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Code Issues Actions 3 Packages Projects Releases Wiki Activity

Finalize training and image selection step

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This commit is contained in:
Nicolas Mowen 2025-10-22 14:30:33 -06:00
parent 6773a9aeb1
commit 7f093d81a9
5 changed files with 183 additions and 22 deletions
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7
frigate/api/app.py
View File

@ -401,6 +401,13 @@ def config_set(request: Request, body: AppConfigSetBody):
CameraConfigUpdateTopic(CameraConfigUpdateEnum[field], camera),
settings,
)
elif body.update_topic and "/config/" in body.update_topic[1:]:
# Handle nested config updates (e.g., config/classification/custom/{name})
settings = config.get_nested_object(body.update_topic)
if settings:
request.app.config_publisher.publisher.publish(
body.update_topic, settings
)
return JSONResponse(
content=(

57
frigate/embeddings/maintainer.py
View File

@ -9,6 +9,7 @@ from typing import Any
from peewee import DoesNotExist
from frigate.comms.config_updater import ConfigSubscriber
from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
from frigate.comms.embeddings_updater import (
EmbeddingsRequestEnum,
@ -95,6 +96,9 @@ class EmbeddingMaintainer(threading.Thread):
CameraConfigUpdateEnum.semantic_search,
],
)
self.classification_config_subscriber = ConfigSubscriber(
"config/classification/custom/"
)
# Configure Frigate DB
db = SqliteVecQueueDatabase(
@ -255,6 +259,7 @@ class EmbeddingMaintainer(threading.Thread):
"""Maintain a SQLite-vec database for semantic search."""
while not self.stop_event.is_set():
self.config_updater.check_for_updates()
self._check_classification_config_updates()
self._process_requests()
self._process_updates()
self._process_recordings_updates()
@ -265,6 +270,7 @@ class EmbeddingMaintainer(threading.Thread):
self._process_event_metadata()
self.config_updater.stop()
self.classification_config_subscriber.stop()
self.event_subscriber.stop()
self.event_end_subscriber.stop()
self.recordings_subscriber.stop()
@ -275,6 +281,57 @@ class EmbeddingMaintainer(threading.Thread):
self.requestor.stop()
logger.info("Exiting embeddings maintenance...")
def _check_classification_config_updates(self) -> None:
"""Check for classification config updates and add new processors."""
topic, model_config = self.classification_config_subscriber.check_for_update()
if topic and model_config:
# Extract model name from topic: config/classification/custom/{model_name}
model_name = topic.split("/")[-1]
logger.info(
f"Received classification config update for model: {model_name}"
)
# Update config
self.config.classification.custom[model_name] = model_config
# Check if processor already exists for this model
existing_processor_index = None
for i, processor in enumerate(self.realtime_processors):
if isinstance(
processor,
(
CustomStateClassificationProcessor,
CustomObjectClassificationProcessor,
),
):
if processor.model_config.name == model_name:
existing_processor_index = i
break
# Remove existing processor if found
if existing_processor_index is not None:
logger.info(
f"Removing existing classification processor for model: {model_name}"
)
self.realtime_processors.pop(existing_processor_index)
# Add new processor
if model_config.state_config is not None:
processor = CustomStateClassificationProcessor(
self.config, model_config, self.requestor, self.metrics
)
else:
processor = CustomObjectClassificationProcessor(
self.config,
model_config,
self.event_metadata_publisher,
self.metrics,
)
self.realtime_processors.append(processor)
logger.info(f"Added classification processor for model: {model_name}")
def _process_requests(self) -> None:
"""Process embeddings requests"""

18
frigate/util/classification.py
View File

@ -225,13 +225,13 @@ def collect_state_classification_examples(
# Step 4: Select 24 most visually distinct images (they're already cropped)
distinct_images = _select_distinct_images(keyframes, target_count=24)
# Step 5: Save to dataset directory (in "unknown" subfolder for unlabeled data)
unknown_dir = os.path.join(dataset_dir, "unknown")
os.makedirs(unknown_dir, exist_ok=True)
# Step 5: Save to train directory for later classification
train_dir = os.path.join(CLIPS_DIR, model_name, "train")
os.makedirs(train_dir, exist_ok=True)
saved_count = 0
for idx, image_path in enumerate(distinct_images):
dest_path = os.path.join(unknown_dir, f"example_{idx:03d}.jpg")
dest_path = os.path.join(train_dir, f"example_{idx:03d}.jpg")
try:
img = cv2.imread(image_path)
@ -549,13 +549,13 @@ def collect_object_classification_examples(
distinct_images = _select_distinct_images(thumbnails, target_count=24)
logger.debug(f"Selected {len(distinct_images)} distinct images")
# Step 5: Save to dataset directory
unknown_dir = os.path.join(dataset_dir, "unknown")
os.makedirs(unknown_dir, exist_ok=True)
# Step 5: Save to train directory for later classification
train_dir = os.path.join(CLIPS_DIR, model_name, "train")
os.makedirs(train_dir, exist_ok=True)
saved_count = 0
for idx, image_path in enumerate(distinct_images):
dest_path = os.path.join(unknown_dir, f"example_{idx:03d}.jpg")
dest_path = os.path.join(train_dir, f"example_{idx:03d}.jpg")
try:
img = cv2.imread(image_path)
@ -573,7 +573,7 @@ def collect_object_classification_examples(
logger.warning(f"Failed to clean up temp directory: {e}")
logger.debug(
f"Successfully collected {saved_count} classification examples in {unknown_dir}"
f"Successfully collected {saved_count} classification examples in {train_dir}"
)

