def collect_state(self):
mc = self.mc
target = ['lapis_block', 4.5, 46, 12.5]
target_pos = target[1:4]
mc.observeProc()
aPos = mc.getAgentPos()
logging.debug(aPos)
while aPos is None:
time.sleep(0.05)
mc.observeProc()
aPos = mc.getAgentPos()
if not all(mc.isAlive):
raise DeadException()
# grid
grid = mc.getNearGrid()
grid_vec = grid_to_vec_walking(grid[:27])
# position encoding
grid_enc = torch.as_tensor(grid_vec)
# target
pitch, yaw, dist = direction_to_target(mc, target_pos)
target_enc = torch.as_tensor([pitch, yaw, dist])
# 'XPos', 'YPos', 'ZPos', 'Pitch', 'Yaw'
# take pitch, yaw
# take XPos, YPos, ZPos modulo 1
self_pitch = toRadAndNorm(aPos[3])
self_yaw = toRadAndNorm(aPos[4])
xpos, ypos, zpos = [_ % 1 for _ in aPos[0:3]]
logging.debug("%.2f %.2f %.2f ", xpos, ypos, zpos)
self_pos_enc = torch.as_tensor(
[self_pitch, self_yaw, xpos, ypos, zpos])
data = dict(grid_vec=grid_enc, target=target_enc, pos=self_pos_enc)
return data
def collect_state(self):
while True:
self.mc.observeProc()
data = self.mc.getImage()
aPos = self.mc.getAgentPos()
if not any(x is None for x in (data, aPos)):
pitch_raw = degree2rad(aPos[3])
yaw_raw = degree2rad(aPos[4])
self_pitch = toRadAndNorm(aPos[3])
self_yaw = toRadAndNorm(aPos[4])
data = data.reshape(
(240, 320, 3 + self.want_depth)).transpose(2, 0, 1) / 255.
pitch_yaw = torch.as_tensor([self_pitch, self_yaw])
pitch_yaw_raw = torch.as_tensor([pitch_raw, yaw_raw])
height = 1.6025
x, y, z = aPos[0:3]
y += height
visible = self.observe_by_line()
return dict(image=torch.as_tensor(data).float(),
pitch_yaw=pitch_yaw,
pitch_yaw_raw=pitch_yaw_raw,
coordinates=[x, y, z],
visible=visible)
else:
time.sleep(0.05)
def collect_state(self):
while True:
self.mc.observeProc()
data = self.mc.getImage()
aPos = self.mc.getAgentPos()
if not any(x is None for x in (data, aPos)):
self_pitch = toRadAndNorm(aPos[3])
self_yaw = toRadAndNorm(aPos[4])
data = data.reshape((240, 320, 3 + self.want_depth)).transpose(2, 0, 1) / 255.
pitch_yaw = torch.as_tensor([self_pitch, self_yaw])
return dict(image=torch.as_tensor(data).float(), position=pitch_yaw)
else:
time.sleep(0.05)
def collect_state(self):
"""
state has format:
image, prev_action, prev_prev_action,
pitch, prev_pitch,
yaw - prev_yaw, prev_yaw - prev_prev_yaw
"""
mc = self.mc
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
logging.debug(aPos)
while aPos is None or (img_data is None):
time.sleep(0.05)
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
height = aPos[1]
if height < 30: # avoid ponds and holes
raise DeadException()
pitch = aPos[3]
yaw = aPos[4]
data = dict()
img_data = img_data.reshape((240 * 4, 320 * 4, 3 + self.want_depth))
img_data = cv2.resize(img_data, (320, 240))
img = img_data.reshape((240, 320, 3 + self.want_depth)).transpose(2, 0, 1) / 255.
