mirror of
https://github.com/blakeblackshear/frigate.git
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Settings i18n improvements (#22571)
* i18n improvements for settings UI - deduplicate shared detector translation keys and centralize config translation resolution - add missing i18n keys * formatting
This commit is contained in:
Josh Hawkins
GitHub
parent
74c89beaf9
commit
b6c03c99de
@ -190,20 +190,24 @@ def generate_section_translation(config_class: type) -> Dict[str, Any]:
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def get_detector_translations(
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config_schema: Dict[str, Any],
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) -> tuple[Dict[str, Any], set[str]]:
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"""Build detector type translations with nested fields based on schema definitions."""
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) -> tuple[Dict[str, Any], Dict[str, Any], set[str]]:
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"""Build detector type translations with nested fields based on schema definitions.
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Returns a tuple of (type_translations, shared_fields, nested_field_keys).
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Shared fields (identical across all detector types) are returned separately
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to avoid duplication in the output.
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"""
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defs = config_schema.get("$defs", {})
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detector_schema = defs.get("DetectorConfig", {})
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discriminator = detector_schema.get("discriminator", {})
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mapping = discriminator.get("mapping", {})
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type_translations: Dict[str, Any] = {}
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nested_field_keys: set[str] = set()
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for detector_type, ref in mapping.items():
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if not isinstance(ref, str):
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continue
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# First pass: collect all nested fields per detector type
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all_nested: Dict[str, Dict[str, Any]] = {}
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type_meta: Dict[str, Dict[str, str]] = {}
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if not ref.startswith("#/$defs/"):
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for detector_type, ref in mapping.items():
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if not isinstance(ref, str) or not ref.startswith("#/$defs/"):
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continue
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ref_name = ref.split("/")[-1]
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@ -211,26 +215,49 @@ def get_detector_translations(
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if not ref_schema:
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continue
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type_entry: Dict[str, str] = {}
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meta: Dict[str, str] = {}
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title = ref_schema.get("title")
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description = ref_schema.get("description")
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if title:
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type_entry["label"] = title
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meta["label"] = title
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if description:
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type_entry["description"] = description
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meta["description"] = description
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type_meta[detector_type] = meta
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nested = extract_translations_from_schema(ref_schema, defs=defs)
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nested_without_root = {
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all_nested[detector_type] = {
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k: v for k, v in nested.items() if k not in ("label", "description")
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}
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if nested_without_root:
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type_entry.update(nested_without_root)
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nested_field_keys.update(nested_without_root.keys())
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# Find fields that are identical across all types that have them
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shared_fields: Dict[str, Any] = {}
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if all_nested:
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# Collect all field keys across all types
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all_keys: set[str] = set()
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for nested in all_nested.values():
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all_keys.update(nested.keys())
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for key in all_keys:
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values = [nested[key] for nested in all_nested.values() if key in nested]
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if len(values) == len(all_nested) and all(v == values[0] for v in values):
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shared_fields[key] = values[0]
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# Build per-type translations with only unique (non-shared) fields
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type_translations: Dict[str, Any] = {}
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nested_field_keys: set[str] = set()
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for detector_type, nested in all_nested.items():
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type_entry: Dict[str, Any] = {}
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type_entry.update(type_meta.get(detector_type, {}))
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unique_fields = {k: v for k, v in nested.items() if k not in shared_fields}
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if unique_fields:
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type_entry.update(unique_fields)
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nested_field_keys.update(unique_fields.keys())
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if type_entry:
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type_translations[detector_type] = type_entry
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return type_translations, nested_field_keys
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return type_translations, shared_fields, nested_field_keys
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def main():
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@ -303,9 +330,12 @@ def main():
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section_data.update(nested_without_root)
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if field_name == "detectors":
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detector_types, detector_field_keys = get_detector_translations(
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config_schema
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detector_types, shared_fields, detector_field_keys = (
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get_detector_translations(config_schema)
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)
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# Add shared fields at the base detectors level
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section_data.update(shared_fields)
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# Add per-type translations (only unique fields per type)
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section_data.update(detector_types)
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for key in detector_field_keys:
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if key == "type":
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@ -287,118 +287,63 @@
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"label": "Detector hardware",
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"description": "Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
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"type": {
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"label": "Detector Type",
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"description": "Type of detector to use for object detection (for example 'cpu', 'edgetpu', 'openvino')."
