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8
Commits
tweaks
...
1831c5818a
| Author | SHA1 | Date | |
|---|---|---|---|
 Nicolas Mowen
|
1831c5818a | ||
|
Ersa Oktavian RamadanandJosh Hawkins
|
74c932c3f5 | ||
|
Josh Hawkins
|
a0042b8d7f | ||
|
Nicolas MowenandJosh Hawkins
|
659d26658b | ||
|
|
6c47bbfbbc | ||
|
DoFabienandJosh Hawkins
|
c4d53484b7 | ||
|
Nicolas MowenandJosh Hawkins
|
928dac506a | ||
|
Nicolas MowenandJosh Hawkins
|
c1bde8ca20 |
@@ -1,7 +1,7 @@
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||||
default_target: local
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COMMIT_HASH := $(shell git log -1 --pretty=format:"%h"|tail -1)
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VERSION = 0.18.0
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VERSION = 0.19.0
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IMAGE_REPO ?= ghcr.io/blakeblackshear/frigate
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GITHUB_REF_NAME ?= $(shell git rev-parse --abbrev-ref HEAD)
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BOARDS= #Initialized empty
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@@ -7,10 +7,9 @@ edgeTPU:
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download: A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
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ui: Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
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yaml: |-
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detectors:
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coral:
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type: edgetpu
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device: usb
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models:
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- devices:
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- edgetpu:usb
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- key: yolov9
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label: YOLOv9
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recommended: false
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@@ -29,17 +28,14 @@ edgeTPU:
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| **Model Input D Type** | `int` (Frigate's default value) |
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| **Object Detection Model Type** | `yolo-generic` |
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yaml: |-
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detectors:
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coral:
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type: edgetpu
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device: usb
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model:
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize of the model, typically 320
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height: 320 # <--- should match the imgsize of the model, typically 320
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path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
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labelmap_path: /config/labels-coco17.txt
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models:
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- devices:
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- edgetpu:usb
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model_type: yolo-generic
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width: 320 # <--- should match the imgsize of the model, typically 320
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height: 320 # <--- should match the imgsize of the model, typically 320
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path: /config/model_cache/yolov9-s-relu6-best_320_int8_edgetpu.tflite
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labelmap_path: /config/labels-coco17.txt
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hailo8l:
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title: Hailo-8/Hailo-8L
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models:
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@@ -62,32 +58,29 @@ hailo8l:
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The detector automatically selects the default model based on your hardware. Optionally, specify a local model path or URL to override.
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yaml: |-
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detectors:
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hailo:
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type: hailo8l
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device: PCIe
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models:
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- devices:
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- hailo8l:PCIe
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width: 320
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height: 320
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input_tensor: nhwc
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input_pixel_format: rgb
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input_dtype: int
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model_type: yolo-generic
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labelmap_path: /labelmap/coco-80.txt
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model:
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width: 320
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height: 320
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input_tensor: nhwc
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input_pixel_format: rgb
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input_dtype: int
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model_type: yolo-generic
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labelmap_path: /labelmap/coco-80.txt
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# The detector automatically selects the default model based on your hardware:
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# - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
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# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
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#
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# Optionally, you can specify a local model path to override the default.
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# If a local path is provided and the file exists, it will be used instead of downloading.
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# Example:
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# path: /config/model_cache/hailo/yolov6n.hef
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#
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# You can also override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
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# just make sure to give it the write configuration based on the model
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# The detector automatically selects the default model based on your hardware:
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# - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
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# - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
|
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#
|
||||
# Optionally, you can specify a local model path to override the default.
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||||
# If a local path is provided and the file exists, it will be used instead of downloading.
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||||
# Example:
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# path: /config/model_cache/hailo/yolov6n.hef
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#
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# You can also override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
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# just make sure to give it the write configuration based on the model
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- key: ssd
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label: SSD MobileNet v1
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recommended: false
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@@ -106,23 +99,20 @@ hailo8l:
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Specify the local model path or URL for SSD MobileNet v1.
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yaml: |-
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detectors:
|
||||
hailo:
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type: hailo8l
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device: PCIe
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||||
|
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model:
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width: 300
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height: 300
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input_tensor: nhwc
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input_pixel_format: rgb
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model_type: ssd
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# Specify the local model path (if available) or URL for SSD MobileNet v1.
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# Example with a local path:
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# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
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#
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# Or override using a custom URL:
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# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
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||||
models:
|
||||
- devices:
|
||||
- hailo8l:PCIe
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width: 300
|
||||
height: 300
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||||
input_tensor: nhwc
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input_pixel_format: rgb
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||||
model_type: ssd
|
||||
# Specify the local model path (if available) or URL for SSD MobileNet v1.
|
||||
# Example with a local path:
|
||||
# path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
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||||
#
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||||
# Or override using a custom URL:
|
||||
# path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef
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openvino:
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title: OpenVINO
|
||||
models:
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||||
@@ -166,19 +156,16 @@ openvino:
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||||
| **Model Input D Type** | `float` |
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||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: ssd
|
||||
label: SSDLite MobileNet v2
|
||||
recommended: false
|
||||
@@ -197,18 +184,15 @@ openvino:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU # Or NPU
|
||||
|
||||
model:
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
input_pixel_format: bgr
|
||||
path: /openvino-model/ssdlite_mobilenet_v2.xml
|
||||
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
input_pixel_format: bgr
|
||||
path: /openvino-model/ssdlite_mobilenet_v2.xml
|
||||
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
|
||||
- key: yolo-legacy
|
||||
label: YOLO (v3, v4, v7)
|
||||
recommended: false
|
||||
@@ -235,19 +219,16 @@ openvino:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolonas
|
||||
label: YOLO-NAS
|
||||
recommended: false
|
||||
@@ -275,19 +256,16 @@ openvino:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU
|
||||
|
||||
model:
|
||||
model_type: yolonas
|
||||
width: 320 # <--- should match whatever was set in notebook
|
||||
height: 320 # <--- should match whatever was set in notebook
|
||||
input_tensor: nchw
|
||||
input_pixel_format: bgr
|
||||
path: /config/yolo_nas_s.onnx
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
model_type: yolonas
|
||||
width: 320 # <--- should match whatever was set in notebook
|
||||
height: 320 # <--- should match whatever was set in notebook
|
||||
input_tensor: nchw
|
||||
input_pixel_format: bgr
|
||||
path: /config/yolo_nas_s.onnx
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolox
|
||||
label: YOLOX
|
||||
recommended: false
|
||||
@@ -303,15 +281,12 @@ openvino:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU
|
||||
|
||||
model:
|
||||
model_type: yolox
|
||||
path: /config/model_cache/yolox.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
model_type: yolox
|
||||
path: /config/model_cache/yolox.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: rfdetr
|
||||
label: RF-DETR
|
||||
recommended: false
|
||||
@@ -345,18 +320,15 @@ openvino:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `rfdetr` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: GPU
|
||||
|
||||
model:
|
||||
model_type: rfdetr
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
model_type: rfdetr
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
|
||||
- key: dfine
|
||||
label: D-FINE / DEIMv2
|
||||
recommended: false
|
||||
@@ -443,19 +415,16 @@ openvino:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `dfine` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: CPU
|
||||
|
||||
model:
|
||||
model_type: dfine
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/dfine-s.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- openvino:CPU
|
||||
model_type: dfine
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/dfine-s.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
appleSilicon:
|
||||
title: Apple Silicon
|
||||
models:
|
||||
@@ -499,19 +468,16 @@ appleSilicon:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
apple-silicon:
|
||||
type: zmq
|
||||
endpoint: tcp://host.docker.internal:5555
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- zmq:tcp://host.docker.internal:5555
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolo-legacy
|
||||
label: YOLO (v3, v4, v7)
|
||||
recommended: false
|
||||
@@ -538,19 +504,16 @@ appleSilicon:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
apple-silicon:
|
||||
type: zmq
|
||||
endpoint: tcp://host.docker.internal:5555
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- zmq:tcp://host.docker.internal:5555
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
onnx:
|
||||
title: ONNX
|
||||
models:
|
||||
@@ -594,18 +557,16 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: rfdetr
|
||||
label: RF-DETR
|
||||
recommended: false
|
||||
@@ -639,17 +600,15 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `rfdetr` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: rfdetr
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: rfdetr
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/rfdetr.onnx # use the filename you generated above
|
||||
- key: yolonas
|
||||
label: YOLO-NAS
|
||||
recommended: false
|
||||
@@ -677,18 +636,16 @@ onnx:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: yolonas
|
||||
width: 320 # <--- should match whatever was set in notebook
|
||||
height: 320 # <--- should match whatever was set in notebook
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nchw
|
||||
path: /config/yolo_nas_s.onnx
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: yolonas
|
||||
width: 320 # <--- should match whatever was set in notebook
|
||||
height: 320 # <--- should match whatever was set in notebook
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nchw
|
||||
path: /config/yolo_nas_s.onnx
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolox
|
||||
label: YOLOX
|
||||
recommended: false
|
||||
@@ -707,18 +664,16 @@ onnx:
|
||||
| **Model Input D Type** | `float_denorm` |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: yolox
|
||||
width: 416 # <--- should match the imgsize set during model export
|
||||
height: 416 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float_denorm
|
||||
path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: yolox
|
||||
width: 416 # <--- should match the imgsize set during model export
|
||||
height: 416 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float_denorm
|
||||
path: /config/model_cache/yolox_tiny.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: dfine
|
||||
label: D-FINE / DEIMv2
|
||||
recommended: false
|
||||
@@ -805,18 +760,16 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `dfine` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: dfine
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: dfine
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/dfine_m_obj2coco.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolo-legacy
|
||||
label: YOLO (v3, v4, v7)
|
||||
recommended: false
|
||||
@@ -843,18 +796,16 @@ onnx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
onnx:
|
||||
type: onnx
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
model_type: yolo-generic
|
||||
width: 320 # <--- should match the imgsize set during model export
|
||||
height: 320 # <--- should match the imgsize set during model export
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
path: /config/model_cache/yolo.onnx # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
cpu:
|
||||
title: CPU
|
||||
models:
|
||||
@@ -870,10 +821,9 @@ cpu:
|
||||
| **Detector type** | `cpu` |
|
||||
| **Num threads** | `3` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
cpu1:
|
||||
type: cpu
|
||||
num_threads: 3
|
||||
models:
|
||||
- devices:
|
||||
- cpu:3
|
||||
deepstack:
|
||||
title: DeepStack / CodeProject.AI
|
||||
models:
|
||||
@@ -889,11 +839,9 @@ deepstack:
|
||||
| **API URL** | `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection` |
|
||||
| **API Timeout** | `0.1` (seconds) |
|
||||
yaml: |-
|
||||
detectors:
|
||||
deepstack:
|
||||
api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
|
||||
type: deepstack
|
||||
api_timeout: 0.1 # seconds
|
||||
models:
|
||||
- devices:
|
||||
- deepstack:http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
|
||||
memryx:
|
||||
title: MemryX
|
||||
models:
|
||||
@@ -923,23 +871,20 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
type: memryx
|
||||
device: PCIe:0
|
||||
|
||||
model:
|
||||
model_type: yolonas
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolonas.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
models:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
model_type: yolonas
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolonas.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolonas.dfp (a file ending with .dfp)
|
||||
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
- key: yolov9
|
||||
label: YOLOv9
|
||||
recommended: false
|
||||
@@ -960,22 +905,19 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
type: memryx
|
||||
device: PCIe:0
|
||||
|
||||
model:
|
||||
model_type: yolo-generic
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolov9.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolov9.dfp (a file ending with .dfp)
|
||||
models:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
model_type: yolo-generic
|
||||
width: 320 # (Can be set to 640 for higher resolution)
|
||||
height: 320 # (Can be set to 640 for higher resolution)
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolov9.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolov9.dfp (a file ending with .dfp)
|
||||
- key: yolox
|
||||
label: YOLOX
|
||||
recommended: false
|
||||
@@ -996,22 +938,19 @@ memryx:
|
||||
| **Model Input D Type** | `float_denorm` |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
type: memryx
|
||||
device: PCIe:0
|
||||
|
||||
model:
|
||||
model_type: yolox
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float_denorm
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolox.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolox.dfp (a file ending with .dfp)
|
||||
models:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
model_type: yolox
|
||||
width: 640
|
||||
height: 640
|
||||
input_tensor: nchw
|
||||
input_dtype: float_denorm
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/yolox.zip
|
||||
# The .zip file must contain:
|
||||
# ├── yolox.dfp (a file ending with .dfp)
|
||||
- key: ssd
|
||||
label: SSDLite MobileNet v2
|
||||
recommended: false
|
||||
@@ -1032,23 +971,20 @@ memryx:
|
||||
| **Model Input D Type** | `float` |
|
||||
| **Object Detection Model Type** | `ssd` |
|
||||
yaml: |-
|
||||
detectors:
|
||||
memx0:
|
||||
type: memryx
|
||||
device: PCIe:0
|
||||
|
||||
model:
|
||||
model_type: ssd
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/ssdlite_mobilenet.zip
|
||||
# The .zip file must contain:
|
||||
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
|
||||
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
models:
|
||||
- devices:
|
||||
- memryx:PCIe:0
|
||||
model_type: ssd
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nchw
|
||||
input_dtype: float
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
|
||||
# path: /config/ssdlite_mobilenet.zip
|
||||
# The .zip file must contain:
|
||||
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
|
||||
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
|
||||
tensorrt:
|
||||
title: TensorRT
|
||||
models:
|
||||
@@ -1082,18 +1018,15 @@ tensorrt:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
|
||||
yaml: |-
|
||||
detectors:
|
||||
tensorrt:
|
||||
type: tensorrt
|
||||
device: 0 #This is the default, select the first GPU
|
||||
|
||||
model:
|
||||
path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
input_tensor: nchw
|
||||
input_pixel_format: rgb
|
||||
width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
|
||||
height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
|
||||
models:
|
||||
- devices:
|
||||
- tensorrt:0
|
||||
path: /config/model_cache/tensorrt/yolov7-320.trt # use the filename you generated above
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
input_tensor: nchw
|
||||
input_pixel_format: rgb
|
||||
width: 320 # MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
|
||||
height: 320 # MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
|
||||
synaptics:
|
||||
title: Synaptics
|
||||
models:
|
||||
@@ -1115,16 +1048,15 @@ synaptics:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `ssd` (Frigate's default value) |
|
||||
yaml: |-
|
||||
detectors: # required
|
||||
synap_npu: # required
|
||||
type: synaptics # required
|
||||
|
||||
model: # required
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
input_tensor: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
models:
|
||||
- # required
|
||||
devices:
|
||||
- synaptics
|
||||
path: /synaptics/mobilenet.synap # required
|
||||
width: 224 # required
|
||||
height: 224 # required
|
||||
input_tensor: nhwc # default value (optional. If you change the model, it is required)
|
||||
labelmap_path: /labelmap/coco-80.txt # required
|
||||
rknn:
|
||||
title: RKNN
|
||||
models:
|
||||
@@ -1149,21 +1081,23 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - frigate-fp16-yolov9-t
|
||||
# - frigate-fp16-yolov9-s
|
||||
# - frigate-fp16-yolov9-m
|
||||
# - frigate-fp16-yolov9-c
|
||||
# - frigate-fp16-yolov9-e
|
||||
# your yolo_model.rknn
|
||||
path: frigate-fp16-yolov9-t
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - frigate-fp16-yolov9-t
|
||||
# - frigate-fp16-yolov9-s
|
||||
# - frigate-fp16-yolov9-m
|
||||
# - frigate-fp16-yolov9-c
|
||||
# - frigate-fp16-yolov9-e
|
||||
# your yolo_model.rknn
|
||||
path: frigate-fp16-yolov9-t
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolonas
|
||||
label: YOLO-NAS
|
||||
recommended: false
|
||||
@@ -1187,20 +1121,22 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolonas` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - deci-fp16-yolonas_s
|
||||
# - deci-fp16-yolonas_m
|
||||
# - deci-fp16-yolonas_l
|
||||
# your yolonas_model.rknn
|
||||
path: deci-fp16-yolonas_s
|
||||
model_type: yolonas
|
||||
width: 320
|
||||
height: 320
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - deci-fp16-yolonas_s
|
||||
# - deci-fp16-yolonas_m
|
||||
# - deci-fp16-yolonas_l
|
||||
# your yolonas_model.rknn
|
||||
path: deci-fp16-yolonas_s
|
||||
model_type: yolonas
|
||||
width: 320
|
||||
height: 320
|
||||
input_pixel_format: bgr
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
- key: yolox
|
||||
label: YOLOx
|
||||
recommended: false
|
||||
@@ -1222,20 +1158,22 @@ rknn:
|
||||
| **Model Input D Type** | `int` (Frigate's default value) |
|
||||
| **Object Detection Model Type** | `yolox` |
|
||||
yaml: |-
|
||||
model: # required
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - rock-i8-yolox_nano
|
||||
# - rock-i8-yolox_tiny
|
||||
# - rock-fp16-yolox_nano
|
||||
# - rock-fp16-yolox_tiny
|
||||
# your yolox_model.rknn
|
||||
path: rock-i8-yolox_nano
|
||||
model_type: yolox
|
||||
width: 416
|
||||
height: 416
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
models:
|
||||
- devices:
|
||||
- rknn
|
||||
# name of model (will be automatically downloaded) or path to your own .rknn model file
|
||||
# possible values are:
|
||||
# - rock-i8-yolox_nano
|
||||
# - rock-i8-yolox_tiny
|
||||
# - rock-fp16-yolox_nano
|
||||
# - rock-fp16-yolox_tiny
|
||||
# your yolox_model.rknn
|
||||
path: rock-i8-yolox_nano
|
||||
model_type: yolox
|
||||
width: 416
|
||||
height: 416
|
||||
input_tensor: nhwc
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
axengine:
|
||||
title: AXEngine
|
||||
models:
|
||||
@@ -1257,6 +1195,7 @@ axengine:
|
||||
| **Model Input D Type** | `int` |
|
||||
| **Object Detection Model Type** | `yolo-generic` |
|
||||
yaml: |-
|
||||
<<<<<<< HEAD
|
||||
detectors:
|
||||
axengine:
|
||||
type: axengine
|
||||
@@ -1269,3 +1208,96 @@ axengine:
|
||||
input_dtype: int
|
||||
input_pixel_format: bgr
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
=======
|
||||
models:
|
||||
- devices:
|
||||
- axengine
|
||||
path: frigate-yolov9-tiny
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
input_dtype: int
|
||||
input_pixel_format: bgr
|
||||
labelmap_path: /labelmap/coco-80.txt
|
||||
degirumAiServer:
|
||||
title: DeGirum AI Server
|
||||
models:
|
||||
- key: ai-server-inference
|
||||
label: AI Server Inference
|
||||
recommended: true
|
||||
download: |-
|
||||
Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
Set `location` to the server's service name, container name, or `host:port`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `degirum` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:degirum
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
degirumLocal:
|
||||
title: DeGirum Local
|
||||
models:
|
||||
- key: local-inference
|
||||
label: Local Inference
|
||||
recommended: true
|
||||
download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@local` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:@local
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
degirumCloud:
|
||||
title: DeGirum AI Hub Cloud
|
||||
models:
|
||||
- key: ai-hub-cloud-inference
|
||||
label: AI Hub Cloud Inference
|
||||
recommended: true
|
||||
download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
|
||||
ui: |
|
||||
Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
|
||||
|
||||
| Field | Value |
|
||||
| --- | --- |
|
||||
| **Location** | `@cloud` |
|
||||
| **Zoo** | `degirum/public` |
|
||||
| **Token** | your AI Hub token (optional for the public zoo) |
|
||||
yaml: |
|
||||
models:
|
||||
- devices:
|
||||
- degirum:@cloud
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300
|
||||
height: 300
|
||||
input_pixel_format: rgb
|
||||
>>>>>>> 34363affa (Refactor detector and model management)
|
||||
|
||||
@@ -56,17 +56,6 @@ mqtt:
|
||||
# 2 = exactly once
|
||||
qos: 0
|
||||
|
||||
# Optional: Detectors configuration. Defaults to a single CPU detector
|
||||
detectors:
|
||||
# Required: name of the detector
|
||||
detector_name:
|
||||
# Required: type of the detector
|
||||
# Frigate provides many types, see https://docs.frigate.video/configuration/object_detectors for more details (default: shown below)
|
||||
# Additional detector types can also be plugged in.
|
||||
# Detectors may require additional configuration.
|
||||
# Refer to the Detectors configuration page for more information.
|
||||
type: cpu
|
||||
|
||||
# Optional: Database configuration
|
||||
database:
|
||||
# The path to store the SQLite DB (default: shown below)
|
||||
@@ -157,44 +146,56 @@ auth:
|
||||
- front_door
|
||||
- back_yard
|
||||
|
||||
# Optional: model modifications
|
||||
# Optional: object detection models. Defaults to a single model on a CPU detector.
|
||||
# NOTE: The default values are for the EdgeTPU detector.
|
||||
# Other detectors will require the model config to be set.
|
||||
model:
|
||||
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
|
||||
path: /edgetpu_model.tflite
|
||||
# Required: path to the labelmap (default: shown below)
|
||||
labelmap_path: /labelmap.txt
|
||||
# Required: Object detection model input width (default: shown below)
|
||||
width: 320
|
||||
# Required: Object detection model input height (default: shown below)
|
||||
height: 320
|
||||
# Required: Object detection model input colorspace
|
||||
# Valid values are rgb, bgr, or yuv. (default: shown below)
|
||||
input_pixel_format: rgb
|
||||
# Required: Object detection model input tensor format
|
||||
# Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
|
||||
input_tensor: nhwc
|
||||
# Optional: Data type of the model input tensor
|
||||
# Valid values are float, float_denorm, or int (default: shown below)
|
||||
input_dtype: int
|
||||
# Required: Object detection model architecture, used by detectors that support more
|
||||
# than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
|
||||
# Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
|
||||
model_type: ssd
|
||||
# Required: Label name modifications. These are merged into the standard labelmap.
|
||||
labelmap:
|
||||
2: vehicle
|
||||
# Optional: Map of object labels to their attribute labels (default: depends on model)
|
||||
attributes_map:
|
||||
person:
|
||||
- amazon
|
||||
- face
|
||||
car:
|
||||
- amazon
|
||||
- fedex
|
||||
- license_plate
|
||||
- ups
|
||||
models:
|
||||
# Optional: the camera environment this model is for (default: shown below)
|
||||
# Cameras select a model by setting detect -> scene to a matching value, and
|
||||
# a model with a scene of all is used by any camera that does not set one.
|
||||
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
|
||||
- scene: all
|
||||
# Required: hardware this model runs on, as <detector> or <detector>:<device>
|
||||
# See https://docs.frigate.video/configuration/object_detectors for the
|
||||
# detectors available and the devices each one accepts. All of a model's
|
||||
# devices must use the same detector. Listing the same device more than once
|
||||
# runs additional inference processes on it.
|
||||
devices:
|
||||
- edgetpu:pci:0
|
||||
# Required: path to the model. Frigate+ models use plus://<model_id> (default: automatic based on detector)
|
||||
path: /edgetpu_model.tflite
|
||||
# Required: path to the labelmap (default: shown below)
|
||||
labelmap_path: /labelmap.txt
|
||||
# Required: Object detection model input width (default: shown below)
|
||||
width: 320
|
||||
# Required: Object detection model input height (default: shown below)
|
||||
height: 320
|
||||
# Required: Object detection model input colorspace
|
||||
# Valid values are rgb, bgr, or yuv. (default: shown below)
|
||||
input_pixel_format: rgb
|
||||
# Required: Object detection model input tensor format
|
||||
# Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
|
||||
input_tensor: nhwc
|
||||
# Optional: Data type of the model input tensor
|
||||
# Valid values are float, float_denorm, or int (default: shown below)
|
||||
input_dtype: int
|
||||
# Required: Object detection model architecture, used by detectors that support more
|
||||
# than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
|
||||
# Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
|
||||
model_type: ssd
|
||||
# Required: Label name modifications. These are merged into the standard labelmap.
|
||||
labelmap:
|
||||
2: vehicle
|
||||
# Optional: Map of object labels to their attribute labels (default: depends on model)
|
||||
attributes_map:
|
||||
person:
|
||||
- amazon
|
||||
- face
|
||||
car:
|
||||
- amazon
|
||||
- fedex
|
||||
- license_plate
|
||||
- ups
|
||||
|
||||
# Optional: Audio Events Configuration
|
||||
# NOTE: Can be overridden at the camera level
|
||||
@@ -251,11 +252,17 @@ birdseye:
|
||||
# Optional: Encoding quality of the mpeg1 feed (default: shown below)
|
||||
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
|
||||
quality: 8
|
||||
# Optional: Mode of the view. Available options are: objects, motion, and continuous
|
||||
# objects - cameras are included if they have had a tracked object within the last 30 seconds
|
||||
# motion - cameras are included if motion was detected in the last 30 seconds
|
||||
# continuous - all cameras are included always
|
||||
mode: objects
|
||||
# Optional: Activity types that include cameras in Birdseye (default: shown below)
|
||||
# Multiple activity types can be enabled at the same time.
|
||||
mode:
|
||||
# Optional: All cameras are included always (default: shown below)
|
||||
continuous: False
|
||||
# Optional: Cameras are included if motion was detected in the last 30 seconds (default: shown below)
|
||||
motion: False
|
||||
# Optional: Cameras are included if they have had an active tracked object within the last 30 seconds (default: shown below)
|
||||
objects: True
|
||||
# Optional: Cameras are included while they have a stationary tracked object (default: shown below)
|
||||
stationary_objects: False
|
||||
# Optional: Threshold for camera activity to stop showing camera (default: shown below)
|
||||
inactivity_threshold: 30
|
||||
# Optional: Configure the birdseye layout
|
||||
@@ -306,6 +313,10 @@ detect:
|
||||
width: 1280
|
||||
# Optional: height of the frame for the input with the detect role (default: use native stream resolution)
|
||||
height: 720
|
||||
# Optional: the environment this camera looks at, which picks the model it runs on
|
||||
# (default: the model with a scene of all)
|
||||
# Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
|
||||
scene: outdoor
|
||||
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
|
||||
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
|
||||
fps: 5
|
||||
|
||||
@@ -192,12 +192,14 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and open
|
||||
|
||||
```yaml
|
||||
# Optional: model config
|
||||
model:
|
||||
path: /path/to/model
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: "nhwc"
|
||||
input_pixel_format: "bgr"
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
path: /path/to/model
|
||||
width: 320
|
||||
height: 320
|
||||
input_tensor: "nhwc"
|
||||
input_pixel_format: "bgr"
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -214,15 +216,15 @@ If the labelmap is customized then the labels used for alerts will need to be ad
|
||||
The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
labelmap:
|
||||
2: vehicle
|
||||
3: vehicle
|
||||
5: vehicle
|
||||
7: vehicle
|
||||
15: animal
|
||||
16: animal
|
||||
17: animal
|
||||
models:
|
||||
- labelmap:
|
||||
2: vehicle
|
||||
3: vehicle
|
||||
5: vehicle
|
||||
7: vehicle
|
||||
15: animal
|
||||
16: animal
|
||||
17: animal
|
||||
```
|
||||
|
||||
Note that if you rename objects in the labelmap, you will also need to update your `objects -> track` list as well.
|
||||
|
||||
@@ -18,13 +18,14 @@ Each camera tile in Birdseye is composed from the frames of the stream assigned
|
||||
|
||||
## Birdseye Behavior
|
||||
|
||||
### Birdseye Modes
|
||||
### Birdseye Activity Types
|
||||
|
||||
Birdseye offers different modes to customize which cameras show under which circumstances.
|
||||
Birdseye offers independent activity types that control when cameras are shown. Multiple activity types can be enabled together.
|
||||
|
||||
- **continuous:** All cameras are always included
|
||||
- **motion:** Cameras that have detected motion within the last 30 seconds are included
|
||||
- **objects:** Cameras that have tracked an active object within the last 30 seconds are included
|
||||
- **continuous:** The camera is always included
|
||||
- **motion:** The camera is included when motion was detected within the last 30 seconds
|
||||
- **objects:** The camera is included when an active object was tracked within the last 30 seconds
|
||||
- **stationary_objects:** The camera is included while a stationary object is tracked
|
||||
|
||||
### Custom Birdseye Icon
|
||||
|
||||
@@ -39,27 +40,30 @@ To include a camera in Birdseye view only for specific circumstances, or exclude
|
||||
|
||||
**Global settings:** Navigate to <NavPath path="Settings > System > Birdseye" /> to configure the default Birdseye behavior for all cameras.
|
||||
|
||||
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the mode or disable Birdseye for a specific camera.
|
||||
**Per-camera overrides:** Navigate to <NavPath path="Settings > Camera configuration > Birdseye" /> to override the activity types or disable Birdseye for a specific camera.
