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Add frequence_process plotting to compare original with detected

This commit is contained in:
Thibaud 2024-05-29 16:35:33 +02:00
parent d2af367f2d
commit 3d727f3fec
2 changed files with 32 additions and 3 deletions
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29
frequence_process.py Normal file
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@ -0,0 +1,29 @@
import slider as sl
import sound_process
from tkinter import filedialog as fd
from matplotlib import pyplot as plt
def process_with_map():
filename = fd.askopenfilename()
beatmap = sl.Beatmap.from_path(filename)
timing = beatmap.timing_points[0]
bpm = timing.bpm
offset = timing.offset.total_seconds() * 1000
data = sound_process.process_song(beatmap.audio_filename, bpm, offset0=offset)
timings, amplitudes, freqs = [x[0].total_seconds() for x in data], [x[1] for x in data], [x[2] for x in data]
original_times = [x.time.total_seconds() for x in beatmap.hit_objects(spinners=False) if x.time.total_seconds() <= timings[len(timings) - 1]]
#bx.scatter(original_times, ys=10, zs=0)
plt.scatter(timings, amplitudes, marker="x", c="red")
for x in original_times:
plt.axvline(x=x)
plt.show()
def main():
process_with_map()
if __name__ == "__main__":
main()

6
sound_process.py
View File

@ -480,13 +480,13 @@ def process_song(filename, bpm, offset0=0, div_len_factor=1, n_iter_2=-1, thresh
void_freq(filename, offset, min(song_len, offset+div_len*(n_iter+1)+0.01), 4*60/bpm, minfreq=0, maxfreq=220, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name) void_freq(filename, offset, min(song_len, offset+div_len*(n_iter+1)+0.01), 4*60/bpm, minfreq=0, maxfreq=220, upperthr=5000, ampthr=60, ampfreq = 1200, ampval = 5.0, leniency = 0.005, write=True, linear=False, output_file=filtered_name)
#void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=2500, ampthr=60, ampfreq = 1200, ampval = 1/2000, leniency = 0.0, write=True, linear=True, output_file=filtered_name) #void_freq(filename, offset, offset+div_len*(n_iter+1)+0.01, 4*60/bpm, minfreq=0, maxfreq=330, upperthr=2500, ampthr=60, ampfreq = 1200, ampval = 1/2000, leniency = 0.0, write=True, linear=True, output_file=filtered_name)
datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold) datares = filter_n_percent_serial(filtered_name, offset, n_iter, div_len, threshold)
datares = snap(datares, 44100, bpm, 4, True) datares = snap(datares, 44100, bpm, 4, False)
frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, True) frequencies = get_freq(filtered_name, offset, div_len, div_len*n_iter, datares, False)
Path(f"{filename}_trimmed.wav").unlink() Path(f"{filename}_trimmed.wav").unlink()
return convert_tuple(datares, frequencies) return convert_tuple(datares, frequencies)
def main(): def main():
data = process_song("tetris_4.wav", 160, n_iter_2 = 64) data = process_song("audio.mp3", 160, n_iter_2 = 64)
#print(data) #print(data)
print("Program finished with return 0") print("Program finished with return 0")