import image, audio, time from ulab import numpy as np from ulab import scipy as sp CHANNELS = 1 FREQUENCY = 32000 N_SAMPLES = 32 if FREQUENCY == 16000 else 64 SCALE = 1 SIZE = (N_SAMPLES * SCALE) // CHANNELS raw_buf = None fb = image.Image(SIZE+(50*SCALE), SIZE, image.RGB565, copy_to_fb=True) audio.init(channels=CHANNELS, frequency=FREQUENCY, gain_db=16, overflow=False) def audio_callback(buf): # NOTE: do Not call any function that allocates memory. global raw_buf if (raw_buf == None): raw_buf = buf # Start audio streaming audio.start_streaming(audio_callback) def draw_fft(img, fft_buf): fft_buf = (fft_buf / max(fft_buf)) * SIZE fft_buf = np.log10(fft_buf + 1) * 20 color = (0xFF, 0x0F, 0x00) for i in range(0, len(fft_buf)): img.draw_line(i*SCALE, SIZE, i*SCALE, SIZE-int(fft_buf[i]) * SCALE, color, SCALE) def draw_audio_bar(img, level, offset): blk_size = (SIZE//10) color = (0xFF, 0x00, 0xF0) blk_space = (blk_size//4) for i in range(0, int(round(level/10))): fb.draw_rectangle(SIZE+offset, SIZE - ((i+1)*blk_size) + blk_space, 20 * SCALE, blk_size - blk_space, color, 1, True) while (True): if (raw_buf != None): pcm_buf = np.frombuffer(raw_buf, dtype=np.int16) raw_buf = None if CHANNELS == 1: fft_buf = sp.signal.spectrogram(pcm_buf) l_lvl = int((np.mean(abs(pcm_buf[1::2])) / 32768)*100) else: fft_buf = sp.signal.spectrogram(pcm_buf[0::2]) l_lvl = int((np.mean(abs(pcm_buf[1::2])) / 32768)*100) r_lvl = int((np.mean(abs(pcm_buf[0::2])) / 32768)*100) fb.clear() draw_fft(fb, fft_buf) draw_audio_bar(fb, l_lvl, 0) draw_audio_bar(fb, l_lvl, 25*SCALE) if CHANNELS == 2: draw_audio_bar(fb, r_lvl, 25 * SCALE) fb.flush() # Stop streaming audio.stop_streaming()