img = torch.as_tensor(img).float()
data['image'] = img
actions = []
imgs = [torch.as_tensor(img)]
yaws = [yaw]
pitches = [pitch]
for item in reversed(self.state_queue):
actions.append(item['action'])
yaws.append(item['yaw'])
pitches.append(item['pitch'])
while len(yaws) < 3:
actions.append(torch.as_tensor(-1).to(img))
yaws.append(torch.as_tensor(yaw).to(img))
pitches.append(torch.as_tensor(pitch).to(img))
# use relative change for yaws
for i in range(len(yaws) - 1):
yaws[i] = toRadAndNorm(yaws[i] - yaws[i + 1])
yaws.pop()
pitches.pop()
state = torch.as_tensor(actions + pitches + yaws)
data.update(dict(state=state,
images=torch.stack(imgs),
image=img,
reward=torch.as_tensor(0),
yaw=torch.as_tensor(yaw),
pitch=torch.as_tensor(pitch)
))
return data
def lookAt(self, pitch_new, yaw_new):
mc = self.mc
print('look at')
for t in range(2000):
time.sleep(0.02)
mc.observeProc()
aPos = mc.getAgentPos()
if aPos is None:
continue
current_pitch = toRadAndNorm(aPos[3])
current_yaw = toRadAndNorm(aPos[4])
pitch = normAngle(normAngle(pitch_new) - current_pitch)
yaw = normAngle(normAngle(yaw_new) - current_yaw)
if abs(pitch) < 0.02 and abs(yaw) < 0.02: break
yaw = yaw * 0.5
while abs(yaw) > 1:
yaw *= 0.8
pitch = pitch * 0.5
while abs(pitch) > 1:
pitch *= 0.8
mc.sendCommand("turn " + str(yaw))
mc.sendCommand("pitch " + str(pitch))
mc.sendCommand("turn 0")
mc.sendCommand("pitch 0")
def collect_state(self):
mc = self.mc
target = ['lapis_block', self.target_x, 30, self.target_y]
target_pos = target[1:4]
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
logging.debug(aPos)
while aPos is None or (img_data is None):
time.sleep(0.05)
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
if not all(mc.isAlive):
raise DeadException()
# target
pitch, yaw, dist = direction_to_target(mc, target_pos)
# 'XPos', 'YPos', 'ZPos', 'Pitch', 'Yaw'
# take pitch, yaw
# take XPos, YPos, ZPos modulo 1
self_pitch = toRadAndNorm(aPos[3])
self_yaw = toRadAndNorm(aPos[4])
xpos, ypos, zpos = aPos[0:3]
logging.debug("%.2f %.2f %.2f ", xpos, ypos, zpos)
# use relative height
ypos = 30 - aPos[1]
data = dict()
img_data = img_data.reshape((240 * 4, 320 * 4, 3 + self.want_depth))
img_data = cv2.resize(img_data, (320, 240))
img = img_data.reshape((240, 320, 3 + self.want_depth)).transpose(2, 0, 1) / 255.
img = torch.as_tensor(img).float()
data['image'] = img
actions = []
imgs = [torch.as_tensor(img)]
yaws = [torch.as_tensor(yaw)]
dists = [torch.as_tensor(dist)]
heights = [torch.as_tensor(ypos)]
