def gen_hihat(all_data, fs, fps, cand):
fps = librosa.samples_to_frames(fs, hop_length=hop_len, n_fft=win_len)
fps = 100
print(cand)
proc = BeatTrackingProcessor(look_aside=0.2, fps=fps)
act = RNNBeatProcessor()(all_data)
beat_times = proc(act)
song_len = librosa.samples_to_time(data.shape, sr=fs)[0]
hihat = np.zeros(all_data.shape)
idx = np.where(beat_times <= song_len)[0]
new_beat_times = np.zeros(idx.shape)
new_beat_times[idx] = beat_times[idx]
beat_samples = librosa.time_to_samples(new_beat_times, sr=fs)
start = librosa.frames_to_samples(cand[0], hop_len, n_fft=win_len)
end = librosa.frames_to_samples(cand[-1], hop_len, n_fft=win_len)
cand_len = end - start
i = 3
is_hihat = np.zeros(beat_samples.shape)
while i < len(beat_samples):
is_hihat[i] = 1
i = i + 4
for i, s in enumerate(beat_samples):
if is_hihat[i] == 1:
if s + cand_len > hihat.shape:
break
hihat[s:s + cand_len] = data[start:end]
return hihat, new_beat_times, beat_samples
def predict(flac_path,
title="",
model_path="./model",
diff_root_only=True,
max_num_chord=4):
label_path = "chord_labels.txt"
# Estimate the bpm of the audio
beat_proc = RNNBeatProcessor()
tempo_proc = TempoEstimationProcessor(min_bpm=50, max_bpm=180, fps=100)
beat_processed = beat_proc(flac_path)
tempo_estimation = tempo_proc(beat_processed)
BPM = BPM_selector(tempo_estimation)
sec_per_beat = 60 / BPM
sec_per_frame = 2048 / 16000
# set eighth note as the minimum duration of the chord
min_duration = sec_per_beat / 2
# Read chord labels file
with open(label_path) as f:
with torch.no_grad():
chord_labels = ast.literal_eval(f.read())
# Process raw audio
X = cqt_preprocess(flac_path)
X = Variable(
torch.from_numpy(np.expand_dims(X, axis=0)).float().cpu())
# Load model
model = Net(1).cpu()
state_dict = torch.load(model_path,
map_location="cpu")["state_dict"]
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.eval()
# Estimate
estimation = np.zeros((22, X.shape[2]))
estimation = model(X).data.cpu()[0][0]
estimation = to_probability(estimation)
# Post-processing
estimation = dp_post_processing(estimation)
# predict_list_majmin = _predict(estimation, chord_labels[13:], sec_per_frame, min_duration, mapping_majmin)
predict_list_seventh = _predict(estimation, chord_labels[13:],
sec_per_frame, min_duration,
mapping_seventh)
text = ''
for chord in predict_list_seventh:
text += f'{chord[0]}\t{chord[1]}\t{chord[2]}\n'
return text
def calculateBeatsAndSemitones(infile):
print 'Loading audio file...', infile
proc = BeatTrackingProcessor(fps=100,
method='comb',
min_bpm=40,
max_bpm=240,
act_smooth=0.09,
hist_smooth=7,
alpha=0.79)
act = RNNBeatProcessor()(infile)
beats = proc(act).astype('float32')
audio = essentia.standard.MonoLoader(filename=infile)()
#bt = BeatTrackerMultiFeature()
#beats, _ = bt(audio)
# TODO: best partameters.
parameters = {}
#stepsize, chroma = vamp.collect(
# audio, 44100, "nnls-chroma:nnls-chroma", output = "chroma", step_size=2048)["matrix"]
stepsize, semitones = vamp.collect(audio,
44100,
"nnls-chroma:nnls-chroma",
output="semitonespectrum",
step_size=2048)["matrix"]
return len(audio), beats, semitones
def beat_extractor(queue_beat):
kwargs = dict(
fps=100,
correct=True,
infile=None,
outfile=None,
max_bpm=170,
min_bpm=60,
#nn_files = [BEATS_LSTM[0]],
transition_lambda=100,
num_frames=1,
online=True,
verbose=1)
def beat_callback(beats, output=None):
if len(beats) > 0:
# Do something with the beat (for now, just print the array to stdout)
queue_beat.put(beats[0])
#print(beats)
#print('Process to write betas: %s' % os.getpid())
in_processor = RNNBeatProcessor(**kwargs)
beat_processor = DBNBeatTrackingProcessor(**kwargs)
out_processor = [beat_processor, beat_callback]
processor = IOProcessor(in_processor, out_processor)
process_online(processor, **kwargs)
def get_beat_processor():
print('START BEAT PROCESSOR >> ', str(datetime.now()))
from madmom.features.beats import RNNBeatProcessor, DBNBeatTrackingProcessor
from madmom.processors import SequentialProcessor
print('BEAT PROCESSOR >> ', str(datetime.now()))
return SequentialProcessor(
[RNNBeatProcessor(),
DBNBeatTrackingProcessor(fps=100)])
def beatSyncFeature(feature, audio, sr, hop_length):
# Aggregate feature between beat events
fps = SR / HOP_LENGTH
beat_proc = DBNBeatTrackingProcessor(fps=100)
beat_act = RNNBeatProcessor()(audio)
beat_times = beat_proc(beat_act)
# We'll use the median value of each feature between beat frames
feature = librosa.feature.sync(feature, (beat_times * fps).astype(int),
aggregate=np.median)
return feature, beat_times
def get_beats(file_path: str) -> List[float]:
"""
Given the path to an audio file get a list of detected beat timings (in seconds)
"""
print(f"Getting beats for {file_path}")
proc = DBNBeatTrackingProcessor(fps=100)
act = RNNBeatProcessor()(file_path)
res: List[float] = proc(act)
print(f"Got {len(res)} beats")
print(res)
return res
def getMadmomTempo(filename):
"""
Call Madmom Tempo Estimation
:return: Array of tempos sorted in decreasing order of strength
"""
from madmom.features.beats import RNNBeatProcessor
from madmom.features.tempo import TempoEstimationProcessor
act = RNNBeatProcessor()(filename)
proc = TempoEstimationProcessor(fps=100)
res = proc(act)
return res[:, 0]
def getRNNDBNOnsets(filename):
"""
Call Madmom's implementation of RNN + DBN beat tracking
:param filename: Path to audio file
"""
print("Computing madmom beats...")
