def main():
## Simulator
simulator_args = {}
simulator_args['config'] = 'config/config.cfg'
simulator_args['resolution'] = (widthIn, heightIn)
simulator_args['frame_skip'] = 1
simulator_args['color_mode'] = 'RGB24'
simulator_args['game_args'] = "+name ICO +colorset 7"
## Agent
agent_args = {}
# preprocessing
preprocess_input_images = lambda x: x / 255.
agent_args['preprocess_input_images'] = lambda x: x / 255.
agent_args['preprocess_input_measurements'] = lambda x: x / 100. - 0.5
agent_args['num_future_steps'] = 6
pred_scale_coeffs = np.expand_dims(
(np.expand_dims(np.array([8., 40., 1.]), 1) * np.ones(
(1, agent_args['num_future_steps']))).flatten(), 0)
agent_args['meas_for_net_init'] = range(3)
agent_args['meas_for_manual_init'] = range(3, 16)
agent_args['resolution'] = (width, height)
# just use grayscale for nnet inputs
agent_args['num_channels'] = 1
# net parameters
agent_args['conv_params'] = np.array([(16, 5, 4), (32, 3, 2), (64, 3, 2),
(128, 3, 2)],
dtype=[('out_channels', int),
('kernel', int),
('stride', int)])
agent_args['fc_img_params'] = np.array([(128, )],
dtype=[('out_dims', int)])
agent_args['fc_meas_params'] = np.array([(128, ), (128, ), (128, )],
dtype=[('out_dims', int)])
agent_args['fc_joint_params'] = np.array([(256, ), (256, ), (-1, )],
dtype=[('out_dims', int)])
agent_args['target_dim'] = agent_args['num_future_steps'] * len(
agent_args['meas_for_net_init'])
agent_args['n_actions'] = 7
# experiment arguments
agent_args['test_objective_params'] = (np.array([5, 11, 17]),
np.array([1., 1., 1.]))
agent_args['history_length'] = 3
agent_args['history_length_ico'] = 3
historyLen = agent_args['history_length']
print("HistoryLen: ", historyLen)
print('starting simulator')
simulator = DoomSimulator(simulator_args)
num_channels = simulator.num_channels
print('started simulator')
agent_args['state_imgs_shape'] = (historyLen * num_channels,
simulator.resolution[1],
simulator.resolution[0])
agent_args['n_ffnet_input'] = (agent_args['resolution'][0] *
agent_args['resolution'][1])
agent_args['n_ffnet_hidden'] = np.array([50, 5])
agent_args['n_ffnet_output'] = 1
agent_args['n_ffnet_act'] = 7
agent_args['n_ffnet_meas'] = simulator.num_meas
agent_args['learning_rate'] = 1E-4
agent_args['momentum'] = 0.2
modelDir = os.path.join(os.path.expanduser("~"),
"Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/Models")
if 'meas_for_net_init' in agent_args:
agent_args['meas_for_net'] = []
for ns in range(historyLen):
agent_args['meas_for_net'] += [
i + simulator.num_meas * ns
for i in agent_args['meas_for_net_init']
]
agent_args['meas_for_net'] = np.array(agent_args['meas_for_net'])
else:
agent_args['meas_for_net'] = np.arange(historyLen * simulator.num_meas)
if len(agent_args['meas_for_manual_init']) > 0:
agent_args['meas_for_manual'] = np.array([
i + simulator.num_meas * (historyLen - 1)
for i in agent_args['meas_for_manual_init']
]) # current timestep is the last in the stack
else:
agent_args['meas_for_manual'] = []
agent_args['state_meas_shape'] = (len(agent_args['meas_for_net']), )
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
# agent = Agent(sess, agent_args)
# agent.load('/home/paul/Dev/GameAI/vizdoom_cig2017/icolearner/ICO1/checkpoints/ICO-8600')
# print("model loaded..")
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
img_buffer = np.zeros((historyLen, simulator.resolution[1],
simulator.resolution[0], num_channels),
dtype='uint8')
meas_buffer = np.zeros((historyLen, simulator.num_meas))
act_buffer = np.zeros((historyLen, 7))
act_buffer_ico = np.zeros((agent_args['history_length_ico'], 7))
curr_step = 0
old_step = -1
term = False
print("state_meas_shape: ", meas_buffer.shape, " == ",
agent_args['state_meas_shape'])
print("act_buffer_shape: ", act_buffer.shape)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
ag = Agent(sess, agent_args)
if (os.path.isfile("checkpoints/checkpoint")):
ag.load(
'/home/paul/Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/checkpoints/')
print("model loaded..")
