mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-23 19:11:14 +01:00
420bcd7aa0
* refactor detectors * move create_detector and DetectorTypeEnum * fixed code formatting * add detector model config models * fix detector unit tests * adjust SharedMemory size to largest detector model shape * fix detector model config defaults * enable auto-discovery of detectors * simplify config * simplify config changes further * update detectors docs; detect detector configs dynamic * add suggested changes * remove custom detector doc * fix grammar, adjust device defaults
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import logging
|
|
import numpy as np
|
|
import openvino.runtime as ov
|
|
|
|
from frigate.detectors.detection_api import DetectionApi
|
|
from frigate.detectors.detector_config import BaseDetectorConfig
|
|
from typing import Literal
|
|
from pydantic import Extra, Field
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
DETECTOR_KEY = "openvino"
|
|
|
|
|
|
class OvDetectorConfig(BaseDetectorConfig):
|
|
type: Literal[DETECTOR_KEY]
|
|
device: str = Field(default=None, title="Device Type")
|
|
|
|
|
|
class OvDetector(DetectionApi):
|
|
type_key = DETECTOR_KEY
|
|
|
|
def __init__(self, detector_config: OvDetectorConfig):
|
|
self.ov_core = ov.Core()
|
|
self.ov_model = self.ov_core.read_model(detector_config.model.path)
|
|
|
|
self.interpreter = self.ov_core.compile_model(
|
|
model=self.ov_model, device_name=detector_config.device
|
|
)
|
|
logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
|
|
self.output_indexes = 0
|
|
while True:
|
|
try:
|
|
tensor_shape = self.interpreter.output(self.output_indexes).shape
|
|
logger.info(f"Model Output-{self.output_indexes} Shape: {tensor_shape}")
|
|
self.output_indexes += 1
|
|
except:
|
|
logger.info(f"Model has {self.output_indexes} Output Tensors")
|
|
break
|
|
|
|
def detect_raw(self, tensor_input):
|
|
|
|
infer_request = self.interpreter.create_infer_request()
|
|
infer_request.infer([tensor_input])
|
|
|
|
results = infer_request.get_output_tensor()
|
|
|
|
detections = np.zeros((20, 6), np.float32)
|
|
i = 0
|
|
for object_detected in results.data[0, 0, :]:
|
|
if object_detected[0] != -1:
|
|
logger.debug(object_detected)
|
|
if object_detected[2] < 0.1 or i == 20:
|
|
break
|
|
detections[i] = [
|
|
object_detected[1], # Label ID
|
|
float(object_detected[2]), # Confidence
|
|
object_detected[4], # y_min
|
|
object_detected[3], # x_min
|
|
object_detected[6], # y_max
|
|
object_detected[5], # x_max
|
|
]
|
|
i += 1
|
|
|
|
return detections
|