from madmom.features.beats import RNNBeatProcessor, DBNBeatTrackingProcessor
proc = DBNBeatTrackingProcessor(fps=100)
act = RNNBeatProcessor()(filename)
b = proc(act)
return b
def extract(yt_id):
beats = SequentialProcessor(
[RNNBeatProcessor(),
DBNBeatTrackingProcessor(fps=100)])
chordrec = SequentialProcessor(
[CNNChordFeatureProcessor(),
CRFChordRecognitionProcessor()])
processMulti = ParallelProcessor([])
processMulti.append(beats)
processMulti.append(chordrec)
beatSync = SequentialProcessor(
[printTime, processMulti, printTime, arrange, printTime])
return beatSync('tmp/' + yt_id + '.wav')
def madmom_features(self, fps=100):
"""
Call Madmom's implementation of RNN + DBN beat tracking. Madmom's
results are returned in terms of seconds, but round and convert to
be in terms of hop_size so that they line up with the features.
The novelty function is also computed as a side effect (and is
the bottleneck in the computation), so also return that
Parameters
----------
fps: int
Frames per second in processing
Returns
-------
{
'tempos': ndarray(n_levels, 2)
An array of tempo estimates in beats per minute,
along with their confidences
'onsets': ndarray(n_onsets)
Array of onsets, where each onset indexes into a particular window
'novfn': ndarray(n_frames)
Evaluation of the rnn audio novelty function at each audio
frame, in time increments equal to self.hop_length
'snovfn': ndarray(n_frames)
Superflux audio novelty function at each audio frame,
in time increments equal to self.hop_length
}
"""
from madmom.features.beats import RNNBeatProcessor, DBNBeatTrackingProcessor
from madmom.features.tempo import TempoEstimationProcessor
from madmom.features.onsets import SpectralOnsetProcessor
from madmom.audio.filters import LogarithmicFilterbank
beatproc = DBNBeatTrackingProcessor(fps=fps)
tempoproc = TempoEstimationProcessor(fps=fps)
novfn = RNNBeatProcessor()(self.audio_file) # This step is the computational bottleneck
beats = beatproc(novfn)
tempos = tempoproc(novfn)
onsets = np.array(np.round(beats*self.fs/float(self.hop_length)), dtype=np.int64)
# Resample the audio novelty function to correspond to the
# correct hop length
nframes = len(self.librosa_noveltyfn())
novfn = np.interp(np.arange(nframes)*self.hop_length/float(self.fs), np.arange(len(novfn))/float(fps), novfn)
# For good measure, also compute and return superflux
sodf = SpectralOnsetProcessor(onset_method='superflux', fps=fps, \
filterbank=LogarithmicFilterbank,\
num_bands=24, log=np.log10)
snovfn = sodf(self.audio_file)
snovfn = np.interp(np.arange(nframes)*self.hop_length/float(self.fs), np.arange(len(snovfn))/float(fps), snovfn)
return {'tempos':tempos, 'onsets':onsets, 'novfn':novfn, 'snovfn':snovfn}
def getRNNDBNOnsets(filename, Fs, hopSize):
"""
Call Madmom's implementation of RNN + DBN beat tracking
:param filename: Path to audio file
:param Fs: Sample rate
:param hopSize: Hop size of each onset function value
:returns (tempo, beats): Average tempo, numpy array
of beat intervals in seconds
"""
print("Computing madmom beats...")
from madmom.features.beats import RNNBeatProcessor, DBNBeatTrackingProcessor
proc = DBNBeatTrackingProcessor(fps=100)
act = RNNBeatProcessor()(filename)
b = proc(act)
tempo = 60 / np.mean(b[1::] - b[0:-1])
beats = np.array(np.round(b * Fs / hopSize), dtype=np.int64)
return (tempo, beats)
def chordBeats(infile, outfile):
print 'Loading audio file...', infile
#proc = BeatTrackingProcessor(
# fps = 100,
# method='comb', min_bpm=40,
# max_bpm=240, act_smooth=0.09,
# hist_smooth=7, alpha=0.79)
proc = DBNBeatTrackingProcessor(fps=100,
method='comb',
min_bpm=40,
max_bpm=240)
act = RNNBeatProcessor()(infile)
beats = proc(act).astype('float32')
audio = essentia.standard.MonoLoader(filename=infile)()
# TODO: best partameters.
parameters = {}
stepsize, semitones = vamp.collect(audio,
44100,
"nnls-chroma:nnls-chroma",
output="semitonespectrum",
step_size=2048)["matrix"]
np.savez(outfile, [len(audio)], beats, semitones)
def get_tempo(loop):
#tempo
proc2 = bt.TempoEstimationProcessor(fps=100)
act2 = RNNBeatProcessor()(loop)
tempo = proc2(act2)[0][0]
return tempo
from madmom.features.beats import RNNBeatProcessor
from madmom.features.tempo import TempoEstimationProcessor
import os
import numpy as np
proc = TempoEstimationProcessor(fps=100)
source_dir = r"D:\Program Files\StepMania 5\Songs\Albumix 3.I"
np.set_printoptions(suppress=True)
for (dirpath, dirnames, filenames) in os.walk(source_dir):
name = os.path.relpath(dirpath, source_dir)
#print(name)
musicname = ""
chartname = ""
if len(dirnames) > 5:
print(">Subdirectories found in " + name + ", continuing.")
