[M] Move non-api scripts to experiment\
This commit is contained in:
wuliaozhiji
@@ -0,0 +1,60 @@
|
||||
import json
|
||||
|
||||
if __name__ == '__main__':
|
||||
with open('C:\Workspace\EECS 6414\Datasets\CN-Celeb_flac\id_labels.json', 'r', encoding='UTF-8') as f:
|
||||
labels = json.load(f)
|
||||
|
||||
with open('C:\Workspace\EECS 6414\Datasets\CN-Celeb_flac\ina_pf_map.json', 'r', encoding='UTF-8') as f:
|
||||
pf = json.load(f)
|
||||
|
||||
correct_f = []
|
||||
correct_m = []
|
||||
incorrect_f = []
|
||||
incorrect_m = []
|
||||
|
||||
for k in labels:
|
||||
if k not in pf:
|
||||
print(f'Skipped {k}')
|
||||
continue
|
||||
|
||||
if labels[k] == 'f':
|
||||
if pf[k] > 0.5:
|
||||
correct_f += k
|
||||
else:
|
||||
incorrect_f += k
|
||||
|
||||
if labels[k] == 'm':
|
||||
if pf[k] < 0.5:
|
||||
correct_m += k
|
||||
else:
|
||||
incorrect_m += k
|
||||
|
||||
print('Done Reading\n')
|
||||
|
||||
tp = len(correct_f)
|
||||
tn = len(correct_m)
|
||||
fp = len(incorrect_f)
|
||||
fn = len(incorrect_m)
|
||||
|
||||
print('True Positive (F classified as F):', tp)
|
||||
print('True Negative (M classified as M):', tn)
|
||||
print('False Positive (F classified as M):', fp)
|
||||
print('False Negative (M classified as F):', fn)
|
||||
|
||||
acc = (tp + tn) / (tp + tn + fp + fn)
|
||||
precision_f = tp / (tp + fp)
|
||||
recall_f = tp / (tp + fn)
|
||||
precision_m = tn / (tn + fn)
|
||||
recall_m = tn / (tn + fp)
|
||||
|
||||
print('Accuracy:', acc)
|
||||
print('Precision F:', precision_f)
|
||||
print('Recall F:', recall_f)
|
||||
print('Precision M:', precision_m)
|
||||
print('Recall M:', recall_m)
|
||||
|
||||
print('F wrongly classified as M:', fp / (tp + fp))
|
||||
print('M wrongly classified as F:', fn / (tn + fn))
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,116 @@
|
||||
import warnings
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib
|
||||
from telegram import Update, Message
|
||||
from telegram.ext import Updater, CallbackContext, Dispatcher, CommandHandler, MessageHandler, \
|
||||
Filters
|
||||
|
||||
from ina_main import *
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
matplotlib.use('agg')
|
||||
|
||||
|
||||
def r(u: Update, msg: str, md=True):
|
||||
updater.bot.sendMessage(chat_id=u.effective_chat.id, text=msg,
|
||||
parse_mode='Markdown' if md else None)
|
||||
|
||||
|
||||
def cmd_start(u: Update, c: CallbackContext):
|
||||
r(u, '欢迎! 点下面的录音按钮就可以开始啦w')
|
||||
|
||||
|
||||
def process_audio(message: Message):
|
||||
# Only when replying to voice or audio
|
||||
audio = message.audio or message.voice
|
||||
if not audio:
|
||||
return
|
||||
|
||||
# Download audio file
|
||||
date = datetime.now().strftime('%Y-%m-%d %H-%M')
|
||||
try:
|
||||
downloader = bot.getFile(audio.file_id)
|
||||
