
     else:
         last_training_count = metadata.get("last_training_image_count", 0)
+        # Dataset has changed if count is different (either added or deleted images)
+        dataset_changed = current_image_count != last_training_count
+        # Only show positive count for new images (ignore deletions in the count display)
         new_images_count = max(0, current_image_count - last_training_count)
         training_metadata = {
             "has_trained": True,
@@ -605,6 +609,7 @@ def get_classification_dataset(name: str):
             "last_training_image_count": last_training_count,
             "current_image_count": current_image_count,
             "new_images_count": new_images_count,
+            "dataset_changed": dataset_changed,
         }
 
     return JSONResponse(
@@ -948,31 +953,29 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     dependencies=[Depends(require_role(["admin"]))],
     summary="Delete a classification model",
     description="""Deletes a specific classification model and all its associated data.
-    The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
+    Works even if the model is not in the config (e.g., partially created during wizard).
+    Returns a success message.""",
 )
 def delete_classification_model(request: Request, name: str):
-    config: FrigateConfig = request.app.frigate_config
-
-    if name not in config.classification.custom:
-        return JSONResponse(
-            content=(
-                {
-                    "success": False,
-                    "message": f"{name} is not a known classification model.",
-                }
-            ),
-            status_code=404,
-        )
+    sanitized_name = sanitize_filename(name)
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitize_filename(name))
+    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
     if os.path.exists(data_dir):
-        shutil.rmtree(data_dir)
+        try:
+            shutil.rmtree(data_dir)
+            logger.info(f"Deleted classification data directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitize_filename(name))
+    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
     if os.path.exists(model_dir):
-        shutil.rmtree(model_dir)
+        try:
+            shutil.rmtree(model_dir)
+            logger.info(f"Deleted classification model directory for {name}")
+        except Exception as e:
+            logger.debug(f"Failed to delete model directory for {name}: {e}")
 
     return JSONResponse(
         content=(

frigate/data_processing/common/audio_transcription/model.py

@@ -4,7 +4,6 @@ import logging
 import os
 
 import sherpa_onnx
-from faster_whisper.utils import download_model
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import MODEL_CACHE_DIR
@@ -25,6 +24,9 @@ class AudioTranscriptionModelRunner:
 
         if model_size == "large":
             # use the Whisper download function instead of our own
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper.utils import download_model
+
             logger.debug("Downloading Whisper audio transcription model")
             download_model(
                 size_or_id="small" if device == "cuda" else "tiny",

frigate/data_processing/post/audio_transcription.py

@@ -6,7 +6,6 @@ import threading
 import time
 from typing import Optional
 
-from faster_whisper import WhisperModel
 from peewee import DoesNotExist
 
 from frigate.comms.inter_process import InterProcessRequestor
@@ -51,6 +50,9 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
 
     def __build_recognizer(self) -> None:
         try:
+            # Import dynamically to avoid crashes on systems without AVX support
+            from faster_whisper import WhisperModel
+
             self.recognizer = WhisperModel(
                 model_size_or_path="small",
                 device="cuda"

frigate/detectors/detection_runners.py

@@ -394,7 +394,11 @@ class OpenVINOModelRunner(BaseModelRunner):
                     self.infer_request.set_input_tensor(input_index, input_tensor)
 
                 # Run inference
-                self.infer_request.infer()
+                try:
+                    self.infer_request.infer()
+                except Exception as e:
+                    logger.error(f"Error during OpenVINO inference: {e}")
+                    return []
 
             # Get all output tensors
             outputs = []

web/public/locales/en/views/classificationModel.json

@@ -16,6 +16,7 @@
   "tooltip": {
     "trainingInProgress": "Model is currently training",
     "noNewImages": "No new images to train. Classify more images in the dataset first.",
+    "noChanges": "No changes to the dataset since last training.",
     "modelNotReady": "Model is not ready for training"
   },
   "toast": {
@@ -43,7 +44,9 @@
   },
   "deleteCategory": {
     "title": "Delete Class",
-    "desc": "Are you sure you want to delete the class {{name}}? This will permanently delete all associated images and require re-training the model."
+    "desc": "Are you sure you want to delete the class {{name}}? This will permanently delete all associated images and require re-training the model.",
+    "minClassesTitle": "Cannot Delete Class",
+    "minClassesDesc": "A classification model must have at least 2 classes. Add another class before deleting this one."
   },
   "deleteModel": {
     "title": "Delete Classification Model",

