def filter(self, source_label, dest_label='processed', size=(5, 3)):
'''
Median filter a binary spectrogram
Median-filters spectrogram at self.source_label,
saving it at self.dest_label. For instance,
if used as a step in processing, this function
can be used to update the self.processed spectrogram:
audio.filter(source_label = 'processed', dest_label = 'processed')
Inputs:
source_label (str): label of the class attribute
for the source spectrogram, e.g. self.normalized
dest_label (str): label of the class attribute where
the destination spectrogram should be saved.
size (tuple of ints (x, y)): structure of the filter
'''
source = self.get_spect(source_label)
new_spect = utils.median_filter(source.spect, size=size)
self.set_spect(label=dest_label,
spect=new_spect,
freqs=source.freqs,
times=source.times)
def segmentation(F, M, Mg, L, plot=False):
"""Computes the Foote segmentator.
Parameters
----------
F : np.array((N,M))
Features matrix of N beats x M features.
M : int
Median filter size for the audio features (in beats).
Mg : int
Gaussian kernel size (in beats).
L : int
Median filter size for the adaptive threshold
Return
------
bound_idx : np.array
Array containing the indices of the boundaries.
"""
# Filter
F = utils.median_filter(F, M=M)
# Self Similarity Matrix
S = utils.compute_ssm(F)
# Compute gaussian kernel
G = utils.compute_gaussian_krnl(Mg)
# Compute the novelty curve
nc = utils.compute_nc(S, G)
# Find peaks in the novelty curve
return utils.pick_peaks(nc, L=L, plot=plot)
def filter_activation_matrix(G, R):
"""Filters the activation matrix G, and returns a flattened copy."""
idx = np.argmax(G, axis=1)
max_idx = np.arange(G.shape[0])
max_idx = (max_idx, idx.flatten())
G[:, :] = 0
G[max_idx] = idx + 1
G = np.sum(G, axis=1)
G = utils.median_filter(G[:, np.newaxis], R)
return G.flatten()
def compute_ssm(wav_file, h, ssm_read_pk, is_ismir=False, tonnetz=False):
"""Computes the self similarity matrix from an audio file.
Parameters
----------
wav_file: str
Path to the wav file to be read.
h : float
Hop size.
ssm_read_pk : bool
Whether to read the ssm from a pickle file or not (note: this function
utomatically saves the ssm in a pickle file).
is_ismir : bool
Produce the plots that appear on the ISMIR paper.
tonnetz : bool
Compute tonnetz instead of Chroma features.
Returns
-------
X : np.array((N, N))
Self-similarity matrix
"""
if not ssm_read_pk:
# Read WAV file
logging.info("Reading the WAV file...")
C = utils.compute_audio_chromagram(wav_file, h)
C = utils.median_filter(C, L=9)
if is_ismir:
ismir.plot_chroma(C)
# Compute Tonnetz if needed
F = C
if tonnetz:
F = utils.chroma_to_tonnetz(C)
# Compute the self similarity matrix
logging.info("Computing key-invariant self-similarity matrix...")
X = utils.compute_key_inv_ssm(F, h)
#plt.imshow(X, interpolation="nearest", aspect="auto")
#plt.show()
utils.write_cPickle(wav_file + "-audio-ssm.pk", X)
else:
X = utils.read_cPickle(wav_file + "-audio-ssm.pk")
if is_ismir:
#X = X**2.5
ismir.plot_ssm(X)
ismir.plot_score_examples(X)
return X
def segmentation(X, rank, R, h, niter=300, seed=None):
"""
Gets the segmentation (boundaries and labels) from the factorization
matrices.
Parameters
----------
X: np.array()
Features matrix (e.g. chromagram)
rank: int
Rank of decomposition
R: int
Size of the median filter for activation matrix
niter: int
Number of iterations for k-means
bound_idxs : list
Use previously found boundaries (None to detect them)
Returns
-------
bounds_idx: np.array
Bound indeces found
labels: np.array
Indeces of the labels representing the similarity between segments.
