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performancer on inference

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This commit is contained in:
neo-dl 2022-07-22 18:16:52 +02:00
parent 94bef68fd7
commit 7a9c02d4dd
1 changed files with 5 additions and 5 deletions
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10
src/components/vision.py
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@ -10,6 +10,8 @@ import cv2
import numpy as np import numpy as np
from PyQt5.QtWidgets import QApplication from PyQt5.QtWidgets import QApplication
from lib.helpers import time_this
if "--no-gpu" in sys.argv: if "--no-gpu" in sys.argv:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
@ -37,8 +39,8 @@ else:
if "--no-tflite" not in sys.argv: if "--no-tflite" not in sys.argv:
try: try:
Interpreter = tf.lite.Interpreter #Interpreter = tf.lite.Interpreter
# from tflite_runtime.interpreter import Interpreter from tflite_runtime.interpreter import Interpreter
except (ImportError, ModuleNotFoundError): except (ImportError, ModuleNotFoundError):
logging.exception(traceback.format_exc()) logging.exception(traceback.format_exc())
@ -335,6 +337,7 @@ class Vision(Component):
self.log.info(f"initialized model {self.model!r} with mode {self.tf_mode!r}") self.log.info(f"initialized model {self.model!r} with mode {self.tf_mode!r}")
self.loading_model_signal.emit({"status": "done"}) self.loading_model_signal.emit({"status": "done"})
@time_this
def check_features(self, frame, lock=True): def check_features(self, frame, lock=True):
if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]: if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]:
tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0) tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0)
@ -693,10 +696,7 @@ class Vision(Component):
# VISION_CONSUMER TASK # VISION_CONSUMER TASK
if consumable is None: if consumable is None:
return return
t1=time.time()
detections = self.check_features(consumable["frame"]) detections = self.check_features(consumable["frame"])
t2=time.time()
print(f"detection time: {(t2-t1):.3f}")
results = self.process_detections(detections) results = self.process_detections(detections)
return {"detections": detections, "results": results} return {"detections": detections, "results": results}