def step(self, action):
done = False
#action = np.clip(action-self.action_bound[1], *self.action_bound)
r = 0
self.focal_new = int(
np.clip((self.focal_old + action), FOCAL_DOWN, FOCAL_UP))
for i in range(0, self.state_dim - 1):
if i < self.state_dim - 2:
self.hist_focal[i] = self.hist_focal[i + 1]
if i == self.state_dim - 2:
self.hist_focal[i] = self.focal_new
if (self.focal_new == FOCAL_UP) or (self.focal_new == FOCAL_DOWN):
r -= 5
# normalize features
grey_avg_old = 0
grey_avg_new = 0
else:
# normalize features
grey_avg_old = ifa(self.img_stack[self.focal_old])
grey_avg_new = ifa(self.img_stack[self.focal_new])
# done and reward
if (self.focal_new == self.focal_old) and (grey_avg_new * 255 == self.max_grey):
self.focused += 1
if self.focused > 20:
self.hist_focal[self.state_dim - 1] = 1
r += 10
done = True
self.focused = 0
print("hold position at focal plane for 20 times: ",
self.focal_new)
else:
r -= 0.01
else:
self.focused = 0
# r -= np.sum(np.sqrt(np.asarray(feature_new) -
# np.asarray(feature_old)))
r += (grey_avg_new - grey_avg_old) * 255
# state
#s = self.hist_focal
s = np.asarray([self.focal_new, 1 if done else 0])
self.focal_old = self.focal_new
if grey_avg_new > grey_avg_old:
self.max_grey = grey_avg_new * 255
return s, r, done
def reset(self):
self.focal_new = int(np.random.rand() * 200)
self.focal_old = int(np.random.rand() * 200)
self.focused = 0
area_sel_new, grey_sum_new, grey_avg_new, threshold_new, grey_std_new = ifa(
self.img_stack[self.focal_new])
# state
s = self.focal_new
return s
def step(self, action):
done = False
r = 0
exceed_bound = False
area_sel_old = 0
grey_sum_old = 0
grey_avg_old = 0
threshold_old = 0
grey_std_old = 0
area_sel_new = 0
grey_sum_new = 0
grey_avg_new = 0
threshold_new = 0
grey_std_new = 0
if action == 0: # large forward step
if self.focal_old < (FOCAL_UP - 10):
self.focal_new = int(self.focal_old + 10)
else:
exceed_bound = True
elif action == 1: # small forward step
if self.focal_old < (FOCAL_UP - 1):
self.focal_new = int(self.focal_old + 1)
else:
exceed_bound = True
elif action == 2: # hold focal position
self.focal_new = self.focal_old
elif action == 3: # small backward step
if self.focal_old > (FOCAL_DOWN + 1):
self.focal_new = int(self.focal_old - 1)
else:
exceed_bound = True
elif action == 4: # large backward step
if self.focal_old > (FOCAL_DOWN + 10):
self.focal_new = int(self.focal_old - 10)
else:
exceed_bound = True
if (exceed_bound):
done = False
r -= 5
# state
# normalize features
area_sel_old, grey_sum_old, grey_avg_old, threshold_old, grey_std_old = ifa(
self.img_stack[self.focal_old])
s = self.focal_new
else:
# normalize features
area_sel_old, grey_sum_old, grey_avg_old, threshold_old, grey_std_old = ifa(
self.img_stack[self.focal_old])
area_sel_new, grey_sum_new, grey_avg_new, threshold_new, grey_std_new = ifa(
self.img_stack[self.focal_new])
r += (grey_avg_new - grey_avg_old) * grey_avg_new / 50
# done and reward
if (self.focal_new == self.focal_old) and (grey_avg_new
== self.max_grey):
r += 0.001
self.focused += 1
if self.focused > 20:
done = True
print('hold position at focal plane: ', self.focal_new)
else:
self.focused = 0
# state
s = self.focal_new
if grey_avg_new > grey_avg_old:
self.max_grey = grey_avg_new
self.focal_old = self.focal_new
return s, r, done
def step(self, a):
done = False
reward = 0
exceed_bound = False
action = np.argmax(a)
if action == 0: # large forward step
if self.focal_old < (FOCAL_UP - 10):
self.focal_new = int(self.focal_old + 10)
else:
exceed_bound = True
elif action == 1: # small forward step
if self.focal_old < (FOCAL_UP - 1):
self.focal_new = int(self.focal_old + 1)
else:
exceed_bound = True
elif action == 2: # hold focal position
self.focal_new = self.focal_old
elif action == 3: # small backward step
if self.focal_old > (FOCAL_DOWN + 1):
self.focal_new = int(self.focal_old - 1)
else:
exceed_bound = True
elif action == 4: # large backward step
if self.focal_old > (FOCAL_DOWN + 10):
self.focal_new = int(self.focal_old - 10)
else:
exceed_bound = True
if exceed_bound:
reward -= 5
grey_avg_new = 0
grey_avg_old = 0
else:
grey_avg_new = ifa(self.img_stack[self.focal_new])
grey_avg_old = ifa(self.img_stack[self.focal_old])
if (self.focal_new == self.focal_old) and (grey_avg_new*255 == self.max_grey):
# reward += 1
self.focused += 1
if self.focused > 20:
done = True
self.focused = 0
reward += 10
print("hold the position at focal plane 20 times:",
self.focal_new)
else:
reward -= 0.01
else:
self.focused = 0
# np.sign(grey_avg_new - grey_avg_old)
reward += (grey_avg_new - grey_avg_old) * 255
#next_state = self.focal_new
s = np.asarray([self.focal_new, 1 if done else 0])
if grey_avg_new > grey_avg_old:
self.max_grey = grey_avg_new*255
self.focal_old = self.focal_new
return s, reward, done