深度学习Caffe处理工具
对于机器学习、图像处理有时要对图像数据进行分割处理。用python写了一些常用小工具代码。
1 Caffe数据集txt文本制作
很多时候要建立如下数据集txt文本，类似图片所示（图片网上找的)
添加图片注释，不超过 140 字（可选）
生成代码如下：
"""
caffe数据集txt文本制作
"""
import os
#文件保存路径
f =open(r'd:/val.txt',"w")
path = 'D:/smoke_datasets/val/0'
for filename in os.listdir(path) :
#数据标签
count = 0
ff = filename+" "+ "0"+"\n"
f.write(ff)
print('{} class: {}'.format(filename,count))
path = 'D:/smoke_datasets/val/1'
for filename in os.listdir(path) :
#数据标签
count = 1
ff = filename+" "+ "1"+"\n"
f.write(ff)
print('{} class: {}'.format(filename,count))
f.close()2 jpg图像完整性检测
有时爬虫所获得图像可能不完整，需要进行图像完整性检测。代码如下：
"""
jpg图像完整性检测
"""
from skimage import io
from PIL import Image
import numpy as np
import os
def is_valid_jpg(path):
#判断JPG文件下载是否完整
if path.split('.')[-1].lower() == 'jpg':
with open(path, 'rb') as fr:
fr.seek(-2, 2)
#判定jpg是否包含结束字段
return fr.read() == '\xff\xd9'
else:
return False
#文件头的方式
def is_jpg(path):
data = open(path,'rb').read(11)
if data[:4] != '\xff\xd8\xff\xe0' and data[:4]!='\xff\xd8\xff\xe1':
return False
if data[6:] != 'JFIF\0' and data[6:] != 'Exif\0':
return False
return True
def check_pic_PIL(path):
try:
Image.open(path).load()
Image.open(path).verify()
except:
return False
try:
img = Image.open(path)
img = np.array(img, dtype=np.float32)
except:
return False
if len(img.shape)!=3:
return False
return True
def check_pic_skimage(path):
try:
img = io.imread(path)
except:
return False
try:
img = np.array(img, dtype=np.float32)
except:
return False
if len(img.shape)!=3:
return False
return True
if __name__ == '__main__':
#结果
f =open(r'd:/state.txt',"w")
#路径
paths = ["d:/train"]
for path in paths:
print('the current path is : {}\n'.format(path))
#path = "D:/smoke_data/datas/deal/smoke_auto_aug"
#文件头检测
#精细检测
for filename in os.listdir(path):
#print('current jpg is {}'.format(path+"/"+filename))
#文件头检测
status_valid_jpg = is_valid_jpg(path+"/"+filename)
status_jpg = is_jpg(path+"/"+filename)
if( status_valid_jpg == False or status_jpg == False):
ff = filename+"\n"
f.write(ff)
print('{} \n'.format(path+"/"+filename))
continue
#状态检测
status_PIL = check_pic_PIL(path+"/"+filename)
status_skimage = check_pic_skimage(path+"/"+filename)
if (status_PIL == False or status_skimage == False):
ff = filename+"\n"
f.write(ff)
print("=" * 50)
print('{} \n'.format(path+"/"+filename))
print("=" * 50)
#分割线
print("*" * 50)
print("end!")
f.close()3 图像随机移动复制
对于windows系统，移动太慢，也费时。linux也差不多。通过python可以快速移动/复制大量图像，代码如下：
"""
图像随机移动复制
"""
import os, random, shutil
def moveFile(fileDir, tarDir, picknumber):
#取图像原始路径
pathDir = os.listdir(fileDir)
filenumber = len(pathDir)
if filenumber < picknumber:
picknumber = filenumber
#抽取一定比例
sample = random.sample(pathDir, picknumber)
print(sample)
for name in sample:
shutil.move(fileDir+name, tarDir+name)
return
def copyFile(fileDir, tarDir, picknumber):
#取图像原始路径
pathDir = os.listdir(fileDir)
filenumber = len(pathDir)
if filenumber < picknumber:
picknumber = filenumber
#抽取一定比例
sample = random.sample(pathDir, picknumber)
print(sample)
for name in sample:
shutil.copy(fileDir+name, tarDir+name)
return
if __name__ == '__main__':
#图像路径
fileDir = 'D:/datasets/train/'
#移动路径
tarDir = 'D:/datasets/move/'
#从fileDir随机移动500张图像到tarDir文件夹
moveFile(fileDir, tarDir, 500)
#从fileDir随机复制500张图像到tarDir文件夹
copyFile(fileDir, tarDir, 500)4 图像尺寸统计
主要是统计图像尺寸，可以添加过滤条件，滤掉尺寸过小或者过大的图像。代码如下：
"""
统计数据集下图像尺寸
"""
import os
from PIL import Image
import pandas as pd
#数据集路径
path = 'D:/test/'
#存入列表
f = os.listdir(path)
count = 0
df = pd.DataFrame(columns=['width','height'])
for i in f:
#旧文件名
oldname=path+f[count]
im = Image.open(oldname)
df.loc[count,'width']=im.width
df.loc[count,'height']=im.height
print(oldname)
count += 1
#保存结果
df.to_csv('test.csv')
f.close()5 图像名字后缀重命名
对图像的名字以及后缀名重新命名，代码如下：
"""
图像名称后缀重命名
"""
import os
#图像路径
path = 'D:/train/'
#保存路径
save_path = 'D:/result/'
#存入列表
f = os.listdir(path)
count = 0
for i in f:
#旧文件名
oldname=path+f[count]
