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driving_data.py
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driving_data.py
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import scipy.misc
import random
xs = []
ys = []
#points to the end of the last batch
train_batch_pointer = 0
val_batch_pointer = 0
#read data.txt
with open("driving_dataset/data.txt") as f:
for line in f:
xs.append("driving_dataset/" + line.split()[0])
#the paper by Nvidia uses the inverse of the turning radius,
#but steering wheel angle is proportional to the inverse of turning radius
#so the steering wheel angle in radians is used as the output
ys.append(float(line.split()[1]) * scipy.pi / 180)
#get number of images
num_images = len(xs)
#shuffle list of images
c = list(zip(xs, ys))
random.shuffle(c)
xs, ys = zip(*c)
train_xs = xs[:int(len(xs) * 0.8)]
train_ys = ys[:int(len(xs) * 0.8)]
val_xs = xs[-int(len(xs) * 0.2):]
val_ys = ys[-int(len(xs) * 0.2):]
num_train_images = len(train_xs)
num_val_images = len(val_xs)
def LoadTrainBatch(batch_size):
global train_batch_pointer
x_out = []
y_out = []
for i in range(0, batch_size):
x_out.append(scipy.misc.imresize(scipy.misc.imread(train_xs[(train_batch_pointer + i) % num_train_images])[-150:], [66, 200]) / 255.0)
y_out.append([train_ys[(train_batch_pointer + i) % num_train_images]])
train_batch_pointer += batch_size
return x_out, y_out
def LoadValBatch(batch_size):
global val_batch_pointer
x_out = []
y_out = []
for i in range(0, batch_size):
x_out.append(scipy.misc.imresize(scipy.misc.imread(val_xs[(val_batch_pointer + i) % num_val_images])[-150:], [66, 200]) / 255.0)
y_out.append([val_ys[(val_batch_pointer + i) % num_val_images]])
val_batch_pointer += batch_size
return x_out, y_out