def main():
userWantNotToQuit = True
models = f.loadModels()
while userWantNotToQuit:
print("Currently stored models:")
for i in range(len(models)):
print("Nr. " +
str(i +
1) +
": " +
str(models[i].name) +
" | Frames:\t" +
str(len(models[i].features)) +
" | Matches:\t" +
str(models[i].matches) +
" | Influenced by:\t" +
str(models[i].influencedBy) +
" | Threshold:\t" +
str(models[i].threshold) +
" | Score:\t" +
str(models[i].score) +
" | Loaded:\t" +
str(models[i].loaded))
print()
printCommands()
isUserInputWrong = True
while isUserInputWrong:
userInput = raw_input("What do you want to do? ")
if userInput == "a":
userInput = raw_input("Which model do you want to activate? ")
try:
selectedModel = int(userInput) - 1
if selectedModel < len(models):
if selectedModel >= 0:
models[selectedModel].activate()
f.storeSameModel(models[selectedModel])
isUserInputWrong = False
else:
print("There are no models with a number smaller or equal 0.")
else:
print("There is no model with such a high number.")
except ValueError:
print("That was not a number")
elif userInput == "d":
userInput = raw_input("Which model do you want to deactivate? ")
try:
selectedModel = int(userInput) - 1
if selectedModel < len(models):
if selectedModel >= 0:
models[selectedModel].deactivate()
f.storeSameModel(models[selectedModel])
isUserInputWrong = False
else:
print("There are no models with a number smaller or equal 0.")
else:
print("There is no model with such a high number.")
except ValueError:
print("That was not a number")
elif userInput == "del":
userInput = raw_input("Which model do you want to delete? ")
try:
selectedModel = int(userInput) - 1
if selectedModel < len(models):
if selectedModel >= 0:
os.remove(conf.MODELS_DIR + "/" + models[selectedModel].name)
models = f.loadModels()
isUserInputWrong = False
else:
print("There are no models with a number smaller or equal 0.")
else:
print("There is no model with such a high number.")
except ValueError:
print("That was not a number")
elif userInput == "r":
userInput = raw_input("Which model do you want to rename? ")
try:
selectedModel = int(userInput) - 1
if selectedModel < len(models):
if selectedModel >= 0:
oldModelName = models[selectedModel].name
newName = raw_input("Which name should this model have?")
models[selectedModel].name = newName
f.storeModel(models[selectedModel])
os.remove(conf.MODELS_DIR + "/" + oldModelName)
isUserInputWrong = False
else:
print("There are no models with a number smaller or equal 0.")
else:
print("There is no model with such a high number.")
except ValueError:
print("That was not a number")
elif userInput == "c":
userInput = raw_input("For which model do you want to change the path to the script? ")
try:
selectedModel = int(userInput) - 1
if selectedModel < len(models):
if selectedModel >= 0:
oldScriptPath = models[selectedModel].script
newPath = raw_input("Where is the script? (Current script is at " + oldScriptPath + ". ")
while os.path.isfile(newPath) == False:
print("It seems that there is no script at " + newPath)
newPath = raw_input("Where is the script? (Current script is at " + oldScriptPath + ". ")
models[selectedModel].script = newPath
f.storeSameModel(models[selectedModel])
isUserInputWrong = False
else:
print("There are no models with a number smaller or equal 0.")
else:
print("There is no model with such a high number.")
except ValueError:
print("That was not a number")
elif userInput == "h":
isUserInputWrong = False
printCommands()
elif userInput == "q":
isUserInputWrong = False
userWantNotToQuit = False
else:
print("That was not a valid input.")
def main():
global models
global modelThreshold
global modelScore
print("")
print("This script can only process conf.CHUNK (currently: " + str(conf.CHUNK) +
") frames per loop so if the file contains a number of frames which is not divisible by conf.CHUNK the last few frames are dropped")
model = []
wavenumber = 1
fileName, modelName, optimalFrames, scriptpath = interactions.getTrainParameters()
beginning = time.time()
# do it while there are wave files
wf = []
while os.path.isfile(str(fileName) + "/" + str(wavenumber) + ".wav"):
wf.append((wave.open(str(fileName) + "/" + str(wavenumber) + ".wav"), wavenumber))
print("File " + str(fileName) + "/" + str(wavenumber) +
".wav found.")
wavenumber += 1
for i in wf:
model.append(preprocess(i))
for i in wf:
i[0].close()
wavenumber -= 1
print("Processed " + str(wavenumber) + " files in " + str(time.time() - beginning) + " seconds, minimalize them.")
if model != []:
data = []
for i in range(wavenumber):
data.append(
(model[i],
optimalFrames))
beginning = time.time()
f.clearTmpFolder()
pool = multiprocessing.Pool(processes=4)
result = pool.map(f.minimalizeAndCalcThreshold, data)
minimalizedRecords = []
calculatedThresholds = []
for i in result:
minimalizedRecords.append(i[0])
calculatedThresholds.append(i[1])
zeroFrame = np.zeros(conf.FEATURES_PER_FRAME, dtype=np.float64)
models = []
for i in range(len(minimalizedRecords)):
features = copy.deepcopy(minimalizedRecords[i])
tmpFeatures = [copy.deepcopy(zeroFrame) for number in range(optimalFrames)]
tmpCounter = [0 for number in range(optimalFrames)]
counter = 0.
posCounter = [0 for number in range(len(minimalizedRecords))]
# for every frame in this record try if we find mergable frames
for h in range(optimalFrames):
# we try all recordings
for j in range(len(minimalizedRecords)):
if f.compare(minimalizedRecords[j][h], features[h]) < calculatedThresholds[i][h]:
tmpFeatures[h] += minimalizedRecords[j][h]
tmpCounter[h] += 1
for h in range(optimalFrames):
tmpFeatures[h] = np.divide(tmpFeatures[h], tmpCounter[h])
counter += tmpCounter[h]
counter /= optimalFrames
models.append(modelImport.Model(tmpFeatures, calculatedThresholds[i], modelName, tmpCounter, scriptpath))
print()
print("Computed the models in " + str(time.time() - beginning) + " seconds. Compute their score.")
print()
beginning = time.time()
data = []
for i in range(len(models)):
data.append((models[i], fileName, i))
pool = multiprocessing.Pool(processes=4)
pool.map(q.qualityCheck, data)
models = f.loadModels(tmp=True)
print("Computed the scores in " + str(time.time() - beginning) + " seconds.")
print()
for i in range(len(models)):
print("Model Nr:\t" +
str(i +
1) +
" | Frames:\t" +
str(len(models[i].features)) +
" | Matches:\t" +
str(models[i].matches) +
" | Influenced by:\t" +
str(models[i].influencedBy) +
" | Threshold:\t" +
str(models[i].threshold) +
" | Score:\t" +
str(models[i].score))
# get the model number and substract 1 because list indexing starts
# with 0
modelNumber = interactions.getModelNumber(len(models)+1) - 1
print("You selected Model " + str(modelNumber) + " with " + str(models[modelNumber].matches) + " Matches and a Score of: " + str(models[modelNumber].score))
f.storeModel(models[modelNumber])