caffe训练过程中使用的python脚本

C++均值文件转为Python能使用的格式

#! /bin/bash
# -*- coding:utf-8 -*-
import caffe
import numpy as np
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-n","--name",required=True,help="input mean")
ap.add_argument("-l","--lu",required=True,help="input wenjianjia")
args = vars(ap.parse_args())
MEAN_PROTO_PATH = 'examples/'+args['lu']+'/data/'+args['name']+'.binaryproto'               # 待转换的pb格式图像均值文件路径
MEAN_NPY_PATH = 'examples/'+args['lu']+'/data/'+args['name']+'.npy'                         # 转换后的numpy格式图像均值文件路径

blob = caffe.proto.caffe_pb2.BlobProto()           # 创建protobuf blob
data = open(MEAN_PROTO_PATH, 'rb' ).read()         # 读入mean.binaryproto文件内容
blob.ParseFromString(data)                         # 解析文件内容到blob

array = np.array(caffe.io.blobproto_to_array(blob))# 将blob中的均值转换成numpy格式，array的shape （mean_number，channel, hight, width）
mean_npy = array[0]                                # 一个array中可以有多组均值存在，故需要通过下标选择其中一组均值
np.save(MEAN_NPY_PATH ,mean_npy)

使用方法： 在caffe根目录下
python binary2npy.python -n train_mean.binaryproto -l project_name

根据图片名称创建train.txt文件

import os,sys
import argparse
def create_image_list(file_path,txtpath):
    if os.path.isfile(txtpath):
        os.remove(txtpath)
    image_name_list = os.listdir(file_path)

    with open(txtpath,'a') as f:
        print('saving to'+txtpath+'...')
        for image_name in image_name_list:
            if "blue" in image_name:
                image_data = image_name+' '+str(0)
            elif "none" in image_name:
                image_data = image_name+' '+str(1)
            elif "orange" in image_name:
                image_data = image_name+' '+str(2)
            elif "red" in image_name:
                image_data = image_name+' '+str(3)
            elif "yellow" in image_name:
                image_data = image_name+' '+str(4)
            f.write(image_data+'\n')
        print('done')

if __name__=='__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument("-d","--data",required=True,help="input pic dir")
    print(ap.parse_args())
    args = vars(ap.parse_args())
    #print(args)
    create_image_list(os.path.dirname(os.path.realpath(__file__))+'/'+args['data'],'train.txt')

使用方法：在图片文件夹同级目录下使用
python create_list.py -d dirname

从文件夹中随机选取部分图片并copy到指定文件夹

# -*- coding:utf-8 -*-
import os, sys, random, shutil,argparse

def copyFile(fileDir,tarDir,number):  
    # 1  
    pathDir = os.listdir(fileDir)  

    # 2  
    sample = random.sample(pathDir, int(number))  #sample参数为int
    print sample

    # 3  
    for name in sample:  
        shutil.copyfile(fileDir+name, tarDir+name)  


if __name__ == '__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument("-s","--srcfile",required=True,help="input src")
    ap.add_argument("-d","--dstfile",required=True,help="output dst")
    ap.add_argument("-n","--number",required=True,help="select number")
    args = vars(ap.parse_args())
    fileDir = args["srcfile"]
    tarDir = args["dstfile"]
    copyFile(fileDir,tarDir,args["number"])
    print("Done")

使用方法：在任意文件夹下使用
python copy.python -s ./src/ -d ./dst/ -n 1000
# 从src中随机选择1000个不重复文件拷贝到dst文件夹中

文件夹内图片重命名

import os
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-c","--cls",required=True,help="input class")

args = vars(ap.parse_args())

path = './'+args['cls']+'/'
count = 1
for file in os.listdir(path):
    os.rename(os.path.join(path,file),path+"start_"+"frame_"+args['cls']+"_"+str(count)+".jpg")
    count+=1

使用方法：在图片文件夹同级目录使用
python rename.py -c className