6
web/public/locales/en/views/classificationModel.json
View File

@ -98,12 +98,16 @@
},
"retryGenerate": "Retry Generation",
"selectClass": "Select class...",
"none": "None",
"noImages": "No sample images generated",
"classifying": "Classifying & Training...",
"trainingStarted": "Training started successfully",
"errors": {
"noCameras": "No cameras configured",
"noObjectLabel": "No object label selected",
"generateFailed": "Failed to generate examples: {{error}}",
"generationFailed": "Generation failed. Please try again."
"generationFailed": "Generation failed. Please try again.",
"classifyFailed": "Failed to classify images: {{error}}"
},
"generateSuccess": "Successfully generated sample images"
}

117
web/src/components/classification/wizard/Step3ChooseExamples.tsx
View File

@ -45,14 +45,14 @@ export default function Step3ChooseExamples({
[imageName: string]: string;
}>(initialData?.imageClassifications || {});
const { data: dataset, mutate: refreshDataset } = useSWR<{
[id: string]: string[];
}>(hasGenerated ? `classification/${step1Data.modelName}/dataset` : null);
const { data: trainImages, mutate: refreshTrainImages } = useSWR<string[]>(
hasGenerated ? `classification/${step1Data.modelName}/train` : null,
);
const unknownImages = useMemo(() => {
if (!dataset || !dataset.unknown) return [];
return dataset.unknown;
}, [dataset]);
if (!trainImages) return [];
return trainImages;
}, [trainImages]);
const handleClassificationChange = useCallback(
(imageName: string, className: string) => {
@ -104,7 +104,7 @@ export default function Step3ChooseExamples({
setHasGenerated(true);
toast.success(t("wizard.step3.generateSuccess"));
await refreshDataset();
await refreshTrainImages();
} catch (error) {
const axiosError = error as {
response?: { data?: { message?: string; detail?: string } };
@ -122,7 +122,7 @@ export default function Step3ChooseExamples({
} finally {
setIsGenerating(false);
}
}, [step1Data, step2Data, t, refreshDataset]);
}, [step1Data, step2Data, t, refreshTrainImages]);
useEffect(() => {
if (!hasGenerated && !isGenerating) {
@ -131,9 +131,94 @@ export default function Step3ChooseExamples({
// eslint-disable-next-line react-hooks/exhaustive-deps
}, []);
const handleContinue = useCallback(() => {
onNext({ examplesGenerated: true, imageClassifications });
}, [onNext, imageClassifications]);
const handleContinue = useCallback(async () => {
try {
// Step 1: Create config for the new model
const modelConfig: {
enabled: boolean;
name: string;
threshold: number;
state_config?: {
cameras: Record<string, { crop: number[] }>;
motion: boolean;
};
object_config?: { objects: string[]; classification_type: string };
} = {
enabled: true,
name: step1Data.modelName,
threshold: 0.8,
};
if (step1Data.modelType === "state") {
// State model config
const cameras: Record<string, { crop: number[] }> = {};
step2Data?.cameraAreas.forEach((area) => {
cameras[area.camera] = {
crop: area.crop,
};
});
modelConfig.state_config = {
cameras,
motion: true,
};
} else {
// Object model config
modelConfig.object_config = {
objects: step1Data.objectLabel ? [step1Data.objectLabel] : [],
classification_type: step1Data.objectType || "sub_label",
} as { objects: string[]; classification_type: string };
}
// Update config via config API
await axios.put("/config/set", {
requires_restart: 0,
update_topic: `config/classification/custom/${step1Data.modelName}`,
config_data: {
classification: {
custom: {
[step1Data.modelName]: modelConfig,
},
},
},
});
// Step 2: Classify each image by moving it to the correct category folder
for (const [imageName, className] of Object.entries(
imageClassifications,
)) {
if (!className) continue;
await axios.post(
`/classification/${step1Data.modelName}/dataset/categorize`,
{
training_file: imageName,
category: className === "none" ? "none" : className,
},
);
}
// Step 3: Kick off training
await axios.post(`/classification/${step1Data.modelName}/train`);
toast.success(t("wizard.step3.trainingStarted"));
onNext({ examplesGenerated: true, imageClassifications });
} catch (error) {
const axiosError = error as {
response?: { data?: { message?: string; detail?: string } };
message?: string;
};
const errorMessage =
axiosError.response?.data?.message ||
axiosError.response?.data?.detail ||
axiosError.message ||
"Failed to classify images";
toast.error(
t("wizard.step3.errors.classifyFailed", { error: errorMessage }),
);
}
}, [onNext, imageClassifications, step1Data, step2Data, t]);
const allImagesClassified = useMemo(() => {
if (!unknownImages || unknownImages.length === 0) return false;
@ -175,7 +260,7 @@ export default function Step3ChooseExamples({
className="group relative aspect-square cursor-pointer overflow-hidden rounded-lg border bg-background transition-all hover:ring-2 hover:ring-primary"
>
<img
src={`${baseUrl}clips/${step1Data.modelName}/dataset/unknown/${imageName}`}
src={`${baseUrl}clips/${step1Data.modelName}/train/${imageName}`}
alt={`Example ${index + 1}`}
className="h-full w-full object-cover"
/>
@ -192,6 +277,14 @@ export default function Step3ChooseExamples({
/>
</SelectTrigger>
<SelectContent>
{step1Data.modelType === "object" && (
<SelectItem
value="none"
className="cursor-pointer text-xs"
>
{t("wizard.step3.none")}
</SelectItem>
)}
{step1Data.classes.map((className) => (
<SelectItem
key={className}