def play(prediction):
with tf.Session() as sess:
#reload trained tf model
saver = tf.train.import_meta_graph(
'./logs/nn3_logs/canabalt_nn_100_50_20.meta')
saver.restore(sess, tf.train.latest_checkpoint('./logs/nn3_logs'))
sess.run(tf.global_variables_initializer())
# initialize required variables
start = time.time()
dead = False
consecutivedead = 0
train_data = 0
space = False
current = np.ones(4800)
previous1 = np.zeros(4800)
previous2 = np.zeros(4800)
previous3 = np.zeros(4800)
# press enter to start the game
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.2)
keypress.ReleaseKey(0x1C)
while not dead:
# grab screen and process
screen = grab_screen(region=(0, 40, 800, 640))
current, previous1, previous2, previous3 = process_image(
screen), current, previous1, previous2
savable = np.append(current, previous3)
# prepare image for prediction
X = savable.reshape(1, 9600)
feed_dict = {x: X, p_input_keep: 1, p_layer_keep: 1}
result = prediction.eval(feed_dict, session=sess)
# check prediction for chosen action
if result[0][1] > result[0][0]:
if space == False:
keypress.PressKey(0x39)
space = True
#print('space down')
else:
if space == True:
keypress.ReleaseKey(0x39)
space = False
#print('space up')
# check if the player has died by detecting changes in the screen. no changes == dead
if np.array_equal(current, previous3) or np.array_equal(
current, previous1):
if consecutivedead > 20:
print('i am dead, did i do well?')
dead = True
consecutivedead += 1
else:
consecutivedead = 0
# counter to later calculate fps
train_data += 1
# calculate run time and derivatives, and print them
run_time = time.time() - start
print('run took {} seconds, for {} screens, for {} seconds per screen'.
format(run_time, train_data, (run_time / train_data)))
# return train_data[200:-100], run_time
return None, run_time
def play(sess,screenloc,death):
# initialize required variables and reset pressed buttons
start = time.time()
dead = False
train_data = 0
keypress.ReleaseKey(0x39)
space = False
current = np.ones((60,80))
previous1 = np.zeros((60,80))
previous2 = np.zeros((60,80))
previous3 = np.zeros((60,80))
presult = [[0,0,0]]
#restore the required placeholders for the feed dict
graph = tf.get_default_graph()
prediction = graph.get_tensor_by_name("hidden_4_dense/Relu:0")
x = graph.get_tensor_by_name("input_data:0")
x2 = graph.get_tensor_by_name("input_data2:0")
layer_keep_holder = graph.get_tensor_by_name("input_keep:0")
# press enter to start the game
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.2)
keypress.ReleaseKey(0x1C)
time.sleep(0.2)
while not dead:
# grab screen and process
screen = grab_screen(region =screenloc)
if np.array_equal(screen[:][250:280],death):
dead = True
current, previous1, previous2, previous3 = process_image(screen), current, previous1, previous2
savable = np.stack([current,previous3],2)
X = np.reshape(savable,(-1,60,80,2))
# prepare image for prediction
feed_dict={x:X, x2:presult, layer_keep_holder: 1}
result = prediction.eval(feed_dict, session=sess)
# check prediction for chosen action
if result[0][1] > result[0][0] :
presult = [[1] + presult[0][0:2]]
if space == False:
keypress.PressKey(0x39)
space = True
#print('space down')
else:
presult = [[0] + presult[0][0:2]]
if space == True:
keypress.ReleaseKey(0x39)
space = False
#print('space up')
# counter to later calculate fps
train_data += 1
# calculate run time and derivatives, and print them
run_time = time.time()-start
print('run took {} seconds, for {} screens, for {} seconds per screen'.format(run_time, train_data, (run_time/train_data)))
# return train_data[200:-100], run_time
return run_time
def play(estimator, deadcheck):
start = time.time()
dead = False
consecutivedead = 0
train_data = 0