continue
if dirnames:
print("----<5 subdirectories in " + name + ", investigate!")
for f in filenames:
if f.endswith(".sm") or f.endswith(".SM"):
chartname = f #.ssc files are very similar, my packs don't need those .dwi files would need work
elif f.endswith(".mp3") or f.endswith(".ogg"):
musicname = f
if musicname == "" or chartname == "":
print("----Music/Chart (" + musicname + "," + chartname +
") not found in " + name)
continue
print(musicname)
act = RNNBeatProcessor()(os.path.join(dirpath, musicname))
print(str(proc(act)))
test.extend(sapce) test.extend(drum) test.extend(sapce) test.extend(beattt) test.extend(sapce) test.extend(drum) test.extend(drum[int(0.5 * drum.shape[0]):]) sss = np.zeros(data.shape) sss[:np.array(test).shape[0]] = np.array(test) #sd.play(sss*5+data*5, fs) '''gen drum''' fps = librosa.samples_to_frames(fs, hop_length=hop_len, n_fft=win_len) fps = 100 print(fps) proc = BeatTrackingProcessor(look_aside=0.2, fps=fps) act = RNNBeatProcessor()(all_data) beat_times = proc(act) song_len = librosa.samples_to_time(data.shape, sr=fs)[0] beat = np.zeros(all_data.shape) idx = np.where(beat_times <= song_len)[0] new_beat_times = np.zeros(idx.shape) new_beat_times[idx] = beat_times[idx] beat_samples = librosa.time_to_samples(new_beat_times, sr=fs) cand_len = len(drum) end = len(beat_samples) is_drum = np.zeros(beat_samples.shape) group = np.arange(len(beat_samples)) % 8 idx = np.where((group == 1) | (group == 6))
import pydub import numpy as np from pydub import AudioSegment from pydub.playback import play from multiprocessing import Process from madmom.features.tempo import TempoEstimationProcessor from madmom.features.beats import RNNBeatProcessor robot = stretch_body.robot.Robot() robot.startup() robot.stow() filename = "./audios/forest.wav" proc = TempoEstimationProcessor(fps=100) act = RNNBeatProcessor()(filename) tempo = proc(act) tempo = tempo[0][0] t = 60.0 / tempo * 4 # interonsets = np.ediff1d(onsets) # interonsets = np.add.reduceat(interonsets, np.arange(0, len(interonsets), 8)) # print(interonsets) xrotate = 3.14 xtilt = 0.5 xpan = 1 xwrist = 1.5 #start
def main():
video_dir = 'dance_videos\\Danny Ocean - Baby I Wont.mp4'
beat_dir = video_dir.strip('mp4') + 'npy'
interval = [32, 36] #in second
REDU = True
motion_base_dir = 'MyNao\\motion_base\\motion_base.json'
if not os.path.exists(motion_base_dir):
motion_base = {}
with open(motion_base_dir, 'w') as f:
json.dump(motion_base, f)
with open(motion_base_dir, 'r') as f:
motion_base = json.load(f)
if REDU:
pose_save_dir = 'MyNao\\motion_glance\\' + str(len(motion_base) - 1)
else:
pose_save_dir = 'MyNao\\motion_glance\\' + str(len(motion_base))
if not os.path.exists(pose_save_dir):
os.mkdir(pose_save_dir)
motion = {}
motion['feature'] = {}
motion['feature']['bps'] = [None]
motion['feature']['symmetric'] = False
motion['feature']['repeat'] = True
motion['frame'] = {}
#args = parse_args()
#cfg.set_args(args.gpu_ids)
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
time_0 = time.time()
tester = Tester(24)
##loading 3D pose estimation model
tester._make_model()
time_1 = time.time()
print('loading integral pose model elapse:', round(time_1 - time_0, 2),
's')
##loading yolo detector
detector = YOLOv3(
model_def=
"3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\config\\yolov3.cfg",
class_path=
"3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\data\\coco.names",
weights_path=
"3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\weights\\yolov3.weights",
classes=('person', ),
max_batch_size=16,
device=torch.device('cuda:{}'.format(cfg.gpu_ids[0])))
print('loading yolo elapse:', round(time.time() - time_1, 2), 's')
skeleton = ((0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13),
(8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3), (0, 4),
(4, 5), (5, 6))
fig = plt.figure(figsize=(10, 10))
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)
])
##load model
if not os.path.exists(video_dir.strip('mp4') + 'wav'):
videoclip = VideoFileClip(video_dir)
audioclip = videoclip.audio
audioclip.write_audiofile(video_dir.strip('mp4') + 'wav')
video = cv2.VideoCapture(video_dir)
if not os.path.exists(beat_dir):
time_2 = time.time()
videoclip = VideoFileClip(video_dir)
audioclip = videoclip.audio
beat_activation = RNNBeatProcessor()(video_dir.strip('mp4') + 'wav')
processor = DBNBeatTrackingProcessor(fps=100)
beats = processor(beat_activation)
frames_at_beat = (beats / audioclip.duration *
video.get(cv2.CAP_PROP_FRAME_COUNT)).astype(int)
print('extracting beat sequence elapse:',
round(time.time() - time_2, 2), 's')
np.save(beat_dir, frames_at_beat)
frames_at_beat = np.load(beat_dir).tolist()
for beat in frames_at_beat:
if interval[0] * video.get(cv2.CAP_PROP_FPS) > beat:
continue
else:
interval[0] = beat
break
for beat in frames_at_beat:
if interval[1] * video.get(cv2.CAP_PROP_FPS) > beat:
continue
else:
interval[1] = beat
break
video.set(1, interval[0])
frame = 0
next_beat = 0
last_beat = 0
num_beat = 0
num_frame_between_beats = []
with torch.no_grad():
while True:
time_start = time.time()
current_frame = video.get(cv2.CAP_PROP_POS_FRAMES)
ret_val, raw_image = video.read()
if current_frame == interval[1]:
break
input_img = raw_image.copy()
##using yolo to get human bounding box
detections = detector.predict_single(input_img)
# if not detections.cpu().numpy().all():
# detections = (0,0,input_img.shape[1],input_img.shape[0],1,1)
# print('not detected')
if detections is None:
detections = np.array(
[[0, 0, input_img.shape[1], input_img.shape[0], 1, 1, 1]])
print('not detected')
elif detections.size()[0] == 0:
detections = np.array(
[[0, 0, input_img.shape[1], input_img.shape[0], 1, 1, 1]])
print('not detected')
last_conf = 0
last_last_conf = 0
for i, (x1_pred, y1_pred, x2_pred, y2_pred, conf, cls_conf,
cls_pred) in enumerate(detections):
if conf.item() > last_conf:
x1 = int(round(x1_pred.item())) - 40
x2 = int(round(x2_pred.item())) + 40
y1 = int(round(y1_pred.item())) - 20
y2 = int(
round(y2_pred.item())
) + 20 #for getting a larger bounding box to cover the full body, in order to get more accurate pose
last_last_conf = last_conf
last_conf = conf.item()
print(last_conf, last_last_conf)
if last_last_conf != 0:
sys.exit()
#print(x1, x2, y1, y2, last_conf)
img_patch = (input_img[y1:y2,
x1:x2, ::-1]).copy().astype(np.float32)
input_patch = cv2.resize(img_patch, (cfg.input_shape))
input_patch = transform(input_patch).unsqueeze(0)
coord_out = tester.model(input_patch)
print('Running model time:', round(time.time() - time_start, 2),
's')
motion['frame'][frame] = {}
if frame + interval[0] in frames_at_beat:
motion['frame'][frame]['next_beat'] = 0
motion['frame'][frame]['last_beat'] = 0
#frames_at_beat.remove(frame)
next_beat = frames_at_beat.index(frame + interval[0]) + 1
last_beat = frames_at_beat.index(frame + interval[0])
num_beat += 1
num_frame_between_beats.append(frames_at_beat[next_beat] -
frames_at_beat[last_beat])
print('Record key frame with beat:', current_frame)
else:
motion['frame'][frame]['next_beat'] = frames_at_beat[
next_beat] - (frame + interval[0])
motion['frame'][frame]['last_beat'] = (
frame + interval[0]) - frames_at_beat[last_beat]
coord_out = coord_out.cpu().numpy()
coord_out_resize = coord_out * np.array([
img_patch.shape[1] / cfg.input_shape[1],
img_patch.shape[0] / cfg.input_shape[0], 1
])
for idx in range(coord_out_resize.shape[1] - 1):
motion['frame'][frame][idx] = (
coord_out_resize[0][idx][0].item(),
coord_out_resize[0][idx][2].item(),
coord_out_resize[0][idx][1].item())
vis = True
vis_3d = False
if vis:
tmpimg = input_patch[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(
3, 1, 1) + np.array(cfg.pixel_mean).reshape(3, 1, 1)
tmpimg = (tmpimg).astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg, (1, 2, 0)).copy()
tmpkps = np.zeros((3, 18))
tmpkps[:2, :] = coord_out[0, :, :2].transpose(
1, 0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2, :] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, skeleton)
tmpimg = cv2.resize(tmpimg,
(img_patch.shape[1], img_patch.shape[0]))
file_name = pose_save_dir + '\\{0}.png'.format(
str(frame).zfill(4))
cv2.imwrite(file_name, tmpimg)
if vis_3d:
#coord_out = coord_out.cpu().numpy()
#coord_out = coord_out * np.array([img_patch.shape[1]/cfg.input_shape[1], img_patch.shape[0]/cfg.input_shape[0], 1])
pred = coord_out_resize.squeeze(
) #remove first batch dimension
ax = plt.subplot('121', projection='3d')
plt.axis('off')
show3D_pose(pred, ax, skeleton, radius=40)
file_name = pose_save_dir + '\\{0}.png'.format(
str(frame).zfill(4))
plt.savefig(file_name)
# cv2.imwrite(file_name, tmpimg)
frame += 1
print('Processing Frame:', round(time.time() - time_start, 2), 's')
motion['feature']['fpb'] = np.mean(num_frame_between_beats)
if REDU:
motion_base[len(motion_base) - 1] = motion
else:
motion_base[len(motion_base)] = motion
#with open(motion_base_dir, 'w') as f:
# json.dump(motion_base, f)
print('done with', num_beat + 1,
'beats! (This should be even for a normal dance)')
print('num_frame between beats:')
print(num_frame_between_beats)
def __init__(self):
self.pa = pyaudio.PyAudio()
self.c_count = 0
using_callback = True
self.buffer = collections.deque(maxlen=self.RATE * 14)
self.rnn = RNNBeatProcessor(online=True, nn_files=[BEATS_LSTM[0]])
self.act_proc = DBNBeatTrackingProcessor(fps=100,
min_bpm=80.0,
max_bpm=180.0)
self.dcp = DeepChromaProcessor()
self.decode = DeepChromaChordRecognitionProcessor()
self.start_current_time = None
if using_callback:
self.stream = self.pa.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=True,
frames_per_buffer=self.CHUNK,
stream_callback=self.callback)
print(self.pa.get_default_output_device_info())
print(self.pa.get_default_input_device_info())
self.t_start = time.time()
