def getSummary(self, avianz_batch, CNN=False, CNNmodel=None):
autoSegNum = 0
autoSegCT = [[] for i in range(len(self.calltypes))]
ws = WaveletSegment.WaveletSegment()
TP = FP = TN = FN = 0
for root, dirs, files in os.walk(self.testDir):
for file in files:
wavFile = os.path.join(root, file)
if file.lower().endswith('.wav') and os.stat(wavFile).st_size != 0 and \
file + '.tmpdata' in files and file[:-4] + '-res' + str(float(self.window)) + 'sec.txt' in files:
autoSegCTCurrent = [[] for i in range(len(self.calltypes))]
avianz_batch.filename = os.path.join(root, file)
avianz_batch.loadFile([self.filtname], anysound=False)
duration = int(
np.ceil(
len(avianz_batch.audiodata) /
avianz_batch.sampleRate))
for i in range(len(self.calltypes)):
ctsegments = self.findCTsegments(
avianz_batch.filename, i)
post = Segment.PostProcess(
configdir=self.configdir,
audioData=avianz_batch.audiodata,
sampleRate=avianz_batch.sampleRate,
tgtsampleRate=self.sampleRate,
segments=ctsegments,
subfilter=self.currfilt['Filters'][i],
CNNmodel=CNNmodel,
cert=50)
post.wind()
if CNN and CNNmodel:
post.CNN()
if 'F0' in self.currfilt['Filters'][
i] and 'F0Range' in self.currfilt['Filters'][i]:
if self.currfilt['Filters'][i]["F0"]:
print("Checking for fundamental frequency...")
post.fundamentalFrq()
post.joinGaps(
maxgap=self.currfilt['Filters'][i]['TimeRange'][3])
post.deleteShort(minlength=self.currfilt['Filters'][i]
['TimeRange'][0])
if post.segments:
for seg in post.segments:
autoSegCTCurrent[i].append(seg[0])
autoSegCT[i].append(seg[0])
autoSegNum += 1
# back-convert to 0/1:
det01 = np.zeros(duration)
for i in range(len(self.calltypes)):
for seg in autoSegCTCurrent[i]:
det01[int(seg[0]):int(seg[1])] = 1
# get and parse the agreement metrics
GT = self.loadGT(
os.path.join(
root, file[:-4] + '-res' +
str(float(self.window)) + 'sec.txt'), duration)
_, _, tp, fp, tn, fn = ws.fBetaScore(GT, det01)
TP += tp
FP += fp
TN += tn
FN += fn
# Summary
total = TP + FP + TN + FN
if total == 0:
print("ERROR: failed to find any testing data")
return
if TP + FN != 0:
recall = TP / (TP + FN)
else:
recall = 0
if TP + FP != 0:
precision = TP / (TP + FP)
else:
precision = 0
if TN + FP != 0:
specificity = TN / (TN + FP)
else:
specificity = 0
accuracy = (TP + TN) / (TP + FP + TN + FN)
if CNN:
self.outfile.write(
"\n\n-- Wavelet Pre-Processor + CNN detection summary --\n")
else:
self.outfile.write(
"\n-- Wavelet Pre-Processor detection summary --\n")
self.outfile.write(
"TP | FP | TN | FN seconds:\t %.2f | %.2f | %.2f | %.2f\n" %
(TP, FP, TN, FN))
self.outfile.write("Specificity:\t\t%.2f %%\n" % (specificity * 100))
self.outfile.write("Recall (sensitivity):\t%.2f %%\n" % (recall * 100))
self.outfile.write("Precision (PPV):\t%.2f %%\n" % (precision * 100))
self.outfile.write("Accuracy:\t\t%.2f %%\n\n" % (accuracy * 100))
self.outfile.write("Manually labelled segments:\t%d\n" %
(self.manSegNum))
for i in range(len(self.calltypes)):
self.outfile.write("Auto suggested \'%s\' segments:\t%d\n" %
(self.calltypes[i], len(autoSegCT[i])))
self.outfile.write("Total auto suggested segments:\t%d\n\n" %
(autoSegNum))
if CNN:
text = "Wavelet Pre-Processor + CNN detection summary\n\n\tTrue Positives:\t%d seconds (%.2f %%)\n\tFalse Positives:\t%d seconds (%.2f %%)\n\tTrue Negatives:\t%d seconds (%.2f %%)\n\tFalse Negatives:\t%d seconds (%.2f %%)\n\n\tSpecificity:\t%.2f %%\n\tRecall:\t\t%.2f %%\n\tPrecision:\t%.2f %%\n\tAccuracy:\t%.2f %%\n" \
% (TP, TP * 100 / total, FP, FP * 100 / total, TN, TN * 100 / total, FN, FN * 100 / total,
specificity * 100, recall * 100, precision * 100, accuracy * 100)
else:
text = "Wavelet Pre-Processor detection summary\n\n\tTrue Positives:\t%d seconds (%.2f %%)\n\tFalse Positives:\t%d seconds (%.2f %%)\n\tTrue Negatives:\t%d seconds (%.2f %%)\n\tFalse Negatives:\t%d seconds (%.2f %%)\n\n\tSpecificity:\t%.2f %%\n\tRecall:\t\t%.2f %%\n\tPrecision:\t%.2f %%\n\tAccuracy:\t%.2f %%\n" \
% (TP, TP * 100 / total, FP, FP * 100 / total, TN, TN * 100 / total, FN, FN * 100 / total,
specificity * 100, recall * 100, precision * 100, accuracy * 100)
return text
def detectFile(self, speciesStr, filters):
""" Actual worker for a file in the detection loop.
