def document_message(msg):
logger.info("{:s}: [DOCUMENT] {:s}".format(msg.from_user.first_name,
msg.document.file_id))
try:
os.makedirs("./documents")
except FileExistsError:
pass
file_info = bot.get_file(msg.document.file_id)
utils.file_download(file_info, './documents/')
def send_message(message):
if message.text:
if message.text.find('/cancel') != -1:
bot.send_message(message.chat.id,
ru_strings.CANCEL_MESSAGE['strings'][0],
parse_mode='Markdown')
else:
bot.send_message(config.send_chat_id,
message.text,
parse_mode='Markdown')
logger.info("Sending message {:s} to chat {:d}".format(
message.text, config.send_chat_id))
bot.send_message(message.chat.id,
ru_strings.SEND_MSG_MESSAGE['strings'][1],
parse_mode='Markdown')
elif message.photo:
try:
os.makedirs("./photos")
except FileExistsError:
pass
file_id = message.photo[len(message.photo) - 1].file_id
file_info = bot.get_file(file_id)
file_patch = utils.file_download(file_info, './photos/')
with open(file_patch, 'rb') as photo:
bot.send_photo(config.send_chat_id, photo)
logger.info("Sending photo {:s} to chat {:d}".format(
file_id, config.send_chat_id))
bot.send_message(message.chat.id,
ru_strings.SEND_MSG_MESSAGE['strings'][1],
parse_mode='Markdown')
def photo_receive(message):
'''
Recognizes photos with the comment "персик"
Ban for NSFW
'''
file_id = message.photo[len(message.photo) - 1].file_id
if message.caption and message.forward_from is None:
if re.match('(?i)(\W|^).*?!п[eеэ][pр][cс](и|ч[eеи]к).*?(\W|$)',
message.caption):
bot.reply_to(message,
"".join([
message.from_user.first_name,
picturedetect.reply_get_concept_msg(file_id)
]),
parse_mode='Markdown')
logger.info("Photo by Username @{:s} | ID {:s}".format(
(message.from_user.username or "NONE"), file_id))
try:
os.makedirs("./photos")
except FileExistsError:
pass
file_patch = './photos/{:s}.jpg'.format(file_id)
_file = Path(file_patch)
if _file.is_file() is not True:
file_info = bot.get_file(file_id)
file_patch = utils.file_download(file_info, './photos/')
if file_patch is None:
logger.error("File download error!'")
bot.reply_to(message,
ru_strings.SOME_ERROR_MESSAGE['strings'],
parse_mode='Markdown')
bot.send_sticker(message.chat.id,
ru_strings.SOME_ERROR_MESSAGE['stickers'][0])
return
if not allow_nsfw:
if picturedetect.nsfw_test(file_patch, 0.75):
bot.delete_message(message.chat.id, message.message_id)
bot.send_message(
message.chat.id,
"*{} уходит в бан на {} {}! Причина: NSFW*".format(
message.from_user.first_name, 2, 'мин.'),
parse_mode='Markdown')
ban_user(message.chat.id, message.from_user.id, 2 * 60)
def load_submission(self, event):
#global student_track, backing_track, reference_track
sound_id = self.submissions_listbox.get(tk.ANCHOR)
#LOGGER.info(" download files", sound_id)
#submissions = utils.get_submissions_in_exercise(exercise)
submission = utils.get_student_submission_by_sound_id(
sound_id, self.submissions)
#LOGGER.info(submission)
if submission is not None:
#print(submissions_listbox.)