|
||||
|
||||
| Field | Description |
|
||||
| ------------------- | ------------------------------------------------------------- |
|
||||
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
|
||||
| **Tracking mode** | When to show the camera: `continuous`, `motion`, or `objects` |
|
||||
| Field | Description |
|
||||
| ---------------------- | ---------------------------------------------------------- |
|
||||
| **Enable Birdseye** | Whether this camera appears in Birdseye view |
|
||||
| **Activity types** | Conditions that determine when to show the camera |
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml {8-10,12-14}
|
||||
```yaml {8-11,13-15}
|
||||
# Include all cameras by default in Birdseye view
|
||||
birdseye:
|
||||
enabled: True
|
||||
mode: continuous
|
||||
mode:
|
||||
continuous: True
|
||||
|
||||
cameras:
|
||||
front:
|
||||
# Only include the "front" camera in Birdseye view when objects are detected
|
||||
birdseye:
|
||||
mode: objects
|
||||
mode:
|
||||
continuous: False
|
||||
objects: True
|
||||
back:
|
||||
# Exclude the "back" camera from Birdseye view
|
||||
birdseye:
|
||||
|
||||
@@ -172,10 +172,9 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-rpi-64-h264
|
||||
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
|
||||
record:
|
||||
enabled: True
|
||||
@@ -249,10 +248,9 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-vaapi
|
||||
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
|
||||
record:
|
||||
enabled: True
|
||||
@@ -329,15 +327,12 @@ mqtt:
|
||||
ffmpeg:
|
||||
hwaccel_args: preset-vaapi
|
||||
|
||||
detectors:
|
||||
ov:
|
||||
type: openvino
|
||||
device: AUTO
|
||||
|
||||
model:
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
models:
|
||||
- devices:
|
||||
- openvino:AUTO
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
input_pixel_format: bgr
|
||||
path: /openvino-model/ssdlite_mobilenet_v2.xml
|
||||
labelmap_path: /openvino-model/coco_91cl_bkgr.txt
|
||||
|
||||
@@ -68,12 +68,66 @@ Frigate supports multiple different detectors that work on different types of ha
|
||||
|
||||
:::note
|
||||
|
||||
Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).
|
||||
A single model can not be spread across different detector types (ex: OpenVINO and Coral EdgeTPU can not run the same model at the same time). Configuring more than one model, each on its own detector type, is supported.
|
||||
|
||||
This does not affect using hardware for accelerating other tasks such as [semantic search](./semantic_search.md)
|
||||
|
||||
:::
|
||||
|
||||
### Configuring models and hardware
|
||||
|
||||
Object detection is configured with a `models` list. Each entry describes one model and the hardware it runs on:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
path: /config/model_cache/yolov9-s.onnx
|
||||
model_type: yolo-generic
|
||||
width: 320
|
||||
height: 320
|
||||
```
|
||||
|
||||
Each entry in `devices` is a detector type, optionally followed by a colon and a device for that detector, such as `edgetpu:pci:0`, `openvino:NPU`, or `tensorrt:0`. The per-detector sections below document the device values each one accepts. Listing several devices runs the model on all of them, and listing the **same** device more than once runs additional inference processes against it, which can improve throughput on hardware that keeps up with more than one stream:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU
|
||||
- openvino:GPU
|
||||
```
|
||||
|
||||
Coral EdgeTPU and MemryX accelerators can only be opened by one process, so those devices can not be repeated.
|
||||
|
||||
### Running more than one model
|
||||
|
||||
Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- scene: outdoor
|
||||
path: plus://your-outdoor-model
|
||||
devices:
|
||||
- edgetpu:pci:0
|
||||
- scene: indoor
|
||||
path: /config/model_cache/indoor.onnx
|
||||
model_type: yolo-generic
|
||||
devices:
|
||||
- openvino:GPU
|
||||
|
||||
cameras:
|
||||
driveway:
|
||||
detect:
|
||||
scene: outdoor
|
||||
...
|
||||
hallway:
|
||||
detect:
|
||||
scene: indoor
|
||||
...
|
||||
```
|
||||
|
||||
Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
|
||||
|
||||
### Choosing a model size
|
||||
|
||||
Along with picking a detector for your hardware, you will choose a model's **input resolution** (such as `320x320` or `640x640`) and, for model families like YOLOv9, a **variant size** (`tiny`, `small`, etc.). Both affect the balance between accuracy and the inference time your hardware can sustain.
|
||||
@@ -92,11 +146,11 @@ The best detection accuracy comes from a model trained on images that look like
|
||||
|
||||
# Officially Supported Detectors
|
||||
|
||||
Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.
|
||||
Frigate provides a number of builtin detector types. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. Each of a model's devices runs in a dedicated process, and they pull from a common queue of detection requests from the cameras assigned to that model.
|
||||
|
||||
## Edge TPU Detector
|
||||
|
||||
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the `"type"` attribute to `"edgetpu"`.
|
||||
The Edge TPU detector type runs TensorFlow Lite models utilizing the Google Coral delegate for hardware acceleration. To use it, prefix a model's device with `edgetpu`.
|
||||
|
||||
The Edge TPU device can be specified using the `"device"` attribute according to the [Documentation for the TensorFlow Lite Python API](https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api). If not set, the delegate will use the first device it finds.
|
||||
|
||||
@@ -117,10 +171,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -137,13 +190,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral1:
|
||||
type: edgetpu
|
||||
device: usb:0
|
||||
coral2:
|
||||
type: edgetpu
|
||||
device: usb:1
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb:0
|
||||
- edgetpu:usb:1
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -162,10 +212,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: ""
|
||||
models:
|
||||
- devices:
|
||||
- 'edgetpu:'
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -182,10 +231,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: pci
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:pci
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -202,13 +250,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral1:
|
||||
type: edgetpu
|
||||
device: pci:0
|
||||
coral2:
|
||||
type: edgetpu
|
||||
device: pci:1
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:pci:0
|
||||
- edgetpu:pci:1
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -225,13 +270,10 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and selec
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
coral_usb:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
coral_pci:
|
||||
type: edgetpu
|
||||
device: pci
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
- edgetpu:pci
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
@@ -273,7 +315,7 @@ Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-proc
|
||||
|
||||
## OpenVINO Detector
|
||||
|
||||
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To configure an OpenVINO detector, set the `"type"` attribute to `"openvino"`.
|
||||
The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel NPUs. To use it, prefix a model's device with `openvino`.
|
||||
|
||||
The OpenVINO device to be used is specified using the `"device"` attribute according to the naming conventions in the [Device Documentation](https://docs.openvino.ai/2025/openvino-workflow/running-inference/inference-devices-and-modes.html). The most common devices are `CPU`, `GPU`, or `NPU`.
|
||||
|
||||
@@ -286,13 +328,10 @@ OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will al
|
||||
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
ov_0:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
ov_1:
|
||||
type: openvino
|
||||
device: GPU # or NPU
|
||||
models:
|
||||
- devices:
|
||||
- openvino:GPU # or NPU
|
||||
- openvino:GPU # or NPU
|
||||
```
|
||||
|
||||
:::
|
||||
@@ -313,6 +352,12 @@ Intel NPUs cannot be used under Home Assistant OS, which does not include the NP
|
||||
|
||||
## Apple Silicon detector
|
||||
|
||||
:::warning
|
||||
|
||||
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
|
||||
|
||||
:::
|
||||
|
||||
The NPU in Apple Silicon can't be accessed from within a container, so the [Apple Silicon detector client](https://github.com/frigate-nvr/apple-silicon-detector) must first be setup. It is recommended to use the Frigate docker image with `-standard-arm64` suffix, for example `ghcr.io/blakeblackshear/frigate:stable-standard-arm64`.
|
||||
|
||||
### Setup {#setup-apple-silicon}
|
||||
@@ -453,11 +498,10 @@ If the correct build is used for your GPU then the GPU will be detected and used
|
||||
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
onnx_0:
|
||||
type: onnx
|
||||
onnx_1:
|
||||
type: onnx
|
||||
models:
|
||||
- devices:
|
||||
- onnx
|
||||
- onnx
|
||||
```
|
||||
|
||||
:::
|
||||
@@ -470,7 +514,7 @@ detectors:
|
||||
|
||||
## CPU Detector (not recommended)
|
||||
|
||||
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the `"type"` attribute to `"cpu"`.
|
||||
The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To use it, set a model's device to `cpu`.
|
||||
|
||||
:::danger
|
||||
|
||||
@@ -480,7 +524,7 @@ The CPU detector is not recommended for general use. If you do not have GPU or E
|
||||
|
||||
The number of threads used by the interpreter can be specified using the `"num_threads"` attribute, and defaults to `3.`
|
||||
|
||||
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with `model.path`.
|
||||
A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with the model's `path`.
|
||||
|
||||
### Configuration {#configuration-cpu}
|
||||
|
||||
@@ -490,6 +534,12 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
|
||||
|
||||
## Deepstack / CodeProject.AI Server Detector
|
||||
|
||||
:::warning
|
||||
|
||||
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
|
||||
|
||||
:::
|
||||
|
||||
The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.
|
||||
|
||||
### Setup {#setup-deepstack}
|
||||
@@ -552,7 +602,7 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
|
||||
|
||||
3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
|
||||
|
||||
4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
|
||||
4. Bind-mount the `.zip` file into the container and specify its path using the model's `path` in your config.
|
||||
|
||||
5. Update `labelmap_path` to match your custom model's labels.
|
||||
|
||||
@@ -682,13 +732,10 @@ If no custom model is provided, the RKNN detector downloads a default model from
|
||||
When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:
|
||||
|
||||
```yaml
|
||||
detectors:
|
||||
rknn_0:
|
||||
type: rknn
|
||||
num_cores: 0
|
||||
rknn_1:
|
||||
type: rknn
|
||||
num_cores: 0
|
||||
models:
|
||||
- devices:
|
||||
- rknn:0
|
||||
- rknn:0
|
||||
```
|
||||
|
||||
:::
|
||||
@@ -762,6 +809,101 @@ Explanation of the parameters:
|
||||
- **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
|
||||
- `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
|
||||
|
||||
<<<<<<< HEAD
|
||||
=======
|
||||
## DeGirum
|
||||
|
||||
:::warning
|
||||
|
||||
The network-based detectors (Deepstack, DeGirum, and the Apple Silicon client) are being reworked. Their extra options no longer have a place in the config, so only the endpoint carried in the device string is honored right now: Deepstack ignores `api_key` and `api_timeout`, DeGirum ignores `zoo` and `token`, and the Apple Silicon client ignores `request_timeout_ms` and `linger_ms`. Anything else is dropped when your config is migrated.
|
||||
|
||||
:::
|
||||
|
||||
DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
|
||||
|
||||
### Configuration {#configuration-degirum}
|
||||
|
||||
#### AI Server Inference
|
||||
|
||||
Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
|
||||
|
||||
```yaml
|
||||
degirum_detector:
|
||||
container_name: degirum
|
||||
image: degirum/aiserver:latest
|
||||
privileged: true
|
||||
ports:
|
||||
- "8778:8778"
|
||||
```
|
||||
|
||||
All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
|
||||
|
||||
Once completed, configure the detector as follows:
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
|
||||
|
||||
The model is set on the same `models` entry as the DeGirum device. You can set it to:
|
||||
|
||||
- A model listed on the [AI Hub](https://hub.degirum.com)
|
||||
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
|
||||
- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
|
||||
- A path to some model.json.
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- devices:
|
||||
- degirum:<location>
|
||||
path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
#### Local Inference
|
||||
|
||||
It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
|
||||
|
||||
1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
|
||||
2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
|
||||
3. Create a DeGirum detector in your configuration.
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
|
||||
|
||||
Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- devices:
|
||||
- degirum:<location>
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
#### AI Hub Cloud Inference
|
||||
|
||||
If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
|
||||
|
||||
1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
|
||||
2. Get an access token.
|
||||
3. Create a DeGirum detector in your configuration.
|
||||
|
||||
<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
|
||||
|
||||
Once the DeGirum device is set up, you can choose a model on the same `models` entry in the `config.yml` file.
|
||||
|
||||
```yaml
|
||||
models:
|
||||
- devices:
|
||||
- degirum:<location>
|
||||
path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
|
||||
width: 300 # width is in the model name as the first number in the "int"x"int" section
|
||||
height: 300 # height is in the model name as the second number in the "int"x"int" section
|
||||
input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
|
||||
```
|
||||
|
||||
>>>>>>> 34363affa (Refactor detector and model management)
|
||||
## AXERA
|
||||
|
||||
Hardware accelerated object detection is supported on the following SoCs:
|
||||
|
||||
@@ -222,15 +222,12 @@ You need to refer to **Configure hardware acceleration** above to enable the con
|
||||
```yaml {3-6,9-15,20-21}
|
||||
mqtt: ...
|
||||
|
||||
detectors: # <---- add detectors
|
||||
ov:
|
||||
type: openvino # <---- use openvino detector
|
||||
device: GPU
|
||||
|
||||
# We will use the default MobileNet_v2 model from OpenVINO.
|
||||
model:
|
||||
width: 300
|
||||
height: 300
|
||||
models: # <---- add models
|
||||
- devices:
|
||||
- openvino:GPU # <---- use the openvino detector on the GPU
|
||||
# We will use the default MobileNet_v2 model from OpenVINO.
|
||||
width: 300
|
||||
height: 300
|
||||
input_tensor: nhwc
|
||||
input_pixel_format: bgr
|
||||
path: /openvino-model/ssdlite_mobilenet_v2.xml
|
||||
@@ -281,10 +278,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" /> and add a
|
||||
```yaml {3-6,11-12}
|
||||
mqtt: ...
|
||||
|
||||
detectors: # <---- add detectors
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
models: # <---- add models
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
|
||||
cameras:
|
||||
name_of_your_camera:
|
||||
@@ -321,10 +317,9 @@ If you are using YAML to configure Frigate instead of the UI, your configuration
|
||||
mqtt:
|
||||
enabled: False
|
||||
|
||||
detectors:
|
||||
coral:
|
||||
type: edgetpu
|
||||
device: usb
|
||||
models:
|
||||
- devices:
|
||||
- edgetpu:usb
|
||||
|
||||
cameras:
|
||||
name_of_your_camera:
|
||||
@@ -357,7 +352,7 @@ In order to review activity in the Frigate UI, recordings need to be enabled.
|
||||
```yaml {16-17}
|
||||
mqtt: ...
|
||||
|
||||
detectors: ...
|
||||
models: ...
|
||||
|
||||
cameras:
|
||||
name_of_your_camera:
|
||||
|
||||
@@ -555,20 +555,21 @@ Topic with current state of Birdseye for a camera. Published values are `ON` and
|
||||
|
||||
### `frigate/<camera_name>/birdseye_mode/set`
|
||||
|
||||
Topic to set Birdseye mode for a camera. Birdseye offers different modes to customize under which circumstances the camera is shown.
|
||||
Topic to set the Birdseye activity types for a camera. Send one uppercase activity type or combine multiple types with commas, for example `MOTION,OBJECTS,STATIONARY_OBJECTS`.
|
||||
|
||||
_Note: Changing the value from `CONTINUOUS` -> `MOTION | OBJECTS` will take up to 30 seconds for
|
||||
_Note: Changing the value from `CONTINUOUS` to non-continuous activity types will take up to 30 seconds for
|
||||
the camera to be removed from the view._
|
||||
|
||||
| Command | Description |
|
||||
| ------------ | ----------------------------------------------------------------- |
|
||||
| `CONTINUOUS` | Always included |
|
||||
| `MOTION` | Show when detected motion within the last 30 seconds are included |
|
||||
| `OBJECTS` | Shown if an active object tracked within the last 30 seconds |
|
||||
| Command | Description |
|
||||
| -------------------- | ---------------------------------------------------------------- |
|
||||
| `CONTINUOUS` | Always included |
|
||||
| `MOTION` | Shown if motion was detected within the last 30 seconds |
|
||||
| `OBJECTS` | Shown if an active object was tracked within the last 30 seconds |
|
||||
| `STATIONARY_OBJECTS` | Shown while a stationary object is tracked |
|
||||
|
||||
### `frigate/<camera_name>/birdseye_mode/state`
|
||||
|
||||
Topic with current state of the Birdseye mode for a camera. Published values are `CONTINUOUS`, `MOTION`, `OBJECTS`.
|
||||
Topic with the current Birdseye activity types for a camera. Multiple enabled types are published as a comma-separated value in the order `OBJECTS`, `MOTION`, `STATIONARY_OBJECTS`, `CONTINUOUS`.
|
||||
|
||||
### `frigate/<camera_name>/notifications/set`
|
||||
|
||||
|
||||
@@ -62,10 +62,9 @@ Once you have [requested your first model](../plus/first_model.md) and gotten yo
|
||||
You can either choose the new model from the <NavPath path="Settings > System > Detectors and model" /> pane in the Frigate UI (the **Frigate+ Model** tab), or manually set the model at the root level in your config:
|
||||
|
||||
```yaml
|
||||
detectors: ...
|
||||
|
||||
model:
|
||||
path: plus://<your_model_id>
|
||||
models:
|
||||
- devices: ...
|
||||
path: plus://<your_model_id>
|
||||
```
|
||||
|
||||
:::note
|
||||
@@ -79,10 +78,11 @@ Models are downloaded into the `/config/model_cache` folder and only downloaded
|
||||
If needed, you can override the labelmap for Frigate+ models. This is not recommended as renaming labels will break the Submit to Frigate+ feature if the labels are not available in Frigate+.
|
||||
|
||||
```yaml
|
||||
model:
|
||||
path: plus://<your_model_id>
|
||||
labelmap:
|
||||
3: animal
|
||||
4: animal
|
||||
5: animal
|
||||
models:
|
||||
- devices: ...
|
||||
path: plus://<your_model_id>
|
||||
labelmap:
|
||||
3: animal
|
||||
4: animal
|
||||
5: animal
|
||||
```
|
||||
|
||||
@@ -36,10 +36,9 @@ Navigate to <NavPath path="Settings > System > Detectors and model" />. In the *
|
||||
<TabItem value="yaml">
|
||||
|
||||
```yaml
|
||||
detectors: ...
|
||||
|
||||
model:
|
||||
path: plus://<your_model_id>
|
||||
models:
|
||||
- devices: ...
|
||||
path: plus://<your_model_id>
|
||||
```
|
||||
|
||||
:::tip
|
||||
|
||||
Vendored
+38
@@ -2946,6 +2946,44 @@ paths:
|
||||
- frigateUserAuth: []
|
||||
x-required-role: any
|
||||
description: '**Access:** Any authenticated user.'
|
||||
/categorized_object_names:
|
||||
get:
|
||||
tags:
|
||||
- App
|
||||
summary: Get known object names by object type
|
||||
description: |-
|
||||
**Access:** Any authenticated user.
|
||||
|
||||
Returns the sub labels and attributes this install can attach,
|
||||
grouped by object type. Unlike /sub_labels, which reflects what has already been
|
||||
detected, this reads the config and model files, so it covers recognized face
|
||||
names, named license plates, custom object classification categories, and the
|
||||
detector attributes of tracked objects.
|
||||
operationId: categorized_object_names_categorized_object_names_get
|
||||
parameters:
|
||||
- name: object_type
|
||||
in: query
|
||||
required: false
|
||||
schema:
|
||||
anyOf:
|
||||
- type: string
|
||||
- type: 'null'
|
||||
title: Object Type
|
||||
responses:
|
||||
'200':
|
||||
description: Successful Response
|
||||
content:
|
||||
application/json:
|
||||
schema: {}
|
||||
'422':
|
||||
description: Validation Error
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/HTTPValidationError'
|
||||
security:
|
||||
- frigateUserAuth: []
|
||||
x-required-role: any
|
||||
/audio_labels:
|
||||
get:
|
||||
tags:
|
||||
|
||||
+47
-28
@@ -71,6 +71,7 @@ from frigate.util.config import (
|
||||
find_config_file,
|
||||
redact_credential,
|
||||
)
|
||||
from frigate.util.object_names import get_categorized_object_names
|
||||
from frigate.util.schema import get_config_schema
|
||||
from frigate.util.services import (
|
||||
get_nvidia_driver_info,
|
||||
@@ -291,10 +292,6 @@ def config(request: Request):
|
||||
config: dict[str, dict[str, Any]] = config_obj.model_dump(
|
||||
mode="json", warnings="none", exclude_none=True
|
||||
)
|
||||
config["detectors"] = {
|
||||
name: detector.model_dump(mode="json", warnings="none", exclude_none=True)
|
||||
for name, detector in config_obj.detectors.items()
|
||||
}
|
||||
|
||||
# remove environment_vars for non-admin users
|
||||
if request.headers.get("remote-role") != "admin":
|
||||
@@ -375,31 +372,28 @@ def config(request: Request):
|
||||
config["go2rtc"]["streams"][stream_name] = cleaned
|
||||
|
||||
config["plus"] = {"enabled": request.app.frigate_config.plus_api.is_active()}
|
||||
config["model"]["colormap"] = config_obj.model.colormap
|
||||
config["model"]["all_attributes"] = config_obj.model.all_attributes
|
||||
config["model"]["non_logo_attributes"] = config_obj.model.non_logo_attributes
|
||||
|
||||
# Add model plus data if plus is enabled
|
||||
if config["plus"]["enabled"]:
|
||||
model_path = config.get("model", {}).get("path")
|
||||
if model_path:
|
||||
model_json_path = FilePath(model_path).with_suffix(".json")
|
||||
for index, model in enumerate(config_obj.models):
|
||||
model_dict = config["models"][index]
|
||||
model_dict["colormap"] = model.colormap
|
||||
model_dict["all_attributes"] = model.all_attributes
|
||||
model_dict["non_logo_attributes"] = model.non_logo_attributes
|
||||
model_dict["labelmap"] = model.merged_labelmap
|
||||
|
||||
if not config["plus"]["enabled"]:
|
||||
continue
|
||||
|
||||
# Add model plus data if plus is enabled
|
||||
model_dict["plus"] = None
|
||||
|
||||
if model.path:
|
||||
model_json_path = FilePath(model.path).with_suffix(".json")
|
||||
|
||||
try:
|
||||
with open(model_json_path) as f:
|
||||
model_plus_data = json.load(f)
|
||||
config["model"]["plus"] = model_plus_data
|
||||
except FileNotFoundError:
|
||||
config["model"]["plus"] = None
|
||||
except json.JSONDecodeError:
|
||||
config["model"]["plus"] = None
|
||||
else:
|
||||
config["model"]["plus"] = None
|
||||
|
||||
# use merged labelamp
|
||||
for detector_config in config["detectors"].values():
|
||||
detector_config["model"]["labelmap"] = (
|
||||
request.app.frigate_config.model.merged_labelmap
|
||||
)
|
||||
model_dict["plus"] = json.load(f)
|
||||
except (FileNotFoundError, json.JSONDecodeError):
|
||||
pass
|
||||
|
||||
return JSONResponse(content=config)
|
||||
|
||||
@@ -1313,6 +1307,28 @@ def get_sub_labels(
|
||||
return JSONResponse(content=sub_labels)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/categorized_object_names",
|
||||
dependencies=[Depends(allow_any_authenticated())],
|
||||
summary="Get known object names by object type",
|
||||
description="""Returns the sub labels and attributes this install can attach,
|
||||
grouped by object type. Unlike /sub_labels, which reflects what has already been
|
||||
detected, this reads the config and model files, so it covers recognized face
|
||||
names, named license plates, custom object classification categories, and the
|
||||
detector attributes of tracked objects.""",
|
||||
)
|
||||
def categorized_object_names(
|
||||
request: Request,
|
||||
object_type: str | None = None,
|
||||
allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
|
||||
):
|
||||
return JSONResponse(
|
||||
content=get_categorized_object_names(
|
||||
request.app.frigate_config, allowed_cameras, object_type
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
@router.get("/audio_labels", dependencies=[Depends(allow_any_authenticated())])
|
||||
def get_audio_labels():
|
||||
labels = load_labels("/audio-labelmap.txt", prefill=521)
|
||||
@@ -1337,11 +1353,14 @@ def plusModels(request: Request, filterByCurrentModelDetector: bool = False):
|
||||
|
||||
modelList = models["list"]
|
||||
|
||||
config: FrigateConfig = request.app.frigate_config
|
||||
primary_model = config.primary_model
|
||||
|
||||
# current model type
|
||||
modelType = request.app.frigate_config.model.model_type
|
||||
modelType = primary_model.model_type
|
||||
|
||||
# current detectorType for comparing to supportedDetectors
|
||||
detectorType = list(request.app.frigate_config.detectors.values())[0].type
|
||||
detectorType = config.devices_for_model(primary_model)[0].detector
|
||||
|
||||
validModels = []
|
||||
|
||||
|
||||
@@ -83,6 +83,7 @@ def require_admin_by_default():
|
||||
"/nvinfo",
|
||||
"/labels",
|
||||
"/sub_labels",
|
||||
"/categorized_object_names",
|
||||
"/plus/models",
|
||||
"/recognized_license_plates",
|
||||
"/timeline",
|
||||
|
||||
+25
-2
@@ -50,6 +50,7 @@ from frigate.jobs.vlm_watch import (
|
||||
stop_vlm_watch_job,
|
||||
)
|
||||
from frigate.models import Event
|
||||
from frigate.util.object_names import get_categorized_object_names
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -539,6 +540,11 @@ async def execute_tool(
|
||||
if tool_name == "search_objects":
|
||||
return await _execute_search_objects(request, arguments, allowed_cameras)
|
||||
|
||||
if tool_name == "get_categorized_object_names":
|
||||
return JSONResponse(
|
||||
content=_execute_get_categorized_object_names(request, allowed_cameras)
|
||||
)
|
||||
|
||||
if tool_name == "find_similar_objects":
|
||||
result = await _execute_find_similar_objects(
|
||||
request, arguments, allowed_cameras
|
||||
@@ -717,6 +723,21 @@ async def _execute_set_camera_state(
|
||||
return {"success": True, "camera": camera, "feature": feature, "value": value}
|
||||
|
||||
|
||||
def _execute_get_categorized_object_names(
|
||||
request: Request,
|
||||
allowed_cameras: list[str],
|
||||
) -> dict[str, Any]:
|
||||