else:
print("No model file, initialising...")
diff_y = 0
diff_x = 0
diff_z = 0
diff_theta = 0
iter = 1
epoch = 200
radialFlowLeft = 30.
radialFlowRight = 30.
radialFlowInertia = 0.4
radialGain = 4.
rotationGain = 50.
errorThresh = 10.
updatePtsFreq = 50
skipImage = 1
skipImageICO = 5
reflexGain = 0.01
opticFlowGain = 0.03
netGain = 40.
oldHealth = 0.
# create masks for left and right visual fields - note that these only cover the upper half of the image
# this is to help prevent the tracking getting confused by the floor pattern
half_height = round(height / 2)
half_width = round(width / 2)
maskLeft = np.zeros([height, width], np.uint8)
maskLeft[half_height:, :half_width] = 1.
maskRight = np.zeros([height, width], np.uint8)
maskRight[half_height:, half_width:] = 1.
lk_params = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
10, 0.03))
feature_params = dict(maxCorners=500,
qualityLevel=0.03,
minDistance=7,
blockSize=7)
imgCentre = np.array([
int(simulator_args['resolution'][0] / 2),
int(simulator_args['resolution'][1] / 2)
])
print("Image centre: ", imgCentre)
simpleInputs = np.zeros((width, height))
input_buff = np.zeros((1, width * height))
target_buff = np.zeros((1, 1))
meas_buff = np.zeros((1, simulator.num_meas))
netOut = 0.
netErr = np.zeros((width, height))
delta = 0.
reflexOn = False
iter1 = 0
while not term:
if curr_step < historyLen:
curr_act = np.zeros(7).tolist()
img, meas, rwrd, term = simulator.step(curr_act)
print("Image: ", img.shape, " max: ", np.amax(img), " min: ",
np.amin(img))
if curr_step == 0:
p0Left = cv2.goodFeaturesToTrack(img[:, :, 0],
mask=maskLeft,
**feature_params)
p0Right = cv2.goodFeaturesToTrack(img[:, :, 0],
mask=maskRight,
**feature_params)
img_buffer[curr_step % historyLen] = img
meas_buffer[curr_step % historyLen] = meas
act_buffer[curr_step % historyLen] = curr_act[:7]
else:
img1 = img_buffer[(curr_step - 2) % historyLen, :, :, :]
img2 = img_buffer[(curr_step - 1) % historyLen, :, :, :]
state = simulator._game.get_state()
stateImg = state.screen_buffer
greyImg = np.array(np.sum(stateImg, axis=2) / 3, dtype="float64")
# greyImg2 = cv2.resize(stateImg, (width,height))
# greyImg2 = np.array(np.sum(greyImg2, axis=2)/3, dtype='uint8')
if (curr_step % updatePtsFreq == 0):
p0Left = cv2.goodFeaturesToTrack(img[:, :, 0],
mask=maskLeft,
**feature_params)
p0Right = cv2.goodFeaturesToTrack(img[:, :, 0],
mask=maskRight,
**feature_params)
p1Left, st, err = cv2.calcOpticalFlowPyrLK(img1[:, :, 0],
img2[:, :, 0], p0Left,
None, **lk_params)
p1Right, st, err = cv2.calcOpticalFlowPyrLK(
img1[:, :, 0], img2[:, :, 0], p0Right, None, **lk_params)
flowLeft = (p1Left - p0Left)[:, 0, :]
flowRight = (p1Right - p0Right)[:, 0, :]
radialFlowTmpLeft = 0
radialFlowTmpRight = 0
for i in range(0, len(p0Left)):
radialFlowTmpLeft += ((p0Left[i, 0, :] - imgCentre)).dot(
flowLeft[i, :]) / float(len(p0Left))
for i in range(0, len(p0Right)):
radialFlowTmpRight += ((p0Right[i, 0, :] - imgCentre)).dot(
flowRight[i, :]) / float(len(p0Right))
rotation = act_buffer[(curr_step - 1) % historyLen][6]
forward = act_buffer[(curr_step - 1) % historyLen][3]
# keep separate radial errors for left and right fields
radialFlowLeft = radialFlowLeft + radialFlowInertia * (
radialFlowTmpLeft - radialFlowLeft)
radialFlowRight = radialFlowRight + radialFlowInertia * (
radialFlowTmpRight - radialFlowRight)
expectFlowLeft = radialGain * forward + (rotationGain * rotation
if rotation < 0. else 0.)