"""
# Filter
X = utils.median_filter(X, M=h)
X = X.T
# Find non filtered boundaries
bound_idxs = None
while True:
if bound_idxs is None:
try:
F, G = cnmf(X, rank, niter=niter, seed=seed)
except:
return np.empty(0), [1]
# Filter G
G = filter_activation_matrix(G.T, R)
if bound_idxs is None:
bound_idxs = np.where(np.diff(G) != 0)[0] + 1
if len(np.unique(bound_idxs)) <= 2:
rank += 1
bound_idxs = None
else:
break
return bound_idxs
img_list_gauss_005.append(utils.gaussian_noise(img_list[i], 0.1))
img_list_gauss_015.append(utils.gaussian_noise(img_list[i], 0.15))
img_list_impulsive_005.append(utils.salt_and_pepper(img_list[i], 0.05))
img_list_impulsive_015.append(utils.salt_and_pepper(img_list[i], 0.15))
# utils.show_image_list(img_list_gauss_005, "Images with gaussian noise! 0.05")
# utils.show_image_list(img_list_gauss_015, "Images with gaussian noise! 0.15")
# utils.show_image_list(img_list_impulsive_005, "Image with salt and pepper noise! 0.05")
# utils.show_image_list(img_list_impulsive_015, "Image with salt and pepper noise! 0.15")
img_list_impulsive = img_list_impulsive + img_list_impulsive_005 + img_list_impulsive_015
img_list_impulsive_median = []
img_list_impulsive_neavf = []
index = 0
for img in img_list_impulsive:
img_list_impulsive_median.append(
utils.median_filter(img_list_impulsive[index]))
img_list_impulsive_neavf.append(NEAVF.NEAVF(img_list_impulsive[index]))
index += 1
# utils.show_image_list(img_list_impulsive_median, "Image with salt and pepper after median filter")
# utils.show_image_list(img_list_impulsive_neavf, "Image with salt and pepper after NEAVF filter")
img_list_gauss = img_list_gauss + img_list_gauss_005 + img_list_gauss_015
img_list_gauss_mean = []
img_list_gauss_neavf = []
index = 0
for img in img_list_gauss:
img_list_gauss_mean.append(utils.mean_filter(img_list_gauss[index]))
img_list_gauss_neavf.append(NEAVF.NEAVF(img_list_gauss[index]))
index += 1
# utils.show_image_list(img_list_gauss_mean, "Image with gauss after mean filter!")
# utils.show_image_list(img_list_gauss_neavf, "Image with gauss after NEAVF filter!")
import utils
if __name__ == "__main__":
lfiles = glob.glob("data/cycling_Auckland/cycling_counts_????.csv")
lfiles.sort()
df_list = []
for f in lfiles:
d = pd.read_csv(f, index_col=0, parse_dates=True)
df_list.append(d)
df = pd.concat(df_list, axis=0)
df = df.loc[:, ["Tamaki Drive EB", "Tamaki Drive WB"]]
Tamaki = df.loc[:, "Tamaki Drive WB"] + df.loc[:, "Tamaki Drive EB"]
Tamaki = Tamaki.loc["2013":"2018-06-01", ]
Tamaki = Tamaki.to_frame(name="Tamaki Drive")
dfc = Tamaki.copy()
dfc.loc[:, "Tamaki Drive, Filtered"] = utils.median_filter(
dfc, varname="Tamaki Drive")
data = dfc.loc["2013":, ["Tamaki Drive, Filtered"]].resample("1D").sum()
holidays_df = pd.DataFrame([], columns=["ds", "holiday"])
ldates = []
lnames = []
for date, name in sorted(
holidays.NZ(prov="AUK", years=np.arange(2013, 2018 + 1)).items()):
ldates.append(date)
lnames.append(name)
ldates = np.array(ldates)
lnames = np.array(lnames)
holidays_df.loc[:, "ds"] = ldates
holidays_df.loc[:, "holiday"] = lnames
holidays_df.loc[:, "holiday"] = holidays_df.loc[:, "holiday"].apply(
lambda x: x.replace(" (Observed)", ""))
def evaluate(
self,
experiment_path: str,
pred_file='hard_predictions_{}.txt',
tag_file='tagging_predictions_{}.txt',
event_file='event_{}.txt',
segment_file='segment_{}.txt',
class_result_file='class_result_{}.txt',
time_ratio=10. / 500,
postprocessing='double',
threshold=None,
window_size=None,
save_seq=False,
sed_eval=True, # Do evaluation on sound event detection ( time stamps, segemtn/evaluation based)
**kwargs):
"""evaluate
:param experiment_path: Path to already trained model using train
:type experiment_path: str
:param pred_file: Prediction output file, put into experiment dir
:param time_resolution: Resolution in time (1. represents the model resolution)
:param **kwargs: Overwrite standard args, please pass `data` and `label`
"""
# Update config parameters with new kwargs
config = torch.load(list(Path(f'{experiment_path}').glob("run_config*"))[0], map_location='cpu')
# Use previous config, but update data such as kwargs
config_parameters = dict(config, **kwargs)
# Default columns to search for in data
config_parameters.setdefault('colname', ('filename', 'encoded'))
model_parameters = torch.load(
glob.glob("{}/run_model*".format(experiment_path))[0],
map_location=lambda storage, loc: storage)
encoder = torch.load(glob.glob(
'{}/run_encoder*'.format(experiment_path))[0],
map_location=lambda storage, loc: storage)
strong_labels_df = pd.read_csv(config_parameters['label'], sep='\t')
# Evaluation is done via the filenames, not full paths
if not np.issubdtype(strong_labels_df['filename'].dtype, np.number):
strong_labels_df['filename'] = strong_labels_df['filename'].apply(
os.path.basename)
if 'audiofilepath' in strong_labels_df.columns: # In case of ave dataset, the audiofilepath column is the main column
strong_labels_df['audiofilepath'] = strong_labels_df[
'audiofilepath'].apply(os.path.basename)
colname = 'audiofilepath' # AVE
else:
colname = 'filename' # Dcase etc.