names = get_categorized_object_names(request.app.frigate_config, allowed_cameras)
|
||||
|
||||
if not names:
|
||||
return {
|
||||
"names": {},
|
||||
"message": "No names configured; search by label or semantic_query.",
|
||||
}
|
||||
|
||||
return {"names": names}
|
||||
|
||||
|
||||
async def _execute_tool_internal(
|
||||
tool_name: str,
|
||||
arguments: dict[str, Any],
|
||||
@@ -741,6 +762,8 @@ async def _execute_tool_internal(
|
||||
except (json.JSONDecodeError, AttributeError) as e:
|
||||
logger.warning(f"Failed to extract tool result: {e}")
|
||||
return {"error": "Failed to parse tool result"}
|
||||
elif tool_name == "get_categorized_object_names":
|
||||
return _execute_get_categorized_object_names(request, allowed_cameras)
|
||||
elif tool_name == "find_similar_objects":
|
||||
return await _execute_find_similar_objects(request, arguments, allowed_cameras)
|
||||
elif tool_name == "set_camera_state":
|
||||
@@ -773,8 +796,8 @@ async def _execute_tool_internal(
|
||||
else:
|
||||
logger.error(
|
||||
"Tool call failed: unknown tool %r. Expected one of: search_objects, find_similar_objects, "
|
||||
"get_live_context, start_camera_watch, stop_camera_watch, get_profile_status, get_recap. "
|
||||
"Arguments received: %s",
|
||||
"get_categorized_object_names, get_live_context, start_camera_watch, stop_camera_watch, "
|
||||
"get_profile_status, get_recap. Arguments received: %s",
|
||||
tool_name,
|
||||
json.dumps(arguments),
|
||||
)
|
||||
|
||||
@@ -575,11 +575,11 @@ async def vod_ts(
|
||||
Recordings.start_time,
|
||||
)
|
||||
.where(
|
||||
Recordings.start_time.between(start_ts, end_ts)
|
||||
| Recordings.end_time.between(start_ts, end_ts)
|
||||
| ((start_ts > Recordings.start_time) & (end_ts < Recordings.end_time))
|
||||
Recordings.camera == camera_name,
|
||||
Recordings.start_time >= start_ts - MAX_SEGMENT_DURATION,
|
||||
Recordings.start_time <= end_ts,
|
||||
Recordings.end_time >= start_ts,
|
||||
)
|
||||
.where(Recordings.camera == camera_name)
|
||||
.order_by(Recordings.start_time.asc())
|
||||
.iterator()
|
||||
)
|
||||
@@ -814,7 +814,7 @@ async def event_snapshot(
|
||||
timestamp_style=request.app.frigate_config.cameras[
|
||||
event.camera
|
||||
].timestamp_style,
|
||||
colormap=request.app.frigate_config.model.colormap,
|
||||
colormap=request.app.frigate_config.model_for_camera(event.camera).colormap,
|
||||
)
|
||||
except DoesNotExist:
|
||||
# see if the object is currently being tracked
|
||||
|
||||
@@ -25,7 +25,7 @@ from frigate.api.defs.query.recordings_query_parameters import (
|
||||
)
|
||||
from frigate.api.defs.response.generic_response import GenericResponse
|
||||
from frigate.api.defs.tags import Tags
|
||||
from frigate.const import RECORD_DIR
|
||||
from frigate.const import MAX_SEGMENT_DURATION, RECORD_DIR
|
||||
from frigate.models import Event, Recordings
|
||||
from frigate.util.time import get_dst_transitions
|
||||
|
||||
@@ -243,6 +243,7 @@ async def recordings(
|
||||
)
|
||||
.where(
|
||||
Recordings.camera == camera_name,
|
||||
Recordings.start_time >= after - MAX_SEGMENT_DURATION,
|
||||
Recordings.end_time >= after,
|
||||
Recordings.start_time <= before,
|
||||
)
|
||||
|
||||
+39
-22
@@ -49,6 +49,8 @@ from frigate.debug_replay import (
|
||||
DebugReplayManager,
|
||||
cleanup_replay_cameras,
|
||||
)
|
||||
from frigate.detectors.detector_config import SceneEnum
|
||||
from frigate.detectors.device import build_detector_config, runner_names
|
||||
from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
|
||||
from frigate.events.audio import AudioProcessor
|
||||
from frigate.events.cleanup import EventCleanup
|
||||
@@ -69,6 +71,7 @@ from frigate.models import (
|
||||
User,
|
||||
)
|
||||
from frigate.object_detection.base import ObjectDetectProcess
|
||||
from frigate.object_detection.util import detection_frame_size
|
||||
from frigate.output.output import OutputProcess
|
||||
from frigate.ptz.autotrack import PtzAutoTrackerThread
|
||||
from frigate.ptz.onvif import OnvifController
|
||||
@@ -98,7 +101,9 @@ class FrigateApp:
|
||||
self.metrics_manager = manager
|
||||
self.audio_process: mp.Process | None = None
|
||||
self.stop_event = stop_event
|
||||
self.detection_queue: Queue = mp.Queue()
|
||||
self.detection_queues: dict[SceneEnum, Queue] = {
|
||||
model.scene: mp.Queue() for model in config.models
|
||||
}
|
||||
self.detectors: dict[str, ObjectDetectProcess] = {}
|
||||
self.detection_shms: list[mp.shared_memory.SharedMemory] = []
|
||||
self.log_queue: Queue = mp.Queue()
|
||||
@@ -343,20 +348,19 @@ class FrigateApp:
|
||||
self.dispatcher.profile_manager = self.profile_manager
|
||||
|
||||
def start_detectors(self) -> None:
|
||||
model_cameras: dict[SceneEnum, list[str]] = {
|
||||
model.scene: [] for model in self.config.models
|
||||
}
|
||||
|
||||
for name in self.config.cameras.keys():
|
||||
model = self.config.model_for_camera(name)
|
||||
model_cameras[model.scene].append(name)
|
||||
|
||||
try:
|
||||
largest_frame = max(
|
||||
[
|
||||
det.model.height * det.model.width * 3
|
||||
if det.model is not None
|
||||
else 320
|
||||
for det in self.config.detectors.values()
|
||||
]
|
||||
)
|
||||
shm_in = UntrackedSharedMemory(
|
||||
name=name,
|
||||
create=True,
|
||||
size=largest_frame,
|
||||
size=detection_frame_size(model),
|
||||
)
|
||||
except FileExistsError:
|
||||
shm_in = UntrackedSharedMemory(name=name)
|
||||
@@ -371,15 +375,26 @@ class FrigateApp:
|
||||
self.detection_shms.append(shm_in)
|
||||
self.detection_shms.append(shm_out)
|
||||
|
||||
for name, detector_config in self.config.detectors.items():
|
||||
self.detectors[name] = ObjectDetectProcess(
|
||||
name,
|
||||
self.detection_queue,
|
||||
list(self.config.cameras.keys()),
|
||||
self.config,
|
||||
detector_config,
|
||||
self.stop_event,
|
||||
)
|
||||
# a device may be listed more than once to run additional inference
|
||||
# processes on it, so names are only unique once de-duplicated
|
||||
all_devices = [
|
||||
device
|
||||
for model in self.config.models
|
||||
for device in self.config.devices_for_model(model)
|
||||
]
|
||||
names = iter(runner_names(all_devices))
|
||||
|
||||
for model in self.config.models:
|
||||
for device in self.config.devices_for_model(model):
|
||||
name = next(names)
|
||||
self.detectors[name] = ObjectDetectProcess(
|
||||
name,
|
||||
self.detection_queues[model.scene],
|
||||
model_cameras[model.scene],
|
||||
self.config,
|
||||
build_detector_config(device, model),
|
||||
self.stop_event,
|
||||
)
|
||||
|
||||
def start_ptz_autotracker(self) -> None:
|
||||
self.ptz_autotracker_thread = PtzAutoTrackerThread(
|
||||
@@ -410,7 +425,7 @@ class FrigateApp:
|
||||
def start_camera_processor(self) -> None:
|
||||
self.camera_maintainer = CameraMaintainer(
|
||||
self.config,
|
||||
self.detection_queue,
|
||||
self.detection_queues,
|
||||
self.detected_frames_queue,
|
||||
self.camera_metrics,
|
||||
self.ptz_metrics,
|
||||
@@ -674,8 +689,10 @@ class FrigateApp:
|
||||
for detector in self.detectors.values():
|
||||
detector.stop()
|
||||
|
||||
empty_and_close_queue(self.detection_queue)
|
||||
logger.info("Detection queue closed")
|
||||
for detection_queue in self.detection_queues.values():
|
||||
empty_and_close_queue(detection_queue)
|
||||
|
||||
logger.info("Detection queues closed")
|
||||
|
||||
self.detected_frames_processor.join()
|
||||
empty_and_close_queue(self.detected_frames_queue)
|
||||
|
||||
@@ -18,6 +18,7 @@ from frigate.config.camera.updater import (
|
||||
CameraConfigUpdateEnum,
|
||||
CameraConfigUpdateSubscriber,
|
||||
)
|
||||
from frigate.detectors.detector_config import NON_LOGO_ATTRIBUTES
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -178,7 +179,7 @@ class CameraActivityManager:
|
||||
return
|
||||
|
||||
for label in camera_config.objects.track:
|
||||
if label in self.config.model.non_logo_attributes:
|
||||
if label in NON_LOGO_ATTRIBUTES:
|
||||
continue
|
||||
|
||||
new_count = all_objects[label]
|
||||
|
||||
@@ -15,7 +15,9 @@ from frigate.config.camera.updater import (
|
||||
CameraConfigUpdateSubscriber,
|
||||
)
|
||||
from frigate.const import REPLAY_CAMERA_PREFIX
|
||||
from frigate.detectors.detector_config import SceneEnum
|
||||
from frigate.models import Regions
|
||||
from frigate.object_detection.util import detection_frame_size
|
||||
from frigate.util.builtin import empty_and_close_queue
|
||||
from frigate.util.image import SharedMemoryFrameManager, UntrackedSharedMemory
|
||||
from frigate.util.object import get_camera_regions_grid
|
||||
@@ -29,7 +31,7 @@ class CameraMaintainer(threading.Thread):
|
||||
def __init__(
|
||||
self,
|
||||
config: FrigateConfig,
|
||||
detection_queue: Queue,
|
||||
detection_queues: dict[SceneEnum, Queue],
|
||||
detected_frames_queue: Queue,
|
||||
camera_metrics: DictProxy,
|
||||
ptz_metrics: dict[str, PTZMetrics],
|
||||
@@ -38,7 +40,7 @@ class CameraMaintainer(threading.Thread):
|
||||
):
|
||||
super().__init__(name="camera_processor")
|
||||
self.config = config
|
||||
self.detection_queue = detection_queue
|
||||
self.detection_queues = detection_queues
|
||||
self.detected_frames_queue = detected_frames_queue
|
||||
self.stop_event = stop_event
|
||||
self.camera_metrics = camera_metrics
|
||||
@@ -79,10 +81,11 @@ class CameraMaintainer(threading.Thread):
|
||||
# create or update region grids for each camera
|
||||
for camera in self.config.cameras.values():
|
||||
assert camera.name is not None
|
||||
model = self.config.model_for_camera(camera.name)
|
||||
self.region_grids[camera.name] = get_camera_regions_grid(
|
||||
camera.name,
|
||||
camera.detect,
|
||||
max(self.config.model.width, self.config.model.height),
|
||||
max(model.width, model.height),
|
||||
)
|
||||
|
||||
def __calculate_shm_frame_count(self) -> int:
|
||||
@@ -114,6 +117,7 @@ class CameraMaintainer(threading.Thread):
|
||||
return
|
||||
|
||||
camera_stop_event = self.__ensure_camera_stop_event(name)
|
||||
model = self.config.model_for_camera(name)
|
||||
|
||||
if runtime:
|
||||
self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
|
||||
@@ -123,32 +127,24 @@ class CameraMaintainer(threading.Thread):
|
||||
self.region_grids[name] = get_camera_regions_grid(
|
||||
name,
|
||||
config.detect,
|
||||
max(self.config.model.width, self.config.model.height),
|
||||
max(model.width, model.height),
|
||||
)
|
||||
|
||||
try:
|
||||
largest_frame = max(
|
||||
[
|
||||
det.model.height * det.model.width * 3
|
||||
if det.model is not None
|
||||
else 320
|
||||
for det in self.config.detectors.values()
|
||||
]
|
||||
)
|
||||
UntrackedSharedMemory(name=f"out-{name}", create=True, size=20 * 6 * 4)
|
||||
UntrackedSharedMemory(
|
||||
name=name,
|
||||
create=True,
|
||||
size=largest_frame,
|
||||
size=detection_frame_size(model),
|
||||
)
|
||||
except FileExistsError:
|
||||
pass
|
||||
|
||||
camera_process = CameraTracker(
|
||||
config,
|
||||
self.config.model,
|
||||
self.config.model.merged_labelmap,
|
||||
self.detection_queue,
|
||||
model,
|
||||
model.merged_labelmap,
|
||||
self.detection_queues[model.scene],
|
||||
self.detected_frames_queue,
|
||||
self.camera_metrics[name],
|
||||
self.ptz_metrics[name],
|
||||
|
||||
+6
-11
@@ -40,6 +40,7 @@ class CameraState:
|
||||
self.name = name
|
||||
self.config = config
|
||||
self.camera_config = config.cameras[name]
|
||||
self.model = config.model_for_camera(name)
|
||||
self.frame_manager = frame_manager
|
||||
self.best_objects: dict[str, TrackedObject] = {}
|
||||
self.tracked_objects: dict[str, TrackedObject] = {}
|
||||
@@ -101,9 +102,7 @@ class CameraState:
|
||||
thickness = 1
|
||||
else:
|
||||
thickness = 2
|
||||
color = self.config.model.colormap.get(
|
||||
obj["label"], (255, 255, 255)
|
||||
)
|
||||
color = self.model.colormap.get(obj["label"], (255, 255, 255))
|
||||
else:
|
||||
thickness = 1
|
||||
color = (255, 0, 0)
|
||||
@@ -125,9 +124,7 @@ class CameraState:
|
||||
and obj["frame_time"] == frame_time
|
||||
):
|
||||
thickness = 5
|
||||
color = self.config.model.colormap.get(
|
||||
obj["label"], (255, 255, 255)
|
||||
)
|
||||
color = self.model.colormap.get(obj["label"], (255, 255, 255))
|
||||
|
||||
# debug autotracking zooming - show the zoom factor box
|
||||
if (
|
||||
@@ -261,9 +258,7 @@ class CameraState:
|
||||
if draw_options.get("paths"):
|
||||
for obj in tracked_objects.values():
|
||||
if obj["frame_time"] == frame_time and obj["path_data"]:
|
||||
color = self.config.model.colormap.get(
|
||||
obj["label"], (255, 255, 255)
|
||||
)
|
||||
color = self.model.colormap.get(obj["label"], (255, 255, 255))
|
||||
|
||||
path_points = [
|
||||
(
|
||||
@@ -366,7 +361,7 @@ class CameraState:
|
||||
for id in new_ids:
|
||||
logger.debug(f"{self.name}: New tracked object ID: {id}")
|
||||
new_obj = tracked_objects[id] = TrackedObject(
|
||||
self.config.model,
|
||||
self.model,
|
||||
self.camera_config,
|
||||
self.config.ui,
|
||||
self.frame_cache,
|
||||
@@ -510,7 +505,7 @@ class CameraState:
|
||||
sub_label = None
|
||||
|
||||
if obj.obj_data.get("sub_label"):
|
||||
if obj.obj_data["sub_label"][0] in self.config.model.all_attributes:
|
||||
if obj.obj_data["sub_label"][0] in self.model.all_attributes:
|
||||
label = obj.obj_data["sub_label"][0]
|
||||
else:
|
||||
label = f"{object_type}-verified"
|
||||
|
||||
@@ -11,7 +11,7 @@ from frigate.camera.activity_manager import AudioActivityManager, CameraActivity
|
||||
from frigate.comms.base_communicator import Communicator
|
||||
from frigate.comms.runtime_state import RuntimeStatePersistence
|
||||
from frigate.comms.webpush import WebPushClient
|
||||
from frigate.config import BirdseyeModeEnum, FrigateConfig
|
||||
from frigate.config import BirdseyeModeConfig, FrigateConfig
|
||||
from frigate.config.camera.updater import (
|
||||
CameraConfigUpdateEnum,
|
||||
CameraConfigUpdatePublisher,
|
||||
@@ -156,10 +156,11 @@ class Dispatcher:
|
||||
if camera not in self.config.cameras:
|
||||
return None
|
||||
|
||||
model = self.config.model_for_camera(camera)
|
||||
grid = get_camera_regions_grid(
|
||||
camera,
|
||||
self.config.cameras[camera].detect,
|
||||
max(self.config.model.width, self.config.model.height),
|
||||
max(model.width, model.height),
|
||||
)
|
||||
return grid
|
||||
|
||||
@@ -882,8 +883,9 @@ class Dispatcher:
|
||||
def _on_birdseye_mode_command(self, camera_name: str, payload: str) -> None:
|
||||
"""Callback for birdseye mode topic."""
|
||||
|
||||
if payload not in ["CONTINUOUS", "MOTION", "OBJECTS"]:
|
||||
logger.info(f"Invalid birdseye_mode command: {payload}")
|
||||
mode = BirdseyeModeConfig.from_mqtt_payload(payload)
|
||||
if mode is None:
|
||||
logger.info("Invalid birdseye_mode command: %s", payload)
|
||||
return
|
||||
|
||||
birdseye_settings = self.config.cameras[camera_name].birdseye
|
||||
@@ -892,7 +894,7 @@ class Dispatcher:
|
||||
logger.info(f"Birdseye mode not enabled for {camera_name}")
|
||||
return
|
||||
|
||||
birdseye_settings.mode = BirdseyeModeEnum(payload.lower())
|
||||
birdseye_settings.mode = mode
|
||||
logger.info(
|
||||
f"Setting birdseye mode for {camera_name} to {birdseye_settings.mode}"
|
||||
)
|
||||
@@ -901,7 +903,9 @@ class Dispatcher:
|
||||
CameraConfigUpdateTopic(CameraConfigUpdateEnum.birdseye, camera_name),
|
||||
birdseye_settings,
|
||||
)
|
||||
self.publish(f"{camera_name}/birdseye_mode/state", payload, retain=True)
|
||||
self.publish(
|
||||
f"{camera_name}/birdseye_mode/state", mode.to_mqtt_payload(), retain=True
|
||||
)
|
||||
|
||||
def _on_camera_notification_command(self, camera_name: str, payload: str) -> None:
|
||||
"""Callback for camera level notifications topic."""
|
||||
|
||||
@@ -125,7 +125,7 @@ class MqttClient(Communicator):
|
||||
self.publish(
|
||||
f"{camera_name}/birdseye_mode/state",
|
||||
(
|
||||
camera.birdseye.mode.value.upper()
|
||||
camera.birdseye.mode.to_mqtt_payload()
|
||||
if camera.birdseye.enabled
|
||||
else "OFF"
|
||||
),
|
||||
|
||||
@@ -1,5 +1,3 @@
|
||||
from enum import Enum
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from ..base import FrigateBaseModel
|
||||
@@ -8,22 +6,78 @@ __all__ = [
|
||||
"BirdseyeCameraConfig",
|
||||
"BirdseyeConfig",
|
||||
"BirdseyeLayoutConfig",
|
||||
"BirdseyeModeEnum",
|
||||
"BirdseyeModeConfig",
|
||||
]
|
||||
|
||||
BIRDSEYE_ACTIVITY_TYPES = (
|
||||
"objects",
|
||||
"motion",
|
||||
"stationary_objects",
|
||||
"continuous",
|
||||
)
|
||||
|
||||
class BirdseyeModeEnum(str, Enum):
|
||||
objects = "objects"
|
||||
motion = "motion"
|
||||
continuous = "continuous"
|
||||
|
||||
class BirdseyeModeConfig(FrigateBaseModel):
|
||||
continuous: bool = Field(
|
||||
default=False,
|
||||
title="Continuous",
|
||||
description="Always include the camera in Birdseye.",
|
||||
)
|
||||
motion: bool = Field(
|
||||
default=False,
|
||||
title="Motion",
|
||||
description="Include the camera in Birdseye when motion is detected.",
|
||||
)
|
||||
objects: bool = Field(
|
||||
default=False,
|
||||
title="Active objects",
|
||||
description="Include the camera in Birdseye while an active object is tracked.",
|
||||
)
|
||||
stationary_objects: bool = Field(
|
||||
default=False,
|
||||
title="Stationary objects",
|
||||
description="Include the camera in Birdseye while a stationary object is tracked.",
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_index(cls, type):
|
||||
return list(cls).index(type)
|
||||
def from_mqtt_payload(cls, payload: str) -> "BirdseyeModeConfig | None":
|
||||
"""Create mode options from an uppercase MQTT payload."""
|
||||
raw_modes = payload.split(",")
|
||||
if not raw_modes or any(not mode for mode in raw_modes):
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def get(cls, index):
|
||||
return list(cls)[index]
|
||||
modes = [mode.lower() for mode in raw_modes]
|
||||
if any(
|
||||
raw_mode != mode.upper() or mode not in BIRDSEYE_ACTIVITY_TYPES
|
||||
for raw_mode, mode in zip(raw_modes, modes)
|
||||
):
|
||||
return None
|
||||
|
||||
if len(modes) != len(set(modes)):
|
||||
return None
|
||||
|
||||
return cls(**{mode: True for mode in modes})
|
||||
|
||||
def has_enabled_activity(self) -> bool:
|
||||
"""Return whether at least one activity type is enabled."""
|
||||
return any(getattr(self, activity) for activity in BIRDSEYE_ACTIVITY_TYPES)
|
||||
|
||||
def to_mqtt_payload(self) -> str:
|
||||
"""Serialize enabled mode options for MQTT state topics."""
|
||||
payload = ",".join(
|
||||
activity.upper()
|
||||
for activity in BIRDSEYE_ACTIVITY_TYPES
|
||||
if getattr(self, activity)
|
||||
)
|
||||
if not payload:
|
||||
raise ValueError("At least one Birdseye activity type must be enabled")
|
||||
|
||||
return payload
|
||||
|
||||
|
||||
def default_birdseye_mode() -> BirdseyeModeConfig:
|
||||
"""Return the default Birdseye mode configuration."""
|
||||
return BirdseyeModeConfig(objects=True)
|
||||
|
||||
|
||||
class BirdseyeLayoutConfig(FrigateBaseModel):
|
||||
@@ -47,10 +101,10 @@ class BirdseyeConfig(FrigateBaseModel):
|
||||
title="Enable Birdseye",
|
||||
description="Enable or disable the Birdseye view feature.",
|
||||
)
|
||||
mode: BirdseyeModeEnum = Field(
|
||||
default=BirdseyeModeEnum.objects,
|
||||
title="Tracking mode",
|
||||
description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
|
||||
mode: BirdseyeModeConfig = Field(
|
||||
default_factory=default_birdseye_mode,
|
||||
title="Activity types",
|
||||
description="Activity types that include cameras in Birdseye.",
|
||||
)
|
||||
|
||||
restream: bool = Field(
|
||||
@@ -102,10 +156,10 @@ class BirdseyeCameraConfig(BaseModel):
|
||||
title="Enable Birdseye",
|
||||
description="Enable or disable the Birdseye view feature.",
|
||||
)
|
||||
mode: BirdseyeModeEnum = Field(
|
||||
default=BirdseyeModeEnum.objects,
|
||||
title="Tracking mode",
|
||||
description="Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'.",
|
||||
mode: BirdseyeModeConfig = Field(
|
||||
default_factory=default_birdseye_mode,
|
||||
title="Activity types",
|
||||
description="Activity types that include cameras in Birdseye.",
|
||||
)
|
||||
|
||||
order: int = Field(
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
from pydantic import Field, model_validator
|
||||
|
||||
from frigate.detectors.detector_config import SceneEnum
|
||||
|
||||
from ..base import FrigateBaseModel
|
||||
|
||||
__all__ = ["DetectConfig", "StationaryConfig", "StationaryMaxFramesConfig"]
|
||||
@@ -60,6 +62,11 @@ class DetectConfig(FrigateBaseModel):
|
||||
title="Detect width",
|
||||
description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
|
||||
)
|
||||
scene: SceneEnum | None = Field(
|
||||
default=None,
|
||||
title="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: int = Field(
|
||||
default=5,
|
||||
title="Detect FPS",
|
||||
|
||||
+244
-61
@@ -11,7 +11,6 @@ from pydantic import (
|
||||
BaseModel,
|
||||
ConfigDict,
|
||||
Field,
|
||||
TypeAdapter,
|
||||
ValidationInfo,
|
||||
field_validator,
|
||||
model_validator,
|
||||
@@ -19,8 +18,9 @@ from pydantic import (
|
||||
from ruamel.yaml import YAML
|
||||
|
||||
from frigate.const import REGEX_JSON
|
||||
from frigate.detectors import DetectorConfig, ModelConfig
|
||||
from frigate.detectors.detector_config import BaseDetectorConfig
|
||||
from frigate.detectors import ModelConfig
|
||||
from frigate.detectors.detector_config import SceneEnum
|
||||
from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
|
||||
from frigate.plus import PlusApi
|
||||
from frigate.util.builtin import (
|
||||
deep_merge,
|
||||
@@ -41,7 +41,7 @@ from .auth import AuthConfig
|
||||
from .base import FrigateBaseModel
|
||||
from .camera import CameraConfig, CameraLiveConfig
|
||||
from .camera.audio import AudioConfig, AudioFilterConfig
|
||||
from .camera.birdseye import BirdseyeConfig
|
||||
from .camera.birdseye import BirdseyeConfig, BirdseyeModeConfig
|
||||
from .camera.detect import DetectConfig
|
||||
from .camera.ffmpeg import FfmpegConfig
|
||||
from .camera.genai import GenAIConfig, GenAIRoleEnum
|
||||
@@ -79,9 +79,14 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
yaml = YAML()
|
||||
|
||||
# Pydantic field default applied when an existing config omits `detectors:`.
|
||||
# Pydantic field default applied when an existing config omits `models:`.
|
||||
# Kept as cpu tflite for backwards compatibility with 0.17 configs.
|
||||
DEFAULT_DETECTORS = {"cpu": {"type": "cpu"}}
|
||||
DEFAULT_MODELS = [{"devices": ["cpu"]}]
|
||||
|
||||
|
||||
def _default_models() -> list[ModelConfig]:
|
||||
return [ModelConfig.model_validate(model) for model in DEFAULT_MODELS]
|
||||
|
||||
|
||||
# Used by the openvino branch below and rendered into the new-config YAML
|
||||
# template so first-time setups default to openvino on CPU.
|
||||
@@ -93,7 +98,7 @@ DEFAULT_MODEL = {
|
||||
"path": "/openvino-model/ssdlite_mobilenet_v2.xml",
|
||||
"labelmap_path": "/openvino-model/coco_91cl_bkgr.txt",
|
||||
}
|
||||
NEW_CONFIG_DETECTORS = {"ov": {"type": "openvino", "device": "CPU"}}
|
||||
NEW_CONFIG_MODELS = [{"devices": ["openvino:CPU"], **DEFAULT_MODEL}]
|
||||
DEFAULT_DETECT_DIMENSIONS = {"width": 1280, "height": 720}
|
||||
|
||||
|
||||
@@ -109,7 +114,7 @@ DEFAULT_CONFIG = f"""
|
||||
mqtt:
|
||||
enabled: False
|
||||
|
||||
{_render_default_yaml({"detectors": NEW_CONFIG_DETECTORS, "model": DEFAULT_MODEL})}
|
||||
{_render_default_yaml({"models": NEW_CONFIG_MODELS})}
|
||||
cameras: {{}} # No cameras defined, UI wizard should be used
|
||||
version: {CURRENT_CONFIG_VERSION}
|
||||
"""
|
||||
@@ -326,8 +331,20 @@ def verify_required_zones_exist(camera_config: CameraConfig) -> None:
|
||||
|
||||
|
||||
def verify_profile_overrides_match_base(camera_config: CameraConfig) -> None:
|
||||
"""Verify that profile zone and mask IDs reference entries defined on the base camera."""
|
||||
"""Verify profile overrides against the resolved base camera configuration."""
|
||||
for profile_name, profile in camera_config.profiles.items():
|
||||
if profile.birdseye is not None:
|
||||
overrides = profile.birdseye.mode.model_dump(exclude_unset=True)
|
||||
base_mode = camera_config.birdseye.mode.model_dump()
|
||||
resolved_mode = BirdseyeModeConfig.model_validate(
|
||||
deep_merge(overrides, base_mode)
|
||||
)
|
||||
if not resolved_mode.has_enabled_activity():
|
||||
raise ValueError(
|
||||
f"Camera '{camera_config.name}' profile '{profile_name}' must "
|
||||
"enable at least one Birdseye activity type"
|
||||
)
|
||||
|
||||
if profile.zones:
|
||||
for zone_name in profile.zones:
|
||||
if zone_name not in camera_config.zones:
|
||||
@@ -497,16 +514,11 @@ class FrigateConfig(FrigateBaseModel):
|
||||
description="User interface preferences such as timezone, time/date formatting, and units.",
|
||||
)
|
||||
|
||||
# Detector config
|
||||
detectors: dict[str, BaseDetectorConfig] = Field(
|
||||
default=DEFAULT_DETECTORS,
|
||||
title="Detector hardware",
|
||||
description="Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
|
||||
)
|
||||
model: ModelConfig = Field(
|
||||
default_factory=ModelConfig,
|
||||
title="Detection model",
|
||||
description="Settings to configure a custom object detection model and its input shape.",
|
||||
# Detection model config
|
||||
models: list[ModelConfig] = Field(
|
||||
default_factory=_default_models,
|
||||
title="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.",
|
||||
)
|
||||
|
||||
# GenAI config (named provider configs: name -> GenAIConfig)
|
||||
@@ -621,11 +633,202 @@ class FrigateConfig(FrigateBaseModel):
|
||||
)
|
||||
|
||||
_plus_api: PlusApi
|
||||
_model_devices: dict[SceneEnum, list[DeviceSpec]]
|
||||
_camera_models: dict[str, ModelConfig]
|
||||
_all_attributes: list[str]
|
||||
_all_attribute_logos: list[str]
|
||||
_all_attributes_map: dict[str, list[str]]
|
||||
_all_labels: set[str]
|
||||
|
||||
@property
|
||||
def plus_api(self) -> PlusApi:
|
||||
return self._plus_api
|
||||
|
||||
@property
|
||||
def all_attributes(self) -> list[str]:
|
||||
"""Every attribute label across all configured models."""
|
||||
return self._all_attributes
|
||||
|
||||
@property
|
||||
def all_attribute_logos(self) -> list[str]:
|
||||
"""Every logo attribute label across all configured models."""
|
||||
return self._all_attribute_logos
|
||||
|
||||
@property
|
||||
def all_attributes_map(self) -> dict[str, list[str]]:
|
||||
"""Object label to attribute labels, merged across all configured models."""
|
||||
return self._all_attributes_map
|
||||
|
||||
@property
|
||||
def all_labels(self) -> set[str]:
|
||||
"""Every object label across all configured models."""
|
||||
return self._all_labels
|
||||
|
||||
@property
|
||||
def primary_model(self) -> ModelConfig:
|
||||
"""The model used when no specific camera is in play."""
|
||||
for model in self.models:
|
||||
if model.scene == SceneEnum.all:
|
||||
return model
|
||||
|
||||
return self.models[0]
|
||||
|
||||
def model_for_camera(self, camera_name: str) -> ModelConfig:
|
||||
"""Get the detection model a camera runs on.