# first prev, then prev_prev etc..
for item in reversed(self.state_queue):
actions.append(item['action'])
imgs.append(item['image'])
yaws.append(item['yaw'])
dists.append(item['dist'])
heights.append(item['ypos'])
while len(imgs) < 3:
imgs.append(img.to(img))
actions.append(torch.as_tensor(-1).to(img))
yaws.append(torch.as_tensor(yaw).to(img))
dists.append(torch.as_tensor(dist).to(img))
heights.append(torch.as_tensor(ypos).to(img))
state = torch.as_tensor(yaws + dists + heights + actions)
data.update(dict(state=state,
images=torch.stack(imgs),
image=img,
dist=torch.as_tensor(dist),
yaw=torch.as_tensor(yaw),
ypos=torch.as_tensor(ypos)
))
# depth
coords1 = self.collect_visible(data, aPos[:3])
if self.write_visualization:
visualize(yaw, dist)
for key, value in data.items():
if isinstance(value, torch.Tensor):
assert not torch.isnan(value).any()
return data
def collect_state(self):
mc = self.mc
target = ['lapis_block', self.target_x, 30, self.target_y]
target_pos = target[1:4]
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
logging.debug(aPos)
while aPos is None or (img_data is None):
time.sleep(0.05)
mc.observeProc()
aPos = mc.getAgentPos()
img_data = self.mc.getImage()
if not all(mc.isAlive):
raise DeadException()
# grid
grid = mc.getNearGrid()
grid_vec = grid_to_vec_walking(grid[:36])
# position encoding
grid_enc = torch.as_tensor(grid_vec)
# target
pitch, yaw, dist = direction_to_target(mc, target_pos)
target_enc = torch.as_tensor([pitch, yaw, dist])
# 'XPos', 'YPos', 'ZPos', 'Pitch', 'Yaw'
# take pitch, yaw
# take XPos, YPos, ZPos modulo 1
self_pitch = toRadAndNorm(aPos[3])
self_yaw = toRadAndNorm(aPos[4])
xpos, ypos, zpos = [_ % 1 for _ in aPos[0:3]]
# use relative height
ypos = 30 - aPos[1]
logging.debug("%.2f %.2f %.2f ", xpos, ypos, zpos)
self_pos_enc = torch.as_tensor(
[self_pitch, self_yaw, xpos, ypos, zpos])
data = dict(grid_vec=grid_enc,
target=target_enc,
state=target_enc,
pos=self_pos_enc)
img = img_data.reshape(
(240, 320, 3 + self.want_depth)).transpose(2, 0, 1) / 255.
data['image'] = torch.as_tensor(img).float()
# depth
visible = self.mc.getLineOfSight('type')
if visible is not None:
coords = [
self.mc.getLineOfSight('x'),
self.mc.getLineOfSight('y'),
self.mc.getLineOfSight('z')
]
height = 1.6025
coords1 = aPos[:3]
coords1[1] += height
dist = numpy.linalg.norm(
numpy.asarray(coords) - numpy.asarray(coords1), 2)
data['visible'] = [visible] + coords + [dist]
if self.write_visualization:
import cv2
font = cv2.FONT_HERSHEY_SIMPLEX
bottomLeftCornerOfText = (40, 40)
fontScale = 0.9
fontColor = (15, 15, 15)
lineType = 2
img_draw = (img * 255).astype(numpy.uint8)
img_draw = cv2.putText(img_draw.transpose(1, 2, 0),
'distance {0:.1f}'.format(dist),
bottomLeftCornerOfText, font, fontScale,
fontColor, lineType)
#img_draw = cv2.putText(img_draw, 'yaw {0:.1f}'.format(yaw),
# (10, 80),
# font,fontScale,fontColor,lineType)
c_x = 260
c_y = 200
r = 20
img_draw = cv2.circle(img_draw, (c_x, c_y), r, (0, 255, 255), 2)
cos_x = numpy.cos(yaw + numpy.pi / 2) * r
sin_y = numpy.sin(yaw + numpy.pi / 2) * r
img_draw = cv2.line(img_draw, (c_x, c_y),
(round(c_x - cos_x), round(c_y - sin_y)),
(0, 255, 255), 2)
cv2.imwrite('episodes/img{0}.png'.format(self.img_num), img_draw)
self.img_num += 1
#cv2.imshow('1', img_draw)
#cv2.waitKey(100)
#if self.want_depth:
# depth = data['image'][-1]
# h, w = [_ // 2 for _ in depth.shape]
# img_depth = img[-1][h, w]
# norm_depth = (depth * (dist / img_depth))
# data['image'][-1] = norm_depth
for key, value in data.items():
if isinstance(value, torch.Tensor):
assert not torch.isnan(value).any()
return data