Does not return anything - for use with external try/catch
"""
# Segment over pages separately, to allow dealing with large files smoothly:
# TODO: page size fixed for now
samplesInPage = 900 * 16000
# (ceil division for large integers)
numPages = (self.datalength - 1) // samplesInPage + 1
# Actual segmentation happens here:
for page in range(numPages):
print("Segmenting page %d / %d" % (page + 1, numPages))
start = page * samplesInPage
end = min(start + samplesInPage, self.datalength)
thisPageLen = (end - start) / self.sampleRate
if thisPageLen < 2 and self.method != "Click":
print("Warning: can't process short file ends (%.2f s)" %
thisPageLen)
continue
# Process
if speciesStr == "Any sound":
# Create spectrogram for median clipping etc
if not hasattr(self, 'sp'):
self.sp = SignalProc.SignalProc(
self.config['window_width'], self.config['incr'])
self.sp.data = self.audiodata[start:end]
self.sp.sampleRate = self.sampleRate
_ = self.sp.spectrogram(window='Hann',
mean_normalise=True,
onesided=True,
multitaper=False,
need_even=False)
self.seg = Segment.Segmenter(self.sp, self.sampleRate)
# thisPageSegs = self.seg.bestSegments()
thisPageSegs = self.seg.medianClip(thr=3.5)
# Post-process
# 1. Delete windy segments
# 2. Merge neighbours
# 3. Delete short segments
print("Segments detected: ", len(thisPageSegs))
print("Post-processing...")
maxgap = int(self.maxgap.value()) / 1000
minlen = int(self.minlen.value()) / 1000
maxlen = int(self.maxlen.value()) / 1000
post = Segment.PostProcess(configdir=self.configdir,
audioData=self.audiodata[start:end],
sampleRate=self.sampleRate,
segments=thisPageSegs,
subfilter={},
cert=0)
if self.wind:
post.wind()
post.joinGaps(maxgap)
post.deleteShort(minlen)
# avoid extra long segments (for Isabel)
post.splitLong(maxlen)
# adjust segment starts for 15min "pages"
if start != 0:
for seg in post.segments:
seg[0][0] += start / self.sampleRate
seg[0][1] += start / self.sampleRate
# attach mandatory "Don't Know"s etc and put on self.segments
self.makeSegments(post.segments)
del self.seg
gc.collect()
else:
if self.method != "Click":
# read in the page and resample as needed
self.ws.readBatch(self.audiodata[start:end],
self.sampleRate,
d=False,
spInfo=filters,
wpmode="new")
data_test = []
click_label = 'None'
for speciesix in range(len(filters)):
print("Working with recogniser:", filters[speciesix])
if self.method != "Click":
# note: using 'recaa' mode = partial antialias
thisPageSegs = self.ws.waveletSegment(speciesix,
wpmode="new")
else:
click_label, data_test, gen_spec = self.ClickSearch(
self.sp.sg, self.filename)
print('number of detected clicks = ', gen_spec)
thisPageSegs = []
# Post-process:
# CNN-classify, delete windy, rainy segments, check for FundFreq, merge gaps etc.
print("Segments detected (all subfilters): ", thisPageSegs)
if not self.testmode:
print("Post-processing...")
