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Code Issues Projects Releases Wiki Activity

Various fixes & tweaks (#17308)

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* Catch case where returned face box is invalid

* Update detector docs

* Add note for customizing rfdetr resolution
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Nicolas Mowen 2025-03-22 12:58:27 -06:00 committed by GitHub
parent d32949017b
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2 changed files with 24 additions and 4 deletions
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22
docs/docs/configuration/object_detectors.md
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@ -344,6 +344,12 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate. [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
:::warning
Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model
:::
After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration: After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:
```yaml ```yaml
@ -653,7 +659,7 @@ Note that the labelmap uses a subset of the complete COCO label set that has onl
After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration: After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
``` ```yaml
detectors: detectors:
onnx: onnx:
type: onnx type: onnx
@ -671,7 +677,7 @@ model:
[D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate. [D-FINE](https://github.com/Peterande/D-FINE) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-d-fine-model) for more information on downloading the D-FINE model for use in Frigate.
After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration: After placing the downloaded onnx model in your `config/model_cache` folder, you can use the following configuration:
```yaml ```yaml
detectors: detectors:
@ -898,11 +904,21 @@ Make sure you change the batch size to 1 before exporting.
To export as ONNX: To export as ONNX:
1. `pip3 install rfdetr` 1. `pip3 install rfdetr`
2. `python` 2. `python3`
3. `from rfdetr import RFDETRBase` 3. `from rfdetr import RFDETRBase`
4. `x = RFDETRBase()` 4. `x = RFDETRBase()`
5. `x.export()` 5. `x.export()`
#### Additional Configuration
The input tensor resolution can be customized:
```python
from rfdetr import RFDETRBase
x = RFDETRBase(resolution=560) # resolution must be a multiple of 56
x.export()
```
### Downloading YOLO-NAS Model ### Downloading YOLO-NAS Model
You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb). You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).

6
frigate/data_processing/real_time/face.py
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@ -329,7 +329,11 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
max(0, face_box[1]) : min(frame.shape[0], face_box[3]), max(0, face_box[1]) : min(frame.shape[0], face_box[3]),
max(0, face_box[0]) : min(frame.shape[1], face_box[2]), max(0, face_box[0]) : min(frame.shape[1], face_box[2]),
] ]
face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
try:
face_frame = cv2.cvtColor(face_frame, cv2.COLOR_RGB2BGR)
except Exception:
return
else: else:
# don't run for object without attributes # don't run for object without attributes
if not obj_data.get("current_attributes"): if not obj_data.get("current_attributes"):