mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-10-15 11:12:49 +03:00
Refactor detector and model management
This commit is contained in:
Nicolas Mowen
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -102,11 +102,12 @@ def generate_config():
|
||||
snapshot = config.model_dump()
|
||||
|
||||
# Runtime-computed fields not in the Pydantic dump
|
||||
all_attrs = set()
|
||||
for attrs in snapshot.get("model", {}).get("attributes_map", {}).values():
|
||||
all_attrs.update(attrs)
|
||||
snapshot["model"]["all_attributes"] = sorted(all_attrs)
|
||||
snapshot["model"]["colormap"] = {}
|
||||
for model in snapshot.get("models", []):
|
||||
all_attrs = set()
|
||||
for attrs in model.get("attributes_map", {}).values():
|
||||
all_attrs.update(attrs)
|
||||
model["all_attributes"] = sorted(all_attrs)
|
||||
model["colormap"] = {}
|
||||
|
||||
return snapshot
|
||||
|
||||
|
||||
@@ -110,6 +110,10 @@
|
||||
"label": "Detect width",
|
||||
"description": "Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
|
||||
},
|
||||
"scene": {
|
||||
"label": "Detect scene",
|
||||
"description": "The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'."
|
||||
},
|
||||
"fps": {
|
||||
"label": "Detect FPS",
|
||||
"description": "Desired frames per second to run detection on; lower values reduce CPU usage (recommended value is 5, only set higher - at most 10 - if tracking extremely fast moving objects)."
|
||||
|
||||
@@ -275,172 +275,17 @@
|
||||
"description": "Unit system for display (metric or imperial) used in the UI and MQTT."
|
||||
}
|
||||
},
|
||||
"detectors": {
|
||||
"label": "Detector hardware",
|
||||
"description": "Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
|
||||
"type": {
|
||||
"label": "Type"
|
||||
"models": {
|
||||
"label": "Detection models",
|
||||
"description": "Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
|
||||
"scene": {
|
||||
"label": "Model scene",
|
||||
"description": "The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one."
|
||||
},
|
||||
"model": {
|
||||
"label": "Detector specific model configuration",
|
||||
"description": "Detector-specific model configuration options (path, input size, etc.).",
|
||||
"path": {
|
||||
"label": "Custom object detector 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, yolo-generic, rfdetr, dfine) used by some detectors for optimization."
|
||||
}
|
||||
"devices": {
|
||||
"label": "Detection hardware",
|
||||
"description": "Hardware this model runs on, as '<detector>' or '<detector>:<device>' (for example 'edgetpu:pci:0' or 'openvino:GPU'). Listing the same device more than once runs additional inference processes on it."
|
||||
},
|
||||
"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."
|
||||
},
|
||||
"cpu": {
|
||||
"label": "CPU",
|
||||
"description": "CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended.",
|
||||
"num_threads": {
|
||||
"label": "Number of detection threads",
|
||||
"description": "The number of threads used for CPU-based inference."
|
||||
}
|
||||
},
|
||||
"deepstack": {
|
||||
"label": "DeepStack",
|
||||
"description": "DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended.",
|
||||
"api_url": {
|
||||
"label": "DeepStack API URL",
|
||||
"description": "The URL of the DeepStack API."
|
||||
},
|
||||
"api_timeout": {
|
||||
"label": "DeepStack API timeout (in seconds)",
|
||||
"description": "Maximum time allowed for a DeepStack API request."
|
||||
},
|
||||
"api_key": {
|
||||
"label": "DeepStack API key (if required)",
|
||||
"description": "Optional API key for authenticated DeepStack services."
|
||||
}
|
||||
},
|
||||
"edgetpu": {
|
||||
"label": "EdgeTPU",
|
||||
"description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for EdgeTPU inference (e.g. 'usb', 'pci')."
|
||||
}
|
||||
},
|
||||
"hailo8l": {
|
||||
"label": "Hailo-8/Hailo-8L",
|
||||
"description": "Hailo-8/Hailo-8L detector using HEF models and the HailoRT SDK for inference on Hailo hardware.",
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
|
||||
}
|
||||
},
|
||||
"memryx": {
|
||||
"label": "MemryX",
|
||||
"description": "MemryX MX3 detector that runs compiled DFP models on MemryX accelerators.",
|
||||
"device": {
|
||||
"label": "Device Path",
|
||||
"description": "The device to use for MemryX inference (e.g. 'PCIe')."
|
||||
}
|
||||
},
|
||||
"onnx": {
|
||||
"label": "ONNX",
|
||||
"description": "ONNX detector for running ONNX models; will use available acceleration backends (CUDA/ROCm/OpenVINO) when available.",
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for ONNX inference (e.g. 'AUTO', 'CPU', 'GPU')."
|
||||
}
|
||||
},
|
||||
"openvino": {
|
||||
"label": "OpenVINO",
|
||||
"description": "OpenVINO detector for AMD and Intel CPUs, Intel GPUs and Intel VPU hardware.",
|
||||
"device": {
|
||||
"label": "Device Type",
|
||||
"description": "The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU')."
|
||||
}
|
||||
},
|
||||
"rknn": {
|
||||
"label": "RKNN",
|
||||
"description": "RKNN detector for Rockchip NPUs; runs compiled RKNN models on Rockchip hardware.",
|
||||
"num_cores": {
|
||||
"label": "Number of NPU cores to use.",
|
||||
"description": "The number of NPU cores to use (0 for auto)."
|
||||
}
|
||||
},
|
||||
"synaptics": {
|
||||
"label": "Synaptics",
|
||||
"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."
|
||||
},
|
||||
"tensorrt": {
|
||||
"label": "TensorRT",
|
||||
"description": "TensorRT detector for Nvidia Jetson devices using serialized TensorRT engines for accelerated inference.",
|
||||
"device": {
|
||||
"label": "GPU Device Index",
|
||||
"description": "The GPU device index to use."
|
||||
}
|
||||
},
|
||||
"zmq": {
|
||||
"label": "ZMQ IPC",
|
||||
"description": "ZMQ IPC detector that offloads inference to an external process via a ZeroMQ IPC endpoint.",
|
||||
"endpoint": {
|
||||
"label": "ZMQ IPC endpoint",
|
||||
"description": "The ZMQ endpoint to connect to."
|
||||
},
|
||||
"request_timeout_ms": {
|
||||
"label": "ZMQ request timeout in milliseconds",
|
||||
"description": "Timeout for ZMQ requests in milliseconds."
|
||||
},
|
||||
"linger_ms": {
|
||||
"label": "ZMQ socket linger in milliseconds",
|
||||
"description": "Socket linger period in milliseconds."
|
||||
}
|
||||
}
|
||||
},
|
||||
"model": {
|
||||
"label": "Detection model",
|
||||
"description": "Settings to configure a custom object detection model and its input shape.",
|
||||
"path": {
|
||||
"label": "Custom object detector model path",
|
||||
"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
|
||||
@@ -633,6 +478,10 @@
|
||||
"label": "Detect width",
|
||||
"description": "Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
|
||||
},
|
||||
"scene": {
|
||||
"label": "Detect scene",
|
||||
"description": "The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'."
|
||||
},
|
||||
"fps": {
|
||||
"label": "Detect FPS",
|
||||
"description": "Desired frames per second to run detection on; lower values reduce CPU usage (recommended value is 5, only set higher - at most 10 - if tracking extremely fast moving objects)."
|
||||
|
||||
Reference in New Issue
Block a user