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Timthony / self_drivePython

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基于树莓派的自动驾驶小车,利用树莓派和Tensorflow实现小车在赛道的自动驾驶 spread retract

https://github.com/Timthony/self_drive

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process_img_to_npz.py 2.42 KB
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Timthony authored 2018-10-02 22:35 . 修改,完善
# -*- coding: utf-8 -*-
# @Author: biying
# @Date: 2017-06-30 10:20:35
# @Last Modified by: Anderson
# @Last Modified time: 2018-05-29 13:31:03
import os
import numpy as np
import matplotlib.image as mpimg
# from PIL import Image
from time import time
import math
CHUNK_SIZE = 256
def process_img(img_path, key):
print(img_path, key)
# Use PIL to convert image file to numpy array
# image = Image.open(img_path)
# image_array = np.array(image)
# image_array = np.expand_dims(image_array,axis = 0)
# Use matplotlib to convert image file to numpy array
image_array = mpimg.imread(img_path)
image_array = np.expand_dims(image_array,axis = 0)
print(image_array.shape)
if key == 2:
label_array = [ 0., 0., 1., 0., 0.]
elif key ==3:
label_array = [ 0., 0., 0., 1., 0.]
elif key == 0:
label_array = [ 1., 0., 0., 0., 0.]
elif key == 1:
label_array = [ 0., 1., 0., 0., 0.]
elif key == 4:
label_array = [ 0., 0., 0., 0., 1.]
return (image_array, label_array)
if __name__ == '__main__':
path = "training_data"
files= os.listdir(path)
turns = int(math.ceil(len(files) / CHUNK_SIZE))
print("number of files: {}".format(len(files)))
print("turns: {}".format(turns))
for turn in range(0, turns):
train_labels = np.zeros((1,5),'float')
train_imgs = np.zeros([1,120,160,3])
CHUNK_files = files[turn*CHUNK_SIZE: (turn+1)*CHUNK_SIZE]
print("number of CHUNK files: {}".format(len(CHUNK_files)))
for file in CHUNK_files:
if not os.path.isdir(file) and file[len(file)-3:len(file)] == 'jpg':
try:
key = int(file[0])
image_array, label_array = process_img(path+"/"+file, key)
train_imgs = np.vstack((train_imgs, image_array))
train_labels = np.vstack((train_labels, label_array))
except:
print('prcess error')
# 去掉第0位的全零图像数组,全零图像数组是 train_imgs = np.zeros([1,120,160,3]) 初始化生成的
train_imgs = train_imgs[1:, :] # 从第一位开始取,因为第0位是初始化的
train_labels = train_labels[1:, :]
file_name = str(int(time())) # 文件名直接取时间
directory = "training_data_npz" # 文件夹
if not os.path.exists(directory):
os.makedirs(directory)
try:
np.savez(directory + '/' + file_name + '.npz', train_imgs=train_imgs, train_labels=train_labels)
except IOError as e:
print(e)

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