from __future__ import division
import cv2
import os
import numpy as np
import scipy
import pickle
import matplotlib.pyplot as plt
from itertools import islice
LIMIT = None
DATA_FOLDER = 'driving_dataset'
TRAIN_FILE = os.path.join(DATA_FOLDER, 'data.txt')
def preprocess(img):
resized = cv2.resize((cv2.cvtColor(img, cv2.COLOR_RGB2HSV))[:, :, 1], (100, 100))
return resized
def return_data():
X = []
y = []
features = []
with open(TRAIN_FILE) as fp:
for line in islice(fp, LIMIT):
path, angle = line.strip().split()
full_path = os.path.join(DATA_FOLDER, path)
X.append(full_path)
# using angles from -pi to pi to avoid rescaling the atan in the network
y.append(float(angle) * scipy.pi / 180)
for i in range(len(X)):
img = plt.imread(X[i])
features.append(preprocess(img))
features = np.array(features).astype('float32')
labels = np.array(y).astype('float32')
with open("features", "wb") as f:
pickle.dump(features, f, protocol=4)
with open("labels", "wb") as f:
pickle.dump(labels, f, protocol=4)
return_data()
