mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-11-21 19:07:46 +01:00
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
|
import logging
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from frigate.detectors.detection_api import DetectionApi
|
||
|
|
import tflite_runtime.interpreter as tflite
|
||
|
|
from tflite_runtime.interpreter import load_delegate
|
||
|
|
|
||
|
|
logger = logging.getLogger(__name__)
|
||
|
|
|
||
|
|
|
||
|
|
class EdgeTpuTfl(DetectionApi):
|
||
|
|
def __init__(self, det_device=None, model_config=None):
|
||
|
|
device_config = {"device": "usb"}
|
||
|
|
if not det_device is None:
|
||
|
|
device_config = {"device": det_device}
|
||
|
|
|
||
|
|
edge_tpu_delegate = None
|
||
|
|
|
||
|
|
try:
|
||
|
|
logger.info(f"Attempting to load TPU as {device_config['device']}")
|
||
|
|
edge_tpu_delegate = load_delegate("libedgetpu.so.1.0", device_config)
|
||
|
|
logger.info("TPU found")
|
||
|
|
self.interpreter = tflite.Interpreter(
|
||
|
|
model_path=model_config.path or "/edgetpu_model.tflite",
|
||
|
|
experimental_delegates=[edge_tpu_delegate],
|
||
|
|
)
|
||
|
|
except ValueError:
|
||
|
|
logger.error(
|
||
|
|
"No EdgeTPU was detected. If you do not have a Coral device yet, you must configure CPU detectors."
|
||
|
|
)
|
||
|
|
raise
|
||
|
|
|
||
|
|
self.interpreter.allocate_tensors()
|
||
|
|
|
||
|
|
self.tensor_input_details = self.interpreter.get_input_details()
|
||
|
|
self.tensor_output_details = self.interpreter.get_output_details()
|
||
|
|
|
||
|
|
def detect_raw(self, tensor_input):
|
||
|
|
self.interpreter.set_tensor(self.tensor_input_details[0]["index"], tensor_input)
|
||
|
|
self.interpreter.invoke()
|
||
|
|
|
||
|
|
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]
|
||
|
|
count = int(
|
||
|
|
self.interpreter.tensor(self.tensor_output_details[3]["index"])()[0]
|
||
|
|
)
|
||
|
|
|
||
|
|
detections = np.zeros((20, 6), np.float32)
|
||
|
|
|
||
|
|
for i in range(count):
|
||
|
|
if scores[i] < 0.4 or i == 20:
|
||
|
|
break
|
||
|
|
detections[i] = [
|
||
|
|
class_ids[i],
|
||
|
|
float(scores[i]),
|
||
|
|
boxes[i][0],
|
||
|
|
boxes[i][1],
|
||
|
|
boxes[i][2],
|
||
|
|
boxes[i][3],
|
||
|
|
]
|
||
|
|
|
||
|
|
return detections
|