|
||||
|
||||
Args:
|
||||
camera_name: Name of the camera
|
||||
|
||||
Returns:
|
||||
The model matching the camera's detect scene
|
||||
"""
|
||||
return self._camera_models[camera_name]
|
||||
|
||||
def devices_for_model(self, model: ModelConfig) -> list[DeviceSpec]:
|
||||
"""Get the parsed hardware devices a model runs on.
|
||||
|
||||
Args:
|
||||
model: One of the configured models
|
||||
|
||||
Returns:
|
||||
The parsed device specs, in config order
|
||||
"""
|
||||
return self._model_devices[model.scene]
|
||||
|
||||
def _load_model(self, model: ModelConfig, detector: str) -> ModelConfig:
|
||||
"""Apply detector specific defaults to a model and load its weights and labels.
|
||||
|
||||
Args:
|
||||
model: The configured model
|
||||
detector: The detector type the model runs on
|
||||
|
||||
Returns:
|
||||
The loaded model
|
||||
"""
|
||||
model_config = model.model_dump(exclude_unset=True, warnings="none")
|
||||
|
||||
if "path" not in model_config:
|
||||
if detector == "cpu" or detector.endswith("_tfl"):
|
||||
model_config["path"] = "/cpu_model.tflite"
|
||||
elif detector == "edgetpu":
|
||||
model_config["path"] = "/edgetpu_model.tflite"
|
||||
elif detector == "openvino":
|
||||
for default_key, default_value in DEFAULT_MODEL.items():
|
||||
model_config.setdefault(default_key, default_value)
|
||||
|
||||
loaded = ModelConfig.model_validate(model_config)
|
||||
loaded.check_and_load_plus_model(self.plus_api, detector)
|
||||
loaded.compute_model_hash()
|
||||
return loaded
|
||||
|
||||
def _load_models(self) -> None:
|
||||
"""Validate the configured models and load each one."""
|
||||
if not self.models:
|
||||
raise ValueError("At least one model must be configured under models")
|
||||
|
||||
model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
|
||||
# device string -> the scene of the model that already claimed it
|
||||
claimed_devices: dict[str, SceneEnum] = {}
|
||||
|
||||
for index, model in enumerate(self.models):
|
||||
scene = model.scene.value
|
||||
|
||||
if model.scene in model_devices:
|
||||
raise ValueError(
|
||||
f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
|
||||
)
|
||||
|
||||
if not model.devices:
|
||||
raise ValueError(
|
||||
f"Model '{scene}' must list at least one entry under devices."
|
||||
)
|
||||
|
||||
try:
|
||||
devices = [parse_device(device) for device in model.devices]
|
||||
except DeviceParseError as err:
|
||||
raise ValueError(
|
||||
f"Model '{scene}' has an invalid device: {err}"
|
||||
) from err
|
||||
|
||||
detectors = {device.detector for device in devices}
|
||||
|
||||
if len(detectors) > 1:
|
||||
raise ValueError(
|
||||
f"Model '{scene}' mixes the {', '.join(sorted(detectors))} detectors. All of a model's devices must use the same detector."
|
||||
)
|
||||
|
||||
for device in devices:
|
||||
if device.raw in claimed_devices and not device.shareable:
|
||||
other = claimed_devices[device.raw]
|
||||
where = (
|
||||
f"twice by model '{scene}'"
|
||||
if other == model.scene
|
||||
else f"by both the '{other.value}' and '{scene}' models"
|
||||
)
|
||||
raise ValueError(
|
||||
f"Device '{device.raw}' is used {where}, but it can only run one detection process."
|
||||
)
|
||||
|
||||
claimed_devices[device.raw] = model.scene
|
||||
|
||||
self.models[index] = self._load_model(model, devices[0].detector)
|
||||
model_devices[model.scene] = devices
|
||||
|
||||
attributes: set[str] = set()
|
||||
attribute_logos: set[str] = set()
|
||||
attributes_map: dict[str, set[str]] = {}
|
||||
labels: set[str] = set()
|
||||
|
||||
for model in self.models:
|
||||
attributes.update(model.all_attributes)
|
||||
attribute_logos.update(model.all_attribute_logos)
|
||||
labels.update(model.merged_labelmap.values())
|
||||
|
||||
for label, label_attributes in model.attributes_map.items():
|
||||
attributes_map.setdefault(label, set()).update(label_attributes)
|
||||
|
||||
self._model_devices = model_devices
|
||||
self._all_attributes = sorted(attributes)
|
||||
self._all_attribute_logos = sorted(attribute_logos)
|
||||
self._all_attributes_map = {
|
||||
label: sorted(label_attributes)
|
||||
for label, label_attributes in sorted(attributes_map.items())
|
||||
}
|
||||
self._all_labels = labels
|
||||
|
||||
def _resolve_camera_model(self, name: str, scene: SceneEnum | None) -> ModelConfig:
|
||||
"""Resolve which model a camera runs on.
|
||||
|
||||
Args:
|
||||
name: Name of the camera
|
||||
scene: The camera's configured detect scene, if any
|
||||
|
||||
Returns:
|
||||
The model the camera runs on
|
||||
"""
|
||||
by_scene = {model.scene: model for model in self.models}
|
||||
|
||||
if scene is not None:
|
||||
model = by_scene.get(scene)
|
||||
|
||||
if model is None:
|
||||
raise ValueError(
|
||||
f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene."
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
default = by_scene.get(SceneEnum.all) or (
|
||||
self.models[0] if len(self.models) == 1 else None
|
||||
)
|
||||
|
||||
if default is None:
|
||||
raise ValueError(
|
||||
f"Camera '{name}' must set detect -> scene, because more than one model is configured and none of them uses a scene of 'all'."
|
||||
)
|
||||
|
||||
return default
|
||||
|
||||
@model_validator(mode="after")
|
||||
def post_validation(self, info: ValidationInfo) -> Self:
|
||||
# Load plus api from context, if possible.
|
||||
@@ -670,8 +873,10 @@ class FrigateConfig(FrigateBaseModel):
|
||||
"'embeddings' in its roles for semantic search."
|
||||
)
|
||||
|
||||
self._load_models()
|
||||
|
||||
# set default min_score for object attributes
|
||||
for attribute in self.model.all_attributes:
|
||||
for attribute in self.all_attributes:
|
||||
existing = self.objects.filters.get(attribute)
|
||||
if existing is None:
|
||||
self.objects.filters[attribute] = FilterConfig(min_score=0.7)
|
||||
@@ -721,44 +926,7 @@ class FrigateConfig(FrigateBaseModel):
|
||||
exclude_unset=True,
|
||||
)
|
||||
|
||||
for key, detector in self.detectors.items():
|
||||
adapter = TypeAdapter(DetectorConfig)
|
||||
model_dict = (
|
||||
detector
|
||||
if isinstance(detector, dict)
|
||||
else detector.model_dump(warnings="none")
|
||||
)
|
||||
detector_config: BaseDetectorConfig = adapter.validate_python(model_dict)
|
||||
|
||||
# users should not set model themselves
|
||||
if detector_config.model:
|
||||
logger.warning(
|
||||
"The model key should be specified at the root level of the config, not under detectors. The nested model key will be ignored."
|
||||
)
|
||||
detector_config.model = None
|
||||
|
||||
model_config = self.model.model_dump(exclude_unset=True, warnings="none")
|
||||
|
||||
if detector_config.model_path:
|
||||
model_config["path"] = detector_config.model_path
|
||||
|
||||
if "path" not in model_config:
|
||||
if detector_config.type == "cpu" or detector_config.type.endswith(
|
||||
"_tfl"
|
||||
):
|
||||
model_config["path"] = "/cpu_model.tflite"
|
||||
elif detector_config.type == "edgetpu":
|
||||
model_config["path"] = "/edgetpu_model.tflite"
|
||||
elif detector_config.type == "openvino":
|
||||
for default_key, default_value in DEFAULT_MODEL.items():
|
||||
model_config.setdefault(default_key, default_value)
|
||||
|
||||
model = ModelConfig.model_validate(model_config)
|
||||
model.check_and_load_plus_model(self.plus_api, detector_config.type)
|
||||
model.compute_model_hash()
|
||||
labelmap_objects = model.merged_labelmap.values()
|
||||
detector_config.model = model
|
||||
self.detectors[key] = detector_config
|
||||
self._camera_models = {}
|
||||
|
||||
for name, camera in self.cameras.items():
|
||||
modified_global_config = global_config.copy()
|
||||
@@ -785,6 +953,9 @@ class FrigateConfig(FrigateBaseModel):
|
||||
{"name": name, **merged_config}
|
||||
)
|
||||
|
||||
camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
|
||||
self._camera_models[name] = camera_model
|
||||
|
||||
if camera_config.ffmpeg.hwaccel_args == "auto":
|
||||
camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
|
||||
|
||||
@@ -998,6 +1169,10 @@ class FrigateConfig(FrigateBaseModel):
|
||||
self.cameras[name] = camera_config
|
||||
|
||||
verify_config_roles(camera_config)
|
||||
if not camera_config.birdseye.mode.has_enabled_activity():
|
||||
raise ValueError(
|
||||
f"Camera '{name}' must enable at least one Birdseye activity type"
|
||||
)
|
||||
verify_valid_live_stream_names(self, camera_config)
|
||||
verify_recording_segments_setup_with_reasonable_time(camera_config)
|
||||
verify_zone_objects_are_tracked(camera_config)
|
||||
@@ -1005,7 +1180,7 @@ class FrigateConfig(FrigateBaseModel):
|
||||
verify_profile_overrides_match_base(camera_config)
|
||||
verify_autotrack_zones(camera_config)
|
||||
verify_motion_and_detect(camera_config)
|
||||
verify_objects_track(camera_config, labelmap_objects)
|
||||
verify_objects_track(camera_config, camera_model.merged_labelmap.values())
|
||||
verify_lpr_and_face(self, camera_config)
|
||||
|
||||
# Validate camera profiles reference top-level profile definitions
|
||||
@@ -1022,8 +1197,16 @@ class FrigateConfig(FrigateBaseModel):
|
||||
config.name = name
|
||||
|
||||
self.objects.parse_all_objects(self.cameras)
|
||||
self.model.create_colormap(sorted(self.objects.all_objects))
|
||||
self.model.check_and_load_plus_model(self.plus_api)
|
||||
|
||||
# every model shares one colormap so a label is drawn the same color no
|
||||
# matter which model detected it, so filter attributes across all models
|
||||
# rather than letting each model filter with only its own
|
||||
colored_labels = sorted(
|
||||
set(self.objects.all_objects) - set(self.all_attributes)
|
||||
)
|
||||
|
||||
for model in self.models:
|
||||
model.create_colormap(colored_labels)
|
||||
|
||||
# Check audio transcription and audio detection requirements
|
||||
if self.audio_transcription.enabled:
|
||||
|
||||
@@ -43,7 +43,9 @@ SECTION_STATE_TOPICS: dict[str, list[tuple[str, Callable[[Any], Any]]]] = {
|
||||
("birdseye", lambda c: "ON" if c.birdseye.enabled else "OFF"),
|
||||
(
|
||||
"birdseye_mode",
|
||||
lambda c: c.birdseye.mode.value.upper() if c.birdseye.enabled else "OFF",
|
||||
lambda c: (
|
||||
c.birdseye.mode.to_mqtt_payload() if c.birdseye.enabled else "OFF"
|
||||
),
|
||||
),
|
||||
],
|
||||
"detect": [("detect", lambda c: "ON" if c.detect.enabled else "OFF")],
|
||||
|
||||
@@ -72,7 +72,7 @@ class LicensePlateProcessingMixin:
|
||||
# Object config
|
||||
self.lp_objects: list[str] = []
|
||||
|
||||
for obj, attributes in self.config.model.attributes_map.items():
|
||||
for obj, attributes in self.config.all_attributes_map.items():
|
||||
if "license_plate" in attributes:
|
||||
self.lp_objects.append(obj)
|
||||
|
||||
|
||||
@@ -231,8 +231,8 @@ class ReviewDescriptionProcessor(PostProcessorApi):
|
||||
final_data,
|
||||
thumbs,
|
||||
camera_config.review.genai,
|
||||
list(self.config.model.merged_labelmap.values()),
|
||||
self.config.model.all_attributes,
|
||||
sorted(self.config.all_labels),
|
||||
self.config.all_attributes,
|
||||
),
|
||||
).start()
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ import json
|
||||
import logging
|
||||
import os
|
||||
from enum import Enum
|
||||
from typing import Any
|
||||
from typing import Any, ClassVar
|
||||
|
||||
import requests
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
@@ -15,6 +15,9 @@ from frigate.util.builtin import generate_color_palette, load_labels
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# attributes that are recognized rather than shown as a logo
|
||||
NON_LOGO_ATTRIBUTES = ["face", "license_plate"]
|
||||
|
||||
|
||||
class PixelFormatEnum(str, Enum):
|
||||
rgb = "rgb"
|
||||
@@ -44,7 +47,27 @@ class ModelTypeEnum(str, Enum):
|
||||
yologeneric = "yolo-generic"
|
||||
|
||||
|
||||
class SceneEnum(str, Enum):
|
||||
"""The camera environment a detection model is intended for."""
|
||||
|
||||
all = "all"
|
||||
indoor = "indoor"
|
||||
outdoor = "outdoor"
|
||||
indoor_thermal = "indoor_thermal"
|
||||
outdoor_thermal = "outdoor_thermal"
|
||||
|
||||
|
||||
class ModelConfig(BaseModel):
|
||||
scene: SceneEnum = Field(
|
||||
default=SceneEnum.all,
|
||||
title="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.",
|
||||
)
|
||||
devices: list[str] = Field(
|
||||
default_factory=list,
|
||||
title="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.",
|
||||
)
|
||||
path: str | None = Field(
|
||||
None,
|
||||
title="Custom object detector model path",
|
||||
@@ -111,7 +134,7 @@ class ModelConfig(BaseModel):
|
||||
|
||||
@property
|
||||
def non_logo_attributes(self) -> list[str]:
|
||||
return ["face", "license_plate"]
|
||||
return NON_LOGO_ATTRIBUTES
|
||||
|
||||
@property
|
||||
def all_attributes(self) -> list[str]:
|
||||
@@ -201,9 +224,7 @@ class ModelConfig(BaseModel):
|
||||
unique_attributes.update(attributes)
|
||||
|
||||
self._all_attributes = list(unique_attributes)
|
||||
self._all_attribute_logos = list(
|
||||
unique_attributes - set(["face", "license_plate"])
|
||||
)
|
||||
self._all_attribute_logos = list(unique_attributes - set(NON_LOGO_ATTRIBUTES))
|
||||
|
||||
self._merged_labelmap = {
|
||||
**{int(key): val for key, val in model_info["labelMap"].items()},
|
||||
@@ -234,6 +255,14 @@ class ModelConfig(BaseModel):
|
||||
|
||||
|
||||
class BaseDetectorConfig(BaseModel):
|
||||
# how the trailing part of a device string ("openvino:GPU" -> "GPU") maps onto
|
||||
# this detector's fields, and whether the same device may be listed more than
|
||||
# once to run additional inference processes against it. Most accelerators
|
||||
# multiplex fine, so this is opt-out rather than opt-in.
|
||||
device_spec_field: ClassVar[str] = "device"
|
||||
device_spec_type: ClassVar[type] = str
|
||||
shareable: ClassVar[bool] = True
|
||||
|
||||
# the type field must be defined in all subclasses
|
||||
type: str = Field(
|
||||
default="cpu",
|
||||
|
||||
@@ -2,7 +2,7 @@ import importlib
|
||||
import logging
|
||||
import pkgutil
|
||||
from enum import Enum
|
||||
from typing import Annotated, Union
|
||||
from typing import Annotated, Union, get_args
|
||||
|
||||
from pydantic import Field
|
||||
|
||||
@@ -39,3 +39,21 @@ DetectorConfig = Annotated[
|
||||
Union[tuple(BaseDetectorConfig.__subclasses__())], # noqa: UP007
|
||||
Field(discriminator="type"),
|
||||
]
|
||||
|
||||
|
||||
def _discriminator_value(config_class: type[BaseDetectorConfig]) -> str | None:
|
||||
"""Read the Literal value of a detector config class' type field."""
|
||||
field = config_class.model_fields.get("type")
|
||||
|
||||
if field is None:
|
||||
return None
|
||||
|
||||
values = get_args(field.annotation)
|
||||
return values[0] if values else None
|
||||
|
||||
|
||||
config_types: dict[str, type[BaseDetectorConfig]] = {
|
||||
key: config_class
|
||||
for config_class in BaseDetectorConfig.__subclasses__()
|
||||
if (key := _discriminator_value(config_class)) is not None
|
||||
}
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
"""Parsing of detection hardware device strings."""
|
||||
|
||||
import logging
|
||||
from dataclasses import dataclass
|
||||
|
||||
from pydantic import TypeAdapter, ValidationError
|
||||
|
||||
from frigate.detectors.detector_config import BaseDetectorConfig, ModelConfig
|
||||
from frigate.detectors.detector_types import DetectorConfig, config_types
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
_detector_adapter: TypeAdapter[BaseDetectorConfig] = TypeAdapter(DetectorConfig)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class DeviceSpec:
|
||||
"""A parsed `<detector>` or `<detector>:<device>` string."""
|
||||
|
||||
raw: str
|
||||
detector: str
|
||||
device: str | None
|
||||
|
||||
@property
|
||||
def shareable(self) -> bool:
|
||||
"""Whether this device may be listed more than once."""
|
||||
return config_types[self.detector].shareable
|
||||
|
||||
|
||||
class DeviceParseError(ValueError):
|
||||
pass
|
||||
|
||||
|
||||
def parse_device(raw: str) -> DeviceSpec:
|
||||
"""Parse a device string into its detector type and detector specific device.
|
||||
|
||||
Args:
|
||||
raw: The configured device string, for example 'edgetpu:pci:0'
|
||||
|
||||
Returns:
|
||||
The parsed spec
|
||||
|
||||
Raises:
|
||||
DeviceParseError: If the detector type is unknown or the device is not
|
||||
valid for that detector
|
||||
"""
|
||||
detector, separator, device = raw.partition(":")
|
||||
|
||||
if detector not in config_types:
|
||||
raise DeviceParseError(
|
||||
f"'{raw}' does not name a known detector. Available detectors are {', '.join(sorted(config_types))}"
|
||||
)
|
||||
|
||||
spec = DeviceSpec(raw=raw, detector=detector, device=device if separator else None)
|
||||
|
||||
# surface a bad device now rather than when the detection process starts
|
||||
build_detector_config(spec, None)
|
||||
return spec
|
||||
|
||||
|
||||
def build_detector_config(
|
||||
spec: DeviceSpec, model: ModelConfig | None
|
||||
) -> BaseDetectorConfig:
|
||||
"""Build the detector config a device string describes.
|
||||
|
||||
Args:
|
||||
spec: The parsed device spec
|
||||
model: The model this detector runs, if it has been resolved yet
|
||||
|
||||
Returns:
|
||||
The validated detector config
|
||||
|
||||
Raises:
|
||||
DeviceParseError: If the device is not valid for this detector type
|
||||
"""
|
||||
config: dict[str, object] = {"type": spec.detector, "model": model}
|
||||
|
||||
if spec.device is not None:
|
||||
config_class = config_types[spec.detector]
|
||||
|
||||
try:
|
||||
config[config_class.device_spec_field] = config_class.device_spec_type(
|
||||
spec.device
|
||||
)
|
||||
except ValueError as err:
|
||||
raise DeviceParseError(
|
||||
f"'{spec.raw}' is not a valid {spec.detector} device: {err}"
|
||||
) from err
|
||||
|
||||
try:
|
||||
return _detector_adapter.validate_python(config)
|
||||
except ValidationError as err:
|
||||
raise DeviceParseError(f"'{spec.raw}' is not a valid device: {err}") from err
|
||||
|
||||
|
||||
def runner_names(devices: list[DeviceSpec]) -> list[str]:
|
||||
"""Build a unique name for each device, since a shareable device may repeat.
|
||||
|
||||
Args:
|
||||
devices: Every device spec across every configured model, in config order
|
||||
|
||||
Returns:
|
||||
A name per device, suffixed with '#2', '#3', etc. on repeats
|
||||
"""
|
||||
names: list[str] = []
|
||||
seen: dict[str, int] = {}
|
||||
|
||||
for spec in devices:
|
||||
count = seen.get(spec.raw, 0) + 1
|
||||
seen[spec.raw] = count
|
||||
names.append(spec.raw if count == 1 else f"{spec.raw}#{count}")
|
||||
|
||||
return names
|
||||
@@ -1,5 +1,5 @@
|
||||
import logging
|
||||
from typing import Literal
|
||||
from typing import ClassVar, Literal
|
||||
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
@@ -27,6 +27,9 @@ class CpuDetectorConfig(BaseDetectorConfig):
|
||||
title="CPU",
|
||||
)
|
||||
|
||||
device_spec_field: ClassVar[str] = "num_threads"
|
||||
device_spec_type: ClassVar[type] = int
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
num_threads: int = Field(
|
||||
default=3,
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
from typing import Literal
|
||||
from typing import ClassVar, Literal
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -28,6 +28,9 @@ class EdgeTpuDetectorConfig(BaseDetectorConfig):
|
||||
title="EdgeTPU",
|
||||
)
|
||||
|
||||
# a TPU can only be opened by one process
|
||||
shareable: ClassVar[bool] = False
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
device: str = Field(
|
||||
default=None,
|
||||
|
||||
@@ -5,7 +5,7 @@ import shutil
|
||||
import urllib.request
|
||||
import zipfile
|
||||
from queue import Queue
|
||||
from typing import Literal
|
||||
from typing import ClassVar, Literal
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -37,6 +37,9 @@ class MemryXDetectorConfig(BaseDetectorConfig):
|
||||
title="MemryX",
|
||||
)
|
||||
|
||||
# an accelerator can only be opened by one process
|
||||
shareable: ClassVar[bool] = False
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
device: str = Field(
|
||||
default="PCIe",
|
||||
|
||||
@@ -28,7 +28,7 @@ class OvDetectorConfig(BaseDetectorConfig):
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
device: str = Field(
|
||||
default=None,
|
||||
default="AUTO",
|
||||
title="Device Type",
|
||||
description="The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU').",
|
||||
)
|
||||
|
||||
@@ -2,7 +2,7 @@ import logging
|
||||
import os.path
|
||||
import re
|
||||
import urllib.request
|
||||
from typing import Literal
|
||||
from typing import ClassVar, Literal
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@@ -35,6 +35,9 @@ class RknnDetectorConfig(BaseDetectorConfig):
|
||||
title="RKNN",
|
||||
)
|
||||
|
||||
device_spec_field: ClassVar[str] = "num_cores"
|
||||
device_spec_type: ClassVar[type] = int
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
num_cores: int = Field(
|
||||
default=0,
|
||||
|
||||
@@ -14,7 +14,7 @@ try:
|
||||
except ModuleNotFoundError:
|
||||
TRT_SUPPORT = False
|
||||
|
||||
from typing import Literal
|
||||
from typing import ClassVar, Literal
|
||||
|
||||
from pydantic import ConfigDict, Field
|
||||
|
||||
@@ -53,6 +53,8 @@ class TensorRTDetectorConfig(BaseDetectorConfig):
|
||||
title="TensorRT",
|
||||
)
|
||||
|
||||
device_spec_type: ClassVar[type] = int
|
||||
|
||||
type: Literal[DETECTOR_KEY]
|
||||
device: int = Field(
|
||||
default=0, title="GPU Device Index", description="The GPU device index to use."
|
||||
|
||||
@@ -159,7 +159,8 @@ class EventProcessor(threading.Thread):
|
||||
if width is None or height is None:
|
||||
return
|
||||
|
||||
first_detector = list(self.config.detectors.values())[0]
|
||||
camera_model = self.config.model_for_camera(camera)
|
||||
camera_detector = self.config.devices_for_model(camera_model)[0].detector
|
||||
|
||||
start_time = event_data["start_time"]
|
||||
end_time = (
|
||||
@@ -229,13 +230,9 @@ class EventProcessor(threading.Thread):
|
||||
Event.thumbnail: event_data.get("thumbnail"),
|
||||
Event.has_clip: event_data["has_clip"],
|
||||
Event.has_snapshot: event_data["has_snapshot"],
|
||||
Event.model_hash: first_detector.model.model_hash
|
||||
if first_detector.model
|
||||
else None,
|
||||
Event.model_type: first_detector.model.model_type
|
||||
if first_detector.model
|
||||
else None,
|
||||
Event.detector_type: first_detector.type,
|
||||
Event.model_hash: camera_model.model_hash,
|
||||
Event.model_type: camera_model.model_type,
|
||||
Event.detector_type: camera_detector,
|
||||
Event.data: {
|
||||
"box": box,
|
||||
"region": region,
|
||||
|
||||
+65
-121
@@ -262,6 +262,10 @@ def get_tool_definitions(
|
||||
`attribute` parameter is exposed for filtering by their labels. When the
|
||||
embeddings model only understands English (JinaV1), the `semantic_query`
|
||||
description instructs the model to write the query in English.
|
||||
|
||||
Descriptions here stay mechanical: which tool to reach for, and how the
|
||||
filters relate to each other, is stated once in the system prompt so the
|
||||
guidance is not paid for twice on every request.
|
||||
"""
|
||||
search_objects_properties: dict[str, Any] = {
|
||||
"camera": {
|
||||
@@ -270,26 +274,13 @@ def get_tool_definitions(
|
||||
},
|
||||
"label": {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Generic object class to filter by — one of the tracked detector "
|
||||
"labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
|
||||
"this for broad queries like 'show me all cars today'. Combine "
|
||||
"with semantic_query when the user also describes appearance or "
|
||||
"behavior (e.g. label='person', semantic_query='riding a lawn "
|
||||
"mower')."
|
||||
),
|
||||
"description": "Tracked object class to filter by.",
|
||||
},
|
||||
"sub_label": {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Filter by a DISCRETE NAMED entity recognized in the detection. "
|
||||
"Use this for: a known person's name ('John'), a delivery "
|
||||
"company ('Amazon', 'UPS'), a recognized animal species or "
|
||||
"breed ('blue jay', 'cardinal', 'golden retriever'), or a "
|
||||
"license plate string. When filtering by a specific name, set "
|
||||
"only sub_label and leave label unset. Do NOT use sub_label "
|
||||
"for descriptions of appearance, clothing, or actions — those "
|
||||
"belong in semantic_query."
|
||||
"Name recognized in the detection: a person, delivery company, "
|
||||
"animal species or breed, or license plate."
|
||||
),
|
||||
},
|
||||
"after": {
|
||||
@@ -313,20 +304,11 @@ def get_tool_definitions(
|
||||
}
|
||||
|
||||
if attribute_classifications:
|
||||
model_outline = "; ".join(
|
||||
f"{m['name']} (applies to {', '.join(m['objects']) or 'any object'})"
|
||||
for m in attribute_classifications
|
||||
)
|
||||
search_objects_properties["attribute"] = {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Filter by a classification attribute label produced by a "
|
||||
"configured attribute classification model. Use this INSTEAD "
|
||||
"of semantic_query when the user's request matches one of "
|
||||
"these classifications. Configured models: "
|
||||
f"{model_outline}. "
|
||||
"Set the value to the attribute label that matches the user's "
|
||||
"phrasing (case-sensitive)."
|
||||
"Attribute label produced by a configured classification model "
|
||||
"(case-sensitive)."
|
||||
),
|
||||
}
|
||||
|
||||
@@ -334,29 +316,12 @@ def get_tool_definitions(
|
||||
search_objects_properties["semantic_query"] = {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Optional natural-language description of a PHYSICAL "
|
||||
"CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
|
||||
"used to semantically narrow results. Only set this when the "
|
||||
"user describes something beyond what label and sub_label can "
|
||||
"express on their own.\n"
|
||||
"USE for descriptive phrases like: 'riding a lawn mower', "
|
||||
"'wearing a red jacket', 'carrying a package', 'walking a "
|
||||
"dog', 'on a bicycle', 'holding an umbrella'.\n"
|
||||
"DO NOT USE for:\n"
|
||||
"- specific named people, pets, or delivery companies → use sub_label\n"
|
||||
"- animal species or breed names like 'blue jay', 'cardinal', "
|
||||
"'golden retriever' → use sub_label\n"
|
||||
"- license plate strings → use sub_label\n"
|
||||
"- generic object queries like 'all cars today' or 'every "
|
||||
"person' → use label alone with no semantic_query\n"
|
||||
"When set, combine with label/time/camera/zone filters as "
|
||||
"usual (e.g. label='person', semantic_query='riding a lawn "
|
||||
"mower', after='2024-05-01T00:00:00Z')."
|
||||
"Description of an appearance or activity, used to semantically "
|
||||
"narrow results."