space = False
current = np.zeros(4800)
previous1 = np.zeros(4800)
previous2 = np.zeros(4800)
previous3 = np.zeros(4800)
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.2)
keypress.ReleaseKey(0x1C)
while not dead:
screen = grab_screen(region =(0,40,800,640))
current, previous1, previous2, previous3 = process_image(screen), current, previous1, previous2
savable = np.append(current,previous3)
X = savable.reshape(1,-1)
feed_dict={"x": X}
result = estimator.predict(feed_dict)
if train_data == 0:
print(result)
if result == 1 :
if space == False:
keypress.PressKey(0x39)
space = True
#print('space down')
else:
if space == True:
keypress.ReleaseKey(0x39)
space = False
#print('space up')
train_data += 1
if (train_data % 1000) == 0:
print((time.time()-start)/1000)
start = time.time()
run_time = time.time()-start
print('run took {} seconds, for {} screens, for {} seconds per screen'.format(run_time,
train_data,
(run_time/train_data)))
# return train_data[200:-100], run_time
return None, run_time
def play(sess, screenloc, death, model):
# initialize required variables and reset pressed buttons
start = time.time()
dead = False
train_data = 0
keypress.ReleaseKey(0x39)
space = False
current = np.ones((60, 80))
previous1 = np.zeros((60, 80))
previous2 = np.zeros((60, 80))
previous3 = np.zeros((60, 80))
presult = [[0, 0, 0]]
# press enter to start the game
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.2)
keypress.ReleaseKey(0x1C)
time.sleep(0.2)
while not dead:
# grab screen and process
screen = grab_screen(region=screenloc)
if np.array_equal(screen[:][250:280], death):
dead = True
current, previous1, previous2, previous3 = process_image(
screen), current, previous1, previous2
savable = np.stack([current, previous3], 2)
X = np.reshape(savable, (-1, 60, 80, 2))
# prepare image for prediction
result = model.predict(sess, X, presult)
# check prediction for chosen action
if result[0][1] > result[0][0]:
presult = [[1] + presult[0][0:2]]
if space == False:
keypress.PressKey(0x39)
space = True
#print('space down')
else:
presult = [[0] + presult[0][0:2]]
if space == True:
keypress.ReleaseKey(0x39)
space = False
#print('space up')
# counter to later calculate fps
train_data += 1
# calculate run time and derivatives, and print them
run_time = time.time() - start
print('run took {} seconds, for {} screens, for {} seconds per screen'.
format(run_time, train_data, (run_time / train_data)))
# return train_data[200:-100], run_time
return run_time
def save_file(filename):
time.sleep(1)
keypress.PressKey(0x12) #alt
keypress.ReleaseKey(0x12) #~alt #click file
keypress.PressKey(0x28) #down
keypress.ReleaseKey(0x28)
keypress.PressKey(0x28) #down
keypress.ReleaseKey(0x28)
keypress.PressKey(0x27) #right
keypress.ReleaseKey(0x27)
keypress.PressKey(0x28) #down
keypress.ReleaseKey(0x28)
keypress.PressKey(0x0D) #enter
keypress.ReleaseKey(0x0D) #enter
#time.sleep(2)
keypress.Crtla() #select all
time.sleep(0.2)
keypress.type(filename + ".txt") #type in filename
time.sleep(0.5)
keypress.PressKey(0x0D) #enter
keypress.ReleaseKey(0x0D) #enter
def play(screenloc,death):
#initialize variables
start = time.time()
dead = False
train_data = []
savable = np.ones(4800)
previous1 = np.zeros(4800)
previous2 = np.zeros(4800)
previous3 = np.zeros(4800)
#press enter key to start the game
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.1)
keypress.ReleaseKey(0x1C)
time.sleep(0.2)
while not dead:
screen = grab_screen(region = screenloc)
if np.array_equal(screen[:][250:280],death):
dead = True
savable, previous1, previous2, previous3 = process_image(screen), savable, previous1, previous2
#savable = process_image(screen)
#X = savable.reshape(1,-1)
keys = key_check()
output = keys_to_output(keys)