# Problem is that we iterate over the strong_labels_df, which is ambigious
# In order to conserve some time and resources just reduce strong_label to weak_label format
weak_labels_df = strong_labels_df.groupby(
colname)['event_label'].unique().apply(
tuple).to_frame().reset_index()
if "event_labels" in strong_labels_df.columns:
assert False, "Data with the column event_labels are used to train not to evaluate"
weak_labels_array, encoder = utils.encode_labels(
labels=weak_labels_df['event_label'], encoder=encoder)
dataloader = dataset.getdataloader(
{
'filename': weak_labels_df['filename'].values,
'encoded': weak_labels_array,
},
config_parameters['data'],
batch_size=1,
shuffle=False,
colname=config_parameters[
'colname'] # For other datasets with different key names
)
model = getattr(models, config_parameters['model'])(
inputdim=dataloader.dataset.datadim,
outputdim=len(encoder.classes_),
**config_parameters['model_args'])
model.load_state_dict(model_parameters)
model = model.to(DEVICE).eval()
time_predictions, clip_predictions = [], []
sequences_to_save = []
mAP_pred, mAP_tar = [], []
with torch.no_grad():
for batch in tqdm(dataloader, unit='file', leave=False):
_, target, filenames = batch
clip_pred, pred, _ = self._forward(model, batch)
clip_pred = clip_pred.cpu().detach().numpy()
mAP_tar.append(target.numpy().squeeze(0))
mAP_pred.append(clip_pred.squeeze(0))
pred = pred.cpu().detach().numpy()
if postprocessing == 'median':
if threshold is None:
thres = 0.5
else:
thres = threshold
if window_size is None:
window_size = 1
filtered_pred = utils.median_filter(
pred, window_size=window_size, threshold=thres)
decoded_pred = utils.decode_with_timestamps(
encoder, filtered_pred)
elif postprocessing == 'cATP-SDS':
# cATP-SDS postprocessing uses an "Optimal" configurations, assumes we have a prior
# Values are taken from the Surface Disentange paper
# Classes are (DCASE2018 only)
# ['Alarm_bell_ringing' 'Blender' 'Cat' 'Dishes' 'Dog'
# 'Electric_shaver_toothbrush' 'Frying' 'Running_water' 'Speech'
# 'Vacuum_cleaner']
assert pred.shape[
-1] == 10, "Only supporting DCASE2018 for now"
if threshold is None:
thres = 0.5
else:
thres = threshold
if window_size is None:
window_size = [17, 42, 17, 9, 16, 74, 85, 64, 18, 87]
# P(y|x) > alpha
clip_pred = utils.binarize(clip_pred, threshold=thres)
pred = pred * clip_pred
filtered_pred = np.zeros_like(pred)
# class specific filtering via median filter
for cl in range(pred.shape[-1]):
# Median filtering also applies thresholding
filtered_pred[..., cl] = utils.median_filter(
pred[..., cl],
window_size=window_size[cl],
threshold=thres)
decoded_pred = utils.decode_with_timestamps(
encoder, filtered_pred)
elif postprocessing == 'double':
# Double thresholding as described in
# https://arxiv.org/abs/1904.03841
if threshold is None:
hi_thres, low_thres = (0.75, 0.2)
else:
hi_thres, low_thres = threshold
filtered_pred = utils.double_threshold(pred,
high_thres=hi_thres,
low_thres=low_thres)
decoded_pred = utils.decode_with_timestamps(
encoder, filtered_pred)
elif postprocessing == 'triple':
# Triple thresholding as described in
# Using frame level + clip level predictions
if threshold is None:
clip_thres, hi_thres, low_thres = (0.5, 0.75, 0.2)
else:
clip_thres, hi_thres, low_thres = threshold
clip_pred = utils.binarize(clip_pred, threshold=clip_thres)
# Apply threshold to
pred = clip_pred * pred
filtered_pred = utils.double_threshold(pred,
