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 KerasLinear
import pandas as pd
from matplotlib import pyplot as plt
tg = TubGroup(tub_paths)
model_path = os.path.expanduser(model_path)
model = KerasLinear()
model.load(model_path)
gen = tg.get_batch_gen(None,batch_size=len(tg.df),record_transform=None,shuffle=True, 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 plot_predictions(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(batch_size=len(tg.df), shuffle=False)
arr = next(gen)
"""
