def startHL():
# Loop over the timestamps coming from HEARING & LANGUAGE
while True:
time.sleep(5) # Wait 5 seconds to prevent aggressive loop
hearing_timestamps = HearingMemoryUtil.get_timestamps(
) # Get hearing timestamps starting from 0th line
if not hearing_timestamps:
continue
language_timestamps = LanguageMemoryUtil.get_timestamps(
) # Get language timestamps starting from 0th line
if not language_timestamps:
continue
# Cheking all possible combinations for overlapping --- http://stackoverflow.com/questions/35644301/checking-two-time-intervals-are-overlapping-or-not
for (i1, interval1), (i2, interval2) in itertools.product(
enumerate(hearing_timestamps),
enumerate(language_timestamps)
): # Cartesian product of enumareted input iterables
if MapperStarters.overlap(
interval1, interval2
): # If interval1 and interval2 is overlapping
MapperUtil.add_pair(interval1['starting_time'],
interval2['starting_time'],
"H2L") # Write a H2L pair
MapperUtil.add_pair(interval2['starting_time'],
interval1['starting_time'],
"L2H") # Write a L2H pair
def startHL(): # Loop over the timestamps coming from HEARING & LANGUAGE while True: time.sleep(5) # Wait 5 seconds to prevent aggressive loop hearing_timestamps = HearingMemoryUtil.get_timestamps() # Get hearing timestamps starting from 0th line if not hearing_timestamps: continue language_timestamps = LanguageMemoryUtil.get_timestamps() # Get language timestamps starting from 0th line if not language_timestamps: continue # Cheking all possible combinations for overlapping --- http://stackoverflow.com/questions/35644301/checking-two-time-intervals-are-overlapping-or-not for (i1,interval1),(i2,interval2) in itertools.product(enumerate(hearing_timestamps),enumerate(language_timestamps)): # Cartesian product of enumareted input iterables if MapperStarters.overlap(interval1,interval2): # If interval1 and interval2 is overlapping MapperUtil.add_pair(interval1['starting_time'], interval2['starting_time'], "H2L") # Write a H2L pair MapperUtil.add_pair(interval2['starting_time'], interval1['starting_time'], "L2H") # Write a L2H pair
def start():
pairs = MapperUtil.get_allpairs() # Get pairs starting from 0th line
if not pairs:
print("No pairs found.")
sys.exit()
p = pyaudio.PyAudio() # Create a PyAudio session
# Create a stream
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
output=True)
#H2V_cursor = NeuralNetUtil.get_neurons("H2V")
elmH2V = None
# Loop over the pairs coming from CROSSMODAL
for pair in pairs:
#time.sleep(0.5) # Wait 0.5 seconds to prevent aggressive loop
print pair
if pair['direction'] == "H2V":
print "____________________________________________________________\n"
print pair['timestamp1']
hearing_memory = HearingMemoryUtil.get_memory(
pair['timestamp1'])
hearing_memory = hearing_memory.next()['data']
#print hearing_memory.next()['data']
#chunky_array = numpy.fromstring(hearing_memory.next()['data'], 'int16')
#print chunky_array
stream.write(hearing_memory)
numpy_audio = numpy.fromstring(hearing_memory, numpy.uint8)
#print numpy_audio
print "Audio: ", numpy_audio.shape
#print numpy.transpose(numpy_audio.reshape((numpy_audio.shape[0],1))).shape
vision_memory = VisionMemoryUtil.get_memory(pair['timestamp2'])
vision_memory = vision_memory.next()
frame_amodal = numpy.fromstring(vision_memory['amodal'],
numpy.uint8)
print "Frame Threshold: ", frame_amodal.shape
cv2.imshow("Frame Threshhold", frame_amodal.reshape(360, 640))
cv2.moveWindow("Frame Threshhold", 50, 100)
frame_color = numpy.fromstring(vision_memory['color'],
numpy.uint8)
print "Frame Delta Colored: ", frame_color.shape
cv2.imshow("Frame Delta Colored",
frame_color.reshape(360, 640, 3))
cv2.moveWindow("Frame Delta Colored", 1200, 100)
key = cv2.waitKey(500) & 0xFF
#time.sleep(2.0)
modulo = numpy_audio.shape[0] % RATE
numpy_audio = numpy_audio[:-modulo]
for one_second in numpy.array_split(
numpy_audio, int(numpy_audio.shape[0] / RATE)):
X = numpy.transpose(
one_second.reshape((one_second.shape[0], 1)))
T = numpy.transpose(
frame_amodal.reshape((frame_amodal.shape[0], 1)))
X = X.astype(numpy.float32, copy=False)
T = T.astype(numpy.float32, copy=False)
X[0] = X[0] / X[0].max()
T[0] = T[0] / T[0].max()
print X.shape
print T.shape
if elmH2V is None:
elmH2V = HPELM(X.shape[1], T.shape[1])
if os.path.exists(
os.path.expanduser("~/CerebralCortexH2V.pkl")):
#elmH2V.nnet.neurons = H2V_cursor.next()['neurons']
elmH2V.load(
os.path.expanduser("~/CerebralCortexH2V.pkl"))
else:
elmH2V.add_neurons(100, "sigm")
elmH2V.train(X, T, "LOO")
print elmH2V.predict(X)
cv2.imshow(
">>>PREDICTION<<<",
numpy.transpose(elmH2V.predict(X)).reshape(360, 640))
cv2.moveWindow(">>>PREDICTION<<<", 50, 550)
print elmH2V.nnet.neurons
elmH2V.save(os.path.expanduser("~/CerebralCortexH2V.pkl"))
def start():
pairs = MapperUtil.get_allpairs() # Get pairs starting from 0th line
if not pairs:
print ("No pairs found.")