# postProcess currently operates on single-level list of segments,
# so we run it over subfilters for wavelets:
spInfo = filters[speciesix]
for filtix in range(len(spInfo['Filters'])):
if not self.testmode:
# TODO THIS IS FULL POST-PROC PIPELINE FOR BIRDS AND BATS
# -- Need to check how this should interact with the testmode
CNNmodel = None
if 'CNN' in spInfo:
if spInfo['CNN'][
'CNN_name'] in self.CNNDicts.keys():
# This list contains the model itself, plus parameters for running it
CNNmodel = self.CNNDicts[spInfo['CNN']
['CNN_name']]
if self.method == "Click":
# bat-style CNN:
model = CNNmodel[0]
thr1 = CNNmodel[5][0]
thr2 = CNNmodel[5][1]
if click_label == 'Click':
# we enter in the cnn only if we got a click
sg_test = np.ndarray(
shape=(np.shape(data_test)[0],
np.shape(data_test[0][0])[0],
np.shape(data_test[0][0])[1]),
dtype=float)
spec_id = []
print('Number of file spectrograms = ',
np.shape(data_test)[0])
for j in range(np.shape(data_test)[0]):
maxg = np.max(data_test[j][0][:])
sg_test[
j][:] = data_test[j][0][:] / maxg
spec_id.append(data_test[j][1:3])
# CNN classification of clicks
x_test = sg_test
test_images = x_test.reshape(
x_test.shape[0], 6, 512, 1)
test_images = test_images.astype('float32')
# recovering labels
predictions = model.predict(test_images)
# predictions is an array #imagesX #of classes which entries are the probabilities for each class
# Create a label (list of dicts with species, certs) for the single segment
print('Assessing file label...')
label = self.File_label(predictions,
thr1=thr1,
thr2=thr2)
print('CNN detected: ', label)
if len(label) > 0:
# Convert the annotation into a full segment in self.segments
thisPageStart = start / self.sampleRate
self.makeSegments([
thisPageStart, thisPageLen, label
])
else:
# do not create any segments
print("Nothing detected")
else:
# bird-style CNN and other processing:
post = Segment.PostProcess(
configdir=self.configdir,
audioData=self.audiodata[start:end],
sampleRate=self.sampleRate,
tgtsampleRate=spInfo["SampleRate"],
segments=thisPageSegs[filtix],
subfilter=spInfo['Filters'][filtix],
CNNmodel=CNNmodel,
cert=50)
print("Segments detected after WF: ",
len(thisPageSegs[filtix]))
if self.wind and self.useWindF(
spInfo['Filters'][filtix]['FreqRange']
[0], spInfo['Filters'][filtix]['FreqRange']
[1]):
post.wind()
if CNNmodel:
print('Post-processing with CNN')
post.CNN()
if 'F0' in spInfo['Filters'][
filtix] and 'F0Range' in spInfo[
'Filters'][filtix]:
if spInfo['Filters'][filtix]["F0"]:
print(
"Checking for fundamental frequency..."
)
post.fundamentalFrq()
post.joinGaps(maxgap=spInfo['Filters'][filtix]
['TimeRange'][3])
post.deleteShort(minlength=spInfo['Filters']
[filtix]['TimeRange'][0])
# adjust segment starts for 15min "pages"
if start != 0:
for seg in post.segments:
seg[0][0] += start / self.sampleRate
seg[0][1] += start / self.sampleRate
# attach filter info and put on self.segments:
self.makeSegments(post.segments,
self.species[speciesix],
spInfo["species"],
spInfo['Filters'][filtix])
else:
# TODO: THIS IS testmode. NOT USING ANY BAT STUFF THEN
# I.E. testmode not adapted to bats
post = Segment.PostProcess(
configdir=self.configdir,
audioData=self.audiodata[start:end],
sampleRate=self.sampleRate,
tgtsampleRate=spInfo["SampleRate"],
segments=thisPageSegs[filtix],
subfilter=spInfo['Filters'][filtix],
CNNmodel=None,
cert=50)
# adjust segment starts for 15min "pages"
if start != 0:
for seg in post.segments:
seg[0][0] += start / self.sampleRate
seg[0][1] += start / self.sampleRate
# attach filter info and put on self.segments:
self.makeSegments(post.segments,
self.species[speciesix],
spInfo["species"],
spInfo['Filters'][filtix])