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
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}
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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},
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"axengine": {
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"label": "AXEngine NPU",
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"description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime.",
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"type": {
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
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}
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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}
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"description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime."
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},
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"cpu": {
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"label": "CPU",
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"description": "CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended.",
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"type": {
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
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}
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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},
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"num_threads": {
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"label": "Number of detection threads",
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"description": "The number of threads used for CPU-based inference."
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@ -407,57 +352,6 @@
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"deepstack": {
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"label": "DeepStack",
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"description": "DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended.",
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"type": {
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
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}
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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},
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"api_url": {
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"label": "DeepStack API URL",
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"description": "The URL of the DeepStack API."
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@ -474,57 +368,6 @@
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"degirum": {
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"label": "DeGirum",
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"description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
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"type": {
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
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}
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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},
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"location": {
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"label": "Inference Location",
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"description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
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@ -541,57 +384,6 @@
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"edgetpu": {
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"label": "EdgeTPU",
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"description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
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"type": {
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"label": "Type"
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom Object detection model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for EdgeTPU inference (e.g. 'usb', 'pci')."
|
||||
@ -600,57 +392,6 @@
|
||||
"hailo8l": {
|
||||
"label": "Hailo-8/Hailo-8L",
|
||||
"description": "Hailo-8/Hailo-8L detector using HEF models and the HailoRT SDK for inference on Hailo hardware.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
|
||||
@ -659,57 +400,6 @@
|
||||
"memryx": {
|
||||
"label": "MemryX",
|
||||
"description": "MemryX MX3 detector that runs compiled DFP models on MemryX accelerators.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "Device Path",
|
||||
"description": "The device to use for MemryX inference (e.g. 'PCIe')."
|
||||
@ -718,57 +408,6 @@
|
||||
"onnx": {
|
||||
"label": "ONNX",
|
||||
"description": "ONNX detector for running ONNX models; will use available acceleration backends (CUDA/ROCm/OpenVINO) when available.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for ONNX inference (e.g. 'AUTO', 'CPU', 'GPU')."
|
||||
@ -777,57 +416,6 @@
|
||||
"openvino": {
|
||||
"label": "OpenVINO",
|
||||
"description": "OpenVINO detector for AMD and Intel CPUs, Intel GPUs and Intel VPU hardware.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU')."
|
||||
@ -836,57 +424,6 @@
|
||||
"rknn": {
|
||||
"label": "RKNN",
|
||||
"description": "RKNN detector for Rockchip NPUs; runs compiled RKNN models on Rockchip hardware.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"num_cores": {
|
||||
"label": "Number of NPU cores to use.",
|
||||
"description": "The number of NPU cores to use (0 for auto)."
|
||||
@ -894,168 +431,15 @@
|
||||
},
|
||||
"synaptics": {
|
||||
"label": "Synaptics",
|
||||
"description": "Synaptics NPU detector for models in .synap format using the Synap SDK on Synaptics hardware.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
}
|
||||
"description": "Synaptics NPU detector for models in .synap format using the Synap SDK on Synaptics hardware."
|
||||
},
|
||||
"teflon_tfl": {
|
||||
"label": "Teflon",
|
||||
"description": "Teflon delegate detector for TFLite using Mesa Teflon delegate library to accelerate inference on supported GPUs.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
}
|
||||
"description": "Teflon delegate detector for TFLite using Mesa Teflon delegate library to accelerate inference on supported GPUs."
|
||||
},
|
||||
"tensorrt": {
|
||||
"label": "TensorRT",
|
||||
"description": "TensorRT detector for Nvidia Jetson devices using serialized TensorRT engines for accelerated inference.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"device": {
|
||||
"label": "GPU Device Index",
|
||||
"description": "The GPU device index to use."