web/src/components/classification/ClassificationModelWizardDialog.tsx

@@ -15,6 +15,7 @@ import Step3ChooseExamples, {
 } from "./wizard/Step3ChooseExamples";
 import { cn } from "@/lib/utils";
 import { isDesktop } from "react-device-detect";
+import axios from "axios";
 
 const OBJECT_STEPS = [
   "wizard.steps.nameAndDefine",
@@ -120,7 +121,18 @@ export default function ClassificationModelWizardDialog({
     dispatch({ type: "PREVIOUS_STEP" });
   };
 
-  const handleCancel = () => {
+  const handleCancel = async () => {
+    // Clean up any generated training images if we're cancelling from Step 3
+    if (wizardState.step1Data && wizardState.step3Data?.examplesGenerated) {
+      try {
+        await axios.delete(
+          `/classification/${wizardState.step1Data.modelName}`,
+        );
+      } catch (error) {
+        // Silently fail - user is already cancelling
+      }
+    }
+
     dispatch({ type: "RESET" });
     onClose();
   };

web/src/components/classification/wizard/Step3ChooseExamples.tsx

@@ -165,18 +165,15 @@ export default function Step3ChooseExamples({
     const isLastClass = currentClassIndex === allClasses.length - 1;
 
     if (isLastClass) {
-      // Assign remaining unclassified images
-      unknownImages.slice(0, 24).forEach((imageName) => {
-        if (!newClassifications[imageName]) {
-          // For state models with 2 classes, assign to the last class
-          // For object models, assign to "none"
-          if (step1Data.modelType === "state" && allClasses.length === 2) {
-            newClassifications[imageName] = allClasses[allClasses.length - 1];
-          } else {
+      // For object models, assign remaining unclassified images to "none"
+      // For state models, this should never happen since we require all images to be classified
+      if (step1Data.modelType !== "state") {
+        unknownImages.slice(0, 24).forEach((imageName) => {
+          if (!newClassifications[imageName]) {
             newClassifications[imageName] = "none";
           }
-        }
-      });
+        });
+      }
 
       // All done, trigger training immediately
       setImageClassifications(newClassifications);
@@ -316,8 +313,15 @@ export default function Step3ChooseExamples({
       return images;
     }
 
-    return images.filter((img) => !imageClassifications[img]);
-  }, [unknownImages, imageClassifications]);
+    // If we're viewing a previous class (going back), show images for that class
+    // Otherwise show only unclassified images
+    const currentClassInView = allClasses[currentClassIndex];
+    return images.filter((img) => {
+      const imgClass = imageClassifications[img];
+      // Show if: unclassified OR classified with current class we're viewing
+      return !imgClass || imgClass === currentClassInView;
+    });
+  }, [unknownImages, imageClassifications, allClasses, currentClassIndex]);
 
   const allImagesClassified = useMemo(() => {
     return unclassifiedImages.length === 0;
@@ -326,15 +330,26 @@ export default function Step3ChooseExamples({
   // For state models on the last class, require all images to be classified
   const isLastClass = currentClassIndex === allClasses.length - 1;
   const canProceed = useMemo(() => {
-    if (
-      step1Data.modelType === "state" &&
-      isLastClass &&
-      !allImagesClassified
-    ) {
-      return false;
+    if (step1Data.modelType === "state" && isLastClass) {
+      // Check if all 24 images will be classified after current selections are applied
+      const totalImages = unknownImages.slice(0, 24).length;
+
+      // Count images that will be classified (either already classified or currently selected)
+      const allImages = unknownImages.slice(0, 24);
+      const willBeClassified = allImages.filter((img) => {
+        return imageClassifications[img] || selectedImages.has(img);
+      }).length;
+
+      return willBeClassified >= totalImages;
     }
     return true;
-  }, [step1Data.modelType, isLastClass, allImagesClassified]);
+  }, [
+    step1Data.modelType,
+    isLastClass,
+    unknownImages,
+    imageClassifications,
+    selectedImages,
+  ]);
 
