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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_process2.py 3.80 KB
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tianhangz authored 2018-11-05 18:27 . 修改版
# coding=utf-8
# 将原始的jpg图片处理成Inception-v3模型需要的299×299×3的数字矩阵
# 将所有的图片分为训练/验证/测试3个数据集
import glob
import os.path
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
import os
#####################################1.定义需要使用到的常量###########################
# 原始输入数据的目录
INPUT_DATA = 'datasets/training_data'
# 输出文件的地址。我们将整理后的图片数据通过numpy格式保存
OUTPUT_FILE = 'datasets/processed_data.npy'
# 测试数据和验证数据的比例
VALIDATION_PRECENTAGE = 10
TEST_PRECENTAGE = 10
#####################################2.定义数据处理过程###############################
# 读取数据并将数据分割成训练数据/验证数据/测试数据
def create_image_lists(sess, testing_percentage, validation_percentage):
sub_dirs = [x[0] for x in os.walk(INPUT_DATA)]
is_root_dir = True
# 初始化各个数据集。
training_images = []
training_labels = []
testing_images = []
testing_labels = []
validation_images = []
validation_labels = []
current_label = 0
# 读取所有的子目录。
for sub_dir in sub_dirs:
if is_root_dir:
is_root_dir = False
continue
# 获取一个子目录中所有的图片文件。
extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
file_list = []
dir_name = os.path.basename(sub_dir)
for extension in extensions:
file_glob = os.path.join(INPUT_DATA, dir_name, '*.' + extension)
file_list.extend(glob.glob(file_glob))
if not file_list: continue
print("processing:", dir_name)
i = 0
# 处理图片数据。
for file_name in file_list:
i += 1
# 读取并解析图片,将图片转化为299*299以方便inception-v3模型来处理。
image_raw_data = gfile.FastGFile(file_name, 'rb').read()
image = tf.image.decode_jpeg(image_raw_data)
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.image.resize_images(image, [299, 299])
image_value = sess.run(image)
# 随机划分数据聚。
chance = np.random.randint(100)
if chance < validation_percentage:
validation_images.append(image_value)
validation_labels.append(current_label)
elif chance < (testing_percentage + validation_percentage):
testing_images.append(image_value)
testing_labels.append(current_label)
else:
training_images.append(image_value)
training_labels.append(current_label)
if i % 200 == 0:
print(i, "images processed.")
current_label += 1
# 将训练数据随机打乱以获得更好的训练效果。
state = np.random.get_state()
np.random.shuffle(training_images)
np.random.set_state(state)
np.random.shuffle(training_labels)
return np.asarray([training_images, training_labels,
validation_images, validation_labels,
testing_images, testing_labels])
def main():
#config = tf.ConfigProto(allow_soft_placement = True)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
#config.gpu_options.allow_growth = True
with tf.Session(config=tf.ConfigProto(log_device_placement=False,gpu_options=gpu_options)) as sess:
processed_data = create_image_lists(sess, TEST_PRECENTAGE, VALIDATION_PRECENTAGE)
# 通过numpy格式储存处理过的数据
np.save(OUTPUT_FILE, processed_data)
if __name__ == '__main__':
main()

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