beepsnd, _ = librosa.load('block.wav', sr=None)
out1 = (beepsnd).tostring()
#print(beepsnd.size, len(out1))
self.beepsnd = out1
self.Flag = False
self.beep_count = 0
while self.stream.is_active():
if len(self.buffer) == self.RATE * 8:
print('14 sec')
print(self.time_info)
print(time.time() - self.t_start)
self.tmp = np.array(self.buffer)
self.buffer.clear()
print(time.time() - self.t_start)
chroma_thread = threading.Thread(target=self.chroma_rec,
args=())
chroma_thread.start()
#chord = chroma_thread.run()
tmp2 = self.rnn(self.tmp)
# tmp2 = librosa.onset.onset_strength(tmp,sr=self.RATE, hop_length = int(self.RATE / 100),max_size=1,aggregate=np.median, n_mels=256)
# tmp2 /= np.max(tmp2)
#t_axes = librosa.frames_to_time(np.arange(len(tmp2)),sr=self.RATE)
t_proc = time.time() - self.t_start
print(t_proc)
tmp3_2 = self.act_proc(tmp2)
tmp3_1 = 60 / np.mean(np.diff(tmp3_2))
# print(tmp3)
#tmp3_1,tmp3_2 = librosa.beat.beat_track(onset_envelope=tmp2, sr=self.RATE)
print('tempo is %f' % tmp3_1)
print('beat is ', tmp3_2)
t_proc = time.time() - self.t_start
chroma_thread.join()
print(t_proc)
t = threading.Timer(60. / tmp3_1 - t_proc, self.flagit, ())
t.daemon = True
t.start()
# self.stream.write(self.beepsnd)
print(time.time() - self.t_start)
else:
time.sleep(0.001)
else:
self.stream = self.pa.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=True,
frames_per_buffer=self.CHUNK)
self.t_start = time.time()
self.loop()
x_path = join(base, file_name + "_X.npy")
y_path = join(base, file_name + "_y.npy")
return np.load(x_path), np.load(y_path)
processors = [
SpectralOnsetProcessor(fps=200),
RNNOnsetProcessor(),
CNNOnsetProcessor(),
SpectralOnsetProcessor(onset_method='superflux',
fps=200,
filterbank=LogarithmicFilterbank,
num_bands=24,
log=np.log10),
RNNDownBeatProcessor(),
lambda sig: np.array(RNNBeatProcessor(post_processor=None)(sig)).T
]
def rhythm_features_for_signal(signal):
rhythm_features = [process(signal) for process in processors]
return concatenate_and_resample(rhythm_features)
def load_and_rhythm_preprocess(audio_dir, max_samples=-1):
print('...load and preprocess files from folder')
audio_files = get_list_of_files(audio_dir)
if max_samples > 0:
audio_files = audio_files[:max_samples]
def beat_activations(path):
beat_activations_ = RNNBeatProcessor()(path)
return beat_activations_
import sys
import bmaFunctions
import numpy
from madmom.features.chords import DeepChromaChordRecognitionProcessor
from madmom.audio.chroma import DeepChromaProcessor
from madmom.features.beats import DBNBeatTrackingProcessor
from madmom.features.beats import RNNBeatProcessor
#Setting up Deep Chroma Chord Recognition Processor
dcp = DeepChromaProcessor()
decode = DeepChromaChordRecognitionProcessor()
chroma = dcp(sys.argv[1])
chords = decode(chroma)
#Setting up Dynamic Baysian Network Tracking Processor
proc = DBNBeatTrackingProcessor(fps=100)
act = RNNBeatProcessor()(sys.argv[1])
beats = proc(act)
#calculating msi
beatsArray = numpy.array(beats)
msi = numpy.mean(beatsArray[1:] - beatsArray[:-1]) * 1000
beatmap = bmaFunctions.assignKeys(beats, chords, sys.argv[3])
if msi < 360:
del beatmap[1::2]
#generating and printing beatmap
bmaFunctions.fancyPrint(beatmap, msi, sys.argv[2])
#TODO: eliminate trailing Ns
def main():
"""DBNBeatTracker"""
# define parser
p = argparse.ArgumentParser(
formatter_class=argparse.RawDescriptionHelpFormatter,
description='''
The DBNBeatTracker.py program detects all beats in an audio file according to
the method described in:
"A Multi-Model Approach to Beat Tracking Considering Heterogeneous Music
Styles"
Sebastian Böck, Florian Krebs and Gerhard Widmer.
Proceedings of the 15th International Society for Music Information
Retrieval Conference (ISMIR), 2014.
It does not use the multi-model (Section 2.2.) and selection stage (Section
2.3), i.e. this version corresponds to the pure DBN version of the
algorithm for which results are given in Table 2.
Instead of the originally proposed state space and transition model for the
DBN, the following is used:
"An Efficient State Space Model for Joint Tempo and Meter Tracking"
Florian Krebs, Sebastian Böck and Gerhard Widmer.
Proceedings of the 16th International Society for Music Information
Retrieval Conference (ISMIR), 2015.
This program can be run in 'single' file mode to process a single audio
file and write the detected beats to STDOUT or the given output file.
$ DBNBeatTracker.py single INFILE [-o OUTFILE]
If multiple audio files should be processed, the program can also be run
in 'batch' mode to save the detected beats to files with the given suffix.
$ DBNBeatTracker.py batch [-o OUTPUT_DIR] [-s OUTPUT_SUFFIX] FILES
If no output directory is given, the program writes the files with the
detected beats to the same location as the audio files.
The 'pickle' mode can be used to store the used parameters to be able to
exactly reproduce experiments.