   const handleBack = useCallback(() => {
     if (currentClassIndex > 0) {

web/src/components/overlay/ImageShadowOverlay.tsx

@@ -12,13 +12,13 @@ export function ImageShadowOverlay({
     <>
       <div
         className={cn(
-          "pointer-events-none absolute inset-x-0 top-0 z-10 h-[30%] w-full rounded-lg bg-gradient-to-b from-black/20 to-transparent md:rounded-2xl",
+          "pointer-events-none absolute inset-x-0 top-0 z-10 h-[30%] w-full rounded-lg bg-gradient-to-b from-black/20 to-transparent",
           upperClassName,
         )}
       />
       <div
         className={cn(
-          "pointer-events-none absolute inset-x-0 bottom-0 z-10 h-[10%] w-full rounded-lg bg-gradient-to-t from-black/20 to-transparent md:rounded-2xl",
+          "pointer-events-none absolute inset-x-0 bottom-0 z-10 h-[10%] w-full rounded-lg bg-gradient-to-t from-black/20 to-transparent",
           lowerClassName,
         )}
       />

web/src/components/player/BirdseyeLivePlayer.tsx

@@ -77,7 +77,10 @@ export default function BirdseyeLivePlayer({
       )}
       onClick={onClick}
     >
-      <ImageShadowOverlay />
+      <ImageShadowOverlay
+        upperClassName="md:rounded-2xl"
+        lowerClassName="md:rounded-2xl"
+      />
       <div className="size-full" ref={playerRef}>
         {player}
       </div>

web/src/components/player/LivePlayer.tsx

@@ -331,7 +331,10 @@ export default function LivePlayer({
     >
       {cameraEnabled &&
         ((showStillWithoutActivity && !liveReady) || liveReady) && (
-          <ImageShadowOverlay />
+          <ImageShadowOverlay
+            upperClassName="md:rounded-2xl"
+            lowerClassName="md:rounded-2xl"
+          />
         )}
       {player}
       {cameraEnabled &&

web/src/context/detail-stream-context.tsx

@@ -1,4 +1,10 @@
-import React, { createContext, useContext, useState, useEffect } from "react";
+import React, {
+  createContext,
+  useContext,
+  useState,
+  useEffect,
+  useRef,
+} from "react";
 import { FrigateConfig } from "@/types/frigateConfig";
 import useSWR from "swr";
 
@@ -36,6 +42,23 @@ export function DetailStreamProvider({
     () => initialSelectedObjectIds ?? [],
   );
 
+  // When the parent provides a new initialSelectedObjectIds (for example
+  // when navigating between search results) update the selection so children
+  // like `ObjectTrackOverlay` receive the new ids immediately. We only
+  // perform this update when the incoming value actually changes.
+  useEffect(() => {
+    if (
+      initialSelectedObjectIds &&
+      (initialSelectedObjectIds.length !== selectedObjectIds.length ||
+        initialSelectedObjectIds.some((v, i) => selectedObjectIds[i] !== v))
+    ) {
+      setSelectedObjectIds(initialSelectedObjectIds);
+    }
+    // Intentionally include selectedObjectIds to compare previous value and
+    // avoid overwriting user interactions unless the incoming prop changed.
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [initialSelectedObjectIds]);
+
   const toggleObjectSelection = (id: string | undefined) => {
     if (id === undefined) {
       setSelectedObjectIds([]);
@@ -63,10 +86,33 @@ export function DetailStreamProvider({
     setAnnotationOffset(cfgOffset);
   }, [config, camera]);
 
-  // Clear selected objects when exiting detail mode or changing cameras
+  // Clear selected objects when exiting detail mode or when the camera
+  // changes for providers that are not initialized with an explicit
+  // `initialSelectedObjectIds` (e.g., the RecordingView). For providers
+  // that receive `initialSelectedObjectIds` (like SearchDetailDialog) we
+  // avoid clearing on camera change to prevent a race with children that
+  // immediately set selection when mounting.
+  const prevCameraRef = useRef<string | undefined>(undefined);
   useEffect(() => {
-    setSelectedObjectIds([]);
-  }, [isDetailMode, camera]);
+    // Always clear when leaving detail mode
+    if (!isDetailMode) {
+      setSelectedObjectIds([]);
+      prevCameraRef.current = camera;
+      return;
+    }
+
+    // If camera changed and the parent did not provide initialSelectedObjectIds,
+    // clear selection to preserve previous behavior.
+    if (
+      prevCameraRef.current !== undefined &&
+      prevCameraRef.current !== camera &&
+      initialSelectedObjectIds === undefined
+    ) {
+      setSelectedObjectIds([]);
+    }
+
+    prevCameraRef.current = camera;
+  }, [isDetailMode, camera, initialSelectedObjectIds]);
 