sys.exit()
p = pyaudio.PyAudio() # Create a PyAudio session
# Create a stream
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
output=True)
#H2V_cursor = NeuralNetUtil.get_neurons("H2V")
elmH2V = None
# Loop over the pairs coming from CROSSMODAL
for pair in pairs:
#time.sleep(0.5) # Wait 0.5 seconds to prevent aggressive loop
print pair
if pair['direction'] == "H2V":
print "____________________________________________________________\n"
print pair['timestamp1']
hearing_memory = HearingMemoryUtil.get_memory(pair['timestamp1'])
hearing_memory = hearing_memory.next()['data']
#print hearing_memory.next()['data']
#chunky_array = numpy.fromstring(hearing_memory.next()['data'], 'int16')
#print chunky_array
stream.write(hearing_memory)
numpy_audio = numpy.fromstring(hearing_memory, numpy.uint8)
#print numpy_audio
print "Audio: ",numpy_audio.shape
#print numpy.transpose(numpy_audio.reshape((numpy_audio.shape[0],1))).shape
vision_memory = VisionMemoryUtil.get_memory(pair['timestamp2'])
vision_memory = vision_memory.next()
frame_amodal = numpy.fromstring(vision_memory['amodal'], numpy.uint8)
print "Frame Threshold: ",frame_amodal.shape
cv2.imshow("Frame Threshhold", frame_amodal.reshape(360,640))
cv2.moveWindow("Frame Threshhold",50,100)
frame_color = numpy.fromstring(vision_memory['color'], numpy.uint8)
print "Frame Delta Colored: ",frame_color.shape
cv2.imshow("Frame Delta Colored", frame_color.reshape(360,640,3))
cv2.moveWindow("Frame Delta Colored",1200,100)
key = cv2.waitKey(500) & 0xFF
#time.sleep(2.0)
modulo = numpy_audio.shape[0] % RATE
numpy_audio = numpy_audio[:-modulo]
for one_second in numpy.array_split(numpy_audio, int(numpy_audio.shape[0] / RATE)):
X = numpy.transpose(one_second.reshape((one_second.shape[0],1)))
T = numpy.transpose(frame_amodal.reshape((frame_amodal.shape[0],1)))
X = X.astype(numpy.float32, copy=False)
T = T.astype(numpy.float32, copy=False)
X[0] = X[0] / X[0].max()
T[0] = T[0] / T[0].max()
print X.shape
print T.shape
if elmH2V is None:
elmH2V = HPELM(X.shape[1],T.shape[1])
if os.path.exists(os.path.expanduser("~/CerebralCortexH2V.pkl")):
#elmH2V.nnet.neurons = H2V_cursor.next()['neurons']
elmH2V.load(os.path.expanduser("~/CerebralCortexH2V.pkl"))
else:
elmH2V.add_neurons(100, "sigm")
elmH2V.train(X, T, "LOO")
print elmH2V.predict(X)
cv2.imshow(">>>PREDICTION<<<", numpy.transpose(elmH2V.predict(X)).reshape(360,640))
cv2.moveWindow(">>>PREDICTION<<<",50,550)
print elmH2V.nnet.neurons
elmH2V.save(os.path.expanduser("~/CerebralCortexH2V.pkl"))
