def plot_predictions(self, cfg, tub_paths, model_path):
"""
Plot model predictions for angle and throttle against data from tubs.
"""
from donkeycar.parts.datastore import TubGroup
from donkeycar.parts.keras import KerasCategorical
tg = TubGroup(tub_paths)
model_path = os.path.expanduser(model_path)
model = KerasCategorical()
model.load(model_path)
gen = tg.get_batch_gen(None, None, batch_size=len(tg.df),shuffle=False, df=tg.df)
arr = next(gen)
user_angles = []
user_throttles = []
pilot_angles = []
pilot_throttles = []
for tub in tg.tubs:
num_records = tub.get_num_records()
for iRec in tub.get_index(shuffled=False):
record = tub.get_record(iRec)
img = record["cam/image_array"]
user_angle = float(record["user/angle"])
user_throttle = float(record["user/throttle"])
pilot_angle, pilot_throttle = model.run(img)
user_angles.append(user_angle)
user_throttles.append(user_throttle)
pilot_angles.append(pilot_angle)
pilot_throttles.append(pilot_throttle)
angles_df = pd.DataFrame({'user_angle': user_angles, 'pilot_angle': pilot_angles})
throttles_df = pd.DataFrame({'user_throttle': user_throttles, 'pilot_throttle': pilot_throttles})
fig = plt.figure()
title = "Model Predictions\nTubs: {}\nModel: {}".format(tub_paths, model_path)
fig.suptitle(title)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
angles_df.plot(ax=ax1)
throttles_df.plot(ax=ax2)
ax1.legend(loc=4)
ax2.legend(loc=4)
plt.show()
def drive(model_path=None):
kl = KerasCategorical()
if model_path:
kl.load(model_path)
print('Model loaded')
# testImage = Image.open('test.png');
# testImage = numpy.array(testImage)
throttle_history = collections.deque(maxlen=10)
angle_history = collections.deque(maxlen=10)
for i in range(10):
throttle_history.append(0)
angle_history.append(0)
print(len(throttle_history))
print(len(angle_history))
round = 0
while True:
with mss.mss() as sct:
image = captureScreen(sct)
image = numpy.array(image)
if (round > 0):
angle, throttle = kl.run(image, prevImage,
numpy.array(angle_history),
numpy.array(throttle_history))
# print('Raw throttle', throttle)
# throttle = min(0.8, max(0.1, throttle * 3000000))
setThrottle(throttle)
setSteering(angle)
#s = input(">>")
#if len(s) >= 1:
# break
# if (round % 100 == 0):
# print(throttle, angle)
throttle_history.append(throttle)
angle_history.append(angle)
prevImage = image
round = round + 1
def playback(cfg, tub_names, model_name=None):
if not tub_names:
tub_names = os.path.join(cfg.DATA_PATH, '*')
tubgroup = TubGroup(tub_names)
if not model_name is None:
from donkeycar.parts.keras import KerasCategorical
kl = KerasCategorical()
kl.load(model_name)
pilot_angles = []
pilot_throttles = []
print('tub_names', tub_names)
tub_paths = utils.expand_path_arg(tub_names)
print('TubGroup:tubpaths:', tub_paths)
user_angles = []
user_throttles = []
tubs = [Tub(path) for path in tub_paths]
for tub in tubs:
num_records = tub.get_num_records()
print(num_records)
for iRec in tub.get_index(shuffled=False):
record = tub.get_record(iRec)
#record = tubs.get_record(random.randint(1,num_records+1))
img = record["cam/image_array"]
user_angle = float(record["user/angle"])
user_throttle = float(record["user/throttle"])
user_angles.append(user_angle)
user_throttles.append(user_throttle)
if not model_name is None:
pilot_angle, pilot_throttle = kl.run(img)
pilot_angles.append(pilot_angle)
pilot_throttles.append(pilot_throttle)
record = tubs[0].get_record(random.randint(1,num_records+1))
user_angle = float(record["user/angle"])
user_throttle = float(record["user/throttle"])
print(img.shape)
print('-----')
print(user_angle)
print(user_throttle)
plt.figure()
plt.imshow(img)
plt.plot([80,80+10*user_throttle*np.cos(user_angle)],[120,120+100*user_throttle*np.sin(user_angle)])
plt.figure()
plt.plot(user_angles)
plt.plot(user_throttles)
fig = plt.figure()
ax1 = plt.subplot2grid((2,2),(0,0))
record = tubs[0].get_record(1)
img = record["cam/image_array"]
imPlot = ax1.imshow(img,animated=True)
floorImg = lookAtFloorImg(img)
print(floorImg)
ax3 = plt.subplot2grid((2,2),(0,1))
imPlot2 = ax3.imshow(floorImg,animated=True)
ax2 = plt.subplot2grid((2,2),(1,0), colspan=2)
line1, = ax2.plot(user_angles)
line2, = ax2.plot(user_throttles)
if not model_name is None:
line4, = ax2.plot(pilot_angles)
line5, = ax2.plot(pilot_throttles)
line3, = ax2.plot([0,0],[-1,1])
def animate(i):
record = tubs[0].get_record(i)
img = record["cam/image_array"]
imPlot.set_array(img)
imPlot2.set_array(lookAtFloorImg(img))
line3.set_data([i,i],[-1,1])
#print(i)
#sys.stdout.flush()
return imPlot,
# Init only required for blitting to give a clean slate.
def init():
record = tubs[0].get_record(1)
img = record["cam/image_array"]
imPlot.set_array(img)
line3.set_data([0,0],[-1,1])
return imPlot,
ani = animation.FuncAnimation(fig, animate, np.arange(1, tubs[0].get_num_records()),
interval=100, blit=False)
plt.show()