expectFlowRight = radialGain * forward - (rotationGain * rotation
if rotation > 0. else 0.)
flowErrorLeft = forward * (expectFlowLeft - radialFlowLeft) / (
1. + rotationGain * np.abs(rotation))
flowErrorRight = forward * (expectFlowRight - radialFlowRight) / (
1. + rotationGain * np.abs(rotation))
flowErrorLeft = flowErrorLeft if flowErrorLeft > 0. else 0.
flowErrorRight = flowErrorRight if flowErrorRight > 0. else 0.
icoSteer = 0.
if curr_step > 100:
health = meas[1]
if (health < 0.1):
reflexOn = False
iter1 = 0
# Don't run any networks when the player is dead!
if (health < 101. and health > 0.):
#print (curr_step)
icoInLeft = (flowErrorLeft - errorThresh) if (
flowErrorLeft -
errorThresh) > 0. else 0. / opticFlowGain
icoInRight = (flowErrorRight - errorThresh) if (
flowErrorRight -
errorThresh) > 0. else 0. / opticFlowGain
icoInSteer = ((flowErrorRight - errorThresh) if
(flowErrorRight - errorThresh) > 0. else
0. / opticFlowGain -
(flowErrorLeft - errorThresh) if
(flowErrorLeft - errorThresh) > 0. else 0. /
opticFlowGain)
centre, bottomLeft, topRight, colourStrength = getMaxColourPos(
stateImg, [255, 0, 0])
colourSteer = imgCentre[0]
imgRect = img
imgRect = np.zeros(img.shape)
if (len(bottomLeft) > 0 and len(topRight) > 0
and ((topRight[0] - bottomLeft[0]) < width / 2)
and ((topRight[1] - bottomLeft[1]) < height / 2)):
#imgRect = cv2.rectangle(imgRect, (bottomLeft[0], bottomLeft[1]), (topRight[0], topRight[1]), (255,255,255), 2)
#imgRect = cv2.circle(imgRect, (bottomLeft[0] + int(0.5*(topRight[0] - bottomLeft[0])),
# bottomLeft[1] + int(0.5*(topRight[1] - bottomLeft[1]))),
# 3, (255,0,0), 3)
colourSteer = bottomLeft[0] + int(
0.5 * (topRight[0] - bottomLeft[0]))
cv2.arrowedLine(imgRect, (colourSteer, imgCentre[1] + 10),
(colourSteer, imgCentre[1]),
color=(255, 255, 255),
thickness=2)
#cv2.imwrite("/home/paul/tmp/Images/Positive/rect-" + str(curr_step) + ".jpg", imgRect)
# get the setpoint in the -.9/+.9 range
simpleInputs[:, :] = 0.1 * np.random.rand(width, height)
#cv2.arrowedLine(simpleInputs, (colourSteer, imgCentre[1]+10), (colourSteer, imgCentre[1]), color=(0,55,255), thickness=2)
simpleInputs = np.array(simpleInputs, dtype='uint8')
#greyImg = 1.0 * preprocess_input_images(greyImg)
greyImg = cv2.filter2D(greyImg, -1, edge)
# XORin = np.random.rand(2)
# imgRect[...] = 0.
# if XORin[0] > 0.5:
# imgRect[:int(height/2),:] = 1.
# if XORin[1] > 0.5:
# imgRect[int(height/2):,:] = 1.
# XORout = -0.9
# if XORin[0]>0.5 or XORin[1]>0.5:
# XORout = 0.9
# if XORin[0] > 0.5 and XORin[1] > 0.5:
# XORout = -0.9
#cv2.imwrite("/home/paul/tmp/Images/Positive/rect-" + str(curr_step) + ".jpg", 20.*greyImg)
input_buff[0, :] = np.ndarray.flatten(greyImg)
# we want the reflex to be delayed wrt to the image input, so that the image is. Otherwise the learning can
# never reduce the error to zero no matter how good the controller.
if (iter1 > 2):
delta = (float(colourSteer) -
float(imgCentre[0])) / float(width)
else:
delta = 0
# target_buff[...] = delta + netOut
target_buff[...] = delta + netOut
meas_buff[0, :] = meas
ag.act_fcnet(input_buff, meas, target_buff)
netOut = np.ndarray.flatten(
ag.ext_fcnet_output)[0].flatten()[0]
print(curr_step, " ColourSteer: ", colourSteer, "delta: ",
delta, " target: ", target_buff[0], " NetOut: ",
netOut, " mean: ", np.mean(input_buff), " var: ",
np.var(input_buff))
diff_theta = 0.