high_thres=hi_thres,
low_thres=low_thres)
decoded_pred = utils.decode_with_timestamps(
encoder, filtered_pred)
for num_batch in range(len(decoded_pred)):
filename = filenames[num_batch]
cur_pred = pred[num_batch]
cur_clip = clip_pred[num_batch].reshape(1, -1)
# Clip predictions, independent of per-frame predictions
bin_clips = utils.binarize(cur_clip)
# Binarize with default threshold 0.5 For clips
bin_clips = encoder.inverse_transform(
bin_clips.reshape(1,
-1))[0] # 0 since only single sample
# Add each label individually into list
for clip_label in bin_clips:
clip_predictions.append({
'filename': filename,
'event_label': clip_label,
})
# Save each frame output, for later visualization
if save_seq:
labels = weak_labels_df.loc[weak_labels_df['filename']
== filename]['event_label']
to_save_df = pd.DataFrame(pred[num_batch],
columns=encoder.classes_)
# True labels
to_save_df.rename({'variable': 'event'},
axis='columns',
inplace=True)
to_save_df['filename'] = filename
to_save_df['pred_labels'] = np.array(labels).repeat(
len(to_save_df))
sequences_to_save.append(to_save_df)
label_prediction = decoded_pred[num_batch]
for event_label, onset, offset in label_prediction:
time_predictions.append({
'filename': filename,
'event_label': event_label,
'onset': onset,
'offset': offset
})
assert len(time_predictions) > 0, "No outputs, lower threshold?"
pred_df = pd.DataFrame(
time_predictions,
columns=['filename', 'event_label', 'onset', 'offset'])
clip_pred_df = pd.DataFrame(
clip_predictions,
columns=['filename', 'event_label', 'probability'])
test_data_filename = os.path.splitext(
os.path.basename(config_parameters['label']))[0]
if save_seq:
pd.concat(sequences_to_save).to_csv(os.path.join(
experiment_path, 'probabilities.csv'),
index=False,
sep='\t',
float_format="%.4f")
pred_df = utils.predictions_to_time(pred_df, ratio=time_ratio)
if pred_file:
pred_df.to_csv(os.path.join(experiment_path,
pred_file.format(test_data_filename)),
index=False,
sep="\t")
tagging_df = metrics.audio_tagging_results(strong_labels_df, pred_df)
clip_tagging_df = metrics.audio_tagging_results(
strong_labels_df, clip_pred_df)
print("Tagging Classwise Result: \n{}".format(
tabulate(clip_tagging_df,
headers='keys',
showindex=False,
tablefmt='github')))
print("mAP: {}".format(
metrics.mAP(np.array(mAP_tar), np.array(mAP_pred))))
if tag_file:
clip_tagging_df.to_csv(os.path.join(
experiment_path, tag_file.format(test_data_filename)),
index=False,
sep='\t')
if sed_eval:
event_result, segment_result = metrics.compute_metrics(
strong_labels_df, pred_df, time_resolution=1.0)
print("Event Based Results:\n{}".format(event_result))
event_results_dict = event_result.results_class_wise_metrics()
class_wise_results_df = pd.DataFrame().from_dict({
f: event_results_dict[f]['f_measure']
for f in event_results_dict.keys()
}).T
class_wise_results_df.to_csv(os.path.join(
experiment_path, class_result_file.format(test_data_filename)),
sep='\t')
print("Class wise F1-Macro:\n{}".format(
tabulate(class_wise_results_df,
headers='keys',
tablefmt='github')))
if event_file:
with open(
os.path.join(experiment_path,
event_file.format(test_data_filename)),
'w') as wp:
wp.write(event_result.__str__())
print("=" * 100)
print(segment_result)
if segment_file:
with open(
os.path.join(experiment_path,
segment_file.format(test_data_filename)),
'w') as wp:
wp.write(segment_result.__str__())
event_based_results = pd.DataFrame(