|
||||
+ (
|
||||
" The configured embeddings model only understands "
|
||||
"English, so always write semantic_query in English, "
|
||||
"translating the user's description if they phrased it "
|
||||
"in another language."
|
||||
" The configured embeddings model only understands English, so "
|
||||
"always write this in English, translating the user's "
|
||||
"description if they phrased it in another language."
|
||||
if embeddings_language == "english"
|
||||
else ""
|
||||
)
|
||||
@@ -364,26 +329,10 @@ def get_tool_definitions(
|
||||
}
|
||||
|
||||
search_objects_description = (
|
||||
"Search the historical record of detected objects in Frigate. "
|
||||
"Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
|
||||
"'when was the last car?', 'show me detections from yesterday'. "
|
||||
"Do NOT use this for monitoring or alerting requests about future events — "
|
||||
"use start_camera_watch instead for those. "
|
||||
"An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
|
||||
"Choose filters based on what the user is asking for:\n"
|
||||
"- Generic class query ('show me all cars today'): set `label` only.\n"
|
||||
"- Specific NAMED entity (known person, delivery company, animal "
|
||||
"species/breed like 'blue jay' or 'golden retriever', license "
|
||||
"plate): set `sub_label` only and leave `label` unset.\n"
|
||||
"Search the historical record of tracked detections. Use this ONLY for "
|
||||
"questions about the PAST, e.g. 'did anyone come by today?', 'when was the "
|
||||
"last car?'. For alerting on future events use start_camera_watch instead."
|
||||
)
|
||||
if semantic_search_enabled:
|
||||
search_objects_description += (
|
||||
"- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
|
||||
"discrete name ('person riding a lawn mower', 'someone in a red "
|
||||
"jacket', 'person carrying a package'): set `semantic_query` with "
|
||||
"the descriptive phrase, optionally alongside `label` for the "
|
||||
"object class. Do NOT put descriptive phrases in sub_label."
|
||||
)
|
||||
|
||||
return [
|
||||
{
|
||||
@@ -398,20 +347,30 @@ def get_tool_definitions(
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "get_categorized_object_names",
|
||||
"description": (
|
||||
"Every name that can be attached as a sub_label, grouped by object "
|
||||
"type: recognized faces, named license plates, classification "
|
||||
"categories, and delivery logos. Takes no arguments and always "
|
||||
"returns the complete map."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "find_similar_objects",
|
||||
"description": (
|
||||
"Find tracked objects that are visually and semantically similar "
|
||||
"to a specific past event. Use this when the user references a "
|
||||
"particular object they have seen and wants to find other "
|
||||
"sightings of the same or similar one ('that green car', 'the "
|
||||
"person in the red jacket', 'the package that was delivered'). "
|
||||
"Prefer this over search_objects whenever the user's intent is "
|
||||
"'find more like this specific one.' Use search_objects first "
|
||||
"only if you need to locate the anchor event. Requires semantic "
|
||||
"search to be enabled."
|
||||
"Find tracked objects visually and semantically similar to a "
|
||||
"specific past event. Requires semantic search to be enabled."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
@@ -473,9 +432,8 @@ def get_tool_definitions(
|
||||
"function": {
|
||||
"name": "set_camera_state",
|
||||
"description": (
|
||||
"Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
|
||||
"Use camera='*' to apply to all cameras at once. "
|
||||
"Only call this tool when the user explicitly asks to change a camera setting. "
|
||||
"Change a camera's feature state, e.g. turn detection on or off. "
|
||||
"Only call this when the user explicitly asks to change a setting. "
|
||||
"Requires admin privileges."
|
||||
),
|
||||
"parameters": {
|
||||
@@ -510,14 +468,14 @@ def get_tool_definitions(
|
||||
],
|
||||
"description": (
|
||||
"The feature to change. Most features accept ON or OFF. "
|
||||
"birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
|
||||
"birdseye_mode accepts CONTINUOUS, MOTION, OBJECTS, STATIONARY_OBJECTS, or a comma-separated combination. "
|
||||
"motion_contour_area and motion_threshold accept a number. "
|
||||
"profile accepts a profile name or 'none' to deactivate (requires camera='*')."
|
||||
),
|
||||
},
|
||||
"value": {
|
||||
"type": "string",
|
||||
"description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
|
||||
"description": "The value to set, as accepted by the chosen feature.",
|
||||
},
|
||||
},
|
||||
"required": ["camera", "feature", "value"],
|
||||
@@ -529,11 +487,9 @@ def get_tool_definitions(
|
||||
"function": {
|
||||
"name": "get_live_context",
|
||||
"description": (
|
||||
"Get the current live image and detection information for a single camera: objects being tracked, "
|
||||
"zones, timestamps. Use this to understand what is visible in the live view. "
|
||||
"Call this when answering questions about what is happening right now on a specific camera. "
|
||||
"Operates on one camera at a time; call the tool again for each additional camera. "
|
||||
"Wildcards and empty values are not accepted."
|
||||
"Current live image and detections (tracked objects, zones, "
|
||||
"timestamps) for one camera. Use this for questions about what is "
|
||||
"happening right now. Call it again for each additional camera."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
@@ -541,8 +497,8 @@ def get_tool_definitions(
|
||||
"camera": {
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Exact name of a single camera to get live context for. "
|
||||
"Wildcards (e.g. '*', 'all') and empty strings are not accepted."
|
||||
"Exact name of a single camera. Wildcards (e.g. '*', "
|
||||
"'all') and empty strings are not accepted."
|
||||
),
|
||||
},
|
||||
},
|
||||
@@ -555,10 +511,9 @@ def get_tool_definitions(
|
||||
"function": {
|
||||
"name": "start_camera_watch",
|
||||
"description": (
|
||||
"Start a continuous VLM watch job that monitors a camera and sends a notification "
|
||||
"when a specified condition is met. Use this when the user wants to be alerted about "
|
||||
"a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
|
||||
"Only one watch job can run at a time. Returns a job ID."
|
||||
"Start a continuous watch job that monitors a camera and notifies "
|
||||
"the user when a condition is met, e.g. 'tell me when guests "
|
||||
"arrive'. Only one watch job can run at a time. Returns a job ID."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
@@ -598,10 +553,7 @@ def get_tool_definitions(
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "stop_camera_watch",
|
||||
"description": (
|
||||
"Cancel the currently running VLM watch job. Use this when the user wants to "
|
||||
"stop a previously started watch, e.g. 'stop watching the front door'."
|
||||
),
|
||||
"description": "Cancel the currently running watch job.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
@@ -614,11 +566,9 @@ def get_tool_definitions(
|
||||
"function": {
|
||||
"name": "get_profile_status",
|
||||
"description": (
|
||||
"Get the current profile status including the active profile and "
|
||||
"timestamps of when each profile was last activated. Use this to "
|
||||
"determine time periods for recap requests — e.g. when the user asks "
|
||||
"'what happened while I was away?', call this first to find the relevant "
|
||||
"time window based on profile activation history."
|
||||
"Get the active profile and when each profile was last activated. "
|
||||
"Call this before get_recap to derive the time window for requests "
|
||||
"like 'what happened while I was away?'."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
@@ -632,11 +582,9 @@ def get_tool_definitions(
|
||||
"function": {
|
||||
"name": "get_recap",
|
||||
"description": (
|
||||
"Get a recap of all activity (alerts and detections) for a given time period. "
|
||||
"Use this after calling get_profile_status to retrieve what happened during "
|
||||
"a specific window — e.g. 'what happened while I was away?'. Returns a "
|
||||
"chronological list of activity with camera, objects, zones, and GenAI-generated "
|
||||
"descriptions when available. Summarize the results for the user."
|
||||
"Get all activity (alerts and detections) for a time period, as a "
|
||||
"chronological list with camera, objects, zones, and descriptions "
|
||||
"when available. Summarize the results for the user."
|
||||
),
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
@@ -723,14 +671,13 @@ def build_chat_system_prompt(
|
||||
)
|
||||
speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
|
||||
|
||||
semantic_search_section = ""
|
||||
filter_routing_section = (
|
||||
"\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
|
||||
"- Generic class ('show me all cars today'): set `label` only.\n"
|
||||
"- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset. Call get_categorized_object_names first and use the exact spelling it returns; a guessed spelling matches nothing. If the name is absent, say it is not configured rather than searching for it."
|
||||
)
|
||||
if semantic_search_enabled:
|
||||
semantic_search_section = (
|
||||
"\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
|
||||
"- Generic class ('show me all cars today'): set `label` only.\n"
|
||||
"- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
|
||||
"- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
|
||||
)
|
||||
filter_routing_section += "\n- Physical characteristic, appearance, or activity that is NOT a discrete name ('riding a lawn mower', 'someone in a red jacket'): set `semantic_query` with the descriptive phrase, optionally combined with `label`. Never put descriptive phrases in `sub_label`."
|
||||
|
||||
attribute_classification_section = ""
|
||||
if attribute_classifications:
|
||||
@@ -739,9 +686,9 @@ def build_chat_system_prompt(
|
||||
for m in attribute_classifications
|
||||
)
|
||||
attribute_classification_section = (
|
||||
"\n\nAttribute classification models are configured for the following object types:\n"
|
||||
"\n\nConfigured attribute classification models:\n"
|
||||
f"{model_lines}\n"
|
||||
"When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases that fall outside the configured attribute labels."
|
||||
"When the user's request matches one of these classifications, set the search_objects `attribute` field to the matching label (case-sensitive) rather than using `semantic_query`. Reserve `semantic_query` for descriptive phrases outside the configured attribute labels."
|
||||
)
|
||||
|
||||
return f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
|
||||
@@ -750,9 +697,6 @@ Current server local date and time: {current_date_str} at {current_time_str}
|
||||
|
||||
Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
|
||||
|
||||
Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
|
||||
When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
|
||||
When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
|
||||
Always be accurate with time calculations based on the current date provided.
|
||||
Always present times in the server's local timezone. When tool results include start_time_local and end_time_local, quote those strings exactly; never convert or invent timestamps, and fall back to UTC or ISO format only when a result has no local time fields. Resolve relative dates like "today" or "this week" against the current date above, and pass dates to tools in ISO 8601 (e.g. {current_date_str}T00:00:00Z for the start of today).
|
||||
|
||||
When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
|
||||
When the user refers to a specific object they have seen ("that green car", "the person in the red jacket", "a package left today"), prefer find_similar_objects over search_objects, using search_objects only to locate the anchor event and passing its id along. Keep search_objects for generic queries like "show me all cars today". If a user message begins with [attached_event:<id>], treat that id as the anchor for any similarity or "tell me more" request in the same message.{filter_routing_section}{attribute_classification_section}{cameras_section}{speed_units_section}"""
|
||||
|
||||
@@ -5,7 +5,19 @@ import threading
|
||||
|
||||
from numpy import ndarray
|
||||
|
||||
from frigate.detectors.detector_config import InputTensorEnum
|
||||
from frigate.detectors.detector_config import InputTensorEnum, ModelConfig
|
||||
|
||||
|
||||
def detection_frame_size(model: ModelConfig) -> int:
|
||||
"""Get the shared memory size a camera needs to hand frames to a model.
|
||||
|
||||
Args:
|
||||
model: The model the camera runs on
|
||||
|
||||
Returns:
|
||||
Size in bytes of one model input frame
|
||||
"""
|
||||
return model.height * model.width * 3
|
||||
|
||||
|
||||
class RequestStore:
|
||||
|
||||
+129
-120
@@ -9,6 +9,7 @@ import queue
|
||||
import subprocess as sp
|
||||
import threading
|
||||
import traceback
|
||||
from dataclasses import dataclass
|
||||
from multiprocessing.synchronize import Event as MpEvent
|
||||
from typing import Any
|
||||
|
||||
@@ -16,7 +17,7 @@ import cv2
|
||||
import numpy as np
|
||||
|
||||
from frigate.comms.inter_process import InterProcessRequestor
|
||||
from frigate.config import BirdseyeModeEnum, FfmpegConfig, FrigateConfig
|
||||
from frigate.config import BirdseyeModeConfig, FfmpegConfig, FrigateConfig
|
||||
from frigate.const import BASE_DIR, BIRDSEYE_PIPE, INSTALL_DIR, UPDATE_BIRDSEYE_LAYOUT
|
||||
from frigate.output.ws_auth import ws_has_camera_access
|
||||
from frigate.util.image import (
|
||||
@@ -28,6 +29,15 @@ from frigate.util.image import (
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
class BirdseyeActivity:
|
||||
"""Activity signals used to decide whether a camera is shown in Birdseye."""
|
||||
|
||||
has_active_object: bool
|
||||
has_stationary_object: bool
|
||||
has_motion: bool
|
||||
|
||||
|
||||
def get_standard_aspect_ratio(width: int, height: int) -> tuple[int, int]:
|
||||
"""Ensure that only standard aspect ratios are used."""
|
||||
# it is important that all ratios have the same scale
|
||||
@@ -409,18 +419,16 @@ class BirdsEyeFrameManager:
|
||||
)
|
||||
|
||||
def camera_active(
|
||||
self, mode: Any, object_box_count: int, motion_box_count: int
|
||||
self,
|
||||
mode: BirdseyeModeConfig,
|
||||
activity: BirdseyeActivity,
|
||||
) -> bool:
|
||||
if mode == BirdseyeModeEnum.continuous:
|
||||
return True
|
||||
|
||||
if mode == BirdseyeModeEnum.motion and motion_box_count > 0:
|
||||
return True
|
||||
|
||||
if mode == BirdseyeModeEnum.objects and object_box_count > 0:
|
||||
return True
|
||||
|
||||
return False
|
||||
return (
|
||||
mode.continuous
|
||||
or (mode.motion and activity.has_motion)
|
||||
or (mode.objects and activity.has_active_object)
|
||||
or (mode.stationary_objects and activity.has_stationary_object)
|
||||
)
|
||||
|
||||
def get_camera_coordinates(self) -> dict[str, dict[str, int]]:
|
||||
"""Return the coordinates of each camera in the current layout."""
|
||||
@@ -604,112 +612,92 @@ class BirdsEyeFrameManager:
|
||||
) -> list[list[Any]] | None:
|
||||
"""Calculate the optimal layout for 2+ cameras."""
|
||||
|
||||
def map_layout(
|
||||
camera_layout: list[list[Any]], row_height: int
|
||||
) -> tuple[int, int, list[list[Any]] | None]:
|
||||
"""Map the calculated layout."""
|
||||
candidate_layout = []
|
||||
starting_x = 0
|
||||
x = 0
|
||||
max_width = 0
|
||||
y = 0
|
||||
def find_available_x(
|
||||
current_x: int,
|
||||
width: int,
|
||||
reserved_ranges: list[tuple[int, int]],
|
||||
max_width: int,
|
||||
) -> int | None:
|
||||
"""Find the first horizontal slot that does not collide with reservations."""
|
||||
x = current_x
|
||||
|
||||
for row in camera_layout:
|
||||
final_row = []
|
||||
max_width = max(max_width, x)
|
||||
x = starting_x
|
||||
for cameras in row:
|
||||
camera_dims = self.cameras[cameras[0]]["dimensions"].copy()
|
||||
camera_aspect = cameras[1]
|
||||
for reserved_start, reserved_end in sorted(reserved_ranges):
|
||||
if x >= reserved_end:
|
||||
continue
|
||||
|
||||
if camera_dims[1] > camera_dims[0]:
|
||||
scaled_height = int(row_height * 2)
|
||||
scaled_width = int(scaled_height * camera_aspect)
|
||||
starting_x = scaled_width
|
||||
else:
|
||||
scaled_height = row_height
|
||||
scaled_width = int(scaled_height * camera_aspect)
|
||||
if x + width <= reserved_start:
|
||||
return x
|
||||
|
||||
# layout is too large
|
||||
if (
|
||||
x + scaled_width > self.canvas.width
|
||||
or y + scaled_height > self.canvas.height
|
||||
):
|
||||
return x + scaled_width, y + scaled_height, None
|
||||
x = max(x, reserved_end)
|
||||
|
||||
final_row.append((cameras[0], (x, y, scaled_width, scaled_height)))
|
||||
x += scaled_width
|
||||
if x + width <= max_width:
|
||||
return x
|
||||
|
||||
y += row_height
|
||||
candidate_layout.append(final_row)
|
||||
|
||||
if max_width == 0:
|
||||
max_width = x
|
||||
|
||||
return max_width, y, candidate_layout
|
||||
|
||||
canvas_aspect_x, canvas_aspect_y = self.canvas.get_aspect(coefficient)
|
||||
camera_layout: list[list[Any]] = []
|
||||
camera_layout.append([])
|
||||
starting_x = 0
|
||||
x = starting_x
|
||||
y = 0
|
||||
y_i = 0
|
||||
max_y = 0
|
||||
for camera in cameras_to_add:
|
||||
camera_dims = self.cameras[camera]["dimensions"].copy()
|
||||
camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
|
||||
camera, camera_dims[0], camera_dims[1]
|
||||
)
|
||||
|
||||
if camera_dims[1] > camera_dims[0]:
|
||||
portrait = True
|
||||
else:
|
||||
portrait = False
|
||||
|
||||
if (x + camera_aspect_x) <= canvas_aspect_x:
|
||||
# insert if camera can fit on current row
|
||||
camera_layout[y_i].append(
|
||||
(
|
||||
camera,
|
||||
camera_aspect_x / camera_aspect_y,
|
||||
)
|
||||
)
|
||||
|
||||
if portrait:
|
||||
starting_x = camera_aspect_x
|
||||
else:
|
||||
max_y = max(
|
||||
max_y,
|
||||
camera_aspect_y,
|
||||
)
|
||||
|
||||
x += camera_aspect_x
|
||||
else:
|
||||
# move on to the next row and insert
|
||||
y += max_y
|
||||
y_i += 1
|
||||
camera_layout.append([])
|
||||
x = starting_x
|
||||
|
||||
if x + camera_aspect_x > canvas_aspect_x:
|
||||
return None
|
||||
|
||||
camera_layout[y_i].append(
|
||||
(
|
||||
camera,
|
||||
camera_aspect_x / camera_aspect_y,
|
||||
)
|
||||
)
|
||||
x += camera_aspect_x
|
||||
|
||||
if y + max_y > canvas_aspect_y:
|
||||
return None
|
||||
|
||||
row_height = int(self.canvas.height / coefficient)
|
||||
total_width, total_height, standard_candidate_layout = map_layout(
|
||||
camera_layout, row_height
|
||||
)
|
||||
def map_layout(row_height: int) -> tuple[int, int, list[list[Any]] | None]:
|
||||
"""Lay out cameras row by row while reserving portrait spans for the next row."""
|
||||
candidate_layout: list[list[Any]] = []
|
||||
reserved_ranges: dict[int, list[tuple[int, int]]] = {}
|
||||
current_row: list[Any] = []
|
||||
row_index = 0
|
||||
row_y = 0
|
||||
row_x = 0
|
||||
max_width = 0
|
||||
max_height = 0
|
||||
|
||||
for camera in cameras_to_add:
|
||||
camera_dims = self.cameras[camera]["dimensions"].copy()
|
||||
camera_aspect_x, camera_aspect_y = self.canvas.get_camera_aspect(
|
||||
camera, camera_dims[0], camera_dims[1]
|
||||
)
|
||||
portrait = camera_dims[1] > camera_dims[0]
|
||||
scaled_height = row_height * 2 if portrait else row_height
|
||||
scaled_width = int(scaled_height * (camera_aspect_x / camera_aspect_y))
|
||||
|
||||
while True:
|
||||
x = find_available_x(
|
||||
row_x,
|
||||
scaled_width,
|
||||
reserved_ranges.get(row_index, []),
|
||||
self.canvas.width,
|
||||
)
|
||||
|
||||
if x is not None and row_y + scaled_height <= self.canvas.height:
|
||||
current_row.append(
|
||||
(camera, (x, row_y, scaled_width, scaled_height))
|
||||
)
|
||||
row_x = x + scaled_width
|
||||
max_width = max(max_width, row_x)
|
||||
max_height = max(max_height, row_y + scaled_height)
|
||||
|
||||
if portrait:
|
||||
reserved_ranges.setdefault(row_index + 1, []).append(
|
||||
(x, row_x)
|
||||
)
|
||||
|
||||
break
|
||||
|
||||
if current_row:
|
||||
candidate_layout.append(current_row)
|
||||
current_row = []
|
||||
|
||||
row_index += 1
|
||||
row_y = row_index * row_height
|
||||
row_x = 0
|
||||
|
||||
if row_y + scaled_height > self.canvas.height:
|
||||
overflow_width = max(max_width, scaled_width)
|
||||
overflow_height = row_y + scaled_height
|
||||
return overflow_width, overflow_height, None
|
||||
|
||||
if current_row:
|
||||
candidate_layout.append(current_row)
|
||||
|
||||
return max_width, max_height, candidate_layout
|
||||
|
||||
row_height = max(1, int(self.canvas.height / coefficient))
|
||||
total_width, total_height, standard_candidate_layout = map_layout(row_height)
|
||||
|
||||
if not standard_candidate_layout:
|
||||
# if standard layout didn't work
|
||||
@@ -718,9 +706,9 @@ class BirdsEyeFrameManager:
|
||||
total_width / self.canvas.width,
|
||||
total_height / self.canvas.height,
|
||||
)
|
||||
row_height = int(row_height / scale_down_percent)
|
||||
row_height = max(1, int(row_height / scale_down_percent))
|
||||
total_width, total_height, standard_candidate_layout = map_layout(
|
||||
camera_layout, row_height
|
||||
row_height
|
||||
)
|
||||
|
||||
if not standard_candidate_layout:
|
||||
@@ -734,8 +722,8 @@ class BirdsEyeFrameManager:
|
||||
1 / (total_width / self.canvas.width),
|
||||
1 / (total_height / self.canvas.height),
|
||||
)
|
||||
row_height = int(row_height * scale_up_percent)
|
||||
_, _, scaled_layout = map_layout(camera_layout, row_height)
|
||||
row_height = max(1, int(row_height * scale_up_percent))
|
||||
_, _, scaled_layout = map_layout(row_height)
|
||||
|
||||
if scaled_layout:
|
||||
return scaled_layout
|
||||
@@ -745,8 +733,7 @@ class BirdsEyeFrameManager:
|
||||
def update(
|
||||
self,
|
||||
camera: str,
|
||||
object_count: int,
|
||||
motion_count: int,
|
||||
activity: BirdseyeActivity,
|
||||
frame_time: float,
|
||||
frame: np.ndarray,
|
||||
) -> tuple[bool, bool]:
|
||||
@@ -774,7 +761,10 @@ class BirdsEyeFrameManager:
|
||||
# update the last active frame for the camera
|
||||
self.cameras[camera]["current_frame"] = frame.copy()
|
||||
self.cameras[camera]["current_frame_time"] = frame_time
|
||||
if self.camera_active(camera_config.birdseye.mode, object_count, motion_count):
|
||||
if self.camera_active(
|
||||
camera_config.birdseye.mode,
|
||||
activity,
|
||||
):
|
||||
self.cameras[camera]["last_active_frame"] = frame_time
|
||||
|
||||
now = datetime.datetime.now().timestamp()
|
||||
@@ -882,10 +872,29 @@ class Birdseye:
|
||||
frame_time: float,
|
||||
frame: np.ndarray,
|
||||
) -> None:
|
||||
has_active_object = False
|
||||
has_stationary_object = False
|
||||
for tracked_object in current_tracked_objects:
|
||||
if tracked_object["stationary"]:
|
||||
if not tracked_object["false_positive"]:
|
||||
has_stationary_object = True
|
||||
else:
|
||||
# Preserve the existing objects activity behavior, which includes
|
||||
# non-stationary trackers before they are confirmed.
|
||||
has_active_object = True
|
||||
|
||||
if has_active_object and has_stationary_object:
|
||||
break
|
||||
|
||||
activity = BirdseyeActivity(
|
||||
has_active_object=has_active_object,
|
||||
has_stationary_object=has_stationary_object,
|
||||
has_motion=bool(motion_boxes),
|
||||
)
|
||||
|
||||
frame_changed, frame_layout_changed = self.birdseye_manager.update(
|
||||
camera,
|
||||
len([o for o in current_tracked_objects if not o["stationary"]]),
|
||||
len(motion_boxes),
|
||||
activity,
|
||||
frame_time,
|
||||
frame,
|
||||
)
|
||||
|
||||
@@ -476,7 +476,7 @@ class ReviewSegmentMaintainer(threading.Thread):
|
||||
|
||||
if not object["sub_label"]:
|
||||
segment.detections[object["id"]] = object["label"]
|
||||
elif object["sub_label"][0] in self.config.model.all_attributes:
|
||||
elif object["sub_label"][0] in self.config.all_attributes:
|
||||
segment.detections[object["id"]] = object["sub_label"][0]
|
||||
else:
|
||||
segment.detections[object["id"]] = f"{object['label']}-verified"
|
||||
@@ -614,7 +614,7 @@ class ReviewSegmentMaintainer(threading.Thread):
|
||||
for object in activity.get_all_objects():
|
||||
if not object["sub_label"]:
|
||||
detections[object["id"]] = object["label"]
|
||||
elif object["sub_label"][0] in self.config.model.all_attributes:
|
||||
elif object["sub_label"][0] in self.config.all_attributes:
|
||||
detections[object["id"]] = object["sub_label"][0]
|
||||
else:
|
||||
detections[object["id"]] = f"{object['label']}-verified"
|
||||
|
||||
+14
-13
@@ -322,19 +322,20 @@ async def set_gpu_stats(
|
||||
async def set_npu_usages(config: FrigateConfig, all_stats: dict[str, Any]) -> None:
|
||||
stats: dict[str, dict] = {}
|
||||
|
||||
for detector in config.detectors.values():
|
||||
if detector.type == "rknn":
|
||||
# Rockchip NPU usage
|
||||
rk_usage = get_rockchip_npu_stats()
|
||||
stats["rockchip"] = rk_usage
|
||||
elif detector.type == "openvino" and detector.device == "NPU":
|
||||
# OpenVINO NPU usage
|
||||
ov_usage = get_openvino_npu_stats()
|
||||
stats["openvino"] = ov_usage
|
||||
elif detector.type == "axengine":
|
||||
# AXERA NPU usage
|
||||
axcl_usage = get_axcl_npu_stats()
|
||||
stats["axengine"] = axcl_usage
|
||||
for model in config.models:
|
||||
for device in config.devices_for_model(model):
|
||||
if device.detector == "rknn":
|
||||
# Rockchip NPU usage
|
||||
rk_usage = get_rockchip_npu_stats()
|
||||
stats["rockchip"] = rk_usage
|
||||
elif device.detector == "openvino" and device.device == "NPU":
|
||||
# OpenVINO NPU usage
|
||||
ov_usage = get_openvino_npu_stats()
|
||||
stats["openvino"] = ov_usage
|
||||
elif device.detector == "axengine":
|
||||
# AXERA NPU usage
|
||||
axcl_usage = get_axcl_npu_stats()
|
||||
stats["axengine"] = axcl_usage
|
||||
|
||||
if stats:
|
||||
all_stats["npu_usages"] = stats
|
||||
|
||||
@@ -386,9 +386,19 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
|
||||
guess (mode still equals the previous global) wrongly claims a camera
|
||||
whose explicit yaml mode happens to match.
|
||||
"""
|
||||
self.minimal_config["birdseye"] = {"enabled": True, "mode": "motion"}
|
||||
self.minimal_config["birdseye"] = {
|
||||
"enabled": True,
|
||||
"mode": {"motion": True},
|
||||
}
|
||||
# explicit override that matches the global value being replaced
|
||||
self.minimal_config["cameras"]["front_door"]["birdseye"] = {"mode": "motion"}
|
||||
self.minimal_config["cameras"]["front_door"]["birdseye"] = {
|
||||
"mode": {
|
||||
"continuous": False,
|
||||
"motion": True,
|
||||
"objects": False,
|
||||
"stationary_objects": False,
|
||||
}
|
||||
}
|
||||
|
||||
config_path = self._write_config_file()
|
||||
mock_find_config.return_value = config_path
|
||||
@@ -399,7 +409,16 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
|
||||
resp = client.put(
|
||||
"/config/set",
|
||||
json={
|
||||
"config_data": {"birdseye": {"mode": "continuous"}},
|
||||
"config_data": {
|
||||
"birdseye": {
|
||||
"mode": {
|
||||
"continuous": True,
|
||||
"motion": False,
|
||||
"objects": False,
|
||||
"stationary_objects": False,
|
||||
}
|
||||
}
|
||||
},
|
||||
"update_topic": "config/birdseye",
|
||||
"requires_restart": 0,
|
||||
},
|
||||
@@ -411,7 +430,7 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
|
||||
mock_publisher.publisher.publish.assert_called_once()
|
||||
topic, settings = mock_publisher.publisher.publish.call_args[0]
|
||||
self.assertEqual(topic, "config/birdseye")
|
||||
self.assertEqual(settings.mode.value, "continuous")
|
||||
self.assertEqual(settings.mode.to_mqtt_payload(), "CONTINUOUS")
|
||||
|
||||
published = {
|
||||
call[0][0].camera: call[0][1]
|
||||
@@ -425,8 +444,12 @@ class TestConfigSetWildcardPropagation(BaseTestHttp):
|
||||
)
|
||||
|
||||
# the override survives, the inheriting camera follows global
|
||||
self.assertEqual(published["front_door"].mode.value, "motion")
|
||||
self.assertEqual(published["back_yard"].mode.value, "continuous")
|
||||
self.assertEqual(
|
||||
published["front_door"].mode.to_mqtt_payload(), "MOTION"
|
||||
)
|
||||
self.assertEqual(
|
||||
published["back_yard"].mode.to_mqtt_payload(), "CONTINUOUS"
|
||||
)
|
||||
finally:
|
||||
os.unlink(config_path)
|
||||
|
||||
|
||||
@@ -1,15 +1,73 @@
|
||||
"""Unit tests for recordings/media API endpoints."""
