def run(self, img_arr):
img_arr = img_arr[60:, :]
img_arr = img_arr.reshape((1, ) + img_arr.shape)
angle_binned, throttle = self.model.predict(img_arr)
#angle_certainty = max(angle_binned[0])
angle_unbinned = utils.linear_unbin(angle_binned)
return angle_unbinned, throttle[0][0]
def run(self, img_arr, son_arr): ##@@## take sonic data as 'son_arr'
img_arr = img_arr.reshape((1,) + img_arr.shape)
son_arr= np.array(son_arr).reshape(1,3) ##@@##
angle_binned, throttle = self.model.predict([img_arr, son_arr]) ##@@## put son_arr into model
#angle_certainty = max(angle_binned[0])
angle_unbinned = utils.linear_unbin(angle_binned)
return angle_unbinned, throttle[0][0]
def run(self, img_arr):
img_arr = undistort(img_arr, balance=0.55)[9:79,:,:]
img_arr = img_arr.reshape((1,) + img_arr.shape)
angle_binned, throttle = self.model.predict(img_arr)
#angle_certainty = max(angle_binned[0])
angle_unbinned = utils.linear_unbin(angle_binned)
return angle_unbinned, throttle[0][0]
def parse_outputs(self, outputs):
res = []
for iO, output in enumerate(outputs):
if len(output.shape) == 2:
if iO == self.STEERING:
steering_angle = linear_unbin(output)
res.append(steering_angle)
elif iO == self.THROTTLE:
throttle = linear_unbin(output, N=output.shape[1], offset=0.0, R=0.5)
res.append(throttle)
else:
res.append( np.argmax(output) )
else:
for i in range(output.shape[0]):
res.append(output[i])
self.on_parsed_outputs(res)
def run(self, son_arr):
##@@##img_arr = img_arr.reshape((1,) + img_arr.shape)
son_arr = np.array(son_arr).reshape(1, 3) ####################
angle_binned, throttle = self.model.predict([son_arr
]) ####################
#angle_certainty = max(angle_binned[0])
angle_unbinned = utils.linear_unbin(angle_binned)
return angle_unbinned, throttle[0][0]
def inference(self, img_arr, other_arr):
# print("get action")
#print("return max q prediction")
q_value = self.model.predict(img_arr)
# convert q array to steering value
return linear_unbin(q_value[0]), 0.7
