mirror of
https://github.com/blakeblackshear/frigate.git
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71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
import logging
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import sys
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import os
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import numpy as np
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import ctypes
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from pydantic import Field
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from typing_extensions import Literal
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import glob
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from frigate.detectors.detection_api import DetectionApi
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from frigate.detectors.detector_config import BaseDetectorConfig
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from frigate.detectors.util import preprocess, yolov8_postprocess
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "onnx"
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class ONNXDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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class ONNXDetector(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: ONNXDetectorConfig):
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try:
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import onnxruntime
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logger.info(f"ONNX: loaded onnxruntime module")
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except ModuleNotFoundError:
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logger.error(
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"ONNX: module loading failed, need 'pip install onnxruntime'?!?"
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)
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raise
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assert (
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detector_config.model.model_type == "yolov8"
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), "ONNX: detector_config.model.model_type: only yolov8 supported"
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assert (
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detector_config.model.input_tensor == "nhwc"
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), "ONNX: detector_config.model.input_tensor: only nhwc supported"
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if detector_config.model.input_pixel_format != "rgb":
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logger.warn(
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"ONNX: detector_config.model.input_pixel_format: should be 'rgb' for yolov8, but '{detector_config.model.input_pixel_format}' specified!"
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)
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assert detector_config.model.path is not None, (
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"ONNX: No model.path configured, please configure model.path and model.labelmap_path; some suggestions: "
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+ ", ".join(glob.glob("/config/model_cache/yolov8/*.onnx"))
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+ " and "
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+ ", ".join(glob.glob("/config/model_cache/yolov8/*_labels.txt"))
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)
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path = detector_config.model.path
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logger.info(f"ONNX: loading {detector_config.model.path}")
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self.model = onnxruntime.InferenceSession(path)
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logger.info(f"ONNX: {path} loaded")
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def detect_raw(self, tensor_input):
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model_input_name = self.model.get_inputs()[0].name
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model_input_shape = self.model.get_inputs()[0].shape
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tensor_input = preprocess(tensor_input, model_input_shape, np.float32)
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tensor_output = self.model.run(None, {model_input_name: tensor_input})[0]
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return yolov8_postprocess(model_input_shape, tensor_output)
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