event_result.results_class_wise_average_metrics()['f_measure'],
index=['event_based'])
segment_based_results = pd.DataFrame(
segment_result.results_class_wise_average_metrics()
['f_measure'],
index=['segment_based'])
result_quick_report = pd.concat((
event_based_results,
segment_based_results,
))
# Add two columns
tagging_macro_f1, tagging_macro_pre, tagging_macro_rec = tagging_df.loc[
tagging_df['label'] == 'macro'].values[0][1:]
static_tagging_macro_f1, static_tagging_macro_pre, static_tagging_macro_rec = clip_tagging_df.loc[
clip_tagging_df['label'] == 'macro'].values[0][1:]
result_quick_report.loc['Time Tagging'] = [
tagging_macro_f1, tagging_macro_pre, tagging_macro_rec
]
result_quick_report.loc['Clip Tagging'] = [
static_tagging_macro_f1, static_tagging_macro_pre,
static_tagging_macro_rec
]
with open(
os.path.join(
experiment_path,
'quick_report_{}.md'.format(test_data_filename)),
'w') as wp:
print(tabulate(result_quick_report,
headers='keys',
tablefmt='github'),
file=wp)
print("Quick Report: \n{}".format(
tabulate(result_quick_report,
headers='keys',
tablefmt='github')))
def wcal(filename, telluric_name):
tel = fits.getdata(telluric_name)
fTel = tel['trans']
wTel = tel['lam'] * 1E3
cs_tell = interpolate.splrep(wTel, fTel, s=0.0)
hdul = fits.open(filename)
no = len(hdul) - 1
nx = len(hdul[1].data)
# Set starting parameters
x1 = 255
x2 = 511
x3 = 767
# Initialise vectors
for io in range(no):
print('Processing detector {:1}'.format(io + 1))
wlout = np.zeros(nx)
d = hdul[io + 1].data
wlen = d['Wavelength']
# Identify and mask bad pixels
spc = d['Extracted_OPT']
### Two-step filtering to attempt to clean the bad pixels out.
for i in range(5):
spc = median_filter(spc, 128, False)
spc = median_filter(spc, 4, True)
'''
plt.figure(figsize=(12,3), dpi=120)
plt.title("Before")
plt.plot(wlen, spc, c='r', label="input_spectrum")
plt.plot(wTel, fTel*np.nanpercentile(spc, 70), c='k', label="telluric_model")
plt.xlim(wlen[0], wlen[-1])
plt.show()
'''
l1 = wlen[x1]
l2 = wlen[x2]
l3 = wlen[x3]
pars = (l1, l2, l3)
deltas = (1E-3, 1E-3, 1E-3)
### Before feeding to MCMC we do a grid search around the initial params,
### which helps the MCMC converge properly. This takes about as long as
### one of the MCMC chunks below, so it's a good tradeoff.
print("Initialise grid search")
dim1 = np.linspace(l1 - 0.25, l1 + 0.25, 30)
dim2 = np.linspace(l2 - 0.25, l2 + 0.25, 30)
dim3 = np.linspace(l3 - 0.25, l3 + 0.25, 30)
chisq_cube = np.ones((len(dim1), len(dim2), len(dim3)))
for i in range(len(dim1)):
for j in range(len(dim2)):
for k in range(len(dim3)):
theta = (dim1[i], dim2[j], dim3[k])
chisq_cube[i][j][k] = get_logL(theta, cs_tell, spc)
tup = np.unravel_index(chisq_cube.argmax(), chisq_cube.shape)
i, j, k = tup
pars = (dim1[i], dim2[j], dim3[k])
print("MCMC running")
### Here we run a short set of MCMC chains, really refining the solution
### produced by the grid search.
for i in range(3):
ll1, ll2, ll3 = run_emcee(pars, deltas, cs_tell, spc, plot=False)
pars = ll1, ll2, ll3
print(pars)
wlout = get_wl_sol(*pars)
d['WAVELENGTH'] = wlout
hdul[io + 1].data = d
'''
plt.figure(figsize=(12,3), dpi=120)
plt.title("After")
plt.plot(wlout, spc, c='r', label="input_spectrum")
plt.plot(wTel, fTel*np.nanpercentile(spc, 70), c='k', label="telluric_model")
plt.xlim(wlen[0], wlen[-1])
plt.show()
'''
### Write out our updated spectra to a new file.
out_file = filename[:-5] + "_proc.fits"
hdul.writeto(out_file, overwrite=True)
return