except:
|
||||
downloader = bot.getFile(audio.file_id)
|
||||
file = Path(tmpdir).joinpath(f'{date} {message.from_user.name[1:]}.mp3')
|
||||
print(downloader, '->', file)
|
||||
downloader.download(file)
|
||||
|
||||
# Segment file
|
||||
seg = Segmenter()
|
||||
result = process(seg, [str(file.absolute())]).results[0]
|
||||
|
||||
# Null case
|
||||
print(result.frames)
|
||||
if len(result.frames) == 0:
|
||||
bot.send_message(message.chat_id, '分析失败, 大概是音量太小或者时长太短吧, 再试试w')
|
||||
return
|
||||
|
||||
# Draw results
|
||||
with draw_result(str(file), result) as buf:
|
||||
f, m, o, pf = get_result_percentages(result)
|
||||
msg = f"分析结果: {f*100:.0f}% 🙋♀️ | {m*100:.0f}% 🙋♂️ | {o*100:.0f}% 🚫\n" \
|
||||
f"(结果仅供参考, 如果结果不是你想要的，那就是模型的问题，欢迎反馈)\n" \
|
||||
f"" \
|
||||
f"(因为这个模型基于法语数据, 和中文发音习惯有差异, 所以这个识别结果可能不准)"
|
||||
bot.send_photo(message.chat_id, photo=buf, caption=msg,
|
||||
reply_to_message_id=message.message_id)
|
||||
|
||||
|
||||
def cmd_analyze(u: Update, c: CallbackContext):
|
||||
reply = u.effective_message.reply_to_message
|
||||
|
||||
# Parse command
|
||||
text = u.effective_message.text
|
||||
if not text:
|
||||
return
|
||||
cmd = text.lower().split()[0].strip()
|
||||
|
||||
if cmd[0] not in '!/':
|
||||
return
|
||||
cmd = cmd[1:]
|
||||
|
||||
if cmd not in ['analyze', 'analyze-raw']:
|
||||
return
|
||||
|
||||
if cmd == 'analyze-raw':
|
||||
raw = True
|
||||
|
||||
if u.effective_user.id == reply.from_user.id:
|
||||
process_audio(reply)
|
||||
else:
|
||||
r(u, '只有自己能分析自己的音频哦 👀')
|
||||
|
||||
|
||||
def on_audio(u: Update, c: CallbackContext):
|
||||
process_audio(u.effective_message)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
tmpdir = Path('audio_tmp')
|
||||
tmpdir.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
# Find telegram token
|
||||
path = Path(os.path.abspath(__file__)).parent
|
||||
db_path = path.joinpath('voice-bot-db.json')
|
||||
if 'tg_token' in os.environ:
|
||||
tg_token = os.environ['tg_token']
|
||||
else:
|
||||
with open(path.joinpath('voice-bot-token.txt'), 'r', encoding='utf-8') as f:
|
||||
tg_token = f.read().strip()
|
||||
|
||||
# Telegram login
|
||||
updater = Updater(token=tg_token, use_context=True)
|
||||
dispatcher: Dispatcher = updater.dispatcher
|
||||
bot = updater.bot
|
||||
|
||||
dispatcher.add_handler(CommandHandler('start', cmd_start, filters=Filters.chat_type.private))
|
||||
dispatcher.add_handler(CommandHandler('analyze', cmd_analyze, filters=Filters.reply))
|
||||
dispatcher.add_handler(MessageHandler(Filters.reply, cmd_analyze))
|
||||
dispatcher.add_handler(MessageHandler(Filters.voice & Filters.chat_type.private, on_audio))
|
||||
dispatcher.add_handler(MessageHandler(Filters.audio & Filters.chat_type.private, on_audio))
|
||||
|
||||
print('Starting bot...')
