mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-10-27 10:52:11 +01:00
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
import logging
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import os
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import numpy as np
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from pydantic import Field
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from typing_extensions import Literal
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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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try:
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from tflite_runtime.interpreter import Interpreter, load_delegate
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except ModuleNotFoundError:
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from tensorflow.lite.python.interpreter import Interpreter, load_delegate
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logger = logging.getLogger(__name__)
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DETECTOR_KEY = "edgetpu"
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class EdgeTpuDetectorConfig(BaseDetectorConfig):
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type: Literal[DETECTOR_KEY]
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device: str = Field(default=None, title="Device Type")
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class EdgeTpuTfl(DetectionApi):
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type_key = DETECTOR_KEY
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def __init__(self, detector_config: EdgeTpuDetectorConfig):
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device_config = {}
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if detector_config.device is not None:
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device_config = {"device": detector_config.device}
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edge_tpu_delegate = None
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try:
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device_type = (
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device_config["device"] if "device" in device_config else "auto"
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)
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logger.info(f"Attempting to load TPU as {device_type}")
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edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
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logger.info("TPU found")
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self.interpreter = Interpreter(
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model_path=detector_config.model.path,
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experimental_delegates=[edge_tpu_delegate],
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)
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except ValueError:
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_, ext = os.path.splitext(detector_config.model.path)
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if ext and ext != ".tflite":
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logger.error(
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"Incorrect model used with EdgeTPU. Only .tflite models can be used with a Coral EdgeTPU."
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)
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else:
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logger.error(
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"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
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)
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raise
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self.interpreter.allocate_tensors()
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self.tensor_input_details = self.interpreter.get_input_details()
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self.tensor_output_details = self.interpreter.get_output_details()
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self.model_type = detector_config.model.model_type
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def detect_raw(self, tensor_input):
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self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
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self.interpreter.invoke()
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boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
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class_ids = self.interpreter.tensor(self.tensor_output_details[1]["index"])()[0]
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scores = self.interpreter.tensor(self.tensor_output_details[2]["index"])()[0]
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count = int(
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self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
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)
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detections = np.zeros((20, 6), np.float32)
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for i in range(count):
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if scores[i] < 0.4 or i == 20:
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break
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detections[i] = [
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class_ids[i],
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float(scores[i]),
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boxes[i][0],
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boxes[i][1],
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boxes[i][2],
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boxes[i][3],
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]
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return detections
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