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scripts/libraries: Add ML extensions module.
This module extends the built-in ML module with the Micro Speech keyword spotter implementation.
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scripts/libraries/ml.py
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102
scripts/libraries/ml.py
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# This file is part of the OpenMV project.
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#
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# Copyright (c) 2023 Ibrahim Abdelkader <iabdalkader@openmv.io>
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# Copyright (c) 2023 Kwabena W. Agyeman <kwagyeman@openmv.io>
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#
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# This work is licensed under the MIT license, see the file LICENSE for details.
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#
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# This is an extension to the display C user-module. Add or import any display-related
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# drivers here, and freeze this module in the board's manifest, and those drivers will
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# be importable from display.
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import time
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from uml import * # noqa
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from micropython import const
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from ulab import numpy as np
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try:
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import audio
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except (ImportError, AttributeError):
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pass
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def draw_predictions(img, boxes, labels, colors, format="pascal_voc", text_color=(255, 255, 255)):
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CHAR_W = 8
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CHAR_H = 10
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img_w = img.width()
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img_h = img.height()
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for i, (x, y, w, h) in enumerate(boxes):
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label = labels[i]
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box_color = colors[i]
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if format == "pascal_voc":
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x = int(x * img_w)
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y = int(y * img_h)
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w = int(w * img_w) - x
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h = int(h * img_h) - y
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img.draw_rectangle(x, y, w, h, color=box_color)
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img.draw_rectangle(
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x, y - CHAR_H, len(label) * CHAR_W, CHAR_H, fill=True, color=box_color
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)
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img.draw_string(x, y - CHAR_H, label.upper(), text_color)
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class MicroSpeech:
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_SLICE_SIZE = const(40)
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_SLICE_COUNT = const(49)
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_SLICE_TIME_MS = const(30)
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_AUDIO_FREQUENCY = const(16000)
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_SAMPLES_PER_STEP = const(10 * (_AUDIO_FREQUENCY // 1000)) # 10ms * 16 Samples/ms
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_CATEGORY_COUNT = const(4)
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_AVERAGE_WINDOW_SAMPLES = const(1020 // _SLICE_TIME_MS)
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def __init__(self, preprocessor=None, micro_speech=None, labels=None):
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self.preprocessor = preprocessor
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if preprocessor is None:
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self.preprocessor = Model("audio_preprocessor")[1]
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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.labels, self.micro_speech = Model("micro_speech")
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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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self.spectrogram = np.zeros((1, _SLICE_COUNT * _SLICE_SIZE), dtype=np.int8)
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self.pred_history = np.zeros((_AVERAGE_WINDOW_SAMPLES, _CATEGORY_COUNT), dtype=np.float)
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audio.init(channels=1, frequency=_AUDIO_FREQUENCY, gain_db=24, samples=_SAMPLES_PER_STEP * 2)
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def audio_callback(self, buf):
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# Roll the audio buffer to the left, and add the new samples.
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self.audio_buffer = np.roll(self.audio_buffer, -(_SAMPLES_PER_STEP * 2), axis=1)
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self.audio_buffer[0, _SAMPLES_PER_STEP:] = np.frombuffer(buf, dtype=np.int16)
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# Roll the spectrogram to the left and add the new slice.
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self.spectrogram = np.roll(self.spectrogram, -_SLICE_SIZE, axis=1)
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self.spectrogram[0, -_SLICE_SIZE:] = self.preprocessor.predict(
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self.audio_buffer
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)
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# Roll the prediction history and add the new prediction.
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self.pred_history = np.roll(self.pred_history, -1, axis=0)
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self.pred_history[-1] = self.micro_speech.predict(self.spectrogram)[0]
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def listen(self, timeout=0, callback=None, threshold=0.65, filter=["Yes", "No"]):
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self.spectrogram[:] = 0
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self.pred_history[:] = 0
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# Start audio streaming
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audio.start_streaming(self.audio_callback)
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stat_ms = time.ticks_ms()
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while True:
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average_scores = np.mean(self.pred_history, axis=0)
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max_score_index = np.argmax(average_scores)
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max_score = average_scores[max_score_index]
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label = self.labels[max_score_index]
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if max_score > threshold and label in filter:
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self.pred_history[:] = 0
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self.spectrogram[:] = 0
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if callback is None:
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audio.stop_streaming()
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return (label, average_scores)
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callback(label, average_scores)
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if timeout != 0 and (time.ticks_ms() - stat_ms) > timeout:
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audio.stop_streaming()
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return (None, None)
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time.sleep_ms(1)
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