|
||||
updater.start_polling()
|
||||
@@ -0,0 +1,212 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import io
|
||||
import os
|
||||
import subprocess
|
||||
import tempfile
|
||||
import time
|
||||
import warnings
|
||||
from subprocess import Popen, PIPE
|
||||
from typing import NamedTuple, Callable
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import scipy.io.wavfile
|
||||
from PIL import Image
|
||||
from inaSpeechSegmenter import *
|
||||
from inaSpeechSegmenter.segmenter import featGenerator
|
||||
from matplotlib.figure import Figure, Axes
|
||||
|
||||
|
||||
class ResultFrame(NamedTuple):
|
||||
gender: str
|
||||
start: float
|
||||
end: float
|
||||
|
||||
|
||||
class Result(NamedTuple):
|
||||
frames: list[ResultFrame]
|
||||
file: str
|
||||
|
||||
|
||||
class BatchResults(NamedTuple):
|
||||
results: list[Result]
|
||||
time_full: float
|
||||
time_avg: float
|
||||
successes: int
|
||||
messages: list[tuple[str, int]]
|
||||
|
||||
|
||||
def process(self: Segmenter, inp: list[str], tmpdir=None, verbose=False, skip_if_exist=False,
|
||||
nbtry=1, try_delay=2.) -> BatchResults:
|
||||
t_batch_start = time.time()
|
||||
|
||||
results: list[Result] = []
|
||||
lmsg = []
|
||||
fg = featGenerator(inp.copy(), inp.copy(), tmpdir, self.ffmpeg, skip_if_exist, nbtry, try_delay)
|
||||
i = 0
|
||||
for feats, msg in fg:
|
||||
lmsg += msg
|
||||
i += len(msg)
|
||||
if verbose:
|
||||
print('%d/%d' % (i, len(inp)), msg)
|
||||
if feats is None:
|
||||
break
|
||||
mspec, loge, diff_len = feats
|
||||
lseg = self.segment_feats(mspec, loge, diff_len, 0)
|
||||
results.append(Result([ResultFrame(*s) for s in lseg], inp[len(lmsg) - 1]))
|
||||
|
||||
t_batch_dur = time.time() - t_batch_start
|
||||
nb_processed = len([e for e in lmsg if e[1] == 0])
|
||||
avg = t_batch_dur / nb_processed if nb_processed else -1
|
||||
return BatchResults(results, t_batch_dur, avg, nb_processed, lmsg)
|
||||
|
||||
|
||||
def to_wav(file: str, callback: Callable, start_sec: float = 0, stop_sec: float = 0):
|
||||
"""
|
||||
Convert media to temp wav 16k file and return features
|
||||
"""
|
||||
base, _ = os.path.splitext(os.path.basename(file))
|
||||
|
||||
with tempfile.TemporaryDirectory() as tmpdir_name:
|
||||
# build ffmpeg command line
|
||||
tmp_wav = tmpdir_name + '/' + base + '.wav'
|
||||
args = ['ffmpeg', '-y', '-i', file, '-ar', '16000', '-ac', '1']
|
||||
|
||||
if start_sec != 0:
|
||||
args += ['-ss', '%f' % start_sec]
|
||||
if stop_sec != 0:
|
||||
args += ['-to', '%f' % stop_sec]
|
||||
|
||||
args += [tmp_wav]
|
||||
|
||||
# launch ffmpeg
|
||||
p = Popen(args, stdout=PIPE, stderr=PIPE)
|
||||
output, error = p.communicate()
|
||||
assert p.returncode == 0, error
|
||||
|
||||
return callback(tmp_wav)
|
||||
|
||||
|
||||
def show_image_buffer(buf):
|
||||
im = Image.open(buf)
|
||||
im.show()
|
||||
buf.close()
|
||||
|
||||
|
||||
def draw_result(file: str, result: Result):
|
||||
"""
|
||||
Draw segmentation result
|
||||
|
||||
:param file: Audio file
|
||||
:param result: Segmentation result
|
||||
:return: Result image in bytes (please close it after use)
|
||||
"""
|
||||
def wav_callback(wavfile: str):
|
||||
sample_rate, audio = scipy.io.wavfile.read(wavfile)
|
||||
_time = np.linspace(0, len(audio) / sample_rate, num=len(audio))
|
||||
|
||||
fig: Figure = plt.gcf()
|
||||
ax: Axes = plt.gca()
|
||||
|
||||
# Plot audio
|
||||