|
||||
@ -1064,57 +448,6 @@
|
||||
"zmq": {
|
||||
"label": "ZMQ IPC",
|
||||
"description": "ZMQ IPC detector that offloads inference to an external process via a ZeroMQ IPC endpoint.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom Object detection model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
},
|
||||
"labelmap_path": {
|
||||
"label": "Label map for custom object detector",
|
||||
"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
|
||||
},
|
||||
"width": {
|
||||
"label": "Object detection model input width",
|
||||
"description": "Width of the model input tensor in pixels."
|
||||
},
|
||||
"height": {
|
||||
"label": "Object detection model input height",
|
||||
"description": "Height of the model input tensor in pixels."
|
||||
},
|
||||
"labelmap": {
|
||||
"label": "Labelmap customization",
|
||||
"description": "Overrides or remapping entries to merge into the standard labelmap."
|
||||
},
|
||||
"attributes_map": {
|
||||
"label": "Map of object labels to their attribute labels",
|
||||
"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
|
||||
},
|
||||
"input_tensor": {
|
||||
"label": "Model Input Tensor Shape",
|
||||
"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
|
||||
},
|
||||
"input_pixel_format": {
|
||||
"label": "Model Input Pixel Color Format",
|
||||
"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
|
||||
},
|
||||
"input_dtype": {
|
||||
"label": "Model Input D Type",
|
||||
"description": "Data type of the model input tensor (for example 'float32')."
|
||||
},
|
||||
"model_type": {
|
||||
"label": "Object Detection Model Type",
|
||||
"description": "Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization."
|
||||
}
|
||||
},
|
||||
"model_path": {
|
||||
"label": "Detector specific model path",
|
||||
"description": "File path to the detector model binary if required by the chosen detector."
|
||||
},
|
||||
"endpoint": {
|
||||
"label": "ZMQ IPC endpoint",
|
||||
"description": "The ZMQ endpoint to connect to."
|
||||
|
||||
@ -116,5 +116,10 @@
|
||||
"nzpost": "NZPost",
|
||||
"postnord": "PostNord",
|
||||
"gls": "GLS",
|
||||
"dpd": "DPD"
|
||||
"dpd": "DPD",
|
||||
"canada_post": "Canada Post",
|
||||
"royal_mail": "Royal Mail",
|
||||
"school_bus": "School Bus",
|
||||
"skunk": "Skunk",
|
||||
"kangaroo": "Kangaroo"
|
||||
}
|
||||
|
||||
@ -92,6 +92,7 @@
|
||||
"triggers": "Triggers",
|
||||
"debug": "Debug",
|
||||
"frigateplus": "Frigate+",
|
||||
"maintenance": "Maintenance",
|
||||
"mediaSync": "Media sync",
|
||||
"regionGrid": "Region grid"
|
||||
},
|
||||
|
||||
@ -75,7 +75,9 @@ export default function CameraReviewStatusToggles({
|
||||
/>
|
||||
<div className="space-y-0.5">
|
||||
<Label htmlFor="detections-enabled">
|
||||
<Trans ns="views/settings">camera.review.detections</Trans>
|
||||
<Trans ns="views/settings">
|
||||
cameraReview.review.detections
|
||||
</Trans>
|
||||
</Label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -1136,7 +1136,7 @@ export function ConfigSection({
|
||||
)}
|
||||
{hasChanges && (
|
||||
<Badge variant="outline" className="text-xs">
|
||||
{t("modified", {
|
||||
{t("button.modified", {
|
||||
ns: "common",
|
||||
defaultValue: "Modified",
|
||||
})}
|
||||
@ -1210,7 +1210,10 @@ export function ConfigSection({
|
||||
variant="secondary"
|
||||
className="cursor-default bg-danger text-xs text-white hover:bg-danger"
|
||||
>
|
||||
{t("modified", { ns: "common", defaultValue: "Modified" })}
|
||||
{t("button.modified", {
|
||||
ns: "common",
|
||||
