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Settings i18n improvements (#22571)

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* 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
2026-03-22 13:03:24 -05:00
committed by GitHub
parent 74c89beaf9
commit b6c03c99de
12 changed files with 217 additions and 809 deletions
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web/public/locales/en/config/global.json
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@@ -287,118 +287,63 @@
"label": "Detector hardware",
"description": "Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
"type": {
"label": "Detector Type",
"description": "Type of detector to use for object detection (for example 'cpu', 'edgetpu', 'openvino')."
"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."
},
"axengine": {
"label": "AXEngine NPU",
"description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime.",
"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": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime."
},
"cpu": {
"label": "CPU",
"description": "CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended.",
"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_threads": {
"label": "Number of detection threads",
"description": "The number of threads used for CPU-based inference."
@@ -407,57 +352,6 @@
"deepstack": {
"label": "DeepStack",
"description": "DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended.",
"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."
},
"api_url": {
"label": "DeepStack API URL",
"description": "The URL of the DeepStack API."
@@ -474,57 +368,6 @@
"degirum": {
"label": "DeGirum",
"description": "DeGirum detector for running models via DeGirum cloud or local inference services.",
"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."
},
"location": {
"label": "Inference Location",
"description": "Location of the DeGirim inference engine (e.g. '@cloud', '127.0.0.1')."
@@ -541,57 +384,6 @@
"edgetpu": {
"label": "EdgeTPU",
"description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
"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 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."

7
web/public/locales/en/objects.json
View File

@@ -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"
}

1
web/public/locales/en/views/settings.json
View File

@@ -92,6 +92,7 @@
"triggers": "Triggers",
"debug": "Debug",
"frigateplus": "Frigate+",
"maintenance": "Maintenance",
"mediaSync": "Media sync",
"regionGrid": "Region grid"
},

4
web/src/components/config-form/sectionExtras/CameraReviewStatusToggles.tsx
View File

@@ -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>

7
web/src/components/config-form/sections/BaseSection.tsx
View File

@@ -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>

31
web/src/components/config-form/theme/templates/ErrorListTemplate.tsx
View File

@@ -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);

47
web/src/components/config-form/theme/templates/FieldTemplate.tsx
View File

@@ -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;
}

40
web/src/components/config-form/theme/templates/ObjectFieldTemplate.tsx
View File

@@ -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 =

44
web/src/components/config-form/theme/utils/i18n.ts
View File

@@ -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".

1
web/src/components/config-form/theme/utils/index.ts
View File

@@ -4,6 +4,7 @@
export {
buildTranslationPath,
resolveConfigTranslation,
getFilterObjectLabel,
humanizeKey,
getDomainFromNamespace,

7
web/src/views/settings/SingleSectionPage.tsx
View File

@@ -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>