plt.plot(_time, audio, color='white')
|
||||
|
||||
# Set size
|
||||
# fig.set_dpi(400)
|
||||
fig.set_size_inches(18, 6)
|
||||
|
||||
# Cutoff frequency so that the plot looks centered
|
||||
cutoff = min(abs(min(audio)), abs(max(audio)))
|
||||
ax.set_ylim([-cutoff, cutoff])
|
||||
ax.set_xlim([result.frames[0].start, result.frames[-1].end])
|
||||
|
||||
# Draw segmentation areas
|
||||
colors = {'female': '#F5A9B8', 'male': '#5BCEFA', 'default': 'gray'}
|
||||
for r in result.frames:
|
||||
color = colors[r.gender] if r.gender in colors else colors['default']
|
||||
ax.axvspan(r.start, r.end - 0.01, alpha=.5, color=color)
|
||||
|
||||
# Savefig to bytes
|
||||
buf = io.BytesIO()
|
||||
plt.axis('off')
|
||||
plt.savefig(buf, bbox_inches='tight', pad_inches=0, transparent=False)
|
||||
buf.seek(0)
|
||||
plt.clf()
|
||||
plt.close()
|
||||
return buf
|
||||
|
||||
return to_wav(file, wav_callback)
|
||||
|
||||
|
||||
def get_result_percentages(result: Result) -> tuple[float, float, float, float]:
|
||||
"""
|
||||
Get percentages
|
||||
|
||||
:param result: Result
|
||||
:return: %female, %male, %other, %female-vs-female+male
|
||||
"""
|
||||
# Count total and categorical durations
|
||||
total_dur = 0
|
||||
durations: dict[str, int] = {f.gender: 0 for f in result.frames}
|
||||
for f in result.frames:
|
||||
dur = f.end - f.start
|
||||
durations[f.gender] += dur
|
||||
total_dur += dur
|
||||
|
||||
# Convert durations to ratios
|
||||
for d in durations:
|
||||
durations[d] /= total_dur
|
||||
|
||||
# Return results
|
||||
f = durations.get('female', 0)
|
||||
m = durations.get('male', 0)
|
||||
|
||||
fm_total = f + m
|
||||
pf = 0 if fm_total == 0 else f / fm_total
|
||||
|
||||
return f, m, 1 - f - m, pf
|
||||
|
||||
|
||||
def test():
|
||||
# results: BatchResults = BatchResults(
|
||||
# [Result([ResultFrame('female', 0.0, 10.48), ResultFrame('male', 10.48, 12.780000000000001)],
|
||||
# '../test.csv')],
|
||||
# 1.7032792568206787, 1.7032792568206787, 1,
|
||||
# [('../test.csv', 0)])
|
||||
|
||||
warnings.filterwarnings("ignore")
|
||||
seg = Segmenter()
|
||||
audio_file = '../test.flac'
|
||||
|
||||
# Warmup run
|
||||
results = process(seg, [audio_file])
|
||||
print(results)
|
||||
|
||||
# # Actual run
|
||||
# results = process(seg, ['../test.flac'])
|
||||
# print(results)
|
||||
|
||||
# Benchmark
|
||||
iterations = 60
|
||||
total_time = 0
|
||||
audio_len = float(subprocess.getoutput(f'ffprobe -i {audio_file} -show_entries format=duration -v quiet -of csv="p=0"'))
|
||||
print(f'Audio length: {audio_len}')
|
||||
|
||||
for i in range(iterations):
|
||||
results = process(seg, [audio_file])
|
||||
total_time += results.time_full
|
||||
|
||||
time_per_second = total_time / iterations / audio_len
|
||||
print(f'Benchmark result: {total_time}s / {iterations} iterations = {time_per_second} seconds of processing per second in audio')
|
||||
print(f'Score: {1 / time_per_second}')
|
||||
|
||||
# Draw results
|
||||
# with draw_result(audio_file, results.results[0]) as buf:
|
||||
# show_image_buffer(buf)
|
||||
# print(get_result_percentages(results.results[0]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test()
|
||||
pass
|
||||
@@ -0,0 +1,49 @@
|
||||
def ansi_rgb(r: int, g: int, b: int, foreground: bool = True) -> str:
|
||||
"""
|
||||
Convert rgb color into ANSI escape code format
|
||||
|
||||
:param r:
|
||||
:param g:
|