|
||||
|
||||
from dataclasses import dataclass
|
||||
from datetime import UTC, datetime
|
||||
from unittest.mock import patch
|
||||
|
||||
import pytz
|
||||
from fastapi import Request
|
||||
|
||||
from frigate.api.auth import get_allowed_cameras_for_filter, get_current_user
|
||||
from frigate.const import MAX_SEGMENT_DURATION
|
||||
from frigate.models import Recordings
|
||||
from frigate.test.http_api.base_http_test import AuthTestClient, BaseTestHttp
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class RangeCase:
|
||||
"""Expected behavior for one segment relative to the requested range.
|
||||
|
||||
Offsets are seconds from REQUEST_START; the request ends at +100 seconds.
|
||||
"""
|
||||
|
||||
name: str
|
||||
start_offset: float
|
||||
end_offset: float
|
||||
included_in_recordings: bool
|
||||
vod_clip_from_ms: int | None = None
|
||||
vod_duration_ms: int | None = None
|
||||
|
||||
|
||||
REQUEST_START = 1000
|
||||
REQUEST_END = 1100
|
||||
RANGE_CASES = (
|
||||
RangeCase("before", -MAX_SEGMENT_DURATION + 1, -1, False),
|
||||
RangeCase("meets_start", -10, 0, True),
|
||||
RangeCase(
|
||||
"overlaps_start",
|
||||
-MAX_SEGMENT_DURATION + 0.5,
|
||||
0.25,
|
||||
True,
|
||||
vod_clip_from_ms=599500,
|
||||
vod_duration_ms=250,
|
||||
),
|
||||
RangeCase("starts_at_start", 0, 10, True, vod_duration_ms=10000),
|
||||
RangeCase("inside", 20, 80, True, vod_duration_ms=60000),
|
||||
RangeCase("ends_at_end", 90, 100, True, vod_duration_ms=10000),
|
||||
RangeCase("matches_range", 0, 100, True, vod_duration_ms=100000),
|
||||
RangeCase("starts_with_range", 0, 110, True, vod_duration_ms=100000),
|
||||
RangeCase(
|
||||
"covers_range",
|
||||
-20,
|
||||
120,
|
||||
True,
|
||||
vod_clip_from_ms=20000,
|
||||
vod_duration_ms=100000,
|
||||
),
|
||||
RangeCase(
|
||||
"ends_with_range",
|
||||
-10,
|
||||
100,
|
||||
True,
|
||||
vod_clip_from_ms=10000,
|
||||
vod_duration_ms=100000,
|
||||
),
|
||||
RangeCase("overlaps_end", 95, 105, True, vod_duration_ms=5000),
|
||||
RangeCase("starts_at_end", 100, 110, True),
|
||||
RangeCase("after", 101, 110, False),
|
||||
)
|
||||
|
||||
|
||||
class TestHttpMedia(BaseTestHttp):
|
||||
"""Test media API endpoints, particularly recordings with DST handling."""
|
||||
|
||||
@@ -44,6 +102,26 @@ class TestHttpMedia(BaseTestHttp):
|
||||
self.app.dependency_overrides.clear()
|
||||
super().tearDown()
|
||||
|
||||
def _assert_vod_response(
|
||||
self,
|
||||
response,
|
||||
expected_clips: list[tuple[str, int | None, int]],
|
||||
) -> None:
|
||||
"""Assert VOD clip metadata and its derived duration fields."""
|
||||
assert response.status_code == 200
|
||||
vod = response.json()
|
||||
assert [
|
||||
(
|
||||
clip["path"],
|
||||
clip.get("clipFrom"),
|
||||
clip["keyFrameDurations"][0],
|
||||
)
|
||||
for clip in vod["sequences"][0]["clips"]
|
||||
] == expected_clips
|
||||
expected_durations = [clip[2] for clip in expected_clips]
|
||||
assert vod["durations"] == expected_durations
|
||||
assert vod["segment_duration"] == max(expected_durations)
|
||||
|
||||
def test_recordings_summary_across_dst_spring_forward(self):
|
||||
"""
|
||||
Test recordings summary across spring DST transition (spring forward).
|
||||
@@ -404,6 +482,102 @@ class TestHttpMedia(BaseTestHttp):
|
||||
assert "2024-03-10" in summary
|
||||
assert summary["2024-03-10"] is True
|
||||
|
||||
def test_recordings_handles_all_range_relations(self):
|
||||
"""Recordings return every interval relation that touches the range."""
|
||||
with AuthTestClient(self.app) as client:
|
||||
for case in RANGE_CASES:
|
||||
with self.subTest(case=case.name):
|
||||
Recordings.delete().execute()
|
||||
super().insert_mock_recording(
|
||||
case.name,
|
||||
REQUEST_START + case.start_offset,
|
||||
REQUEST_START + case.end_offset,
|
||||
)
|
||||
|
||||
response = client.get(
|
||||
"/front_door/recordings",
|
||||
params={"after": REQUEST_START, "before": REQUEST_END},
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
expected_ids = [case.name] if case.included_in_recordings else []
|
||||
assert [
|
||||
recording["id"] for recording in response.json()
|
||||
] == expected_ids
|
||||
|
||||
def test_vod_handles_all_range_relations(self):
|
||||
"""VOD clips every interval relation with positive playback duration."""
|
||||
with (
|
||||
AuthTestClient(self.app) as client,
|
||||
patch(
|
||||
"frigate.api.media.get_keyframe_before",
|
||||
side_effect=lambda _path, offset: offset,
|
||||
),
|
||||
):
|
||||
for case in RANGE_CASES:
|
||||
with self.subTest(case=case.name):
|
||||
Recordings.delete().execute()
|
||||
super().insert_mock_recording(
|
||||
case.name,
|
||||
REQUEST_START + case.start_offset,
|
||||
REQUEST_START + case.end_offset,
|
||||
)
|
||||
|
||||
response = client.get(
|
||||
f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
|
||||
)
|
||||
|
||||
if case.vod_duration_ms is None:
|
||||
assert response.status_code == 404
|
||||
continue
|
||||
|
||||
self._assert_vod_response(
|
||||
response,
|
||||
[
|
||||
(
|
||||
case.name,
|
||||
case.vod_clip_from_ms,
|
||||
case.vod_duration_ms,
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
def test_vod_handles_segment_ending_at_start_with_keyframe_fallbacks(self):
|
||||
"""VOD keeps a boundary segment when keyframe lookup extends it."""
|
||||
|
||||
def keyframe_before(path: str, offset: int) -> int | None:
|
||||
return offset - 1000 if path == "previous_keyframe" else None
|
||||
|
||||
with (
|
||||
AuthTestClient(self.app) as client,
|
||||
patch(
|
||||
"frigate.api.media.get_keyframe_before",
|
||||
side_effect=keyframe_before,
|
||||
),
|
||||
):
|
||||
super().insert_mock_recording(
|
||||
"previous_keyframe",
|
||||
REQUEST_START - 10,
|
||||
REQUEST_START,
|
||||
)
|
||||
super().insert_mock_recording(
|
||||
"missing_keyframe",
|
||||
REQUEST_START - 5,
|
||||
REQUEST_START,
|
||||
)
|
||||
|
||||
response = client.get(
|
||||
f"/vod/front_door/start/{REQUEST_START}/end/{REQUEST_END}"
|
||||
)
|
||||
|
||||
self._assert_vod_response(
|
||||
response,
|
||||
[
|
||||
("previous_keyframe", 9000, 1000),
|
||||
("missing_keyframe", None, 5000),
|
||||
],
|
||||
)
|
||||
|
||||
def test_recordings_unavailable_reports_gap_between_recordings(self):
|
||||
"""A gap between two recordings is reported as an unavailable segment."""
|
||||
with AuthTestClient(self.app) as client:
|
||||
|
||||
@@ -1,13 +1,70 @@
|
||||
"""Test camera user and password cleanup."""
|
||||
"""Tests for Birdseye canvas sizing and layout behavior."""
|
||||
|
||||
import multiprocessing as mp
|
||||
import unittest
|
||||
from unittest.mock import Mock
|
||||
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.output.birdseye import BirdsEyeFrameManager, get_canvas_shape
|
||||
from frigate.config import BirdseyeModeConfig, FrigateConfig
|
||||
from frigate.output.birdseye import (
|
||||
Birdseye,
|
||||
BirdseyeActivity,
|
||||
BirdsEyeFrameManager,
|
||||
get_canvas_shape,
|
||||
)
|
||||
|
||||
|
||||
class TestBirdseye(unittest.TestCase):
|
||||
def _build_manager(
|
||||
self, camera_dimensions: dict[str, tuple[int, int]]
|
||||
) -> BirdsEyeFrameManager:
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"birdseye": {"width": 1280, "height": 720},
|
||||
"cameras": {},
|
||||
}
|
||||
|
||||
for order, (camera, dimensions) in enumerate(
|
||||
camera_dimensions.items(), start=1
|
||||
):
|
||||
config["cameras"][camera] = {
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{
|
||||
"path": f"rtsp://10.0.0.1:554/{camera}",
|
||||
"roles": ["detect"],
|
||||
}
|
||||
]
|
||||
},
|
||||
"detect": {
|
||||
"width": dimensions[0],
|
||||
"height": dimensions[1],
|
||||
"fps": 5,
|
||||
},
|
||||
"birdseye": {"order": order},
|
||||
}
|
||||
|
||||
return BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
|
||||
|
||||
def _assert_no_overlaps(
|
||||
self, layout: list[list[tuple[str, tuple[int, int, int, int]]]]
|
||||
):
|
||||
rectangles = [position for row in layout for _, position in row]
|
||||
|
||||
for index, rect in enumerate(rectangles):
|
||||
x1, y1, width1, height1 = rect
|
||||
for other in rectangles[index + 1 :]:
|
||||
x2, y2, width2, height2 = other
|
||||
overlap = (
|
||||
x1 < x2 + width2
|
||||
and x2 < x1 + width1
|
||||
and y1 < y2 + height2
|
||||
and y2 < y1 + height1
|
||||
)
|
||||
self.assertFalse(
|
||||
overlap,
|
||||
msg=f"Overlapping rectangles found: {rect} and {other}",
|
||||
)
|
||||
|
||||
def test_16x9(self):
|
||||
"""Test 16x9 aspect ratio works as expected for birdseye."""
|
||||
width = 1280
|
||||
@@ -48,6 +105,212 @@ class TestBirdseye(unittest.TestCase):
|
||||
assert canvas_width == width # width will be the same
|
||||
assert canvas_height != height
|
||||
|
||||
def test_portrait_camera_does_not_overlap_next_row(self):
|
||||
"""Portrait cameras should reserve their real horizontal position on the next row."""
|
||||
manager = self._build_manager(
|
||||
{
|
||||
"cam_a": (1280, 720),
|
||||
"cam_p": (360, 640),
|
||||
"cam_b": (1280, 720),
|
||||
"cam_c": (640, 480),
|
||||
}
|
||||
)
|
||||
|
||||
layout = manager.calculate_layout(["cam_a", "cam_p", "cam_b", "cam_c"], 3)
|
||||
|
||||
self.assertIsNotNone(layout)
|
||||
assert layout is not None
|
||||
self._assert_no_overlaps(layout)
|
||||
|
||||
cam_c = [
|
||||
position for row in layout for camera, position in row if camera == "cam_c"
|
||||
][0]
|
||||
self.assertEqual(cam_c[0], 0)
|
||||
|
||||
def test_portrait_reservation_only_applies_to_next_row(self):
|
||||
"""Portrait reservations should not push later rows after the span ends."""
|
||||
manager = self._build_manager(
|
||||
{
|
||||
"cam_a": (1280, 720),
|
||||
"cam_p": (360, 640),
|
||||
"cam_b": (1280, 720),
|
||||
"cam_c": (1280, 720),
|
||||
"cam_d": (1280, 720),
|
||||
"cam_e": (1280, 720),
|
||||
}
|
||||
)
|
||||
|
||||
layout = manager.calculate_layout(
|
||||
["cam_a", "cam_p", "cam_b", "cam_c", "cam_d", "cam_e"],
|
||||
3,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(layout)
|
||||
assert layout is not None
|
||||
self._assert_no_overlaps(layout)
|
||||
|
||||
cam_e = [
|
||||
position for row in layout for camera, position in row if camera == "cam_e"
|
||||
][0]
|
||||
self.assertEqual(cam_e[0], 0)
|
||||
|
||||
def test_multiple_portraits_reserve_distinct_ranges(self):
|
||||
"""Multiple portrait cameras in one row should reserve separate spans below them."""
|
||||
manager = self._build_manager(
|
||||
{
|
||||
"cam_a": (640, 480),
|
||||
"cam_p1": (360, 640),
|
||||
"cam_p2": (360, 640),
|
||||
"cam_b": (640, 480),
|
||||
"cam_c": (1280, 720),
|
||||
"cam_d": (640, 480),
|
||||
}
|
||||
)
|
||||
|
||||
layout = manager.calculate_layout(
|
||||
["cam_a", "cam_p1", "cam_p2", "cam_b", "cam_c", "cam_d"],
|
||||
4,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(layout)
|
||||
assert layout is not None
|
||||
self._assert_no_overlaps(layout)
|
||||
|
||||
def test_two_landscapes_then_portrait_then_two_landscapes(self):
|
||||
"""A portrait after two landscapes should reserve only its own tail span."""
|
||||
manager = self._build_manager(
|
||||
{
|
||||
"cam_a": (1280, 720),
|
||||
"cam_b": (1280, 720),
|
||||
"cam_p": (360, 640),
|
||||
"cam_c": (1280, 720),
|
||||
"cam_d": (1280, 720),
|
||||
}
|
||||
)
|
||||
|
||||
layout = manager.calculate_layout(
|
||||
["cam_a", "cam_b", "cam_p", "cam_c", "cam_d"],
|
||||
3,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(layout)
|
||||
assert layout is not None
|
||||
self._assert_no_overlaps(layout)
|
||||
|
||||
cam_c = [
|
||||
position for row in layout for camera, position in row if camera == "cam_c"
|
||||
][0]
|
||||
cam_d = [
|
||||
position for row in layout for camera, position in row if camera == "cam_d"
|
||||
][0]
|
||||
self.assertEqual(cam_c[0], 0)
|
||||
self.assertEqual(cam_d[0], cam_c[0] + cam_c[2])
|
||||
|
||||
|
||||
class TestBirdseyeActivity(unittest.TestCase):
|
||||
"""Test which camera activity is included in each Birdseye mode."""
|
||||
|
||||
def setUp(self):
|
||||
config = {
|
||||
"mqtt": {"enabled": False},
|
||||
"birdseye": {
|
||||
"enabled": True,
|
||||
"mode": {
|
||||
"motion": True,
|
||||
"objects": True,
|
||||
"stationary_objects": True,
|
||||
},
|
||||
},
|
||||
"cameras": {
|
||||
"front": {
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]}
|
||||
]
|
||||
},
|
||||
"detect": {"height": 1080, "width": 1920, "fps": 5},
|
||||
}
|
||||
},
|
||||
}
|
||||
self.manager = BirdsEyeFrameManager(FrigateConfig(**config), mp.Event())
|
||||
|
||||
def test_existing_modes_keep_their_activity_rules(self):
|
||||
continuous = BirdseyeModeConfig(continuous=True)
|
||||
motion = BirdseyeModeConfig(motion=True)
|
||||
objects = BirdseyeModeConfig(objects=True)
|
||||
|
||||
no_activity = BirdseyeActivity(False, False, False)
|
||||
motion_activity = BirdseyeActivity(False, False, True)
|
||||
stationary_activity = BirdseyeActivity(False, True, False)
|
||||
active_object_activity = BirdseyeActivity(True, False, False)
|
||||
|
||||
assert self.manager.camera_active(continuous, no_activity)
|
||||
assert self.manager.camera_active(motion, motion_activity)
|
||||
assert not self.manager.camera_active(motion, stationary_activity)
|
||||
assert self.manager.camera_active(objects, active_object_activity)
|
||||
assert not self.manager.camera_active(objects, stationary_activity)
|
||||
|
||||
def test_modes_can_be_combined(self):
|
||||
mode = BirdseyeModeConfig(motion=True, stationary_objects=True)
|
||||
|
||||
assert self.manager.camera_active(mode, BirdseyeActivity(False, False, True))
|
||||
assert self.manager.camera_active(mode, BirdseyeActivity(False, True, False))
|
||||
assert not self.manager.camera_active(
|
||||
mode, BirdseyeActivity(False, False, False)
|
||||
)
|
||||
|
||||
def test_stationary_objects_are_independent_from_active_objects(self):
|
||||
stationary_objects = BirdseyeModeConfig(stationary_objects=True)
|
||||
|
||||
assert self.manager.camera_active(
|
||||
stationary_objects, BirdseyeActivity(False, True, False)
|
||||
)
|
||||
assert not self.manager.camera_active(
|
||||
stationary_objects, BirdseyeActivity(True, False, False)
|
||||
)
|
||||
|
||||
def test_write_data_preserves_active_and_confirms_stationary_activity(self):
|
||||
birdseye = Birdseye.__new__(Birdseye)
|
||||
birdseye.birdseye_manager = Mock()
|
||||
birdseye.birdseye_manager.update.return_value = (False, False)
|
||||
birdseye._idle_interval = None
|
||||
frame = Mock()
|
||||
|
||||
birdseye.write_data(
|
||||
"front",
|
||||
[
|
||||
{"stationary": True, "false_positive": True},
|
||||
{"stationary": False, "false_positive": True},
|
||||
{"stationary": True, "false_positive": False},
|
||||
],
|
||||
[[0, 0, 10, 10]],
|
||||
1.0,
|
||||
frame,
|
||||
)
|
||||
|
||||
birdseye.birdseye_manager.update.assert_called_once_with(
|
||||
"front", BirdseyeActivity(True, True, True), 1.0, frame
|
||||
)
|
||||
|
||||
def test_stationary_false_positive_does_not_activate_birdseye(self):
|
||||
birdseye = Birdseye.__new__(Birdseye)
|
||||
birdseye.birdseye_manager = Mock()
|
||||
birdseye.birdseye_manager.update.return_value = (False, False)
|
||||
birdseye._idle_interval = None
|
||||
frame = Mock()
|
||||
|
||||
birdseye.write_data(
|
||||
"front",
|
||||
[{"stationary": True, "false_positive": True}],
|
||||
[],
|
||||
1.0,
|
||||
frame,
|
||||
)
|
||||
|
||||
birdseye.birdseye_manager.update.assert_called_once_with(
|
||||
"front", BirdseyeActivity(False, False, False), 1.0, frame
|
||||
)
|
||||
|
||||
|
||||
class TestBirdseyeCameraOrder(unittest.TestCase):
|
||||
"""Test that birdseye reacts to camera order changes without a restart."""
|
||||
@@ -55,7 +318,7 @@ class TestBirdseyeCameraOrder(unittest.TestCase):
|
||||
def setUp(self):
|
||||
config = {
|
||||
"mqtt": {"enabled": False},
|
||||
"birdseye": {"enabled": True, "mode": "continuous"},
|
||||
"birdseye": {"enabled": True, "mode": {"continuous": True}},
|
||||
"cameras": {
|
||||
camera: {
|
||||
"ffmpeg": {
|
||||
|
||||
+260
-46
@@ -7,9 +7,11 @@ import numpy as np
|
||||
from pydantic import ValidationError
|
||||
from ruamel.yaml.constructor import DuplicateKeyError
|
||||
|
||||
from frigate.config import BirdseyeModeEnum, FrigateConfig
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.const import MODEL_CACHE_DIR
|
||||
from frigate.detectors import DetectorTypeEnum
|
||||
from frigate.detectors.detector_config import SceneEnum
|
||||
from frigate.detectors.device import build_detector_config, runner_names
|
||||
from frigate.util.builtin import deep_merge
|
||||
|
||||
|
||||
@@ -64,49 +66,171 @@ class TestConfig(unittest.TestCase):
|
||||
|
||||
def test_config_class(self):
|
||||
frigate_config = FrigateConfig(**self.minimal)
|
||||
assert "cpu" in frigate_config.detectors.keys()
|
||||
assert frigate_config.detectors["cpu"].type == DetectorTypeEnum.cpu
|
||||
assert frigate_config.detectors["cpu"].model.width == 320
|
||||
model = frigate_config.primary_model
|
||||
assert model.scene == SceneEnum.all
|
||||
assert model.width == 320
|
||||
assert frigate_config.devices_for_model(model)[0].detector == (
|
||||
DetectorTypeEnum.cpu
|
||||
)
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_detector_custom_model_path(self, mock_labels):
|
||||
def test_model_custom_path(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {
|
||||
"detectors": {
|
||||
"cpu": {
|
||||
"type": "cpu",
|
||||
"model_path": "/cpu_model.tflite",
|
||||
"models": [
|
||||
# needs to be a file that will exist, doesn't matter what
|
||||
{"path": "/etc/hosts", "width": 512, "devices": ["openvino:GPU"]},
|
||||
],
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
model = frigate_config.primary_model
|
||||
|
||||
assert model.path == "/etc/hosts"
|
||||
assert model.width == 512
|
||||
|
||||
detector_config = build_detector_config(
|
||||
frigate_config.devices_for_model(model)[0], model
|
||||
)
|
||||
assert detector_config.type == DetectorTypeEnum.openvino
|
||||
assert detector_config.device == "GPU"
|
||||
assert detector_config.model.path == "/etc/hosts"
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_model_default_paths_per_detector(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
|
||||
for devices, expected in (
|
||||
(["cpu"], "/cpu_model.tflite"),
|
||||
(["edgetpu:pci:0"], "/edgetpu_model.tflite"),
|
||||
(["openvino:CPU"], "/openvino-model/ssdlite_mobilenet_v2.xml"),
|
||||
):
|
||||
config = {"models": [{"devices": devices}]}
|
||||
frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
assert frigate_config.primary_model.path == expected
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_camera_picks_model_by_scene(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {
|
||||
"models": [
|
||||
{"scene": "outdoor", "devices": ["cpu"], "width": 320},
|
||||
{"scene": "indoor", "devices": ["openvino:CPU"], "width": 300},
|
||||
],
|
||||
"cameras": {
|
||||
"back": {
|
||||
"detect": {"scene": "outdoor"},
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
|
||||
]
|
||||
},
|
||||
},
|
||||
"edgetpu": {
|
||||
"type": "edgetpu",
|
||||
"model_path": "/edgetpu_model.tflite",
|
||||
},
|
||||
"openvino": {
|
||||
"type": "openvino",
|
||||
"front": {
|
||||
"detect": {"scene": "indoor"},
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{"path": "rtsp://10.0.0.2:554/video", "roles": ["detect"]},
|
||||
]
|
||||
},
|
||||
},
|
||||
},
|
||||
# needs to be a file that will exist, doesn't matter what
|
||||
"model": {"path": "/etc/hosts", "width": 512},
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
assert "cpu" in frigate_config.detectors.keys()
|
||||
assert "edgetpu" in frigate_config.detectors.keys()
|
||||
assert "openvino" in frigate_config.detectors.keys()
|
||||
assert frigate_config.model_for_camera("back").scene == SceneEnum.outdoor
|
||||
assert frigate_config.model_for_camera("front").scene == SceneEnum.indoor
|
||||
assert frigate_config.model_for_camera("back").width == 320
|
||||
assert frigate_config.model_for_camera("front").width == 300
|
||||
|
||||
assert frigate_config.detectors["cpu"].type == DetectorTypeEnum.cpu
|
||||
assert frigate_config.detectors["edgetpu"].type == DetectorTypeEnum.edgetpu
|
||||
assert frigate_config.detectors["openvino"].type == DetectorTypeEnum.openvino
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_camera_requires_a_scene_without_a_default(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {
|
||||
"models": [
|
||||
{"scene": "outdoor", "devices": ["cpu"]},
|
||||
{"scene": "indoor", "devices": ["openvino:CPU"]},
|
||||
],
|
||||
}
|
||||
|
||||
assert frigate_config.detectors["cpu"].num_threads == 3
|
||||
assert frigate_config.detectors["edgetpu"].device is None
|
||||
assert frigate_config.detectors["openvino"].device is None
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
assert frigate_config.model.path == "/etc/hosts"
|
||||
assert frigate_config.detectors["cpu"].model.path == "/cpu_model.tflite"
|
||||
assert frigate_config.detectors["edgetpu"].model.path == "/edgetpu_model.tflite"
|
||||
assert frigate_config.detectors["openvino"].model.path == "/etc/hosts"
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_camera_scene_must_match_a_model(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {
|
||||
"models": [{"devices": ["cpu"]}],
|
||||
"cameras": {
|
||||
"back": {
|
||||
"detect": {"scene": "outdoor"},
|
||||
"ffmpeg": {
|
||||
"inputs": [
|
||||
{"path": "rtsp://10.0.0.1:554/video", "roles": ["detect"]},
|
||||
]
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_models_must_use_unique_scenes(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {
|
||||
"models": [
|
||||
{"scene": "outdoor", "devices": ["cpu"]},
|
||||
{"scene": "outdoor", "devices": ["openvino:CPU"]},
|
||||
],
|
||||
}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_model_devices_must_share_a_detector(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {"models": [{"devices": ["cpu", "openvino:CPU"]}]}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_model_requires_a_known_detector(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {"models": [{"devices": ["not_a_detector:0"]}]}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_model_requires_a_device(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {"models": [{"devices": []}]}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_shareable_devices_may_repeat(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {"models": [{"devices": ["openvino:GPU", "openvino:GPU"]}]}
|
||||
|
||||
frigate_config = FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
devices = frigate_config.devices_for_model(frigate_config.primary_model)
|
||||
|
||||
assert runner_names(devices) == ["openvino:GPU", "openvino:GPU#2"]
|
||||
|
||||
@patch("frigate.detectors.detector_config.load_labels")
|
||||
def test_exclusive_devices_may_not_repeat(self, mock_labels):
|
||||
mock_labels.return_value = {}
|
||||
config = {"models": [{"devices": ["edgetpu:pci:0", "edgetpu:pci:0"]}]}
|
||||
|
||||
with self.assertRaises(ValidationError):
|
||||
FrigateConfig(**(deep_merge(config, self.minimal)))
|
||||
|
||||
def test_invalid_mqtt_config(self):
|
||||
config = {
|
||||
@@ -170,7 +294,7 @@ class TestConfig(unittest.TestCase):
|
||||
def test_override_birdseye(self):
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"birdseye": {"enabled": True, "mode": "continuous"},
|
||||
"birdseye": {"enabled": True, "mode": {"continuous": True}},
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -183,19 +307,30 @@ class TestConfig(unittest.TestCase):
|
||||
"width": 1920,
|
||||
"fps": 5,
|
||||
},
|
||||
"birdseye": {"enabled": False, "mode": "motion"},
|
||||
"birdseye": {
|
||||
"enabled": False,
|
||||
"mode": {"continuous": False, "motion": True},
|
||||
},
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert not frigate_config.cameras["back"].birdseye.enabled
|
||||
assert frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.motion
|
||||
mode = frigate_config.cameras["back"].birdseye.mode
|
||||
assert mode.motion
|
||||
assert not mode.continuous
|
||||
assert not mode.objects
|
||||
assert not mode.stationary_objects
|
||||
|
||||
def test_override_birdseye_non_inheritable(self):
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"birdseye": {"enabled": True, "mode": "continuous", "height": 1920},
|
||||
"birdseye": {
|
||||
"enabled": True,
|
||||
"mode": {"continuous": True},
|
||||
"height": 1920,
|
||||
},
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -218,7 +353,7 @@ class TestConfig(unittest.TestCase):
|
||||
def test_inherit_birdseye(self):
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"birdseye": {"enabled": True, "mode": "continuous"},
|
||||
"birdseye": {"enabled": True, "mode": {"continuous": True}},
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -237,9 +372,89 @@ class TestConfig(unittest.TestCase):
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert frigate_config.cameras["back"].birdseye.enabled