#if (False):
#net_output = np.ndarray.flatten(agent.test_ffnet(input_buff))[0]
#else:
#net_output = np.ndarray.flatten(agent.learn_ffnet(input_buff, target_buff))[0]
netErr[:, :] = 0.
diff_theta = diff_theta + reflexGain * colourStrength * delta
curr_act = np.zeros(7).tolist()
curr_act[0] = 0
curr_act[1] = 0
curr_act[2] = 0
curr_act[3] = 0. #curr_act[3] + diff_z
curr_act[3] = 0.
curr_act[4] = 0
curr_act[5] = 0
curr_act[6] = diff_theta + netGain * netOut
iter1 += 1
if (curr_step % epoch == 0):
ag.save(
'/home/paul/Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/checkpoints/BP',
curr_step)
img, meas, rwrd, term = simulator.step(curr_act)
if (not (meas is None)) and meas[0] > 30.:
meas[0] = 30.
if not term:
img_buffer[curr_step % historyLen] = img
meas_buffer[curr_step % historyLen] = meas
act_buffer[curr_step % historyLen] = curr_act[:7]
curr_step += 1
simulator.close_game()
ag.save(
'/home/paul/Dev/GameAI/vizdoom_cig2017/icolearner/ICO1/checkpoints/' +
'hack-' + str(iter))
def main():
## Simulator
simulator_args = {}
simulator_args['config'] = 'config/config.cfg'
simulator_args['resolution'] = (width, height)
simulator_args['frame_skip'] = 1
simulator_args['color_mode'] = 'RGB24'
simulator_args['game_args'] = "+name ICO +colorset 7"
## Agent
agent_args = {}
# preprocessing
preprocess_input_images = lambda x: x / 255. - 0.5
agent_args['preprocess_input_images'] = lambda x: x / 255. - 0.5
agent_args['preprocess_input_measurements'] = lambda x: x / 100. - 0.5
agent_args['num_future_steps'] = 6
pred_scale_coeffs = np.expand_dims(
(np.expand_dims(np.array([8., 40., 1.]), 1) * np.ones(
(1, agent_args['num_future_steps']))).flatten(), 0)
agent_args['meas_for_net_init'] = range(3)
agent_args['meas_for_manual_init'] = range(3, 16)
agent_args['resolution'] = (width, height)
# just use grayscale for nnet inputs
agent_args['num_channels'] = 1
# net parameters
agent_args['net_type'] = "fc"
agent_args['conv_params'] = np.array([(16, 5, 4), (32, 3, 2), (64, 3, 2),
(128, 3, 2)],
dtype=[('out_channels', int),
('kernel', int),
('stride', int)])
agent_args['fc_img_params'] = np.array([(128, )],
dtype=[('out_dims', int)])
agent_args['fc_meas_params'] = np.array([(128, ), (128, ), (128, )],
dtype=[('out_dims', int)])
agent_args['fc_joint_params'] = np.array([(256, ), (256, ), (-1, )],
dtype=[('out_dims', int)])
agent_args['target_dim'] = agent_args['num_future_steps'] * len(
agent_args['meas_for_net_init'])
agent_args['n_actions'] = 7
# experiment arguments
agent_args['test_objective_params'] = (np.array([5, 11, 17]),
np.array([1., 1., 1.]))
