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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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zth_process_img.py 2.80 KB
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tianhangz authored 2018-11-05 18:27 . 修改版
# 将图片处理为npz格式
# 自动驾驶模型真实道路模拟行驶
import os
import numpy as np
import matplotlib.image as mpimg
from time import time
import math
from PIL import Image
CHUNK_SIZE = 128 # 将图片压缩,每256个做一次处理
# 本段不一样
def process_img(img_path, key):
print(img_path, key)
image = Image.open(img_path)
image_array = np.array(image)
image_array = np.expand_dims(image_array, axis=0) # 增加一个维度
#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)
# 返回图片的数据(矩阵),和对应的标签值
if __name__ == '__main__':
path = "training_data"
files = os.listdir(path) # 将该路径下的文件名都存入列表
turns = int(math.ceil(len(files) / CHUNK_SIZE)) # 取整,把所有图片分为这么多轮,每CHUNK_SIZE张一轮
print("number of files: {}".format(len(files)))
print("turns: {}".format(turns))
for turn in range(0, turns):
train_labels = np.zeros((1, 5), 'float') # 初始化标签数组
train_imgs = np.zeros([1, 120, 160, 3]) # 初始化图像数组
CHUNK_files = files[turn * CHUNK_SIZE: (turn + 1) * CHUNK_SIZE] # 取出当前这一轮图片
print("number of CHUNK files: {}".format(len(CHUNK_files)))
for file in CHUNK_files:
# 不是文件夹,并且是jpg文件
if not os.path.isdir(file) and file[len(file) - 3:len(file)] == 'jpg':
try:
key = int(file[0]) # 取第一个字符为key
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:, :]
train_labels = train_labels[1:, :]
file_name = str(int(time()))
directory = "training_data_npz"
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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