||||
:param b:
|
||||
:param foreground: Whether the color applies to forground
|
||||
:return: Escape code
|
||||
"""
|
||||
c = '38' if foreground else '48'
|
||||
return f'\033[{c};2;{r};{g};{b}m'
|
||||
|
||||
|
||||
def color(msg: str) -> str:
|
||||
"""
|
||||
Replace extended minecraft color codes in string
|
||||
|
||||
:param msg: Message with minecraft color codes
|
||||
:return: Message with escape codes
|
||||
"""
|
||||
replacements = ["&0/\033[0;30m", "&1/\033[0;34m", "&2/\033[0;32m", "&3/\033[0;36m", "&4/\033[0;31m", "&5/\033[0;35m", "&6/\033[0;33m", "&7/\033[0;37m", "&8/\033[1;30m", "&9/\033[1;34m", "&a/\033[1;32m", "&b/\033[1;36m", "&c/\033[1;31m", "&d/\033[1;35m", "&e/\033[1;33m", "&f/\033[1;37m", "&r/\033[0m", "&n/\n"]
|
||||
for r in replacements:
|
||||
msg = msg.replace(r[:2], r[3:])
|
||||
|
||||
while '&gf(' in msg or '&gb(' in msg:
|
||||
i = msg.index('&gf(') if '&gf(' in msg else msg.index('&gb(')
|
||||
end = msg.index(')', i)
|
||||
code = msg[i + 4:end]
|
||||
fore = msg[i + 2] == 'f'
|
||||
|
||||
if code.startswith('#'):
|
||||
rgb = tuple(int(code.lstrip('#')[i:i+2], 16) for i in (0, 2, 4))
|
||||
else:
|
||||
code = code.replace(',', ' ').replace(';', ' ').replace(' ', ' ')
|
||||
rgb = tuple(int(c) for c in code.split(' '))
|
||||
|
||||
msg = msg[:i] + ansi_rgb(*rgb, foreground=fore) + msg[end + 1:]
|
||||
|
||||
return msg
|
||||
|
||||
|
||||
def printc(msg: str):
|
||||
"""
|
||||
Print with color
|
||||
|
||||
:param msg: Message with minecraft color codes
|
||||
"""
|
||||
print(color(msg + '&r'))
|
||||
@@ -0,0 +1,168 @@
|
||||
import json
|
||||
import os
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import pygame
|
||||
from inaSpeechSegmenter import Segmenter
|
||||
|
||||
from ina_main import process, get_result_percentages
|
||||
from utils import color, printc
|
||||
|
||||
|
||||
def segment_all():
|
||||
# Create segmenter
|
||||
seg = Segmenter()
|
||||
np.seterr(invalid='ignore')
|
||||
|
||||
# Loop through all celebrities
|
||||
for id in ids:
|
||||
id_dir = data_dir.joinpath(id)
|
||||
|
||||
# Loop through all recordings (Exclude singing for now)
|
||||
utters = [r for r in os.listdir(id_dir) if r.endswith('.flac')
|
||||
and not r.startswith('singing')]
|
||||
|
||||
# Exclude existing
|
||||
utters = [id_dir.joinpath(u) for u in utters]
|
||||
utters = [u for u in utters if not u.with_suffix('.json').exists()]
|
||||
|
||||
if len(utters) == 0:
|
||||
continue
|
||||
|
||||
# Analyze
|
||||
results = process(seg, [str(u) for u in utters], verbose=True)
|
||||
|
||||
# Write results
|
||||
total = [0, 0, 0, 0, 0]
|
||||
type_totals = {}
|
||||
for result in results.results:
|
||||
file = Path(result.file).with_suffix('.json')
|
||||
|
||||
# Get results
|
||||
# f: Frames, r: Ratios
|
||||
ratios = [round(r, 3) for r in get_result_percentages(result)]
|
||||
stored = {'f': result.frames, 'r': ratios}
|
||||
|
||||
# Count type total (type_totals[utter_type][-1] is the count)
|
||||
file_name = file.name
|
||||
utter_type = file_name[:file_name.index('-')]
|
||||
type_totals.setdefault(utter_type, [0, 0, 0, 0, 0])
|
||||
for i in range(4):
|
||||
type_totals[utter_type][i] += ratios[i]
|
||||
total[i] += ratios[i]
|
||||
type_totals[utter_type][-1] += 1
|
||||
total[-1] += 1
|
||||
|
||||
# Write result
|
||||
file.write_text(json.dumps(stored))
|
||||
|
||||
# Write type averages
|
||||