''')
# version
p.add_argument('--version',
action='version',
version='DBNBeatTracker.py.2016')
# input/output options
io_arguments(p, output_suffix='.beats.txt', online=True)
ActivationsProcessor.add_arguments(p)
# signal processing arguments
SignalProcessor.add_arguments(p, norm=False, gain=0)
# peak picking arguments
DBNBeatTrackingProcessor.add_arguments(p)
NeuralNetworkEnsemble.add_arguments(p, nn_files=None)
# parse arguments
args = p.parse_args()
# set immutable arguments
args.fps = 100
# print arguments
if args.verbose:
print(args)
# input processor
if args.load:
# load the activations from file
in_processor = ActivationsProcessor(mode='r', **vars(args))
else:
# use a RNN to predict the beats
in_processor = RNNBeatProcessor(**vars(args))
# output processor
if args.save:
# save the RNN beat activations to file
out_processor = ActivationsProcessor(mode='w', **vars(args))
else:
# track the beats with a DBN
beat_processor = DBNBeatTrackingProcessor(**vars(args))
# output handler
from madmom.utils import write_events as writer
# sequentially process everything
out_processor = [beat_processor, writer]
# create an IOProcessor
processor = IOProcessor(in_processor, out_processor)
# and call the processing function
args.func(processor, **vars(args))
def main():
video_list = ['Cant stop the feeling - Justin Timberlake - Easy Dance for Kids', 'Dance like yo daddy', 'Danny Ocean - Baby I Wont', 'Si una vez - If I Once', 'Vaiven - MegaMix']
for video in video_list:
video_dir = 'dance_videos\\' + video + '.mp4'
beat_dir = video_dir.strip('mp4') + 'npy'
cudnn.fastest = True
cudnn.benchmark = True
cudnn.deterministic = False
cudnn.enabled = True
time_0 = time.time()
tester = Tester(24)
##loading 3D pose estimation model
tester._make_model()
time_1 = time.time()
print('loading integral pose model elapse:',round(time_1-time_0,2),'s')
##loading yolo detector
detector = YOLOv3( model_def="3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\config\\yolov3.cfg",
class_path="3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\data\\coco.names",
weights_path="3DMPPE_POSENET_RELEASE\\common\\detectors\\yolo\\weights\\yolov3.weights",
classes=('person',),
max_batch_size=16,
device=torch.device('cuda:{}'.format(cfg.gpu_ids[0])))
print('loading yolo elapse:',round(time.time()-time_1,2),'s')
skeleton = ( (0, 7), (7, 8), (8, 9), (9, 10), (8, 11), (11, 12), (12, 13), (8, 14), (14, 15), (15, 16), (0, 1), (1, 2), (2, 3), (0, 4), (4, 5), (5, 6) )
#fig = plt.figure(figsize=(10,10))
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize(mean=cfg.pixel_mean, std=cfg.pixel_std)]
)
if not os.path.exists(video_dir.strip('mp4')+'wav'):
videoclip = VideoFileClip(video_dir)
audioclip = videoclip.audio
audioclip.write_audiofile(video_dir.strip('mp4')+'wav')
video = cv2.VideoCapture(video_dir)
if not os.path.exists(beat_dir):
time_2 = time.time()
videoclip = VideoFileClip(video_dir)
audioclip = videoclip.audio
beat_activation = RNNBeatProcessor()(video_dir.strip('mp4')+'wav')
processor = DBNBeatTrackingProcessor(fps=100)
beats = processor(beat_activation)
frames_at_beat = (beats/audioclip.duration*video.get(cv2.CAP_PROP_FRAME_COUNT)).astype(int)
print('extracting beat sequence elapse:', round(time.time()-time_2, 2), 's')
np.save(beat_dir, frames_at_beat)
frames_at_beat = np.load(beat_dir).tolist()
##########################################
dance_primitives_dir = '.\\danceprimitives_trial'
if not os.path.exists(dance_primitives_dir):
os.mkdir(dance_primitives_dir)
motion_index = len(os.listdir(dance_primitives_dir))
for i in range(len(frames_at_beat)-1):
motion_dir = os.path.join(dance_primitives_dir, '{0}'.format(str(motion_index).zfill(5)))
if not os.path.exists(motion_dir):
os.mkdir(motion_dir)
start = frames_at_beat[i]
end =frames_at_beat[i+1]
dance_primitive = np.empty((0, 17*3)) # for motion control
#dance_primitive_norm = np.empty((0, 17*3)) # for motion clustering
video.set(1, start)
jump_flag = 0
frame = 0
with torch.no_grad():
time_start = time.time()
while True:
current_frame = video.get(cv2.CAP_PROP_POS_FRAMES)
ret_val, raw_image = video.read()
if current_frame == end:
break
##using yolo to get human bounding box
input_img = raw_image.copy()
detections = detector.predict_single(input_img)
if detections is None or detections.size()[0] == 0:
jump_flag = 1
break
last_conf = 0
for i, (x1_pred, y1_pred, x2_pred, y2_pred, conf, cls_conf, cls_pred) in enumerate(detections):
if conf.item() > last_conf:
x1 = max(int(round(x1_pred.item())) - 40, 0)
x2 = min(int(round(x2_pred.item())) + 40, input_img.shape[1]-1)
y1 = max(int(round(y1_pred.item())) - 20, 0)
y2 = min(int(round(y2_pred.item())) + 20, input_img.shape[0]-1) #for getting a larger bounding box to cover the full body, in order to get more accurate pose
last_conf = conf.item()
img_patch = (input_img[y1:y2, x1:x2, ::-1]).copy().astype(np.float32)
##using ResPoseNet to get 3D human pose
input_patch = cv2.resize(img_patch,(cfg.input_shape))
input_patch = transform(input_patch).unsqueeze(0)
coord_out = tester.model(input_patch).cpu().numpy() #dimention: 1 X 18 X 3, where '3' refers to x, z, y in sequence.