|
||||
assert (
|
||||
frigate_config.cameras["back"].birdseye.mode is BirdseyeModeEnum.continuous
|
||||
)
|
||||
mode = frigate_config.cameras["back"].birdseye.mode
|
||||
assert mode.continuous
|
||||
assert not mode.motion
|
||||
assert not mode.objects
|
||||
assert not mode.stationary_objects
|
||||
|
||||
def test_combine_birdseye_activity_types(self):
|
||||
config = {
|
||||
**self.minimal,
|
||||
"birdseye": {
|
||||
"mode": {
|
||||
"motion": True,
|
||||
"stationary_objects": True,
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
mode = frigate_config.cameras["back"].birdseye.mode
|
||||
assert mode.motion
|
||||
assert mode.stationary_objects
|
||||
assert not mode.continuous
|
||||
assert not mode.objects
|
||||
|
||||
def test_birdseye_requires_an_activity_type(self):
|
||||
config = {
|
||||
**self.minimal,
|
||||
"birdseye": {
|
||||
"mode": {
|
||||
"continuous": False,
|
||||
"motion": False,
|
||||
"objects": False,
|
||||
"stationary_objects": False,
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
ValidationError, "must enable at least one Birdseye activity type"
|
||||
):
|
||||
FrigateConfig(**config)
|
||||
|
||||
def test_camera_can_disable_an_inherited_activity_type(self):
|
||||
config = {
|
||||
**self.minimal,
|
||||
"birdseye": {"mode": {"motion": True, "objects": True}},
|
||||
}
|
||||
config["cameras"]["back"]["birdseye"] = {"mode": {"motion": False}}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
mode = frigate_config.cameras["back"].birdseye.mode
|
||||
assert not mode.motion
|
||||
assert mode.objects
|
||||
|
||||
def test_profile_must_leave_an_activity_type_enabled(self):
|
||||
config = {
|
||||
**self.minimal,
|
||||
"profiles": {"away": {"friendly_name": "Away"}},
|
||||
"birdseye": {"mode": {"objects": True}},
|
||||
}
|
||||
config["cameras"]["back"]["profiles"] = {
|
||||
"away": {"birdseye": {"mode": {"objects": False}}}
|
||||
}
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
ValidationError, "must enable at least one Birdseye activity type"
|
||||
):
|
||||
FrigateConfig(**config)
|
||||
|
||||
def test_camera_birdseye_activity_types_override_global_values(self):
|
||||
config = {
|
||||
**self.minimal,
|
||||
"birdseye": {"mode": {"motion": True, "objects": True}},
|
||||
}
|
||||
config["cameras"]["back"]["birdseye"] = {
|
||||
"mode": {"motion": False, "stationary_objects": True}
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
mode = frigate_config.cameras["back"].birdseye.mode
|
||||
assert not mode.motion
|
||||
assert mode.objects
|
||||
assert mode.stationary_objects
|
||||
|
||||
def test_override_tracked_objects(self):
|
||||
config = {
|
||||
@@ -956,7 +1171,7 @@ class TestConfig(unittest.TestCase):
|
||||
def test_merge_labelmap(self):
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"model": {"labelmap": {7: "truck"}},
|
||||
"models": [{"labelmap": {7: "truck"}, "devices": ["cpu"]}],
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -977,7 +1192,7 @@ class TestConfig(unittest.TestCase):
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert frigate_config.model.merged_labelmap[7] == "truck"
|
||||
assert frigate_config.primary_model.merged_labelmap[7] == "truck"
|
||||
|
||||
def test_default_labelmap_empty(self):
|
||||
config = {
|
||||
@@ -1002,12 +1217,12 @@ class TestConfig(unittest.TestCase):
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert frigate_config.model.merged_labelmap[0] == "person"
|
||||
assert frigate_config.primary_model.merged_labelmap[0] == "person"
|
||||
|
||||
def test_default_labelmap(self):
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"model": {"width": 320, "height": 320},
|
||||
"models": [{"width": 320, "height": 320, "devices": ["cpu"]}],
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -1028,7 +1243,7 @@ class TestConfig(unittest.TestCase):
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert frigate_config.model.merged_labelmap[0] == "person"
|
||||
assert frigate_config.primary_model.merged_labelmap[0] == "person"
|
||||
|
||||
def test_plus_labelmap(self):
|
||||
with open(os.path.join(MODEL_CACHE_DIR, "test"), "w") as f:
|
||||
@@ -1038,8 +1253,7 @@ class TestConfig(unittest.TestCase):
|
||||
|
||||
config = {
|
||||
"mqtt": {"host": "mqtt"},
|
||||
"detectors": {"cpu": {"type": "cpu"}},
|
||||
"model": {"path": "plus://test"},
|
||||
"models": [{"path": "plus://test", "devices": ["cpu"]}],
|
||||
"cameras": {
|
||||
"back": {
|
||||
"ffmpeg": {
|
||||
@@ -1060,7 +1274,7 @@ class TestConfig(unittest.TestCase):
|
||||
}
|
||||
|
||||
frigate_config = FrigateConfig(**config)
|
||||
assert frigate_config.model.merged_labelmap[0] == "amazon"
|
||||
assert frigate_config.primary_model.merged_labelmap[0] == "amazon"
|
||||
|
||||
def test_fails_on_invalid_role(self):
|
||||
config = {
|
||||
|
||||
@@ -0,0 +1,225 @@
|
||||
"""Tests for migrating detectors and model into the models list."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
import unittest
|
||||
from unittest.mock import patch
|
||||
|
||||
from ruamel.yaml import YAML
|
||||
|
||||
from frigate.util.config import (
|
||||
CURRENT_CONFIG_VERSION,
|
||||
migrate_frigate_config,
|
||||
migrate_models,
|
||||
)
|
||||
|
||||
|
||||
class TestMigrateModels(unittest.TestCase):
|
||||
def test_single_cpu_detector(self):
|
||||
migrated = migrate_models({"detectors": {"cpu": {"type": "cpu"}}})
|
||||
|
||||
self.assertEqual(migrated["models"], [{"scene": "all", "devices": ["cpu"]}])
|
||||
self.assertNotIn("detectors", migrated)
|
||||
|
||||
def test_model_settings_are_carried_over(self):
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {"coral": {"type": "edgetpu", "device": "pci:0"}},
|
||||
"model": {"path": "plus://abc", "width": 320},
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
migrated["models"],
|
||||
[
|
||||
{
|
||||
"scene": "all",
|
||||
"path": "plus://abc",
|
||||
"width": 320,
|
||||
"devices": ["edgetpu:pci:0"],
|
||||
}
|
||||
],
|
||||
)
|
||||
self.assertNotIn("model", migrated)
|
||||
|
||||
def test_multiple_corals_become_multiple_devices(self):
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"coral1": {"type": "edgetpu", "device": "pci:0"},
|
||||
"coral2": {"type": "edgetpu", "device": "pci:1"},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
migrated["models"][0]["devices"], ["edgetpu:pci:0", "edgetpu:pci:1"]
|
||||
)
|
||||
|
||||
def test_several_detectors_on_one_device_stay_separate(self):
|
||||
# a repeated device is now what running two inference processes on one
|
||||
# piece of hardware looks like
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"ov_0": {"type": "openvino", "device": "GPU"},
|
||||
"ov_1": {"type": "openvino", "device": "GPU"},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
migrated["models"][0]["devices"], ["openvino:GPU", "openvino:GPU"]
|
||||
)
|
||||
|
||||
def test_repeated_exclusive_devices_are_collapsed(self):
|
||||
# two detectors both grabbing the first TPU was never really two TPUs
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"coral_0": {"type": "edgetpu", "device": "usb"},
|
||||
"coral_1": {"type": "edgetpu", "device": "usb"},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["models"][0]["devices"], ["edgetpu:usb"])
|
||||
|
||||
def test_detectors_that_named_the_device_field_differently(self):
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"rk": {"type": "rknn", "num_cores": 2},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["models"][0]["devices"], ["rknn:2"])
|
||||
|
||||
def test_empty_edgetpu_device_is_kept(self):
|
||||
# an empty device selects a native Coral, which is not the same as
|
||||
# letting the delegate pick
|
||||
migrated = migrate_models(
|
||||
{"detectors": {"coral": {"type": "edgetpu", "device": ""}}}
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["models"][0]["devices"], ["edgetpu:"])
|
||||
|
||||
def test_model_path_overrides_the_model(self):
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"coral": {"type": "edgetpu", "model_path": "/custom.tflite"}
|
||||
},
|
||||
"model": {"path": "/ignored.tflite", "width": 320},
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["models"][0]["path"], "/custom.tflite")
|
||||
|
||||
def test_no_detectors_falls_back_to_cpu(self):
|
||||
migrated = migrate_models({"model": {"width": 320}})
|
||||
|
||||
self.assertEqual(migrated["models"][0]["devices"], ["cpu"])
|
||||
|
||||
def test_dropped_remote_detector_options_are_logged(self):
|
||||
with self.assertLogs("frigate.util.config", level=logging.ERROR) as logs:
|
||||
migrated = migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"ds": {
|
||||
"type": "deepstack",
|
||||
"api_url": "http://host:5000/v1/vision/detection",
|
||||
"api_key": "secret",
|
||||
}
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
migrated["models"][0]["devices"],
|
||||
["deepstack:http://host:5000/v1/vision/detection"],
|
||||
)
|
||||
self.assertTrue(any("api_key" in message for message in logs.output))
|
||||
|
||||
def test_mixed_detector_types_are_logged(self):
|
||||
with self.assertLogs("frigate.util.config", level=logging.ERROR) as logs:
|
||||
migrate_models(
|
||||
{
|
||||
"detectors": {
|
||||
"ov": {"type": "openvino", "device": "GPU"},
|
||||
"coral": {"type": "edgetpu", "device": "pci:0"},
|
||||
}
|
||||
}
|
||||
)
|
||||
|
||||
self.assertTrue(any("more than one type" in message for message in logs.output))
|
||||
|
||||
def test_other_keys_are_untouched(self):
|
||||
migrated = migrate_models(
|
||||
{"mqtt": {"host": "mqtt"}, "detectors": {"cpu": {"type": "cpu"}}}
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["mqtt"], {"host": "mqtt"})
|
||||
|
||||
|
||||
class TestMigrateConfigFile(unittest.TestCase):
|
||||
"""The full file migration, which is gated on shape as well as version."""
|
||||
|
||||
def setUp(self):
|
||||
self.temp_dir = tempfile.TemporaryDirectory()
|
||||
self.addCleanup(self.temp_dir.cleanup)
|
||||
self.config_file = os.path.join(self.temp_dir.name, "config.yml")
|
||||
patcher = patch("frigate.util.config.CONFIG_DIR", self.temp_dir.name)
|
||||
patcher.start()
|
||||
self.addCleanup(patcher.stop)
|
||||
|
||||
def _migrate(self, config: str) -> dict:
|
||||
with open(self.config_file, "w") as f:
|
||||
f.write(config)
|
||||
|
||||
migrate_frigate_config(self.config_file)
|
||||
|
||||
with open(self.config_file) as f:
|
||||
return YAML().load(f)
|
||||
|
||||
def test_migrates_a_config_already_stamped_with_the_current_version(self):
|
||||
# 0.19 is unreleased, so a dev config can be current and still use
|
||||
# the pre-models keys
|
||||
migrated = self._migrate(
|
||||
"mqtt:\n"
|
||||
" enabled: false\n"
|
||||
"detectors:\n"
|
||||
" ov:\n"
|
||||
" type: openvino\n"
|
||||
" device: GPU\n"
|
||||
"cameras: {}\n"
|
||||
f"version: {CURRENT_CONFIG_VERSION}\n"
|
||||
)
|
||||
|
||||
self.assertEqual(migrated["models"][0]["devices"], ["openvino:GPU"])
|
||||
self.assertNotIn("detectors", migrated)
|
||||
|
||||
def test_a_migrated_config_is_left_alone(self):
|
||||
migrated = self._migrate(
|
||||
"mqtt:\n"
|
||||
" enabled: false\n"
|
||||
"models:\n"
|
||||
" - scene: all\n"
|
||||
" devices:\n"
|
||||
" - openvino:GPU\n"
|
||||
"cameras: {}\n"
|
||||
f"version: {CURRENT_CONFIG_VERSION}\n"
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
migrated["models"], [{"scene": "all", "devices": ["openvino:GPU"]}]
|
||||
)
|
||||
self.assertFalse(
|
||||
os.path.exists(os.path.join(self.temp_dir.name, "backup_config.yaml"))
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main(verbosity=2)
|
||||
@@ -0,0 +1,94 @@
|
||||
"""Tests for parsing detection hardware device strings."""
|
||||
|
||||
import unittest
|
||||
|
||||
from frigate.detectors.detector_config import ModelConfig
|
||||
from frigate.detectors.device import (
|
||||
DeviceParseError,
|
||||
build_detector_config,
|
||||
parse_device,
|
||||
runner_names,
|
||||
)
|
||||
|
||||
|
||||
class TestParseDevice(unittest.TestCase):
|
||||
def test_bare_detector_has_no_device(self):
|
||||
spec = parse_device("cpu")
|
||||
|
||||
self.assertEqual(spec.detector, "cpu")
|
||||
self.assertIsNone(spec.device)
|
||||
|
||||
def test_device_is_everything_after_the_first_colon(self):
|
||||
spec = parse_device("edgetpu:pci:0")
|
||||
|
||||
self.assertEqual(spec.detector, "edgetpu")
|
||||
self.assertEqual(spec.device, "pci:0")
|
||||
|
||||
def test_trailing_colon_keeps_an_empty_device(self):
|
||||
# an empty edgetpu device selects a native Coral
|
||||
spec = parse_device("edgetpu:")
|
||||
|
||||
self.assertEqual(spec.detector, "edgetpu")
|
||||
self.assertEqual(spec.device, "")
|
||||
|
||||
def test_unknown_detector_is_rejected(self):
|
||||
with self.assertRaises(DeviceParseError):
|
||||
parse_device("not_a_detector:0")
|
||||
|
||||
def test_device_that_the_detector_cannot_use_is_rejected(self):
|
||||
# tensorrt takes a gpu index
|
||||
with self.assertRaises(DeviceParseError):
|
||||
parse_device("tensorrt:the-fast-one")
|
||||
|
||||
|
||||
class TestBuildDetectorConfig(unittest.TestCase):
|
||||
def _build(self, raw: str):
|
||||
return build_detector_config(parse_device(raw), ModelConfig())
|
||||
|
||||
def test_device_lands_on_the_detector_field(self):
|
||||
for raw, expected in (
|
||||
("edgetpu:usb", "usb"),
|
||||
("edgetpu:pci:1", "pci:1"),
|
||||
("openvino:GPU.1", "GPU.1"),
|
||||
("onnx:CPU", "CPU"),
|
||||
("memryx:PCIe:0", "PCIe:0"),
|
||||
):
|
||||
with self.subTest(raw=raw):
|
||||
self.assertEqual(self._build(raw).device, expected)
|
||||
|
||||
def test_detectors_that_name_the_field_something_else(self):
|
||||
self.assertEqual(self._build("cpu:4").num_threads, 4)
|
||||
self.assertEqual(self._build("rknn:2").num_cores, 2)
|
||||
|
||||
def test_device_is_coerced_to_the_detector_field_type(self):
|
||||
self.assertEqual(self._build("tensorrt:1").device, 1)
|
||||
|
||||
def test_omitted_device_falls_back_to_the_detector_default(self):
|
||||
self.assertEqual(self._build("cpu").num_threads, 3)
|
||||
self.assertEqual(self._build("rknn").num_cores, 0)
|
||||
self.assertEqual(self._build("openvino").device, "AUTO")
|
||||
self.assertIsNone(self._build("edgetpu").device)
|
||||
|
||||
def test_the_model_is_attached(self):
|
||||
model = ModelConfig(path="/cpu_model.tflite")
|
||||
|
||||
self.assertIs(build_detector_config(parse_device("cpu"), model).model, model)
|
||||
|
||||
|
||||
class TestRunnerNames(unittest.TestCase):
|
||||
def test_unique_devices_keep_their_name(self):
|
||||
devices = [parse_device("edgetpu:pci:0"), parse_device("edgetpu:pci:1")]
|
||||
|
||||
self.assertEqual(runner_names(devices), ["edgetpu:pci:0", "edgetpu:pci:1"])
|
||||
|
||||
def test_repeated_devices_are_numbered(self):
|
||||
devices = [parse_device("openvino:GPU")] * 3
|
||||
|
||||
self.assertEqual(
|
||||
runner_names(devices),
|
||||
["openvino:GPU", "openvino:GPU#2", "openvino:GPU#3"],
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main(verbosity=2)
|
||||
@@ -8,6 +8,7 @@ from unittest.mock import MagicMock, patch
|
||||
from frigate.app import FrigateApp
|
||||
from frigate.comms.dispatcher import Dispatcher
|
||||
from frigate.comms.runtime_state import RuntimeStatePersistence
|
||||
from frigate.config import BirdseyeModeConfig
|
||||
|
||||
|
||||
def _make_camera_mock(
|
||||
@@ -51,6 +52,58 @@ def _build_dispatcher(cameras: dict[str, MagicMock]) -> Dispatcher:
|
||||
return Dispatcher(config, config_updater, onvif, ptz_metrics, communicators)
|
||||
|
||||
|
||||
class TestBirdseyeModeCommands(unittest.TestCase):
|
||||
"""Verify Birdseye mode commands use the boolean mode contract."""
|
||||
|
||||
def setUp(self) -> None:
|
||||
self.camera = _make_camera_mock()
|
||||
self.camera.birdseye.enabled = True
|
||||
self.dispatcher = _build_dispatcher({"front_door": self.camera})
|
||||
self.dispatcher.publish = MagicMock()
|
||||
|
||||
def test_combined_modes_are_accepted(self) -> None:
|
||||
self.dispatcher._on_birdseye_mode_command(
|
||||
"front_door", "STATIONARY_OBJECTS,MOTION"
|
||||
)
|
||||
|
||||
self.assertEqual(
|
||||
self.camera.birdseye.mode,
|
||||
BirdseyeModeConfig(motion=True, stationary_objects=True),
|
||||
)
|
||||
self.dispatcher.config_updater.publish_update.assert_called_once()
|
||||
self.dispatcher.publish.assert_called_once_with(
|
||||
"front_door/birdseye_mode/state",
|
||||
"MOTION,STATIONARY_OBJECTS",
|
||||
retain=True,
|
||||
)
|
||||
|
||||
def test_single_activity_type_is_accepted(self) -> None:
|
||||
self.dispatcher._on_birdseye_mode_command("front_door", "OBJECTS")
|
||||
|
||||
self.assertEqual(
|
||||
self.camera.birdseye.mode,
|
||||
BirdseyeModeConfig(objects=True),
|
||||
)
|
||||
self.dispatcher.publish.assert_called_once_with(
|
||||
"front_door/birdseye_mode/state", "OBJECTS", retain=True
|
||||
)
|
||||
|
||||
def test_unknown_mode_is_rejected(self) -> None:
|
||||
for payload in (
|
||||
"UNKNOWN",
|
||||
"motion",
|
||||
"MOTION_OBJECTS",
|
||||
"NONE",
|
||||
"NONE,MOTION",
|
||||
"MOTION,MOTION",
|
||||
"MOTION,",
|
||||
):
|
||||
with self.subTest(payload=payload):
|
||||
self.dispatcher._on_birdseye_mode_command("front_door", payload)
|
||||
|
||||
self.dispatcher.config_updater.publish_update.assert_not_called()
|
||||
|
||||
|
||||
class TestRestoreRuntimeState(unittest.TestCase):
|
||||
"""Verify replay routes through handlers and tolerates missing entries."""
|
||||
|
||||
|
||||
@@ -1,7 +1,12 @@
|
||||
import unittest
|
||||
from io import StringIO
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
from frigate.util.services import get_amd_gpu_stats, get_intel_gpu_stats
|
||||
from frigate.util.services import (
|
||||
get_amd_gpu_stats,
|
||||
get_intel_gpu_stats,
|
||||
get_openvino_npu_stats,
|
||||
)
|
||||
|
||||
|
||||
class TestGpuStats(unittest.TestCase):
|
||||
@@ -17,6 +22,88 @@ class TestGpuStats(unittest.TestCase):
|
||||
amd_stats = get_amd_gpu_stats()
|
||||
assert amd_stats == {"gpu": "4.17%", "mem": "60.37%"}
|
||||
|
||||
@patch("frigate.util.services.time.sleep")
|
||||
@patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
|
||||
@patch(
|
||||
"frigate.util.services.os.readlink",
|
||||
return_value="/sys/bus/pci/drivers/intel_vpu",
|
||||
)
|
||||
@patch(
|
||||
"frigate.util.services.glob.glob",
|
||||
return_value=["/sys/class/accel/accel0"],
|
||||
)
|
||||
@patch(
|
||||
"builtins.open",
|
||||
side_effect=[StringIO("1000"), StringIO("1250")],
|
||||
)
|
||||
def test_openvino_npu_stats_discovers_accel0(
|
||||
self, open_file, glob, readlink, time, sleep
|
||||
):
|
||||
assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
|
||||
|
||||
open_file.assert_any_call(
|
||||
"/sys/class/accel/accel0/device/power/runtime_active_time"
|
||||
)
|
||||
|
||||
@patch("frigate.util.services.time.sleep")
|
||||
@patch("frigate.util.services.time.time", side_effect=[0.0, 1.0])
|
||||
@patch(
|
||||
"frigate.util.services.os.readlink",
|
||||
side_effect=[
|
||||
"/sys/bus/pci/drivers/other",
|
||||
"/sys/bus/pci/drivers/intel_vpu",
|
||||
],
|
||||
)
|
||||
@patch(
|
||||
"frigate.util.services.glob.glob",
|
||||
return_value=[
|
||||
"/sys/class/accel/accel0",
|
||||
"/sys/class/accel/accel1",
|
||||
],
|
||||
)
|
||||
@patch(
|
||||
"builtins.open",
|
||||
side_effect=[StringIO("1000"), StringIO("1250")],
|
||||
)
|
||||
def test_openvino_npu_stats_skips_non_intel_accelerator(
|
||||
self, open_file, glob, readlink, time, sleep
|
||||
):
|
||||
assert get_openvino_npu_stats() == {"npu": "25.0", "mem": "-%"}
|
||||
|
||||
open_file.assert_any_call(
|
||||
"/sys/class/accel/accel1/device/power/runtime_active_time"
|
||||
)
|
||||
|
||||
@patch(
|
||||
"frigate.util.services.os.readlink",
|
||||
return_value="/sys/bus/pci/drivers/other",
|
||||
)
|
||||
@patch(
|
||||
"frigate.util.services.glob.glob",
|
||||
return_value=["/sys/class/accel/accel0"],
|
||||
)
|
||||
@patch("builtins.open")
|
||||
def test_openvino_npu_stats_no_intel_accelerator(self, open_file, glob, readlink):
|
||||
assert get_openvino_npu_stats() is None
|
||||
open_file.assert_not_called()
|
||||
|
||||
@patch(
|
||||
"frigate.util.services.os.readlink",
|
||||
return_value="/sys/bus/pci/drivers/intel_vpu",
|
||||
)
|
||||
@patch(
|
||||
"frigate.util.services.glob.glob",
|
||||
return_value=["/sys/class/accel/accel0"],
|
||||
)
|
||||
@patch("builtins.open", side_effect=FileNotFoundError)
|
||||
def test_openvino_npu_stats_runtime_counter_unavailable(
|
||||
self, open_file, glob, readlink
|
||||
):
|
||||
assert get_openvino_npu_stats() is None
|
||||
open_file.assert_called_once_with(
|
||||
"/sys/class/accel/accel0/device/power/runtime_active_time"
|
||||
)
|
||||
|
||||
@patch("frigate.stats.intel_gpu_info.intel_gpu_name_resolver.get_names")
|
||||
@patch("frigate.util.services.time.sleep")
|
||||
@patch("frigate.util.services.time.monotonic")
|
||||
|
||||
@@ -560,6 +560,25 @@ class TestProfileManager(unittest.TestCase):
|
||||
assert err is None
|
||||
assert self.config.cameras["front"].enabled is False
|
||||
|
||||
@patch.object(ProfileManager, "_persist_active_profile")
|
||||
def test_profile_can_disable_inherited_birdseye_activity(self, mock_persist):
|
||||
"""A false-only mode override inherits the other base activity types."""
|
||||
self.config.profiles["away"] = ProfileDefinitionConfig(friendly_name="Away")
|
||||
base_mode = self.config.cameras["front"].birdseye.mode
|
||||
base_mode.motion = True
|
||||
base_mode.objects = True
|
||||
self.config.cameras["front"].profiles["away"] = CameraProfileConfig(
|
||||
birdseye={"mode": {"motion": False}}
|
||||
)
|
||||
self.manager = ProfileManager(self.config, self.mock_updater)
|
||||
|
||||
err = self.manager.activate_profile("away")
|
||||
|
||||
assert err is None
|
||||
mode = self.config.cameras["front"].birdseye.mode
|
||||
assert not mode.motion
|
||||
assert mode.objects
|
||||
|
||||
@patch.object(ProfileManager, "_persist_active_profile")
|
||||
def test_deactivate_restores_enabled(self, mock_persist):
|
||||
"""Deactivating a profile restores the camera's base enabled state."""
|
||||
|
||||
@@ -209,7 +209,7 @@ class TrackedObjectProcessor(threading.Thread):
|
||||
if obj.obj_data.get("sub_label"):
|
||||
sub_label = obj.obj_data["sub_label"][0]
|
||||
|
||||
if sub_label in self.config.model.all_attribute_logos:
|
||||
if sub_label in self.config.all_attribute_logos:
|
||||
self.dispatcher.publish(
|
||||
f"{camera}/{sub_label}/snapshot",
|
||||
jpg_bytes,
|
||||
|
||||
+157
-2
@@ -20,9 +20,28 @@ from frigate.util.services import get_video_properties
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
CURRENT_CONFIG_VERSION = "0.18-0"
|
||||
CURRENT_CONFIG_VERSION = "0.19-0"
|
||||
DEFAULT_CONFIG_FILE = os.path.join(CONFIG_DIR, "config.yml")
|
||||
|
||||
# the detector field that used to hold the device, for detectors that named it
|
||||
# something other than "device"
|
||||
DETECTOR_DEVICE_FIELDS = {
|
||||
"cpu": "num_threads",
|
||||
"rknn": "num_cores",
|
||||
"deepstack": "api_url",
|
||||
"degirum": "location",
|
||||
"zmq": "endpoint",
|
||||
}
|
||||
|
||||
# detector options that have no equivalent in a device string. The remote
|
||||
# detectors that use them are being reworked, so they are dropped rather than
|
||||
# carried over.
|
||||
DROPPED_DETECTOR_OPTIONS = {
|
||||
"deepstack": ["api_timeout", "api_key"],
|
||||
"degirum": ["zoo", "token"],
|
||||
"zmq": ["request_timeout_ms", "linger_ms"],
|
||||
}
|
||||
|
||||
|
||||
def resolve_ffmpeg_path(path: str, binary: str = "ffmpeg") -> str:
|
||||
"""Resolve an ffmpeg version alias or custom path to a binary path.
|
||||
@@ -87,12 +106,17 @@ def migrate_frigate_config(config_file: str):
|
||||
|
||||
previous_version = str(config.get("version", "0.13"))
|
||||
|
||||
if previous_version == CURRENT_CONFIG_VERSION:
|
||||
# 0.19 is unreleased, so a config may already be stamped with the current
|
||||
# version and still use the pre-models detectors and model keys
|
||||
needs_models = "detectors" in config or "model" in config
|
||||
|
||||
if previous_version == CURRENT_CONFIG_VERSION and not needs_models:
|
||||
logger.info("frigate config does not need migration...")
|
||||
return
|
||||
|
||||
logger.info("copying config as backup...")
|
||||
shutil.copy(config_file, os.path.join(CONFIG_DIR, "backup_config.yaml"))
|
||||
new_config = config
|
||||
|
||||
if previous_version < "0.14":
|
||||
logger.info(f"Migrating frigate config from {previous_version} to 0.14...")
|
||||
@@ -147,6 +171,19 @@ def migrate_frigate_config(config_file: str):
|
||||
yaml.dump(new_config, f)
|
||||
previous_version = "0.18-0"
|
||||
|
||||
if previous_version < "0.19-0":
|
||||
logger.info(f"Migrating frigate config from {previous_version} to 0.19-0...")
|
||||
new_config = migrate_019_0(new_config)
|
||||
with open(config_file, "w") as f:
|
||||
yaml.dump(new_config, f)
|
||||
previous_version = "0.19-0"
|
||||
|
||||
if needs_models:
|
||||
logger.info("Migrating frigate detectors and model to models...")