train_data.append([savable, output])
# cv2.imshow('processed view', savable)
# if cv2.waitKey(25) & 0xFF == ord('q'):
# cv2.destroyAllWindows()
# break
run_time = time.time()-start
print('run took {} seconds, for {} screens, for {} seconds per screen'.format(run_time,
len(train_data),
(run_time/len(train_data))))
# cut of the last 250 screens as that playing resulted in death, and should not be learned by the network
return train_data[:-250], run_time
def main():
with open('iternumber.pickle','rb') as f:
iternumber = pickle.load(f)
iternumber = 2
# if os.path.isfile(file_name):
# print('file exists. loading data')
# training_data = list(np.load(file_name))
# else:
# print('file does not exist. creating new file')
# training_data = []
print('opening: MLPtrained_{}.pickle'.format(iternumber - 1))
with open('MLPtrained_{}.pickle'.format(iternumber - 1 ),'rb') as f:
mlp = pickle.load(f)
with open('MLPtrained_dead.pickle','rb') as f:
deadcheck = pickle.load(f)
if os.path.isfile('run_times_{}.npy'.format(iternumber -1)):
print('run times exists. loading data')
run_times = list(np.load('run_times_{}.npy '.format(iternumber-1)))
else:
run_times = []
#
# startdata = len(training_data)
# startdata2 = startdata
for i in range(4,0,-1):
print(i)
time.sleep(1)
while True:
print('starting to play')
keypress.PressKey(0x1C)
time.sleep(0.2)
keypress.ReleaseKey(0x1C)
run_data, run_time = play(mlp,deadcheck)
run_times.append(run_time)
np.save('run_times_{}.npy'.format(iternumber-1), run_times)
def play(mlp, deadcheck):
start = time.time()
dead = False
consecutivedead = 0
## not saving the training data gives significant speed inmprovements.
## replaced by a counter under the same name to preserve speed reports
# train_data = []
train_data = 0
space = False
# roundtime = time.time()
current = np.zeros(4800)
previous1 = np.zeros(4800)
previous2 = np.zeros(4800)
previous3 = np.zeros(4800)
while not dead:
screen = grab_screen(region =(0,40,800,640))
current, previous1, previous2, previous3 = process_image(screen), current, previous1, previous2
savable = np.append(current,previous3)
X = savable.reshape(1,-1)
button = mlp.predict(X)
if button == 1:
if space == False:
keypress.PressKey(0x39)
space = True
print('space down')
else:
if space == True:
keypress.ReleaseKey(0x39)
space = False
print('space up')
# train_data.append([savable, space])
train_data += 1
Xdead = current.reshape(1,-1)
died = deadcheck.predict(Xdead)
if died == 1:
consecutivedead += 1
if consecutivedead > 50:
dead = True
if died == 0:
consecutivedead = 0
# timediff = time.time()-roundtime
# if timediff < 0.023:
# time.sleep(0.01)
# roundtime=time.time()
run_time = time.time()-start
print('run took {} seconds, for {} screens, for {} seconds per screen'.format(run_time,
train_data,
(run_time/train_data)))
# return train_data[200:-100], run_time
return None, run_time
audiobuffer = stream.read(buffer_size)
signal = np.fromstring(audiobuffer, dtype=np.float32)
pitch = pitch_o(signal)[0]
confidence = pitch_o.get_confidence()
window.makeActive()
print("{} / {}".format(pitch, confidence))
#print("{} / {}".format(pitch,confidence))
for tone in pitchdict:
if (abs((round(pitch) - tone)) < 1):
print(pitch, tone)
if (pitchdict[tone] == e):
epress = epress + 1
print(epress, epress % 100)
if (epress % 10 == 9):
keypress.PressKey(pitchdict[tone])
time.sleep(0.05)
keypress.ReleaseKey(pitchdict[tone])
break
keypress.PressKey(pitchdict[tone])
time.sleep(0.05)
keypress.ReleaseKey(pitchdict[tone])
if (round(pitch) == F2):
keypress.moveMouse(-10, 0)
if (round(pitch) == G2):
keypress.moveMouse(0, 10)
if (round(pitch) == A2):
keypress.moveMouse(0, -10)
if (round(pitch) == B2):
keypress.moveMouse(10, 0)
if (round(pitch) == leftClick):