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https://github.com/blakeblackshear/frigate.git
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66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import logging
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import numpy as np
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from .detection_api import DetectionApi
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from .config import EdgeTpuDetectorConfig
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import tflite_runtime.interpreter as tflite
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from tflite_runtime.interpreter import load_delegate
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logger = logging.getLogger(__name__)
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class EdgeTpuTfl(DetectionApi):
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def __init__(self, detector_config: EdgeTpuDetectorConfig):
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device_config = {"device": "usb"}
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if not detector_config.device is 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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logger.info(f"Attempting to load TPU as {device_config['device']}")
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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 = tflite.Interpreter(
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model_path=detector_config.model.path or "/edgetpu_model.tflite",
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experimental_delegates=[edge_tpu_delegate],
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)
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except ValueError:
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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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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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