def preprocess_state(s_, position_, env, resize=arg.resize):
if (not arg.preprocess_state):
s_ = np.array(s_)
return s_
for i in range(len(s_)):
s_[i] = pre_process_screen(s_[i])
prep_s = []
for i in range(arg.num_frames):
img = s_[i]
if (i == arg.num_frames - 1 and arg.resize):
add_rects(img=img, point=position_, env=env)
img = cv2.resize(img, arg.wind_conv_wh, interpolation=cv2.INTER_AREA)
# img = cv2.resize(img, (arg.wind_w, arg.wind_h), interpolation=cv2.INTER_AREA)
prep_s.append(img)
# if (arg.resize):
# for i in range(arg.num_frames):
# img = s_[i]
# if(i == arg.num_frames -1 ):
# add_rects(img=img, point=position_, env=env)
# img = cv2.resize(img, arg.wind_conv_wh, interpolation=cv2.INTER_AREA)
# # img = cv2.resize(img, (arg.wind_w, arg.wind_h), interpolation=cv2.INTER_AREA)
# prep_s.append(img)
# # print(len(s_), env.num_frames)
# else:
# img = s_[-1]
# add_rects(img=img, point=position_, env=env)
# prep_s = s_
prep_s = np.array(prep_s)
return prep_s # s_
def get_pre_state(self, screens=None):
if (screens == None):
self.get_state()
screens_0 = []
for screen in screens:
screen = pre_process_screen(screen)
screens_0.append(screen)
return screens_0
def step(self, action):
s_ = self.get_state(action)
position = self.get_position(s, template=env.my_underwear_bgr)
s_ = pre_process_screen(s_)
r, done, info = self.get_feedback(s_)
# r, s_,info = 0,0,[position]
r = 0
info = [self.position]
self.s = s_
self.steps += 1
return s_, r, done, info
# print(sum(env.rewards), sum(env.rewards_d), env.rewards_p)
# print('{5:2f}, {:5.2f}, {:5.3f}, {:5.3f},'.format( (ep_r, sum(env.rewards), sum(env.rewards_d), sum(env.rewards_p)) )
# print('{:5.2f}, {:5.3f}, {:5.3f},'.format( ep_r, sum(env.rewards_d), sum(env.rewards_p) ))
# r, r_d, r_p = sum(env.rewards), sum(env.rewards_d), sum(env.rewards_p)
# if (r_p != 0):
# d_p = r_d / r_p
# else:
# d_p = -1
# plot
if (arg.show_pre_image):
# img = s_[-1]
if (arg.show_orig_img):
img = pre_process_screen(img, ret_type=arg.img_ret_type)
add_rects(img=img, point=position_, env=env)
# img = cv2.resize(img, (800, 600), interpolation=cv2.INTER_AREA)
else:
img = s_[-1]
if (arg.show_resize_img):
img = cv2.resize(img, (800, 600),
interpolation=cv2.INTER_AREA)
if (cv_img(img)):
break_flag = 1
break
if (model.memory_counter > MEMORY_CAPACITY
and steps % arg.LEARN_TMP_STEP == 0):
if (len(s) != env.num_frames): print('error len!')
############################################################
action = np.random.choice(env.action_space) # action = model(s)
s_ = env.get_state(action)
r, done, info = env.get_feedback(s_)
################### -------------------
if (not done): # plot cv_img
position_ = info[0]
img = s_[-1]
# point = position_
if (arg.control_preprocess_img):
img = pre_process_screen(img)
# if (cv_img(img)): break_flag = 1; break
img_add_rect(img=img,
point=position_,
ptype=arg.ptype,
pcolor=arg.pcolor,
ww=10,
hh=20,
cut=1)
img_add_rect(img=img,
point=env.end,
ptype=arg.ptype_dest,
pcolor=arg.pcolor_dest,
ww=20,
hh=20)
# if (cv_img(img)): break_flag = 1; break
