Remove all AGPL licensed YOLO references from Frigate (#10717)

* Remove yolov8 support from Frigate

* Remove yolov8 from dev

* Remove builds

* Formatting and remove yolov5

* Fix lint

* remove models download

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

frigate/detectors/plugins/onnx.py

@@ -1,4 +1,3 @@
-import glob
 import logging
 
 import numpy as np
@@ -6,7 +5,7 @@ from typing_extensions import Literal
 
 from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detector_config import BaseDetectorConfig
-from frigate.detectors.util import preprocess, yolov8_postprocess
+from frigate.detectors.util import preprocess
 
 logger = logging.getLogger(__name__)
 
@@ -31,24 +30,6 @@ class ONNXDetector(DetectionApi):
             )
             raise
 
-        assert (
-            detector_config.model.model_type == "yolov8"
-        ), "ONNX: detector_config.model.model_type: only yolov8 supported"
-        assert (
-            detector_config.model.input_tensor == "nhwc"
-        ), "ONNX: detector_config.model.input_tensor: only nhwc supported"
-        if detector_config.model.input_pixel_format != "rgb":
-            logger.warn(
-                "ONNX: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!"
-            )
-
-        assert detector_config.model.path is not None, (
-            "ONNX: No model.path configured, please configure model.path and model.labelmap_path; some suggestions: "
-            + ", ".join(glob.glob("/config/model_cache/yolov8/*.onnx"))
-            + " and "
-            + ", ".join(glob.glob("/config/model_cache/yolov8/*_labels.txt"))
-        )
-
         path = detector_config.model.path
         logger.info(f"ONNX: loading {detector_config.model.path}")
         self.model = onnxruntime.InferenceSession(path)
@@ -57,9 +38,10 @@ class ONNXDetector(DetectionApi):
     def detect_raw(self, tensor_input):
         model_input_name = self.model.get_inputs()[0].name
         model_input_shape = self.model.get_inputs()[0].shape
-
         tensor_input = preprocess(tensor_input, model_input_shape, np.float32)
-
+        # ruff: noqa: F841
         tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
 
-        return yolov8_postprocess(model_input_shape, tensor_output)
+        raise Exception(
+            "No models are currently supported via onnx. See the docs for more info."
+        )