def classifyFolderWrapper(inputFolder, model_type, model_name,
outputMode=False):
if not os.path.isfile(model_name):
raise Exception("Input model_name not found!")
types = ('*.wav', '*.aif', '*.aiff', '*.mp3')
wavFilesList = []
for files in types:
wavFilesList.extend(glob.glob((inputFolder + files)))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print("No WAV files found!")
return
Results = []
for wavFile in wavFilesList:
[Result, P, classNames] = aT.file_classification(wavFile, model_name,
model_type)
Result = int(Result)
Results.append(Result)
if outputMode:
print("{0:s}\t{1:s}".format(wavFile, classNames[Result]))
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results,
bins=numpy.arange(len(classNames) + 1))
for i, h in enumerate(Histogram):
print("{0:20s}\t\t{1:d}".format(classNames[i], h))
def classifyFileWrapper(inputFile, model_type, model_name):
if not os.path.isfile(model_name):
raise Exception("Input model_name not found!")
if not os.path.isfile(inputFile):
raise Exception("Input audio file not found!")
[Result, P, classNames] = aT.file_classification(inputFile, model_name,
model_type)
print("{0:s}\t{1:s}".format("Class", "Probability"))
for i, c in enumerate(classNames):
print("{0:s}\t{1:.2f}".format(c, P[i]))
print("Winner class: " + classNames[int(Result)])
def classifyFolderWrapper(inputFolder, modelType, modelName, outputMode=False):
if not os.path.isfile(modelName):
raise Exception("Input modelName not found!")
if modelType == 'svm':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
compute_beat
] = aT.load_model(modelName)
elif modelType == 'knn':
[
Classifier, MEAN, STD, classNames, mtWin, mtStep, stWin, stStep,
compute_beat
] = aT.load_model_knn(modelName)
PsAll = numpy.zeros((len(classNames), ))
files = "*.wav"
if os.path.isdir(inputFolder):
strFilePattern = os.path.join(inputFolder, files)
else:
strFilePattern = inputFolder + files
wavFilesList = []
wavFilesList.extend(glob.glob(strFilePattern))
wavFilesList = sorted(wavFilesList)
if len(wavFilesList) == 0:
print "No WAV files found!"
return
Results = []
for wavFile in wavFilesList:
[Fs, x] = audioBasicIO.readAudioFile(wavFile)
signalLength = x.shape[0] / float(Fs)
[Result, P,
classNames] = aT.file_classification(wavFile, modelName, modelType)
PsAll += (numpy.array(P) * signalLength)
Result = int(Result)
Results.append(Result)
if outputMode:
print "{0:s}\t{1:s}".format(wavFile, classNames[Result])
Results = numpy.array(Results)
# print distribution of classes:
[Histogram, _] = numpy.histogram(Results,
bins=numpy.arange(len(classNames) + 1))
if outputMode:
for i, h in enumerate(Histogram):
print "{0:20s}\t\t{1:d}".format(classNames[i], h)
PsAll = PsAll / numpy.sum(PsAll)
if outputMode:
fig = plt.figure()
ax = fig.add_subplot(111)
plt.title("Classes percentage " + inputFolder.replace('Segments', ''))
ax.axis((0, len(classNames) + 1, 0, 1))
ax.set_xticks(numpy.array(range(len(classNames) + 1)))
ax.set_xticklabels([" "] + classNames)
ax.bar(numpy.array(range(len(classNames))) + 0.5, PsAll)
plt.show()
return classNames, PsAll
