wip
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@ -336,6 +336,8 @@ class Vision(Component):
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self.loading_model_signal.emit({"status": "done"})
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self.loading_model_signal.emit({"status": "done"})
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def check_features(self, frame, lock=True):
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def check_features(self, frame, lock=True):
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if frame is None:
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return []
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if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]:
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if self.interpreter is not None and frame.shape != self.interpreter.get_input_details()[0]["shape"][1:3]:
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tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0)
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tensor = np.expand_dims(cv2.resize(frame, self.interpreter.get_input_details()[0]["shape"][1:3], interpolation=cv2.INTER_LINEAR), axis=0)
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else:
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else:
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@ -691,7 +693,7 @@ class Vision(Component):
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def vision_consumer_work(self, consumable=None):
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def vision_consumer_work(self, consumable=None):
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# VISION_CONSUMER TASK
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# VISION_CONSUMER TASK
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if consumable is None:
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if consumable is None or consumable["frame"] is None:
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return
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return
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detections = self.check_features(consumable["frame"])
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detections = self.check_features(consumable["frame"])
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results = self.process_detections(detections)
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results = self.process_detections(detections)
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