defaultValue: "Modified",
|
||||
})}
|
||||
</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
||||
@ -7,7 +7,11 @@ import type {
|
||||
import { Alert, AlertDescription, AlertTitle } from "@/components/ui/alert";
|
||||
import { LuCircleAlert } from "react-icons/lu";
|
||||
import { useTranslation } from "react-i18next";
|
||||
import { buildTranslationPath, humanizeKey } from "../utils";
|
||||
import {
|
||||
buildTranslationPath,
|
||||
resolveConfigTranslation,
|
||||
humanizeKey,
|
||||
} from "../utils";
|
||||
import type { ConfigFormContext } from "@/types/configForm";
|
||||
|
||||
type ErrorSchemaNode = RJSFSchema & {
|
||||
@ -114,22 +118,15 @@ const resolveErrorFieldLabel = ({
|
||||
);
|
||||
|
||||
if (effectiveNamespace && translationPath) {
|
||||
const prefixedTranslationKey =
|
||||
sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
? `${sectionI18nPrefix}.${translationPath}.label`
|
||||
: undefined;
|
||||
const translationKey = `${translationPath}.label`;
|
||||
|
||||
if (
|
||||
prefixedTranslationKey &&
|
||||
i18n.exists(prefixedTranslationKey, { ns: effectiveNamespace })
|
||||
) {
|
||||
return t(prefixedTranslationKey, { ns: effectiveNamespace });
|
||||
}
|
||||
|
||||
if (i18n.exists(translationKey, { ns: effectiveNamespace })) {
|
||||
return t(translationKey, { ns: effectiveNamespace });
|
||||
}
|
||||
const translated = resolveConfigTranslation(
|
||||
i18n,
|
||||
t,
|
||||
translationPath,
|
||||
"label",
|
||||
sectionI18nPrefix,
|
||||
effectiveNamespace,
|
||||
);
|
||||
if (translated) return translated;
|
||||
}
|
||||
|
||||
const schemaNode = resolveSchemaNodeForPath(schema, segments);
|
||||
|
||||
@ -20,6 +20,7 @@ import { requiresRestartForFieldPath } from "@/utils/configUtil";
|
||||
import RestartRequiredIndicator from "@/components/indicators/RestartRequiredIndicator";
|
||||
import {
|
||||
buildTranslationPath,
|
||||
resolveConfigTranslation,
|
||||
getFilterObjectLabel,
|
||||
hasOverrideAtPath,
|
||||
humanizeKey,
|
||||
@ -219,20 +220,16 @@ export function FieldTemplate(props: FieldTemplateProps) {
|
||||
// Try to get translated label, falling back to schema title, then RJSF label
|
||||
let finalLabel = label;
|
||||
if (effectiveNamespace && translationPath) {
|
||||
// Prefer camera-scoped translations when a section prefix is provided
|
||||
const prefixedTranslationKey =
|
||||
sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
? `${sectionI18nPrefix}.${translationPath}.label`
|
||||
: undefined;
|
||||
const translationKey = `${translationPath}.label`;
|
||||
|
||||
if (
|
||||
prefixedTranslationKey &&
|
||||
i18n.exists(prefixedTranslationKey, { ns: effectiveNamespace })
|
||||
) {
|
||||
finalLabel = t(prefixedTranslationKey, { ns: effectiveNamespace });
|
||||
} else if (i18n.exists(translationKey, { ns: effectiveNamespace })) {
|
||||
finalLabel = t(translationKey, { ns: effectiveNamespace });
|
||||
const translatedLabel = resolveConfigTranslation(
|
||||
i18n,
|
||||
t,
|
||||
translationPath,
|
||||
"label",
|
||||
sectionI18nPrefix,
|
||||
effectiveNamespace,
|
||||
);
|
||||
if (translatedLabel) {
|
||||
finalLabel = translatedLabel;
|
||||
} else if (schemaTitle) {
|
||||
finalLabel = schemaTitle;
|
||||
} else if (translatedFilterObjectLabel) {
|
||||
@ -330,18 +327,16 @@ export function FieldTemplate(props: FieldTemplateProps) {
|
||||
// Try to get translated description, falling back to schema description
|
||||
let finalDescription = description || "";
|
||||
if (effectiveNamespace && translationPath) {
