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project_image.py
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project_image.py
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import csv
import cv2
import numpy as np
import pandas as pd
import os
import datetime
import random
import math
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
lines = []
#load csv file
with open('./data/driving_log.csv') as csvfile:
reader = csv.reader(csvfile)
next(reader) #Skip the first header row, else it causes problems later
for line in reader:
lines.append(line)
train_samples, validation_samples = train_test_split(lines, test_size=0.2)
#crop resize and change color space of image
def crop_and_resize_change_color_space(image):
dim = (32, 32)
#print(image.shape)
image=np.array(image[80:140,:])
#print(image.shape)
image = cv2.cvtColor(cv2.resize(image, dim), cv2.COLOR_BGR2RGB)
#print(image.shape)
return image
i=0
for sample in train_samples:
if(i<3):
name = './data/IMG/'+sample[1].split('/')[-1]
left_img = cv2.imread(name)
img = cv2.cvtColor(left_img, cv2.COLOR_BGR2RGB)
path='./output_images/left_img_'+ str(i) +'.jpg'
cv2.imwrite(path,left_img)
new_img=crop_and_resize_change_color_space(left_img)
path='./output_images/cropandresized_img_'+ str(i) +'.jpg'
cv2.imwrite(path,new_img)
flipped_img=np.fliplr(left_img)
path='./output_images/flipped_img_'+ str(i) +'.jpg'
cv2.imwrite(path,flipped_img)
print(name)
i+=1