#sub_ind = [item for item in submissions_listbox]
#submissions_listbox.itemconfig(sub_ind, {'bg':'red'})
version = submission['version']
self.backing_track = utils.get_backing_track(
self.exercise, version)
self.reference_track = utils.get_reference_track(
self.exercise, version)
#LOGGER.info(version, self.backing_track['download_url'], self.reference_track['download_url'])
self.student_track = utils.file_download(
download_url=submission['download_url'],
filetype='submissions')
self.backing_track = utils.file_download(
download_url=self.backing_track['download_url'],
filetype='backing_tracks')
self.reference_track = utils.file_download(
download_url=self.reference_track['download_url'],
filetype='reference_tracks')
#play(reference_track, backing_track, student_track)
# image_file = os.path.join('performance_feedback',str(sound_id)+'.png')
image_file = os.path.join(
PLOTS_PATH,
os.path.basename(self.student_track)[:-4] + '.png')
render = ImageTk.PhotoImage(Image.open(image_file))
self.img.configure(image=render)
self.img.image = render
# figure = self.visualizer(version)
# line = FigureCanvasTkAgg(figure, self.fig)
# line.get_tk_widget().pack(side=tk.LEFT, fill=tk.BOTH)
return
def extract_submission_features(exercise, submission_sound, plots_path=None):
version = submission_sound['version']
ref_annotation_file = os.path.join(
'reference_tracks',
exercise['name'] + '_' + version + '_ref_segments.csv')
reference_track = utils.get_reference_track(exercise, version)
ref_audio_file = utils.file_download(
download_url=reference_track['download_url'],
filetype='reference_tracks')
std_audio_file = utils.file_download(
download_url=submission_sound['download_url'], filetype='submissions')
features = notes_singing.get_features_from_student_audio(
ref_audio_file,
std_audio_file,
ref_annotation_file,
plots_path=plots_path,
method='MAST')
features['file_name'] = std_audio_file
return features
def reply_get_concept_msg(photo_id):
try:
os.makedirs("./photos")
except FileExistsError:
pass
file_patch = './photos/{:s}.jpg'.format(photo_id)
_file = Path(file_patch)
if not _file.is_file():
file_info = utils.bot.get_file(photo_id)
file_patch = utils.file_download(file_info, './photos/')
concepts = itertools.islice(analise_photo(file_patch), config.CONCEPTS_COUNT)
message, word_sets = process_concepts(concepts)
logger.info("[WHATISTHIS] Photo ID {} - [{}]".format(photo_id, "|".join(word_sets)))
return message
print(len(annotated_files))
for f in annotated_files:
sound_id = os.path.basename(f).split('_')[0]
# print(sound_id)
for af in audio_files:
# print(af)
if sound_id in os.path.basename(af)[:4]:
audio_file = os.path.basename(af)
# print(sub_ref_map[audio_file])
break
# print(audio_file)
try:
exercise = utils.get_exercise_by_name(sub_ref_map[audio_file]['exercise_name'], exercises)
version = sub_ref_map[audio_file]['version']
ref = utils.get_reference_track(exercise, version)
ref = utils.file_download(ref['download_url'], filetype='reference_tracks')
# print(ref)
std = os.path.join('submissions', audio_file)
ref_audio = analysis.load_audio(ref)
std_audio = analysis.load_audio(std)
if series == 'hpcp':
params = {'normalize_method':HPCP_NORMALIZE,'hpcp_size':HPCP_SIZE, 'dist_function':DIST_FUNCTION}
ref_series = analysis.hpcp_extract(ref_audio,params['normalize_method'],params['hpcp_size'])
std_series = analysis.hpcp_extract(std_audio,params['normalize_method'],params['hpcp_size'])
if series =='pitch':
params = {'dist_function':DIST_FUNCTION}
def extract_submission_features(exercise, submission):
version = submission['version']
segmentAnnotationFile = os.path.join(
'reference_tracks',
exercise['name'] + '_' + version + '_ref_segments.csv')
#print(segmentAnnotationFile)
reference_track = utils.get_reference_track(exercise, version)
ref_audio_file = utils.file_download(
download_url=reference_track['download_url'],
filetype='reference_tracks')
# print(ref_audio_file)
ref_audio = analysis.load_audio(audio_file=ref_audio_file)
# thresholding
# ref_energy_mask = analysis.energy_mask(ref_audio, REF_ENERGY_THRESH)
# # ref_audio = ref_audio * ref_energy_mask
# ref_energy = analysis.short_time_energy(ref_audio)
# ref_energy = ref_energy/np.max(ref_energy)
# mean_ref_energy = np.mean(ref_energy)
# ref_energy[np.where(ref_energy > 0.01*mean_ref_energy)] = 1
# ref_energy[np.where(ref_energy <= 0.01*mean_ref_energy)] = 0
ref_hpcp_vector = analysis.hpcp_extract(audio=ref_audio,
normalize_method=HPCP_NORMALIZE,
hpcp_size=HPCP_SIZE)
# ref_hpcp_vector = analysis.hpcp_extract(audio=ref_audio,hpcp_size=HPCP_SIZE)
#ref_hpcp_vector = np.array([ref_hpcp_vector[i] * ref_energy[i] for i in range(len(ref_hpcp_vector))], dtype='float32')