|
||||
const prefixedDescriptionKey =
|
||||
sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
? `${sectionI18nPrefix}.${translationPath}.description`
|
||||
: undefined;
|
||||
const descriptionKey = `${translationPath}.description`;
|
||||
if (
|
||||
prefixedDescriptionKey &&
|
||||
i18n.exists(prefixedDescriptionKey, { ns: effectiveNamespace })
|
||||
) {
|
||||
finalDescription = t(prefixedDescriptionKey, { ns: effectiveNamespace });
|
||||
} else if (i18n.exists(descriptionKey, { ns: effectiveNamespace })) {
|
||||
finalDescription = t(descriptionKey, { ns: effectiveNamespace });
|
||||
const translatedDescription = resolveConfigTranslation(
|
||||
i18n,
|
||||
t,
|
||||
translationPath,
|
||||
"description",
|
||||
sectionI18nPrefix,
|
||||
effectiveNamespace,
|
||||
);
|
||||
if (translatedDescription) {
|
||||
finalDescription = translatedDescription;
|
||||
} else if (schemaDescription) {
|
||||
finalDescription = schemaDescription;
|
||||
}
|
||||
|
||||
@ -17,6 +17,7 @@ import { requiresRestartForFieldPath } from "@/utils/configUtil";
|
||||
import { ConfigFormContext } from "@/types/configForm";
|
||||
import {
|
||||
buildTranslationPath,
|
||||
resolveConfigTranslation,
|
||||
getDomainFromNamespace,
|
||||
getFilterObjectLabel,
|
||||
humanizeKey,
|
||||
@ -263,16 +264,14 @@ export function ObjectFieldTemplate(props: ObjectFieldTemplateProps) {
|
||||
|
||||
let inferredLabel: string | undefined;
|
||||
if (i18nNs && translationPath) {
|
||||
const prefixedLabelKey =
|
||||
sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
? `${sectionI18nPrefix}.${translationPath}.label`
|
||||
: undefined;
|
||||
const labelKey = `${translationPath}.label`;
|
||||
if (prefixedLabelKey && i18n.exists(prefixedLabelKey, { ns: i18nNs })) {
|
||||
inferredLabel = t(prefixedLabelKey, { ns: i18nNs });
|
||||
} else if (i18n.exists(labelKey, { ns: i18nNs })) {
|
||||
inferredLabel = t(labelKey, { ns: i18nNs });
|
||||
}
|
||||
inferredLabel = resolveConfigTranslation(
|
||||
i18n,
|
||||
t,
|
||||
translationPath,
|
||||
"label",
|
||||
sectionI18nPrefix,
|
||||
i18nNs,
|
||||
);
|
||||
}
|
||||
if (!inferredLabel && translatedFilterLabel) {
|
||||
inferredLabel = translatedFilterLabel;
|
||||
@ -286,19 +285,14 @@ export function ObjectFieldTemplate(props: ObjectFieldTemplateProps) {
|
||||
|
||||
let inferredDescription: string | undefined;
|
||||
if (i18nNs && translationPath) {
|
||||
const prefixedDescriptionKey =
|
||||
sectionI18nPrefix && !translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
? `${sectionI18nPrefix}.${translationPath}.description`
|
||||
: undefined;
|
||||
const descriptionKey = `${translationPath}.description`;
|
||||
if (
|
||||
prefixedDescriptionKey &&
|
||||
i18n.exists(prefixedDescriptionKey, { ns: i18nNs })
|
||||
) {
|
||||
inferredDescription = t(prefixedDescriptionKey, { ns: i18nNs });
|
||||
} else if (i18n.exists(descriptionKey, { ns: i18nNs })) {
|
||||
inferredDescription = t(descriptionKey, { ns: i18nNs });
|
||||
}
|
||||
inferredDescription = resolveConfigTranslation(
|
||||
i18n,
|
||||
t,
|
||||
translationPath,
|
||||
"description",
|
||||
sectionI18nPrefix,
|
||||
i18nNs,
|
||||
);
|
||||
}
|
||||
const schemaDescription = schema?.description;
|
||||
const fallbackDescription =
|
||||
|
||||
@ -124,6 +124,50 @@ export function buildTranslationPath(
|
||||
return stringSegments.join(".");
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolve a translated label or description for a config form field.