type_averages = {t: [r / type_totals[t][-1] for r in type_totals[t][:-1]] for t in type_totals}
|
||||
total_average = [r / total[-1] for r in total[:-1]]
|
||||
obj = {'type_averages': type_averages, 'total_averages': total_average}
|
||||
id_dir.joinpath('total.json').write_text(json.dumps(obj))
|
||||
|
||||
|
||||
def graph_histogram():
|
||||
closest_to_half = 1000
|
||||
closest_to_half_id = ''
|
||||
id_pf_map = {}
|
||||
for id in ids:
|
||||
id_dir = data_dir.joinpath(id)
|
||||
json_path = id_dir.joinpath('total.json')
|
||||
|
||||
if not json_path.exists():
|
||||
continue
|
||||
|
||||
obj = json.loads(json_path.read_text())
|
||||
f, m, o, pf = obj['total_averages']
|
||||
|
||||
# Recalculate pf (pf is actually calculated incorrectly)
|
||||
if f + m == 0:
|
||||
continue
|
||||
pf = f / (f + m)
|
||||
id_pf_map[id] = pf
|
||||
|
||||
# Save fixed json
|
||||
obj['total_averages'][3] = pf
|
||||
json_path.write_text(json.dumps(obj))
|
||||
|
||||
# Find pf closest to .5
|
||||
dist = abs(pf - .5)
|
||||
if dist < closest_to_half:
|
||||
closest_to_half = dist
|
||||
closest_to_half_id = id
|
||||
|
||||
data_dir.joinpath('id_pf_map.json').write_text(json.dumps(id_pf_map))
|
||||
plt.hist(id_pf_map.values(), bins=50)
|
||||
plt.show()
|
||||
print(closest_to_half_id)
|
||||
|
||||
|
||||
def manually_label_data():
|
||||
"""
|
||||
Since CN-Celeb isn't labelled with the speaker's gender, this script is used to manually label
|
||||
them.
|
||||
"""
|
||||
pygame.mixer.init()
|
||||
|
||||
# Load existing labels
|
||||
labels_json = data_dir.joinpath('id_labels.json')
|
||||
id_labels = json.loads(labels_json.read_text()) if labels_json.exists() else {}
|
||||
|
||||
# Load pf table
|
||||
id_pfs = json.loads(data_dir.joinpath('id_pf_map.json').read_text())
|
||||
|
||||
# Loop through all speaker
|
||||
for id in sorted(ids):
|
||||
id_dir = data_dir.joinpath(id)
|
||||
|
||||
# Skip already identified labels
|
||||
if id in id_labels:
|
||||
continue
|
||||
|
||||
# Get ina choice
|
||||
pf = id_pfs.get(id, -1)
|
||||
ina_choice = 'f' if pf > 0.5 else 'm'
|
||||
|
||||
# Loop through all tracks until identified
|
||||
tracks = [f for f in os.listdir(id_dir) if f.endswith('.flac')]
|
||||
for track_i, audio in enumerate(tracks):
|
||||
# Play track
|
||||
sound = pygame.mixer.Sound(id_dir.joinpath(audio))
|
||||
sound.play()
|
||||
i = input(color(
|
||||
f'\n&7Playing speaker {id[-3:]}/{len(ids)} - track {track_i}/{len(tracks)} - {audio}&r'
|
||||
f'\n- Press f / m, or anything else to play next track: '))\
|
||||
.lower().strip()
|
||||
sound.stop()
|
||||
|
||||
# Skip
|
||||
if i == 's':
|
||||
break
|
||||
|
||||
# Labeled
|
||||
if i == 'f' or i == 'm':
|
||||
id_labels[id] = i
|
||||
labels_json.write_text(json.dumps(id_labels))
|
||||
|
||||
# Print choice match
|
||||
if pf != -1:
|
||||
agree = '&aINA agrees' if ina_choice == i else '&cINA disagree'
|
||||
printc(f'{agree} with confidence {abs(pf - 0.5) * 200:.0f}%')
|
||||
else:
|
||||
printc(f"&7INA didn't identify any voice")
|
||||
break
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
cn_celeb_root = Path('C:/Users/me/Workspace/Data/CN-Celeb_flac')
|
||||
data_dir = cn_celeb_root.joinpath('data')
|
||||
ids = [id for id in os.listdir(data_dir) if id.startswith('id0')]
|
||||
|
||||
# segment_all()
|
||||
# graph_histogram()
|
||||
manually_label_data()
|
||||
Reference in New Issue
Block a user