#show_pose(input_patch, img_patch, coord_out, skeleton, motion_dir, frame)
coord_out_resize = coord_out * np.array([img_patch.shape[1]/cfg.input_shape[1], img_patch.shape[0]/cfg.input_shape[0], 1]) #transform to original scale
coord_out = coord_out_resize[:, :-1, :] # neglect the key point for "throx"
#coord_out_norm = (coord_out-np.mean(coord_out, axis=1))/np.std(coord_out, axis=1)
dance_primitive = np.vstack((dance_primitive, np.reshape(coord_out[0], -1)))
#dance_primitive_norm = np.vstack((dance_primitive_norm, np.reshape(coord_out_norm[0], -1)))
frame += 1
print('Processing Time Elapse:', round(time.time()-time_start,2), 's')
if jump_flag == 1:
continue
#norm_sample = np.empty((0, 17*3))
#num_sample = 10
#print(dance_primitive_norm.shape[0])
#sample_step = (dance_primitive_norm.shape[0]-1)/(num_sample-1)
#for i in range(num_sample):
# norm_sample = np.vstack((norm_sample, dance_primitive_norm[round(i * sample_step)]))
#print(norm_sample.shape)
print(dance_primitive.shape)
#np.save(os.path.join(motion_dir, 'dance_motion_normlized_'+ str(motion_index)), norm_sample)
np.save(os.path.join(motion_dir, 'dance_motion_'+ str(motion_index)), dance_primitive)
motion_index+=1
###########################################
sys.exit()
video.set(1, interval[0])
frame=0
next_beat = 0
last_beat = 0
num_beat = 0
num_frame_between_beats = []
with torch.no_grad():
while True:
time_start = time.time()
current_frame = video.get(cv2.CAP_PROP_POS_FRAMES)
ret_val, raw_image = video.read()
if current_frame == interval[1]:
break
input_img = raw_image.copy()
##using yolo to get human bounding box
detections = detector.predict_single(input_img)
# if not detections.cpu().numpy().all():
# detections = (0,0,input_img.shape[1],input_img.shape[0],1,1)
# print('not detected')
if detections is None:
detections = np.array([[0,0,input_img.shape[1],input_img.shape[0],1,1,1]])
print('not detected')
elif detections.size()[0] == 0:
detections = np.array([[0,0,input_img.shape[1],input_img.shape[0],1,1,1]])
print('not detected')
last_conf = 0
last_last_conf = 0
for i, (x1_pred, y1_pred, x2_pred, y2_pred, conf, cls_conf, cls_pred) in enumerate(detections):
if conf.item() > last_conf:
x1 = int(round(x1_pred.item())) - 40
x2 = int(round(x2_pred.item())) + 40
y1 = int(round(y1_pred.item())) - 20
y2 = int(round(y2_pred.item())) + 20 #for getting a larger bounding box to cover the full body, in order to get more accurate pose
last_last_conf = last_conf
last_conf = conf.item()
print(last_conf, last_last_conf)
if last_last_conf != 0:
sys.exit()
#print(x1, x2, y1, y2, last_conf)
img_patch = (input_img[y1:y2, x1:x2, ::-1]).copy().astype(np.float32)
input_patch = cv2.resize(img_patch,(cfg.input_shape))
input_patch = transform(input_patch).unsqueeze(0)
coord_out = tester.model(input_patch)
print('Running model time:',round(time.time()-time_start,2),'s')
motion['frame'][frame] = {}
if frame+interval[0] in frames_at_beat:
motion['frame'][frame]['next_beat'] = 0
motion['frame'][frame]['last_beat'] = 0
#frames_at_beat.remove(frame)
next_beat = frames_at_beat.index(frame+interval[0]) + 1
last_beat = frames_at_beat.index(frame+interval[0])
num_beat += 1
num_frame_between_beats.append(frames_at_beat[next_beat] - frames_at_beat[last_beat])
print('Record key frame with beat:', current_frame)
else:
motion['frame'][frame]['next_beat'] = frames_at_beat[next_beat] - (frame+interval[0])
motion['frame'][frame]['last_beat'] = (frame+interval[0]) - frames_at_beat[last_beat]
coord_out = coord_out.cpu().numpy()
coord_out_resize = coord_out * np.array([img_patch.shape[1]/cfg.input_shape[1], img_patch.shape[0]/cfg.input_shape[0], 1])
for idx in range(coord_out_resize.shape[1]-1):
motion['frame'][frame][idx]=(coord_out_resize[0][idx][0].item(), coord_out_resize[0][idx][2].item(), coord_out_resize[0][idx][1].item())
vis = True
vis_3d = False
if vis:
tmpimg = input_patch[0].cpu().numpy()
tmpimg = tmpimg * np.array(cfg.pixel_std).reshape(3,1,1) + np.array(cfg.pixel_mean).reshape(3,1,1)
tmpimg = (tmpimg).astype(np.uint8)
tmpimg = tmpimg[::-1, :, :]
tmpimg = np.transpose(tmpimg,(1,2,0)).copy()
tmpkps = np.zeros((3,18))
tmpkps[:2,:] = coord_out[0,:,:2].transpose(1,0) / cfg.output_shape[0] * cfg.input_shape[0]
tmpkps[2,:] = 1
tmpimg = vis_keypoints(tmpimg, tmpkps, skeleton)
tmpimg = cv2.resize(tmpimg,(img_patch.shape[1],img_patch.shape[0]))
file_name = pose_save_dir+'\\{0}.png'.format(str(frame).zfill(4))
cv2.imwrite(file_name, tmpimg)
if vis_3d:
#coord_out = coord_out.cpu().numpy()