|
||||
*
|
||||
* Tries keys in priority order:
|
||||
* 1. Type-specific prefixed key (e.g. "detectors.edgetpu.device.label")
|
||||
* 2. Shared prefixed key with type stripped (e.g. "detectors.device.label")
|
||||
* 3. Unprefixed key (e.g. "device.label")
|
||||
*
|
||||
* @returns The translated string, or undefined if no key matched.
|
||||
*/
|
||||
export function resolveConfigTranslation(
|
||||
i18n: { exists: (key: string, opts?: Record<string, unknown>) => boolean },
|
||||
t: (key: string, opts?: Record<string, unknown>) => string,
|
||||
translationPath: string,
|
||||
suffix: "label" | "description",
|
||||
sectionI18nPrefix?: string,
|
||||
ns?: string,
|
||||
): string | undefined {
|
||||
const opts = ns ? { ns } : undefined;
|
||||
|
||||
if (
|
||||
sectionI18nPrefix &&
|
||||
!translationPath.startsWith(`${sectionI18nPrefix}.`)
|
||||
) {
|
||||
// 1. Type-specific prefixed key (e.g. detectors.edgetpu.device.label)
|
||||
const prefixed = `${sectionI18nPrefix}.${translationPath}.${suffix}`;
|
||||
if (i18n.exists(prefixed, opts)) return t(prefixed, opts);
|
||||
|
||||
// 2. Shared prefixed key — strip leading type segment
|
||||
// e.g. detectors.edgetpu.model.path → detectors.model.path
|
||||
const dot = translationPath.indexOf(".");
|
||||
if (dot !== -1) {
|
||||
const shared = `${sectionI18nPrefix}.${translationPath.substring(dot + 1)}.${suffix}`;
|
||||
if (i18n.exists(shared, opts)) return t(shared, opts);
|
||||
}
|
||||
}
|
||||
|
||||
// 3. Unprefixed key
|
||||
const base = `${translationPath}.${suffix}`;
|
||||
if (i18n.exists(base, opts)) return t(base, opts);
|
||||
|
||||
return undefined;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract the filter object label from a path containing "filters" segment.
|
||||
* Returns the segment immediately after "filters".
|
||||
|
||||
@ -4,6 +4,7 @@
|
||||
|
||||
export {
|
||||
buildTranslationPath,
|
||||
resolveConfigTranslation,
|
||||
getFilterObjectLabel,
|
||||
humanizeKey,
|
||||
getDomainFromNamespace,
|
||||
|
||||
@ -207,7 +207,10 @@ export function SingleSectionPage({
|
||||
variant="secondary"
|
||||
className="cursor-default bg-danger text-xs text-white hover:bg-danger"
|
||||
>
|
||||
{t("modified", { ns: "common", defaultValue: "Modified" })}
|
||||
{t("button.modified", {
|
||||
ns: "common",
|
||||
defaultValue: "Modified",
|
||||
})}
|
||||
</Badge>
|
||||
)}
|
||||
</div>
|
||||
@ -242,7 +245,7 @@ export function SingleSectionPage({
|
||||
variant="secondary"
|
||||
className="cursor-default bg-danger text-xs text-white hover:bg-danger"
|
||||
>
|
||||
{t("modified", { ns: "common", defaultValue: "Modified" })}
|
||||
{t("button.modified", { ns: "common", defaultValue: "Modified" })}
|
||||
</Badge>
|
||||
)}
|
||||
</div>
|
||||
|
||||
Loading…
Reference in New Issue
Block a user