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scripts/examples: Update examples.
Use romfs paths.
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@ -16,7 +16,7 @@ sensor.set_pixformat(sensor.RGB565)
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sensor.set_framesize(sensor.QVGA)
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sensor.skip_frames(time=2000)
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model = ml.Model("person_detect", load_to_fb=True)
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model = ml.Model("/rom/person_detect.tflite")
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print(model)
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clock = time.clock()
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@ -23,7 +23,7 @@ min_confidence = 0.4
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threshold_list = [(math.ceil(min_confidence * 255), 255)]
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# Load built-in FOMO face detection model
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model = ml.Model("fomo_face_detection")
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model = ml.Model("/rom/fomo_face_detection.tflite")
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print(model)
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# Alternatively, models can be loaded from the filesystem storage.
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@ -18,7 +18,7 @@ from ulab import numpy as np
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# The model is built-in on the RT1062. On other OpenMV Cam's with limited flash space please grab
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# the model from here: https://github.com/openmv/openmv/tree/master/src/lib/tflm/models and
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# copy it to the OpenMV Cam's file system. E.g. model = ml.Model("force_int_quant.tflite")
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model = ml.Model("force_int_quant")
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model = ml.Model("/rom/force_int_quant.tflite")
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print(model)
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i = np.array([-3, -1, -2, 5, -2, 10, -1, 9, 0, # noqa
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@ -49,10 +49,10 @@ class MicroSpeech:
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def __init__(self, preprocessor=None, micro_speech=None, labels=None, **kwargs):
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self.preprocessor = preprocessor
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if preprocessor is None:
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self.preprocessor = Model("audio_preprocessor")
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self.preprocessor = Model("/rom/audio_preprocessor.tflite")
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self.labels, self.micro_speech = (labels, micro_speech)
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if micro_speech is None:
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self.micro_speech = Model("micro_speech")
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self.micro_speech = Model("/rom/micro_speech.tflite")
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self.labels = self.micro_speech.labels
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# 16 samples/1ms
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self.audio_buffer = np.zeros((1, _SAMPLES_PER_STEP * 3), dtype=np.int16)
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