print(oldname)
#新文件名
newname=save_path+'smoke.'+str(count)+'.jpg'
os.rename(oldname,newname)
count += 16 两文件夹文件比对
判断两个文件夹里面文件的文件名是不是相互对应，并移动文件名不对应的文件
# -*- coding: utf-8 -*-
"""
判断两个文件夹里面文件的文件名是不是相互对应，并移动不对应的文件
"""
import os,shutil
# 文件少的文件夹路径 传入存储的list
def listdir(path, list_name):
for file in os.listdir(path):
list_name.append(file.split('.')[0])
def del_dir(checkpath,input_name,savepath):
if not os.path.exists(savepath):
os.makedirs(savepath)
for file in os.listdir(checkpath):
check_name=file.split('.')[0]
if check_name in input_name:
print('{} 在列表中'.format(check_name))
#不在列表中，移动文件
else:
srcfile=os.path.join(checkpath, file)
shutil.move(srcfile,savepath)
name=[]
#保存路径
listdir('./2',name)
del_dir('./1',name,'./3')7 绘制caffe模型的ROC曲线(二分类)
主要原理为通过OpenCV DNN 调用caffe模型，其他模型一样套路。然后对每张图像进行检测，保存该图像真实标签，以及该标签分类的模型预测概率。调用sklearn绘制ROC，并计算AUC值。通过约登指数获得ROC曲线中最佳阈值。最后通过matplotlib绘图。约登指数为TPR- FPR或者TPR+TNR-1。具体ROC原理可参考：
https://www.jianshu.com/p/c61ae11cc5f6
https://www.jianshu.com/p/82903edb58dc
https://blog.csdn.net/u014264373/article/details/80487766
'''
opencv调用caffe并计算roc
'''
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
from sklearn import metrics
# 真实图像标签为0的图像路径
imagePath_0 = ['0']
# 真实图像标签为1的图像路径
imagePath_1 = ['1']
# 正类标签
poslabel = 1
# 模型路径
prototxtFile = 'deploy_227.prototxt'
modelFile = 'model_227.caffemodel'
# 真实分类结果
trueResult = []
# 检测结果
detectProbs = []
# 图像检测
def detectCaffe(srcImg):
detectImg = srcImg.copy()
blob = cv2.dnn.blobFromImage(
detectImg, 1, (227, 227), (92.713, 106.446, 118.115), swapRB=False)
net = cv2.dnn.readNetFromCaffe(prototxtFile, modelFile)
net.setInput(blob)
detections = net.forward()
# 分类结果
order = detections[0].argmax()
prob = detections[0].max()
#print('the predict class is:',order)
#print('the positive class prob is: ', prob)
# 返回分类结果和概率
return order, prob
# 图像检测
def imageDetect(detectImagePath, trueLabel):
for imageFileName in os.listdir(detectImagePath):
imageFilePath = os.path.join(detectImagePath, imageFileName)
print("current detect image is: ", imageFileName)
srcImg = cv2.imread(imageFilePath)
if srcImg is None:
print("error image is: ", imageFilePath)
continue
detectOrder, detectProb = detectCaffe(srcImg)
trueResult.append(trueLabel)
# 如果正样本编号和检测结果标签一致直接保存分类概率
if detectOrder == poslabel:
detectProbs.append(detectProb)
# 如果不一致保存正样本的分类概率
else:
detectProbs.append(1-detectProb)
# 画ROC图，输入真实标签，正样本模型分类概率，正样本编号
def drawROC(trueResult, detectProbs, poslabel):
fpr, tpr, thresholds = metrics.roc_curve(
trueResult, detectProbs, pos_label=poslabel)
#auc = metrics.roc_auc_score(y, scores)
roc_auc = metrics.auc(fpr, tpr)
# 计算约登指数Youden Index（TPR-FPR或者TPR+TNR-1）
tpr_fpr = list(tpr-fpr)
bestIndex = tpr_fpr.index(max(tpr_fpr))
print("约登指数为{}".format(max(tpr_fpr)))
tprBest = tpr[bestIndex]
fprBest = fpr[bestIndex]
thresholdsBest = thresholds[bestIndex]
print("最佳约登指数阈值为:", thresholdsBest)
# 假正率为横坐标，真正率为纵坐标做曲线
plt.plot(fpr, tpr, color='darkorange',
label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
#plt.xlim([0.0, 1.0])
#plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
# 画出约登指数最大值
plt.plot(fprBest, tprBest, "ro")
plt.savefig("roc.png", dpi=300)
plt.show()
return fpr, tpr, thresholds, bestIndex
def main():
# 0标签图像遍历
for imagePath in imagePath_0:
imageDetect(imagePath, 0)
for imagePath in imagePath_1:
imageDetect(imagePath, 1)
# poslabel正例标签
fpr, tpr, thresholds, bestIndex = drawROC(
trueResult, detectProbs, poslabel)
np.save('fpr.npy', fpr)
np.save('tpr.npy', tpr)
np.save('thresholds', thresholds)
return fpr, tpr, thresholds
if __name__ == '__main__':
fpr, tpr, thresholds = main()结果如图所示：
添加图片注释，不超过 140 字（可选）
引用链接
[1] https://www.jianshu.com/p/c61ae11cc5f6 : https://www.jianshu.com/p/c61ae11cc5f6 [2] https://www.jianshu.com/p/82903edb58dc : https://www.jianshu.com/p/82903edb58dc [3] https://blog.csdn.net/u014264373/article/details/80487766: https://blog.csdn.net/u014264373/article/details/80487766