# ref_hpcp_vector[np.where(ref_energy < REF_ENERGY_THRESH)]=0
# # ref_hpcp_vector = np.array([(np.convolve(ref_hpcp_vector[:,i], np.ones(MOV_AVG_SIZE), 'valid')/MOV_AVG_SIZE) for i in range(HPCP_SIZE)]).T
ref_pitch, ref_conf = analysis.pitch_extractor(ref_audio)
# ref_pitch_mask = np.zeros(len(ref_pitch))
# ref_pitch_mask[np.where(ref_pitch > 0)] = 1
# ref_pitch_mask = ref_pitch_mask[:len(ref_hpcp_vector)]
#ref_hpcp_vector = np.array([ref_hpcp_vector[i] * ref_energy_mask[i] for i in range(len(ref_hpcp_vector))], dtype='float32')
#ref_hpcp_vector = ref_hpcp_vector * ref_energy_mask
submission_sound = submission['sounds'][0]
sound_id = str(submission_sound['id'])
std_audio_file = utils.file_download(
download_url=submission_sound['download_url'], filetype='submissions')
print(std_audio_file)
std_audio = analysis.load_audio(audio_file=std_audio_file)
std_pitch, std_conf = analysis.pitch_extractor(std_audio)
# std_energy_mask = analysis.energy_mask(std_audio, STD_ENERGY_THRESH)
# std_audio = std_audio * std_energy_mask
# std_energy = analysis.short_time_energy(std_audio)
# std_energy = std_energy/np.max(std_energy)
# mean_std_energy = np.mean(std_energy)
# std_energy[np.where(std_energy > 0.01*mean_std_energy)] = 1
# std_energy[np.where(std_energy <= 0.01*mean_std_energy)] = 0
std_hpcp_vector = analysis.hpcp_extract(audio=std_audio,
normalize_method=HPCP_NORMALIZE,
hpcp_size=HPCP_SIZE)
# std_hpcp_vector = analysis.hpcp_extract(audio=std_audio,hpcp_size=HPCP_SIZE)
#std_hpcp_vector = np.array([std_hpcp_vector[i] * std_energy[i] for i in range(len(std_hpcp_vector))], dtype='float32')
# std_hpcp_vector[np.where(std_energy < STD_ENERGY_THRESH)]=0
# std_pitch_mask = np.zeros(len(std_pitch))
# std_pitch_mask[np.where(std_pitch > 0)] = 1
# std_pitch_mask = std_pitch_mask[:len(std_hpcp_vector)]
# std_hpcp_vector = np.array([std_hpcp_vector[i] * std_pitch_mask[i] for i in range(len(std_hpcp_vector))], dtype='float32')
#std_hpcp_vector = np.array([(np.convolve(std_hpcp_vector[:,i], np.ones(MOV_AVG_SIZE), 'valid')/MOV_AVG_SIZE) for i in range(HPCP_SIZE)]).T
# std_pitch_mask = np.zeros(len(std_pitch))
# std_pitch_mask[np.where(std_pitch > 0)] = 1
# std_pitch_mask = std_pitch_mask[:len(std_hpcp_vector)]
#std_hpcp_vector = np.array([std_hpcp_vector[i] * std_energy_mask[i] for i in range(len(std_hpcp_vector))], dtype='float32')
#std_hpcp_vector = std_hpcp_vector * std_energy_mask
#plotter.plot_pitch_contour(ref_pitch, save_file_name=Path(std_audio_file).stem)
#cost, path, matrix = analysis.audio_align(ref_energy, std_energy)
cost, path, matrix = analysis.audio_align(ref_hpcp_vector, std_hpcp_vector)
#cost, path = fastdtw.fastdtw(ref_hpcp_vector, std_hpcp_vector, dist=dist_cosine)
# ref_ser =np.append(ref_hpcp_vector.T, [ref_energy], axis=0).T
# std_ser =np.append(std_hpcp_vector.T, [std_energy], axis=0).T
# cost, path = fastdtw.fastdtw(ref_ser, std_ser, dist=dist_combination)
#cost, path = fastdtw.fastdtw(ref_hpcp_vector, std_hpcp_vector, dist=dist_cos_W)
#path = np.array(path)
plotter.plot_dtw_alignment(ref_audio,
std_audio,
path,
save_file_name=Path(std_audio_file).stem)
ref_time_ticks = analysis.get_annotation(segmentAnnotationFile)
std_time_ticks_dtw = analysis.get_std_time_ticks_dtw(ref_time_ticks, path)
figure = plotter.performance_visualize(analysis, ref_audio, std_audio,
ref_pitch, std_pitch,
ref_time_ticks, std_time_ticks_dtw)
figure.savefig(fname=os.path.join(image_save_path, sound_id))
plt.close()
try:
features = analysis.features_extract(segmentAnnotationFile, path,
ref_pitch, std_pitch)
if sound_id in grades:
features['grade'] = grades[sound_id]
else:
features['grade'] = '0'
features['sound_id'] = sound_id
#print(features)
all_features.append(features)
except:
print(sound_id)
gc.collect()
return
import settings
import utils
import mcclient
import json
#context_id = settings.CONTEXT_ID
exercises = mcclient.get_full_context(settings.CONTEXT_ID)['exercises']
with open('data_{}.json'.format(settings.CONTEXT_ID), 'w') as fp:
json.dump(exercises, fp)
#print(len(exercises), type(exercises))
for exercise in exercises:
for backing_track in exercise['backing_tracks']:
utils.file_download(download_url=backing_track['download_url'],
filetype='backing_tracks')
for reference_track in exercise['reference_tracks']:
utils.file_download(download_url=reference_track['download_url'],
filetype='reference_tracks')
submissions = utils.get_submissions_in_exercise(exercise)
#print(len(submissions), type(submissions))
for submission in submissions:
sound_files = submission['sounds']
# print(sound_files)
for sound_file in sound_files:
# print(sound_file['download_url'])
utils.file_download(download_url=sound_file['download_url'],
filetype='submissions')