#coord_out = coord_out * np.array([img_patch.shape[1]/cfg.input_shape[1], img_patch.shape[0]/cfg.input_shape[0], 1])
pred=coord_out_resize.squeeze() #remove first batch dimension
ax=plt.subplot('121',projection='3d')
plt.axis('off')
show3D_pose(pred,ax,skeleton,radius=40)
file_name = pose_save_dir + '\\{0}.png'.format(str(frame).zfill(4))
plt.savefig(file_name)
# cv2.imwrite(file_name, tmpimg)
frame+=1
print('Processing Frame:',round(time.time()-time_start,2),'s')
motion['feature']['fpb'] = np.mean(num_frame_between_beats)
if REDU:
motion_base[len(motion_base)-1] = motion
else:
motion_base[len(motion_base)] = motion
#with open(motion_base_dir, 'w') as f:
# json.dump(motion_base, f)
print('done with', num_beat + 1, 'beats! (This should be even for a normal dance)')
print('num_frame between beats:')
print(num_frame_between_beats)
def __init__(self):
self.pa = pyaudio.PyAudio()
self.c_count = 0
using_callback = True
self.buffer = collections.deque(maxlen=self.RATE * 14)
self.rnn = RNNBeatProcessor(online=True, nn_files=[BEATS_LSTM[0]])
self.act_proc = DBNBeatTrackingProcessor(fps=100,
min_bpm=80.0,
max_bpm=180.0)
self.dcp = DeepChromaProcessor()
self.decode = DeepChromaChordRecognitionProcessor()
self.start_current_time = None
self.beep_count = 0
source_path = 'tool'
style_name = 'test_midi_folder'
self.test = InstScheduler(FoxDot.lib.Clock, source_path)
self.test.AddMidiFolder(style_name)
self.test.Live_event(
) # Online random playing event determined by prosperity function
self.test.set_tempo_pattern(
4, 4
) # if the meta file is exist, calling this routine is not required
if using_callback:
self.stream = self.pa.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=True,
frames_per_buffer=self.CHUNK,
stream_callback=self.callback)
print(self.pa.get_default_output_device_info())
print(self.pa.get_default_input_device_info())
self.t_start = time.time()
beepsnd, _ = librosa.load('block.wav', sr=None)
out1 = (beepsnd).tostring()
#print(beepsnd.size, len(out1))
self.beepsnd = out1
self.Flag = False
while self.stream.is_active():
if len(self.buffer) == self.RATE * 14:
print('14 sec')
print(self.time_info)
print(time.time() - self.t_start)
self.tmp = np.array(self.buffer)
self.buffer.clear()
print(time.time() - self.t_start)
chroma_thread = threading.Thread(target=self.chroma_rec,
args=())
chroma_thread.start()
#chord = chroma_thread.run()
tmp2 = self.rnn(self.tmp)
# tmp2 = librosa.onset.onset_strength(tmp,sr=self.RATE, hop_length = int(self.RATE / 100),max_size=1,aggregate=np.median, n_mels=256)
# tmp2 /= np.max(tmp2)
#t_axes = librosa.frames_to_time(np.arange(len(tmp2)),sr=self.RATE)
t_proc = time.time() - self.t_start
print(t_proc)
tmp3_2 = self.act_proc(tmp2)
tmp3_1 = 60 / np.mean(np.diff(tmp3_2))
# print(tmp3)
#tmp3_1,tmp3_2 = librosa.beat.beat_track(onset_envelope=tmp2, sr=self.RATE)
print('tempo is %f' % tmp3_1)
print('beat is ', tmp3_2)
t_proc = time.time() - self.t_start
chroma_thread.join()
print(t_proc)
t = threading.Timer(60. / tmp3_1 - t_proc, self.flagit, ())
t.daemon = True
t.start()
print(int(tmp3_1))
self.test.StartInTime(
np.mean(np.diff(tmp3_2)) * 4 - (14 - tmp3_2[-1]) -
t_proc, int(tmp3_1))
break
# self.stream.write(self.beepsnd)
print(time.time() - self.t_start)
else:
time.sleep(0.001)
while (1):
time.sleep(0.01)
else:
self.stream = self.pa.open(format=self.FORMAT,
channels=self.CHANNELS,
rate=self.RATE,
input=True,
output=True,
frames_per_buffer=self.CHUNK)
self.t_start = time.time()
self.loop()
"""kwargs = dict(
fps = 100,
correct = True,
infile = 'C:\\Users\\lenovo\\Desktop\\dance_videos\\better_when_im_dancing.wav',
outfile = 'C:\\Users\\lenovo\\Desktop\\dance_videos\\beats.txt',
max_bpm = 170,
min_bpm = 60,
#nn_files = [BEATS_LSTM[0]],
transition_lambda = 100,
num_frames = 1,
online = False,
verbose = 0
)
def beat_callback(beats, output=None):
if len(beats) > 0:
# Do something with the beat (for now, just print the array to stdout)
print(beats)
in_processor = RNNBeatProcessor(**kwargs)
beat_processor = DBNBeatTrackingProcessor(**kwargs)
out_processor = [beat_processor, write_beats]
processor = IOProcessor(in_processor, out_processor)
#process_offline(processor, **kwargs)
processor.process('C:\\Users\\lenovo\\Desktop\\dance_videos\\better_when_im_dancing.wav')
"""
act = RNNBeatProcessor()(
'C:\\Users\\lenovo\\Desktop\\dance_videos\\better_when_im_dancing.wav')
proc = DBNBeatTrackingProcessor(fps=100)
beat = proc(act)
write_beats(beat, 'C:\\Users\\lenovo\\Desktop\\dance_videos\\beats.txt')