   const value: DetailStreamContextType = {
     selectedObjectIds,

web/src/views/classification/ModelTrainingView.tsx

@@ -126,6 +126,7 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
       last_training_image_count: number;
       current_image_count: number;
       new_images_count: number;
+      dataset_changed: boolean;
     } | null;
   }>(`classification/${model.name}/dataset`);
 
@@ -264,10 +265,11 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
               );
             }
 
+            // Always refresh dataset to update the categories list
+            refreshDataset();
+
             if (pageToggle == "train") {
               refreshTrain();
-            } else {
-              refreshDataset();
             }
           }
         })
@@ -445,7 +447,7 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
                   variant={modelState == "failed" ? "destructive" : "select"}
                   disabled={
                     (modelState != "complete" && modelState != "failed") ||
-                    (trainingMetadata?.new_images_count ?? 0) === 0
+                    !trainingMetadata?.dataset_changed
                   }
                 >
                   {modelState == "training" ? (
@@ -466,14 +468,14 @@ export default function ModelTrainingView({ model }: ModelTrainingViewProps) {
                   )}
                 </Button>
               </TooltipTrigger>
-              {((trainingMetadata?.new_images_count ?? 0) === 0 ||
+              {(!trainingMetadata?.dataset_changed ||
                 (modelState != "complete" && modelState != "failed")) && (
                 <TooltipPortal>
                   <TooltipContent>
                     {modelState == "training"
                       ? t("tooltip.trainingInProgress")
-                      : trainingMetadata?.new_images_count === 0
-                        ? t("tooltip.noNewImages")
+                      : !trainingMetadata?.dataset_changed
+                        ? t("tooltip.noChanges")
                         : t("tooltip.modelNotReady")}
                   </TooltipContent>
                 </TooltipPortal>
@@ -571,27 +573,44 @@ function LibrarySelector({
       >
         <DialogContent>
           <DialogHeader>
-            <DialogTitle>{t("deleteCategory.title")}</DialogTitle>
+            <DialogTitle>
+              {Object.keys(dataset).length <= 2
+                ? t("deleteCategory.minClassesTitle")
+                : t("deleteCategory.title")}
+            </DialogTitle>
             <DialogDescription>
-              {t("deleteCategory.desc", { name: confirmDelete })}
+              {Object.keys(dataset).length <= 2
+                ? t("deleteCategory.minClassesDesc")
+                : t("deleteCategory.desc", { name: confirmDelete })}
             </DialogDescription>
           </DialogHeader>
           <div className="flex justify-end gap-2">
-            <Button variant="outline" onClick={() => setConfirmDelete(null)}>
-              {t("button.cancel", { ns: "common" })}
-            </Button>
-            <Button
-              variant="destructive"
-              className="text-white"
-              onClick={() => {
-                if (confirmDelete) {
-                  handleDeleteCategory(confirmDelete);
-                  setConfirmDelete(null);
-                }
-              }}
-            >
-              {t("button.delete", { ns: "common" })}
-            </Button>
+            {Object.keys(dataset).length <= 2 ? (
+              <Button variant="outline" onClick={() => setConfirmDelete(null)}>
+                {t("button.ok", { ns: "common" })}
+              </Button>
+            ) : (
+              <>
+                <Button
+                  variant="outline"
+                  onClick={() => setConfirmDelete(null)}
+                >
+                  {t("button.cancel", { ns: "common" })}
+                </Button>
+                <Button
+                  variant="destructive"
+                  className="text-white"
+                  onClick={() => {
+                    if (confirmDelete) {
+                      handleDeleteCategory(confirmDelete);
+                      setConfirmDelete(null);
+                    }
+                  }}
+                >
+                  {t("button.delete", { ns: "common" })}
+                </Button>
+              </>
+            )}
           </div>
         </DialogContent>
       </Dialog>