|
||||
new_config = migrate_models(new_config)
|
||||
with open(config_file, "w") as f:
|
||||
yaml.dump(new_config, f)
|
||||
|
||||
logger.info("Finished frigate config migration...")
|
||||
|
||||
|
||||
@@ -525,6 +562,21 @@ def _convert_legacy_mask_to_dict(
|
||||
return result
|
||||
|
||||
|
||||
def _migrate_birdseye_mode(birdseye: dict[str, Any] | None) -> None:
|
||||
"""Convert a scalar Birdseye mode to composable activity types."""
|
||||
if not birdseye or not isinstance(birdseye.get("mode"), str):
|
||||
return
|
||||
|
||||
legacy_mode = birdseye["mode"]
|
||||
activity_types = ("continuous", "motion", "objects", "stationary_objects")
|
||||
if legacy_mode not in activity_types:
|
||||
return
|
||||
|
||||
birdseye["mode"] = {
|
||||
activity_type: activity_type == legacy_mode for activity_type in activity_types
|
||||
}
|
||||
|
||||
|
||||
def migrate_018_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
|
||||
"""Handle migrating frigate config to 0.18-0"""
|
||||
new_config = config.copy()
|
||||
@@ -658,6 +710,109 @@ def migrate_018_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]
|
||||
return new_config
|
||||
|
||||
|
||||
def migrate_019_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
|
||||
"""Handle migrating Frigate config to 0.19-0."""
|
||||
new_config = config.copy()
|
||||
|
||||
_migrate_birdseye_mode(new_config.get("birdseye"))
|
||||
|
||||
for name, camera in new_config.get("cameras", {}).items():
|
||||
camera_config: dict[str, dict[str, Any]] = camera.copy()
|
||||
_migrate_birdseye_mode(camera_config.get("birdseye"))
|
||||
|
||||
for profile in camera_config.get("profiles", {}).values():
|
||||
if isinstance(profile, dict):
|
||||
_migrate_birdseye_mode(profile.get("birdseye"))
|
||||
|
||||
new_config["cameras"][name] = camera_config
|
||||
|
||||
new_config["version"] = "0.19-0"
|
||||
return new_config
|
||||
|
||||
|
||||
def migrate_models(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any]]:
|
||||
"""Merge the detectors and model keys into a single models list.
|
||||
|
||||
Every config before this change ran one model across all of its detectors,
|
||||
so this always produces exactly one model.
|
||||
|
||||
Args:
|
||||
config: The loaded config
|
||||
|
||||
Returns:
|
||||
The config with a models list in place of detectors and model
|
||||
"""
|
||||
# imported lazily so loading the detector plugins is not a cost of importing
|
||||
# this module
|
||||
from frigate.detectors.detector_types import config_types
|
||||
|
||||
new_config = config.copy()
|
||||
detectors: dict[str, Any] = new_config.pop("detectors", None) or {}
|
||||
model: dict[str, Any] = new_config.pop("model", None) or {}
|
||||
|
||||
devices: list[str] = []
|
||||
model_path: str | None = None
|
||||
|
||||
for name, detector in detectors.items():
|
||||
detector = detector or {}
|
||||
detector_type = detector.get("type", "cpu")
|
||||
device = detector.get(DETECTOR_DEVICE_FIELDS.get(detector_type, "device"))
|
||||
device_string = detector_type if device is None else f"{detector_type}:{device}"
|
||||
|
||||
# repeating a device now means running an extra inference process on it,
|
||||
# which is what several detectors on one device used to mean. Only
|
||||
# collapse repeats of hardware that can serve a single process.
|
||||
config_class = config_types.get(detector_type)
|
||||
shareable = config_class.shareable if config_class else True
|
||||
|
||||
if shareable or device_string not in devices:
|
||||
devices.append(device_string)
|
||||
|
||||
dropped = [
|
||||
option
|
||||
for option in DROPPED_DETECTOR_OPTIONS.get(detector_type, [])
|
||||
if option in detector
|
||||
]
|
||||
|
||||
if dropped:
|
||||
logger.error(
|
||||
"Detector '%s' had the %s options set, which are no longer supported and have been removed",
|
||||
name,
|
||||
", ".join(dropped),
|
||||
)
|
||||
|
||||
detector_model_path = detector.get("model_path")
|
||||
|
||||
if detector_model_path:
|
||||
if model_path is None:
|
||||
model_path = detector_model_path
|
||||
elif model_path != detector_model_path:
|
||||
logger.warning(
|
||||
"Detector '%s' set a different model_path than an earlier detector, using '%s' for the migrated model",
|
||||
name,
|
||||
model_path,
|
||||
)
|
||||
|
||||
detector_types = {device.partition(":")[0] for device in devices}
|
||||
|
||||
if len(detector_types) > 1:
|
||||
logger.error(
|
||||
"Detectors of more than one type (%s) were configured. A model now runs on one detector type, so the migrated config will need to be corrected by hand",
|
||||
", ".join(sorted(detector_types)),
|
||||
)
|
||||
|
||||
entry: dict[str, Any] = {"scene": "all", **model}
|
||||
|
||||
if model_path:
|
||||
entry["path"] = model_path
|
||||
|
||||
# a config with no detectors ran a single cpu detector
|
||||
entry["devices"] = devices or ["cpu"]
|
||||
|
||||
new_config["models"] = [entry]
|
||||
return new_config
|
||||
|
||||
|
||||
def get_relative_coordinates(
|
||||
mask: str | list | None,
|
||||
frame_shape: tuple[int, int],
|
||||
|
||||
@@ -0,0 +1,209 @@
|
||||
"""Aggregation of the known sub label names an object can be tagged with."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
|
||||
from pathvalidate import sanitize_filename
|
||||
|
||||
from frigate.config import FrigateConfig
|
||||
from frigate.config.classification import ObjectClassificationType
|
||||
from frigate.const import CLIPS_DIR, FACE_DIR, MODEL_CACHE_DIR
|
||||
from frigate.util.builtin import load_labels
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# subdirectory of FACE_DIR holding unassigned training images, not a face name
|
||||
FACE_TRAIN_DIR = "train"
|
||||
|
||||
# category used by classification models for "no match", never attached to an object
|
||||
CLASSIFICATION_NONE_CATEGORY = "none"
|
||||
|
||||
|
||||
def get_categorized_object_names(
|
||||
config: FrigateConfig,
|
||||
allowed_cameras: list[str],
|
||||
object_type: str | None = None,
|
||||
) -> dict[str, list[str]]:
|
||||
"""Collect every sub label name this install can attach, by object type.
|
||||
|
||||
Unlike the database-backed /sub_labels endpoint, this reads the config and
|
||||
model files, so it also covers names that are configured but have not been
|
||||
detected yet. Names come from the detector's logo attributes (limited to
|
||||
objects the allowed cameras actually track), LPR known plate names,
|
||||
registered face names, and custom object classification categories.
|
||||
|
||||
Structural attributes such as `face` and `license_plate` are excluded: they
|
||||
describe a part of an object rather than naming it, and are never attached
|
||||
as a sub label.
|
||||
|
||||
Args:
|
||||
config: The running Frigate config
|
||||
allowed_cameras: Cameras the requesting user may see
|
||||
object_type: Optional object label to restrict the result to
|
||||
|
||||
Returns:
|
||||
Mapping of object label to its known sub label names, sorted and
|
||||
deduplicated. Object types with no known names are omitted.
|
||||
"""
|
||||
tracked_objects = _get_tracked_objects(config, allowed_cameras)
|
||||
names: dict[str, set[str]] = {}
|
||||
logos = set(config.all_attribute_logos)
|
||||
|
||||
# 1. detector logo attributes, only for objects that are actually tracked
|
||||
for label, label_attributes in config.all_attributes_map.items():
|
||||
if label not in tracked_objects:
|
||||
continue
|
||||
|
||||
label_logos = logos.intersection(label_attributes)
|
||||
|
||||
if label_logos:
|
||||
names.setdefault(label, set()).update(label_logos)
|
||||
|
||||
# 2. LPR known plate names, for objects that can carry a plate
|
||||
if config.lpr.known_plates and _lpr_enabled(config, allowed_cameras):
|
||||
known_plates = set(config.lpr.known_plates)
|
||||
|
||||
for label in _objects_with_attribute(config, tracked_objects, "license_plate"):
|
||||
names.setdefault(label, set()).update(known_plates)
|
||||
|
||||
# 3. registered face names, for objects that can carry a face
|
||||
if _face_recognition_enabled(config, allowed_cameras):
|
||||
face_names = _get_face_names()
|
||||
|
||||
if face_names:
|
||||
for label in _objects_with_attribute(config, tracked_objects, "face"):
|
||||
names.setdefault(label, set()).update(face_names)
|
||||
|
||||
# 4. custom object classification categories
|
||||
for model_key, model_config in config.classification.custom.items():
|
||||
if not model_config.enabled or model_config.object_config is None:
|
||||
continue
|
||||
|
||||
if (
|
||||
model_config.object_config.classification_type
|
||||
!= ObjectClassificationType.sub_label
|
||||
):
|
||||
continue
|
||||
|
||||
categories = _get_classification_categories(model_key)
|
||||
|
||||
if not categories:
|
||||
continue
|
||||
|
||||
for label in model_config.object_config.objects:
|
||||
names.setdefault(label, set()).update(categories)
|
||||
|
||||
return {
|
||||
label: sorted(label_names)
|
||||
for label, label_names in sorted(names.items())
|
||||
if label_names and (object_type is None or label == object_type)
|
||||
}
|
||||
|
||||
|
||||
def _get_tracked_objects(config: FrigateConfig, allowed_cameras: list[str]) -> set[str]:
|
||||
"""Get the union of objects tracked by the cameras the user can see."""
|
||||
tracked: set[str] = set()
|
||||
|
||||
for camera_name in allowed_cameras:
|
||||
camera_config = config.cameras.get(camera_name)
|
||||
|
||||
if camera_config is None:
|
||||
continue
|
||||
|
||||
tracked.update(camera_config.objects.track)
|
||||
|
||||
return tracked
|
||||
|
||||
|
||||
def _objects_with_attribute(
|
||||
config: FrigateConfig, tracked_objects: set[str], attribute: str
|
||||
) -> set[str]:
|
||||
"""Get the tracked objects that a given attribute can be recognized on.
|
||||
|
||||
The attribute may also be tracked as an object in its own right, as
|
||||
`license_plate` is on a dedicated LPR camera, in which case the name is
|
||||
attached to that object directly.
|
||||
"""
|
||||
objects = {
|
||||
label
|
||||
for label, label_attributes in config.all_attributes_map.items()
|
||||
if attribute in label_attributes and label in tracked_objects
|
||||
}
|
||||
|
||||
if attribute in tracked_objects:
|
||||
objects.add(attribute)
|
||||
|
||||
return objects
|
||||
|
||||
|
||||
def _lpr_enabled(config: FrigateConfig, allowed_cameras: list[str]) -> bool:
|
||||
return any(
|
||||
config.cameras[camera_name].lpr.enabled
|
||||
for camera_name in allowed_cameras
|
||||
if camera_name in config.cameras
|
||||
)
|
||||
|
||||
|
||||
def _face_recognition_enabled(
|
||||
config: FrigateConfig, allowed_cameras: list[str]
|
||||
) -> bool:
|
||||
return any(
|
||||
config.cameras[camera_name].face_recognition.enabled
|
||||
for camera_name in allowed_cameras
|
||||
if camera_name in config.cameras
|
||||
)
|
||||
|
||||
|
||||
def _get_face_names() -> set[str]:
|
||||
"""Get the names of every registered face collection."""
|
||||
if not os.path.exists(FACE_DIR):
|
||||
return set()
|
||||
|
||||
try:
|
||||
entries = os.listdir(FACE_DIR)
|
||||
except OSError:
|
||||
logger.debug("Failed to read face directory %s", FACE_DIR)
|
||||
return set()
|
||||
|
||||
return {
|
||||
name
|
||||
for name in entries
|
||||
if name != FACE_TRAIN_DIR and os.path.isdir(os.path.join(FACE_DIR, name))
|
||||
}
|
||||
|
||||
|
||||
def _get_classification_categories(model_key: str) -> set[str]:
|
||||
"""Get the categories a custom classification model can output.
|
||||
|
||||
The trained labelmap is authoritative, but it only exists once the model
|
||||
has been trained, so fall back to the dataset directories that will become
|
||||
the labelmap on the next training run.
|
||||
"""
|
||||
safe_key = sanitize_filename(model_key)
|
||||
categories: set[str] = set()
|
||||
labelmap_path = os.path.join(MODEL_CACHE_DIR, safe_key, "labelmap.txt")
|
||||
|
||||
if os.path.exists(labelmap_path):
|
||||
try:
|
||||
labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
|
||||
except OSError:
|
||||
logger.debug("Failed to read labelmap %s", labelmap_path)
|
||||
labelmap = {}
|
||||
|
||||
categories.update(label for label in labelmap.values() if label)
|
||||
|
||||
dataset_dir = os.path.join(CLIPS_DIR, safe_key, "dataset")
|
||||
|
||||
if os.path.exists(dataset_dir):
|
||||
try:
|
||||
entries = os.listdir(dataset_dir)
|
||||
except OSError:
|
||||
logger.debug("Failed to read dataset directory %s", dataset_dir)
|
||||
entries = []
|
||||
|
||||
categories.update(
|
||||
name for name in entries if os.path.isdir(os.path.join(dataset_dir, name))
|
||||
)
|
||||
|
||||
categories.discard(CLASSIFICATION_NONE_CATEGORY)
|
||||
return categories
|
||||
+9
-38
@@ -2,45 +2,16 @@
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, TypeAdapter
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
def get_config_schema(config_class: type[BaseModel]) -> dict[str, Any]:
|
||||
"""Get the JSON schema for FrigateConfig.
|
||||
|
||||
Args:
|
||||
config_class: The config model to describe
|
||||
|
||||
Returns:
|
||||
The JSON schema
|
||||
"""
|
||||
Returns the JSON schema for FrigateConfig with polymorphic detectors.
|
||||
|
||||
This utility patches the FrigateConfig schema to include the full polymorphic
|
||||
definitions for detectors. By default, Pydantic's schema for Dict[str, BaseDetectorConfig]
|
||||
only includes the base class fields. This function replaces it with a reference
|
||||
to the DetectorConfig union, which includes all available detector subclasses.
|
||||
"""
|
||||
# Import here to ensure all detector plugins are loaded through the detectors module
|
||||
from frigate.detectors import DetectorConfig
|
||||
|
||||
# Get the base schema for FrigateConfig
|
||||
schema = config_class.model_json_schema()
|
||||
|
||||
# Get the schema for the polymorphic DetectorConfig union
|
||||
detector_adapter: TypeAdapter = TypeAdapter(DetectorConfig)
|
||||
detector_schema = detector_adapter.json_schema()
|
||||
|
||||
# Ensure $defs exists in FrigateConfig schema
|
||||
if "$defs" not in schema:
|
||||
schema["$defs"] = {}
|
||||
|
||||
# Merge $defs from DetectorConfig into FrigateConfig schema
|
||||
# This includes the specific schemas for each detector plugin (OvDetectorConfig, etc.)
|
||||
if "$defs" in detector_schema:
|
||||
schema["$defs"].update(detector_schema["$defs"])
|
||||
|
||||
# Extract the union schema (oneOf/discriminator) and add it as a definition
|
||||
detector_union_schema = {k: v for k, v in detector_schema.items() if k != "$defs"}
|
||||
schema["$defs"]["DetectorConfig"] = detector_union_schema
|
||||
|
||||
# Update the 'detectors' property to use the polymorphic DetectorConfig definition
|
||||
if "detectors" in schema.get("properties", {}):
|
||||
schema["properties"]["detectors"]["additionalProperties"] = {
|
||||
"$ref": "#/$defs/DetectorConfig"
|
||||
}
|
||||
|
||||
return schema
|
||||
return config_class.model_json_schema()
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
"""Utilities for services."""
|
||||
|
||||
import asyncio
|
||||
import glob
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
@@ -670,19 +671,33 @@ def get_intel_gpu_stats(
|
||||
|
||||
def get_openvino_npu_stats() -> dict[str, str] | None:
|
||||
"""Get NPU stats using openvino."""
|
||||
NPU_RUNTIME_PATH = "/sys/devices/pci0000:00/0000:00:0b.0/power/runtime_active_time"
|
||||
for accel_path in sorted(glob.glob("/sys/class/accel/accel*")):
|
||||
try:
|
||||
driver = os.path.basename(os.readlink(f"{accel_path}/device/driver"))
|
||||
except OSError:
|
||||
continue
|
||||
|
||||
if driver != "intel_vpu":
|
||||
continue
|
||||
|
||||
try:
|
||||
runtime_path = f"{accel_path}/device/power/runtime_active_time"
|
||||
with open(runtime_path) as f:
|
||||
initial_runtime = float(f.read().strip())
|
||||
break
|
||||
except (FileNotFoundError, PermissionError, ValueError):
|
||||
continue
|
||||
else:
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(NPU_RUNTIME_PATH) as f:
|
||||
initial_runtime = float(f.read().strip())
|
||||
|
||||
initial_time = time.time()
|
||||
|
||||
# Sleep for 1 second to get an accurate reading
|
||||
time.sleep(1.0)
|
||||
|
||||
# Read runtime value again
|
||||
with open(NPU_RUNTIME_PATH) as f:
|
||||
with open(runtime_path) as f:
|
||||
current_runtime = float(f.read().strip())
|
||||
|
||||
current_time = time.time()
|
||||
|
||||
@@ -210,78 +210,6 @@ def generate_section_translation(config_class: type) -> dict[str, Any]:
|
||||
return extract_translations_from_schema(schema)
|
||||
|
||||
|
||||
def get_detector_translations(
|
||||
config_schema: dict[str, Any],
|
||||
) -> tuple[dict[str, Any], dict[str, Any], set[str]]:
|
||||
"""Build detector type translations with nested fields based on schema definitions.
|
||||
|
||||
Returns a tuple of (type_translations, shared_fields, nested_field_keys).
|
||||
Shared fields (identical across all detector types) are returned separately
|
||||
to avoid duplication in the output.
|
||||
"""
|
||||
defs = config_schema.get("$defs", {})
|
||||
detector_schema = defs.get("DetectorConfig", {})
|
||||
discriminator = detector_schema.get("discriminator", {})
|
||||
mapping = discriminator.get("mapping", {})
|
||||
|
||||
# First pass: collect all nested fields per detector type
|
||||
all_nested: dict[str, dict[str, Any]] = {}
|
||||
type_meta: dict[str, dict[str, str]] = {}
|
||||
|
||||
for detector_type, ref in mapping.items():
|
||||
if not isinstance(ref, str) or not ref.startswith("#/$defs/"):
|
||||
continue
|
||||
|
||||
ref_name = ref.split("/")[-1]
|
||||
ref_schema = defs.get(ref_name, {})
|
||||
if not ref_schema:
|
||||
continue
|
||||
|
||||
meta: dict[str, str] = {}
|
||||
title = ref_schema.get("title")
|
||||
description = ref_schema.get("description")
|
||||
if title:
|
||||
meta["label"] = title
|
||||
if description:
|
||||
meta["description"] = description
|
||||
type_meta[detector_type] = meta
|
||||
|
||||
nested = extract_translations_from_schema(ref_schema, defs=defs)
|
||||
all_nested[detector_type] = {
|
||||
k: v for k, v in nested.items() if k not in ("label", "description")
|
||||
}
|
||||
|
||||
# Find fields that are identical across all types that have them
|
||||
shared_fields: dict[str, Any] = {}
|
||||
if all_nested:
|
||||
# Collect all field keys across all types
|
||||
all_keys: set[str] = set()
|
||||
for nested in all_nested.values():
|
||||
all_keys.update(nested.keys())
|
||||
|
||||
for key in all_keys:
|
||||
values = [nested[key] for nested in all_nested.values() if key in nested]
|
||||
if len(values) == len(all_nested) and all(v == values[0] for v in values):
|
||||
shared_fields[key] = values[0]
|
||||
|
||||
# Build per-type translations with only unique (non-shared) fields
|
||||
type_translations: dict[str, Any] = {}
|
||||
nested_field_keys: set[str] = set()
|
||||
for detector_type, nested in all_nested.items():
|
||||
type_entry: dict[str, Any] = {}
|
||||
type_entry.update(type_meta.get(detector_type, {}))
|
||||
|
||||
unique_fields = {k: v for k, v in nested.items() if k not in shared_fields}
|
||||
if unique_fields:
|
||||
type_entry.update(unique_fields)
|
||||
nested_field_keys.update(unique_fields.keys())
|
||||
|
||||
if type_entry:
|
||||
type_translations[detector_type] = type_entry
|
||||
|
||||
return type_translations, shared_fields, nested_field_keys
|
||||
|
||||
|
||||
def main():
|
||||
"""Main function to generate config translations."""
|
||||
|
||||
@@ -337,6 +265,12 @@ def main():
|
||||
if args and len(args) > 1:
|
||||
field_type = args[1] # Get value type from Dict[key, value]
|
||||
|
||||
# Handle List[SomeModel] - extract the item type
|
||||
if origin is list:
|
||||
args = get_args(field_type)
|
||||
if args:
|
||||
field_type = args[0]
|
||||
|
||||
# Start with field's top-level metadata (label, description)
|
||||
section_data = get_field_translations(field_info)
|
||||
|
||||
@@ -351,19 +285,6 @@ def main():
|
||||
}
|
||||
section_data.update(nested_without_root)
|
||||
|
||||
if field_name == "detectors":
|
||||
detector_types, shared_fields, detector_field_keys = (
|
||||
get_detector_translations(config_schema)
|
||||
)
|
||||
# Add shared fields at the base detectors level
|
||||
section_data.update(shared_fields)
|
||||
# Add per-type translations (only unique fields per type)
|
||||
section_data.update(detector_types)
|
||||
for key in detector_field_keys:
|
||||
if key == "type":
|
||||
continue
|
||||
section_data.pop(key, None)
|
||||
|
||||
if field_name == "objects":
|
||||
# Produce a parallel `filters_attribute` block alongside `filters`,
|
||||
# with object-wording rewritten for attribute filters (face,
|
||||
|
||||
@@ -122,7 +122,7 @@ class ProcessClip:
|
||||
self.camera_name,
|
||||
self.frame_queue,
|
||||
self.frame_shape,
|
||||
self.config.model,
|
||||
self.config.model_for_camera(self.camera_name),
|
||||
self.camera_config.detect,
|
||||
self.frame_manager,
|
||||
motion_detector,
|
||||
@@ -248,7 +248,7 @@ def process(path, label, output, debug_path):
|
||||
|
||||
json_config = {
|
||||
"mqtt": {"enabled": False},
|
||||
"detectors": {"coral": {"type": "edgetpu", "device": "usb"}},
|
||||
"models": [{"devices": ["edgetpu:usb"]}],
|
||||
"cameras": {
|
||||
"camera": {
|
||||
"ffmpeg": {
|
||||
|
||||
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
|
||||
|
||||
|
||||
@@ -71,8 +71,24 @@
|
||||
"description": "Enable or disable the Birdseye view feature."
|
||||
},
|
||||
"mode": {
|
||||
"label": "Tracking mode",
|
||||
"description": "Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'."
|
||||
"label": "Activity types",
|
||||
"description": "Activity types that include cameras in Birdseye.",
|
||||
"continuous": {
|
||||
"label": "Continuous",
|
||||
"description": "Always include the camera in Birdseye."
|
||||
},
|
||||
"motion": {
|
||||
"label": "Motion",
|
||||
"description": "Include the camera in Birdseye when motion is detected."
|
||||
},
|
||||
"objects": {
|
||||
"label": "Active objects",
|
||||
"description": "Include the camera in Birdseye while an active object is tracked."
|
||||
},
|
||||
"stationary_objects": {
|
||||
"label": "Stationary objects",
|
||||
"description": "Include the camera in Birdseye while a stationary object is tracked."
|
||||
}
|
||||
},
|
||||
"order": {
|
||||
"label": "Position",
|
||||
@@ -94,6 +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)."
|
||||
@@ -558,8 +403,24 @@
|
||||
"description": "Enable or disable the Birdseye view feature."
|
||||
},
|
||||
"mode": {
|
||||
"label": "Tracking mode",
|
||||
"description": "Mode for including cameras in Birdseye: 'objects', 'motion', or 'continuous'."
|
||||
"label": "Activity types",
|
||||
"description": "Activity types that include cameras in Birdseye.",
|
||||
"continuous": {
|
||||
"label": "Continuous",
|
||||
"description": "Always include the camera in Birdseye."
|
||||
},
|
||||
"motion": {
|
||||
"label": "Motion",
|
||||
"description": "Include the camera in Birdseye when motion is detected."
|
||||
},
|
||||
"objects": {
|
||||
"label": "Active objects",
|
||||
"description": "Include the camera in Birdseye while an active object is tracked."
|
||||
},
|
||||
"stationary_objects": {
|
||||
"label": "Stationary objects",
|
||||
"description": "Include the camera in Birdseye while a stationary object is tracked."
|
||||
}
|
||||
},
|
||||
"restream": {
|
||||
"label": "Restream RTSP",
|
||||
@@ -617,6 +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)."
|
||||
|
||||
@@ -1955,7 +1955,7 @@
|
||||
"noRecordRole": "No streams have the record role defined. Recording will not function."
|
||||
},
|
||||
"birdseye": {
|
||||
"objectsModeDetectDisabled": "Birdseye is set to 'objects' mode, but object detection is disabled for this camera. The camera will not appear in Birdseye."
|
||||
"objectTrackingDetectDisabled": "Birdseye includes tracked objects, but object detection is disabled for this camera. The camera will not appear in Birdseye."
|
||||
},
|
||||
"snapshots": {
|
||||
"detectDisabled": "Object detection is disabled. Snapshots are generated from tracked objects and will not be created."
|
||||
|
||||
@@ -5,13 +5,18 @@ const birdseye: SectionConfigOverrides = {
|
||||
sectionDocs: "/configuration/birdseye",
|
||||
messages: [
|
||||
{
|
||||
key: "objects-mode-detect-disabled",
|
||||
messageKey: "configMessages.birdseye.objectsModeDetectDisabled",
|
||||
key: "object-tracking-detect-disabled",
|
||||
messageKey: "configMessages.birdseye.objectTrackingDetectDisabled",
|
||||
severity: "info",
|
||||
condition: (ctx) => {
|
||||
if (ctx.level !== "camera" || !ctx.fullCameraConfig) return false;
|
||||
const mode = ctx.formData?.mode;
|
||||
if (!mode || typeof mode !== "object" || Array.isArray(mode)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return (
|
||||
ctx.formData?.mode === "objects" &&
|
||||
(mode.objects === true || mode.stationary_objects === true) &&
|
||||
ctx.fullCameraConfig.detect?.enabled === false
|
||||
);
|
||||
},
|
||||
@@ -24,10 +29,10 @@ const birdseye: SectionConfigOverrides = {
|
||||
overrideFields: ["enabled", "mode"],
|
||||
uiSchema: {
|
||||
mode: {
|
||||
"ui:size": "xs",
|
||||
"ui:options": {
|
||||
enumI18nPrefix: "birdseye.trackingMode",
|
||||
},
|
||||
continuous: { "ui:size": "xs" },
|
||||
motion: { "ui:size": "xs" },
|
||||
objects: { "ui:size": "xs" },
|
||||
stationary_objects: { "ui:size": "xs" },
|
||||
},
|
||||
},
|
||||
},
|
||||
@@ -55,7 +60,6 @@ const birdseye: SectionConfigOverrides = {
|
||||
],
|
||||
uiSchema: {
|
||||
mode: {
|
||||
"ui:size": "xs",
|
||||
"ui:after": { render: "BirdseyeCameraReorder" },
|
||||
},
|
||||
},
|
||||
|
||||
@@ -13,12 +13,19 @@ export interface UiConfig {
|
||||
export interface BirdseyeConfig {
|
||||
enabled: boolean;
|
||||
height: number;
|
||||
mode: "objects" | "continuous" | "motion";
|
||||
mode: BirdseyeModeConfig;
|
||||
quality: number;
|
||||
restream: boolean;
|
||||
width: number;
|
||||
}
|
||||
|
||||
export interface BirdseyeModeConfig {
|
||||
continuous: boolean;
|
||||
motion: boolean;
|
||||
objects: boolean;
|
||||
stationary_objects: boolean;
|
||||
}
|
||||
|
||||
export interface FaceRecognitionConfig {
|
||||
enabled: boolean;
|
||||
model_size: SearchModelSize;
|
||||
@@ -49,7 +56,7 @@ export interface CameraConfig {
|
||||
best_image_timeout: number;
|
||||
birdseye: {
|
||||
enabled: boolean;
|
||||
mode: "objects" | "continuous" | "motion";
|
||||
mode: BirdseyeModeConfig;
|
||||
order: number;
|
||||
};
|
||||
detect: {
|
||||
|
||||
Reference in New Issue
Block a user