def run(network):
running = True
ui = uinput.UInput()
while running:
screen.fill((0, 0, 0))
vals = network.activate(image_to_input(get_active_window(), screen))
for (index, weight) in enumerate(vals):
if weight > .47:
#print("PUSHING: " + str(net_mapping[index]))
ui.write(ecodes.EV_KEY, net_mapping[index], 1)
# if(net_mapping[index] == ecodes.KEY_LEFT or net_mapping[index] == ecodes.KEY_RIGHT):
# ui.write(ecodes.EV_KEY, net_mapping[index], 0)
ui.syn()
else:
#print("POPPING: " + str(net_mapping[index]))
ui.write(ecodes.EV_KEY, net_mapping[index], 0)
#ui.write(ecodes.EV_KEY, ecodes.KEY_ENTER, 0)
ui.syn()
#time.sleep(.1)
#ui.write(ecodes.EV_KEY, ecodes.KEY_LEFT, 0)
#ui.write(ecodes.EV_KEY, ecodes.KEY_RIGHT, 0)
#ui.syn()
pygame.display.flip()
def run(network):
running = True
ui = uinput.UInput()
while running:
screen.fill((0,0,0))
vals = network.activate(image_to_input(get_active_window(),screen))
for (index, weight) in enumerate(vals):
if weight > .47:
#print("PUSHING: " + str(net_mapping[index]))
ui.write(ecodes.EV_KEY, net_mapping[index], 1)
# if(net_mapping[index] == ecodes.KEY_LEFT or net_mapping[index] == ecodes.KEY_RIGHT):
# ui.write(ecodes.EV_KEY, net_mapping[index], 0)
ui.syn()
else:
#print("POPPING: " + str(net_mapping[index]))
ui.write(ecodes.EV_KEY, net_mapping[index], 0)
#ui.write(ecodes.EV_KEY, ecodes.KEY_ENTER, 0)
ui.syn()
#time.sleep(.1)
#ui.write(ecodes.EV_KEY, ecodes.KEY_LEFT, 0)
#ui.write(ecodes.EV_KEY, ecodes.KEY_RIGHT, 0)
#ui.syn()
pygame.display.flip()
################################################################################
# Main
################################################################################
net = buildNetwork(network.input_nodes, hidden_nodes, network.output_nodes)
data_set = SupervisedDataSet(network.input_nodes, network.output_nodes)
print("Creating data set...")
for train_file in sys.argv[3:]:
train_file = open(train_file,'r')
training_data = json.load(train_file)
train_file.close()
for time in training_data:
image_file, keys = training_data[time]
input_values = network.image_to_input(Image.open(image_file), None)
output_values = network.keys_to_output(keys)
importance = 1
if output_values[network.key_mapping[ecodes.KEY_LEFT]] or output_values[network.key_mapping[ecodes.KEY_RIGHT]]:
importance = 4
#data_set.addSample(input_values, output_values, importance)
for i in xrange(importance):
data_set.addSample(input_values, output_values)
print("Training the network...")
min_delta = .0001
last_error = None
trainer = BackpropTrainer(net, data_set)
#trainer.trainUntilConvergence() # We want it to be verbose
for i in xrange(50):