agent_args['history_length'] = 3
agent_args['history_length_ico'] = 3
historyLen = agent_args['history_length']
print("HistoryLen: ", historyLen)
print('starting simulator')
simulator = DoomSimulator(simulator_args)
num_channels = simulator.num_channels
print('started simulator')
agent_args['state_imgs_shape'] = (historyLen * num_channels,
simulator.resolution[1],
simulator.resolution[0])
agent_args['n_ffnet_input'] = (agent_args['resolution'][0] *
agent_args['resolution'][1])
agent_args['n_ffnet_hidden'] = np.array([50, 5])
agent_args['n_ffnet_output'] = 1
agent_args['n_ffnet_act'] = 7
agent_args['n_ffnet_meas'] = simulator.num_meas
agent_args['learning_rate'] = 1E-3
modelDir = os.path.join(os.path.expanduser("~"),
"Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/Models")
if 'meas_for_net_init' in agent_args:
agent_args['meas_for_net'] = []
for ns in range(historyLen):
agent_args['meas_for_net'] += [
i + simulator.num_meas * ns
for i in agent_args['meas_for_net_init']
]
agent_args['meas_for_net'] = np.array(agent_args['meas_for_net'])
else:
agent_args['meas_for_net'] = np.arange(historyLen * simulator.num_meas)
if len(agent_args['meas_for_manual_init']) > 0:
agent_args['meas_for_manual'] = np.array([
i + simulator.num_meas * (historyLen - 1)
for i in agent_args['meas_for_manual_init']
]) # current timestep is the last in the stack
else:
agent_args['meas_for_manual'] = []
agent_args['state_meas_shape'] = (len(agent_args['meas_for_net']), )
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
# agent = Agent(sess, agent_args)
# agent.load('/home/paul/Dev/GameAI/vizdoom_cig2017/icolearner/ICO1/checkpoints/ICO-8600')
# print("model loaded..")
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
img_buffer = np.zeros((historyLen, simulator.resolution[1],
simulator.resolution[0], num_channels),
dtype='uint8')
meas_buffer = np.zeros((historyLen, simulator.num_meas))
act_buffer = np.zeros((historyLen, 7))
act_buffer_ico = np.zeros((agent_args['history_length_ico'], 7))
curr_step = 0
old_step = -1
term = False
print("state_meas_shape: ", meas_buffer.shape, " == ",
agent_args['state_meas_shape'])
print("act_buffer_shape: ", act_buffer.shape)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.1)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
ag = Agent(sess, agent_args)
if (os.path.isfile("checkpoints1/checkpoint")):
ag.load(
'/home/paul/Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/checkpoints1/')
print("model loaded..")
else:
print("No model file, initialising...")
diff_y = 0
diff_x = 0
diff_z = 0
diff_theta = 0
epoch = 200
radialFlowLeft = 30.
radialFlowRight = 30.
radialFlowInertia = 0.4
radialGain = 4.
rotationGain = 50.
errorThresh = 10.
updatePtsFreq = 50
skipImage = 1
skipImageICO = 5
reflexGain = 0.1
netGain = 0. #10.
oldHealth = 0.
# create masks for left and right visual fields - note that these only cover the upper half of the image
# this is to help prevent the tracking getting confused by the floor pattern
half_height = round(height / 2)
half_width = round(width / 2)
maskLeft = np.zeros([height, width], np.uint8)
maskLeft[half_height:, :half_width] = 1.
maskRight = np.zeros([height, width], np.uint8)
maskRight[half_height:, half_width:] = 1.
lk_params = dict(winSize=(15, 15),
maxLevel=2,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT,
10, 0.03))
feature_params = dict(maxCorners=500,
qualityLevel=0.03,
minDistance=7,
blockSize=7)
imgCentre = np.array([
int(simulator_args['resolution'][0] / 2),
int(simulator_args['resolution'][1] / 2)
])
print("Image centre: ", imgCentre)
simpleInputs = np.zeros((width, height))
input_buff = np.zeros((1, width * height))
target_buff = np.zeros((1, 1))
meas_buff = np.zeros((1, simulator.num_meas))
netOut = 0.
netErr = np.zeros((width, height))
delta = 0.
