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model-test.py
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import cv2
import numpy as np
from matplotlib import pyplot as plt
import pandas as pd
def showImage(image, title="Image"):
plt.imshow(image)
plt.title(title)
plt.show()
def flip(image):
return cv2.flip(image, 1)
def csv_import(csv_filepath):
return pd.read_csv(csv_filepath)
def agument_dataset(images, measurements):
# flip all images
# invert all measurements to reflect the data
assert (len(images) == len(measurements))
agumented_dataset = []
for image in images:
agumented_dataset.append(flip(image))
agumented_measurements = []
for measurement in measurements:
agumented_measurements.append(-1 * measurement)
return agumented_dataset, agumented_measurements
if __name__ == '__main__':
driving_data = csv_import("data/driving_log_test_1.csv")
dataset2 = csv_import("data/driving_log_test_2.csv")
driving_data = driving_data.append(dataset2, ignore_index=True)
print(dataset2.loc[3])
print(driving_data.append(dataset2, ignore_index=True))
images = []
for row in driving_data.itertuples():
# reading images in the following order:
# center, left, right
#print(row)
for column in np.arange(1, 4):
#print("Reading ", str(row[column]).strip(' '))
img = cv2.imread(str(row[column]).strip(' '))
#print(img.shape)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
images.append(img)
print("Loaded {num} images.".format(num=len(images)))
#plt.imshow(images[int(np.random.random_integers(0, len(images), size=1))])
#plt.show()
steering_angles = []
steering_bias = 0.25 # tune this
for row in driving_data.itertuples():
steering_angles.append(float(row[4]))
# add a corrective bias for each left image and -0.2 for each right camera image
steering_angles.append(float(row[4]) + steering_bias)
steering_angles.append(float(row[4]) - steering_bias)
print("Loaded {num} steering angles.".format(num=len(steering_angles)))
agu_imgs, agu_steer = agument_dataset(images, steering_angles)
#plt.imshow(agu_imgs[int(np.random.random_integers(0, len(agu_imgs), size=1))])
#plt.show()
#print(images)
print(len(images))
images.append(agu_imgs)
#print(images)
print(len(images))
#print(steering_angles)
#steering_angles.extend(agu_steer)
#print(steering_angles)
# Test print
#print(dataset.loc[3][6])
image_name = "data/2016/IMG/left_2016_12_01_13_34_23_036.jpg"
image = cv2.imread(image_name)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
showImage(image, "Original image")
noise = np.zeros_like(image)
noise = cv2.randn(noise, (0, 0, 0), (255, 255, 255))
noise = cv2.cvtColor(noise, cv2.COLOR_BGR2RGB)
showImage(noise, "Random generated noise")
#cv2.imshow("Noise", noise)
image = cv2.addWeighted(image, 0.75, noise, 0.25, 0)
#cv2.imshow("Noisy img", image)
showImage(image, "Noisy image")
#cv2.waitKey(0)
image = flip(image)
showImage(image, "Flipped image")
#cv2.imshow("Flipped image", image)
#cv2.waitKey(0)
cv2.destroyAllWindows()