mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-12-23 19:11:14 +01:00
111 lines
3.6 KiB
Python
111 lines
3.6 KiB
Python
import logging
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from typing_extensions import Literal
|
|
|
|
from frigate.detectors.detection_api import DetectionApi
|
|
from frigate.detectors.detector_config import (
|
|
BaseDetectorConfig,
|
|
ModelTypeEnum,
|
|
PixelFormatEnum,
|
|
)
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
DETECTOR_KEY = "onnx"
|
|
|
|
|
|
class ONNXDetectorConfig(BaseDetectorConfig):
|
|
type: Literal[DETECTOR_KEY]
|
|
|
|
|
|
class ONNXDetector(DetectionApi):
|
|
type_key = DETECTOR_KEY
|
|
|
|
def __init__(self, detector_config: ONNXDetectorConfig):
|
|
try:
|
|
import onnxruntime as ort
|
|
|
|
logger.info("ONNX: loaded onnxruntime module")
|
|
except ModuleNotFoundError:
|
|
logger.error(
|
|
"ONNX: module loading failed, need 'pip install onnxruntime'?!?"
|
|
)
|
|
raise
|
|
|
|
path = detector_config.model.path
|
|
logger.info(f"ONNX: loading {detector_config.model.path}")
|
|
|
|
providers = ort.get_available_providers()
|
|
options = []
|
|
|
|
for provider in providers:
|
|
if provider == "TensorrtExecutionProvider":
|
|
options.append(
|
|
{
|
|
"trt_timing_cache_enable": True,
|
|
"trt_timing_cache_path": "/config/model_cache/tensorrt/ort",
|
|
"trt_engine_cache_enable": True,
|
|
"trt_engine_cache_path": "/config/model_cache/tensorrt/ort/trt-engines",
|
|
}
|
|
)
|
|
elif provider == "OpenVINOExecutionProvider":
|
|
options.append({"cache_dir": "/config/model_cache/openvino/ort"})
|
|
else:
|
|
options.append({})
|
|
|
|
self.model = ort.InferenceSession(
|
|
path, providers=providers, provider_options=options
|
|
)
|
|
|
|
self.h = detector_config.model.height
|
|
self.w = detector_config.model.width
|
|
self.onnx_model_type = detector_config.model.model_type
|
|
self.onnx_model_px = detector_config.model.input_pixel_format
|
|
path = detector_config.model.path
|
|
|
|
logger.info(f"ONNX: {path} loaded")
|
|
|
|
def detect_raw(self, tensor_input):
|
|
model_input_name = self.model.get_inputs()[0].name
|
|
model_input_shape = self.model.get_inputs()[0].shape
|
|
|
|
# adjust input shape
|
|
if self.onnx_model_type == ModelTypeEnum.yolonas:
|
|
tensor_input = cv2.dnn.blobFromImage(
|
|
tensor_input[0],
|
|
1.0,
|
|
(model_input_shape[3], model_input_shape[2]),
|
|
None,
|
|
swapRB=self.onnx_model_px == PixelFormatEnum.bgr,
|
|
).astype(np.uint8)
|
|
|
|
tensor_output = self.model.run(None, {model_input_name: tensor_input})
|
|
|
|
if self.onnx_model_type == ModelTypeEnum.yolonas:
|
|
predictions = tensor_output[0]
|
|
|
|
detections = np.zeros((20, 6), np.float32)
|
|
|
|
for i, prediction in enumerate(predictions):
|
|
if i == 20:
|
|
break
|
|
(_, x_min, y_min, x_max, y_max, confidence, class_id) = prediction
|
|
# when running in GPU mode, empty predictions in the output have class_id of -1
|
|
if class_id < 0:
|
|
break
|
|
detections[i] = [
|
|
class_id,
|
|
confidence,
|
|
y_min / self.h,
|
|
x_min / self.w,
|
|
y_max / self.h,
|
|
x_max / self.w,
|
|
]
|
|
return detections
|
|
else:
|
|
raise Exception(
|
|
f"{self.onnx_model_type} is currently not supported for rocm. See the docs for more info on supported models."
|
|
)
|