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detectors/edgetpu: yolov8 support (#9782)

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* detectors/edgetpu: add support for yolov8 models

* docs: edgetpu yolov8 running

* docs: edgetpu yolov8 attribution and language

* Update docs/docs/configuration/object_detectors.md

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

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
harakas
2024-02-10 21:39:28 +02:00
committed by GitHub
parent cad7cdfb7e
commit cd5f4b1534
2 changed files with 71 additions and 0 deletions
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19
frigate/detectors/plugins/edgetpu_tfl.py
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@@ -6,6 +6,7 @@ from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig
from frigate.detectors.util import yolov8_postprocess
try:
from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -54,11 +55,29 @@ class EdgeTpuTfl(DetectionApi):
self.tensor_input_details = self.interpreter.get_input_details()
self.tensor_output_details = self.interpreter.get_output_details()
self.model_type = detector_config.model.model_type
def detect_raw(self, tensor_input):
if self.model_type == "yolov8":
scale, zero_point = self.tensor_input_details[0]["quantization"]
tensor_input = (
(tensor_input - scale * zero_point * 255) * (1.0 / (scale * 255))
).astype(self.tensor_input_details[0]["dtype"])
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
self.interpreter.invoke()
if self.model_type == "yolov8":
scale, zero_point = self.tensor_output_details[0]["quantization"]
tensor_output = self.interpreter.get_tensor(
self.tensor_output_details[0]["index"]
)
tensor_output = (tensor_output.astype(np.float32) - zero_point) * scale
model_input_shape = self.tensor_input_details[0]["shape"]
tensor_output[:, [0, 2]] *= model_input_shape[2]
tensor_output[:, [1, 3]] *= model_input_shape[1]
return yolov8_postprocess(model_input_shape, tensor_output)
boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]