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reverrted openvino detector

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This commit is contained in:
Indrek Mandre 2024-02-09 14:52:55 +02:00
parent 6a2078c9aa
commit 3a9649515d
1 changed files with 23 additions and 6 deletions
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29
frigate/detectors/plugins/openvino.py
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@ -8,8 +8,6 @@ from typing_extensions import Literal
from frigate.detectors.detection_api import DetectionApi from frigate.detectors.detection_api import DetectionApi
from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum from frigate.detectors.detector_config import BaseDetectorConfig, ModelTypeEnum
import frigate.detectors.yolo_utils as yolo_utils
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
DETECTOR_KEY = "openvino" DETECTOR_KEY = "openvino"
@ -35,7 +33,7 @@ class OvDetector(DetectionApi):
model=self.ov_model, device_name=detector_config.device model=self.ov_model, device_name=detector_config.device
) )
logger.info(f"Model Input Shape: {self.interpreter.input(0).shape} {self.interpreter.input(0).element_type.to_dtype()}") logger.info(f"Model Input Shape: {self.interpreter.input(0).shape}")
self.output_indexes = 0 self.output_indexes = 0
while True: while True:
@ -82,7 +80,6 @@ class OvDetector(DetectionApi):
] ]
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
tensor_input = yolo_utils.preprocess(tensor_input, self.interpreter.inputs[0].shape, self.interpreter.inputs[0].element_type.to_dtype())
infer_request = self.interpreter.create_infer_request() infer_request = self.interpreter.create_infer_request()
infer_request.infer([tensor_input]) infer_request.infer([tensor_input])
@ -135,8 +132,28 @@ class OvDetector(DetectionApi):
) )
return detections return detections
elif self.ov_model_type == ModelTypeEnum.yolov8: elif self.ov_model_type == ModelTypeEnum.yolov8:
out_tensor = infer_request.get_output_tensor().data out_tensor = infer_request.get_output_tensor()
return yolo_utils.yolov8_postprocess(self.interpreter.inputs[0].shape, out_tensor) results = out_tensor.data[0]
output_data = np.transpose(results)
scores = np.max(output_data[:, 4:], axis=1)
if len(scores) == 0:
return np.zeros((20, 6), np.float32)
scores = np.expand_dims(scores, axis=1)
# add scores to the last column
dets = np.concatenate((output_data, scores), axis=1)
# filter out lines with scores below threshold
dets = dets[dets[:, -1] > 0.5, :]
# limit to top 20 scores, descending order
ordered = dets[dets[:, -1].argsort()[::-1]][:20]
detections = np.zeros((20, 6), np.float32)
for i, object_detected in enumerate(ordered):
detections[i] = self.process_yolo(
np.argmax(object_detected[4:-1]),
object_detected[-1],
object_detected[:4],
)
return detections
elif self.ov_model_type == ModelTypeEnum.yolov5: elif self.ov_model_type == ModelTypeEnum.yolov5:
out_tensor = infer_request.get_output_tensor() out_tensor = infer_request.get_output_tensor()
output_data = out_tensor.data[0] output_data = out_tensor.data[0]