delta2 = 0
dontshoot = 1
deltaZeroCtr = 1
curr_act = np.zeros(7).tolist()
reflexOn = False
iter = 0
episodes = 1000
simulator._game.init()
for i in range(episodes):
# print ("Episode ", i)
tc = 0
simulator._game.new_episode()
while (tc < 500):
screen_buf, meas, rwrd, term = simulator.step(curr_act)
if (screen_buf is None):
break
midlinex = int(width / 2)
midliney = int(height * 0.75)
crcb = screen_buf
screen_left = screen_buf[0:midliney, 0:midlinex, 2]
screen_right = screen_buf[0:midliney, midlinex:width, 2]
screen_left = cv2.filter2D(screen_left, -1, sharpen)
screen_right = cv2.filter2D(screen_right, -1, sharpen)
simpleInputs = preprocess_input_images(
np.array(np.sum(crcb, axis=2) / 3))
# simpleInputs = cv2.filter2D(simpleInputs, -1, edge)
# simpleInputs = simpleInputs - np.mean(simpleInputs)
screen_diff = screen_left - np.fliplr(screen_right)
screen_diff = cv2.resize(screen_diff, (width, height))
# cv2.imwrite('/tmp/left.png',screen_left)
# cv2.imwrite('/tmp/right.png',screen_right)
# cv2.imwrite("/home/paul/tmp/Images/diff-" + str(iter) + ".png", screen_diff)
# cv2.imwrite("/home/paul/tmp/Images/raw-" + str(iter) + ".png", crcb)
lavg = np.average(screen_left)
ravg = np.average(screen_right)
shoot = 0
if (dontshoot > 1):
dontshoot = dontshoot - 1
else:
if (tc > 30):
shoot = 1
dontshoot = 5
centre, bottomLeft, topRight, colourStrength = getMaxColourPos(
crcb, [255, 0, 0])
colourSteer = imgCentre[0]
imgRect = np.zeros(crcb.shape)
if (len(bottomLeft) > 0 and len(topRight) > 0
and ((topRight[0] - bottomLeft[0]) < width / 3)
and ((topRight[1] - bottomLeft[1]) < height / 2)):
colourSteer = bottomLeft[0] + int(
0.5 * (topRight[0] - bottomLeft[0]))
cv2.arrowedLine(imgRect, (colourSteer, imgCentre[1] + 10),
(colourSteer, imgCentre[1]),
color=(255, 255, 255),
thickness=2)
# cv2.imwrite("/home/paul/tmp/Images/simple-" + str(iter) + ".jpg", simpleInputs)
# cv2.imwrite("/home/paul/tmp/Images/rect-" + str(iter) + ".jpg", imgRect)
# cv2.imwrite("/home/paul/tmp/Images/" + str(iter) + ".jpg", crcb)
# cv2.imwrite("/home/paul/tmp/Images/Positive/arrow-" + str(iter) + ".jpg", imgRect)
# cv2.imwrite("/home/paul/tmp/Images/Positive/" + str(iter) + ".jpg", crcb)
# blue = cv2.filter2D(blue, -1, edge)
# cv2.imwrite("/home/paul/tmp/Images/Positive/blue-" + str(curr_step) + ".jpg", blue)
meas_buff[0, :] = meas
imgRect = np.array(np.sum(imgRect, axis=2) / 3)
# input_buff[0,:] = np.ndarray.flatten(imgRect)
input_buff[0, :] = np.ndarray.flatten(imgRect)
# input_buff[0,:] = np.random.normal(0.0, 0.01, size=width*height)
# print("mean: ", np.mean(input_buff[0,:]), " var: ", np.var(input_buff[0,:]))
if (tc > 2):
delta = (float(colourSteer) -
float(imgCentre[0])) / float(width)
else:
delta = 0
target_buff[...] = delta + netOut
target_buff[...] = -0.5
ag.act_fcnet(input_buff, meas, target_buff)
netOut = np.ndarray.flatten(ag.ext_fcnet_output)[0].flatten()[0]
netErr[:, :] = 0.
diff_theta = reflexGain * delta
# print(tc, diff_theta, netGain*netOut, target_buff[0,0], delta)
print(tc, reflexGain * delta, netOut)
curr_act = np.zeros(7).tolist()
curr_act[0] = 0
curr_act[1] = 0
curr_act[2] = 1
curr_act[3] = 0. #curr_act[3] + diff_z
curr_act[3] = 0.
curr_act[4] = 0
curr_act[5] = 0
curr_act[6] = diff_theta + netGain * netOut
iter += 1
if (curr_step % epoch == 0):
ag.save(
'/home/paul/Dev/GameAI/vizdoom_cig2017/icodoom/ICO1/checkpoints1/BPBasic',
curr_step)
curr_step += 1
# 30 fps
# time.sleep(0.03)
tc += 1
simulator.close_game()
