caffe安装，python caffe 实现目标分类，识别

caffe安装

此教程未小编亲测一步步手写，安装成功，大家哪一步出问题需要找出原因，不可盲目操作
1、硬件 笔记本cpu+华硕8G内存+1T硬盘
2、软件 Ubuntu 14.04 + python2.7
3、安装方法

安装依赖和相关科学计算库
sudo apt-get install libprotobuf-dev 
sudo apt-get install libleveldb-dev
sudo apt-get install libsnappy-dev 
sudo apt-get install libopencv-dev
sudo apt-get install libhdf5-serial-dev
sudo apt-get install protobuf-compiler
sudo apt-get install libgflags-dev
sudo apt-get install libgoogle-glog-dev
sudo apt-get install liblmdb-dev
sudo apt-get install libatlas-base-dev
sudo apt-get install python-pip
sudo apt-get install gfortran
sudo apt-get install python-protobuf
sudo apt-get install python-skimage
sudo apt-get install --no-install-recommends libboost-all-dev  
安装git cmake
sudo apt-get install git cmake

安装结束后下载caffe，执行以下命令：
git clone git://github.com/BVLC/caffe.git

cd caffe/

生成Makefile.config文件，这里是将caffe目录下自带的Makefile.config.example文件复制一份并更名为Makefile.config，命令如下：
cp Makefile.config.example Makefile.config
1  打开Makefile.config
去掉CPU_ONLY前面的#号
2 配置引用文件路径（主要是HDF5的路径问题） ,加上如下文件如下
#whateber else you need goes here
INCLUDE_DIRS :=$(PYTHON_INCLUDE)/usr/local/include  /usr/include/hdf5/serial
LIBABRY_DIRS :=$(PYTHON_LIB) /usr/local/lib  usr/lib  /usr/lib/x86_64-linux-gnu/hdf5/serial
新增的内容为：
/usr/include/hdf5/serial
/usr/lib/x86_64-linux-gnu/hdf5/serial

3 opencv如果使用3.0版本需要将opencv=3.0的注释修改为1,默认使用2.4.9

4 编译
make  -j8
make pycaffe
make test
make runtest

5 添加环境变量
cd ~/caffe/python
for req in $(cat requirements.txt); do pip install $req; done
sudo pip install -r requirements.txt
先打开配置文件bashrc
sudo gedit ~/.bashrc
在文件的最后面添加
export PYTHONPATH=/home/moqi/caffe/python:$PYTHONPATH
保存退出
sudo ldconfig

6 验证python接口
python
import caffe
未报错即安装成功

其中makefile.config如下

## Refer to http://caffe.berkeleyvision.org/installation.html
# Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
# USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
 CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#   You should not set this flag if you will be reading LMDBs with any
#   possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
 OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
        -gencode arch=compute_20,code=sm_21 \
        -gencode arch=compute_30,code=sm_30 \
        -gencode arch=compute_35,code=sm_35 \
        -gencode arch=compute_50,code=sm_50 \
        -gencode arch=compute_52,code=sm_52 \
        -gencode arch=compute_60,code=sm_60 \
        -gencode arch=compute_61,code=sm_61 \
        -gencode arch=compute_61,code=compute_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := atlas
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
        /usr/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
        # $(ANACONDA_HOME)/include/python2.7 \
        # $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                 /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
 WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/lib/x86_64-linux-gnu/hdf5/serial/include /usr/local/lib/python2.7/dist-packages/numpy/core/include

LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib /usr/lib/x86_64-linux-gnu/hdf5/serial

# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
# USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171
# DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

1.准备图片
2. 将 图片路径写入txt

import os

class_names_to_ids = {'daisy': 0, 'dandelion': 1, 'roses': 2, 'sunflowers': 3, 'tulips': 4}
data_dir = 'flower_photos/'
output_path = 'list.txt'

fd = open(output_path, 'w')
for class_name in class_names_to_ids.keys():
    images_list = os.listdir(data_dir + class_name)
    for image_name in images_list:
        fd.write('{}/{} {}\n'.format(class_name, image_name, class_names_to_ids[class_name]))

fd.close()

3.转换格式
还是参考这篇文章

4.训练模型
参考这篇

参考这篇

参考这篇

5.测试模型
看过这篇转换均值文件

看过这篇

—————————————————————————————正文————————————————————————————————” />



Ubuntu下caffe：用自己的图片训练并测试AlexNet模型 - CSDN博客

目录

扫描二维码关注公众号，回复： 2705890 查看本文章

1.准备图片

2. 将 图片路径写入txt

参考 这篇文章

3.转换格式

还是参考这篇文章

4.训练模型

参考这篇

参考这篇

参考这篇

5.测试模型

看过这篇转换均值文件

看过这篇

—————————————————————————————正文——————————————————————————————————————

1.准备图片

在data下新建文件夹myself ，在myself文件夹下新建两个文件夹 train和val。

train用来存放 训练的图片，在train文件夹下新建两个文件夹0和1 。图片有2类，包包（文件夹0）和裤子（文件夹1），每类55种。

Tips：大家从网上找的图片可能命名不规范，身为强迫症当然无法忍受了，一个一个修改太麻烦。

我分两步重命名图片：

第一，在每个图片名字前面加上类别名，这样就会规整很多 ；

rename 's/^/bag/' *

第二，把jpg的后缀改为jpeg，别问我为啥，小白看别人这么做，我也这么做了。

  rename 's/.jpg $/.jpeg/' *

val 用来放训练过程中用来验证的图片（来计算准确率），val中的图片和train中的不一样。我这里放了15张包包和15张裤子。只将图片后缀重命名了一下。

2. 将 图片路径写入txt

在data/myself/中新建train.txt 和val.txt

需要将图片的路径以及标签都写进去，包包标签为0，裤子标签为1

① 写入路径

find -name *jpeg | grep train | cut -d / -f 3-4  > train.txt
find -name *jpeg | grep val | cut -d / -f 3  > val.txt

 ② 在写入标签，val.txt 图片比较少，我是手动标记的。。。

sed -i "1,55s/.*/& 1/" train.txt    # 1~55是裤子，标签为1
sed -i "55,110s/.*/& 0/" train.txt    # 55~110是包包，标签为0

3. 转换数据

在caffe/example目录下新建目录myself。并将caffe/examples/imagenet 目录下create_imagenet.sh文件拷贝到myself中。

注释里是需要改的地方

EXAMPLE=examples/myself     #这里修改为自己的路径 

DATA=data/myself           #  修改为自己的路径 

TOOLS=build/tools

TRAIN_DATA_ROOT=/home/caffe/data/myself/train/ # 修改为自己的路径
VAL_DATA_ROOT=/home/caffe/data/myself/val/ #修改为自己的路径

# Set RESIZE=true to resize the images to 256x256. Leave as false if images have # already been resized using another tool. RESIZE=true #这里一定要改成true！！！！！！ if $RESIZE; then RESIZE_HEIGHT=256 RESIZE_WIDTH=256 else RESIZE_HEIGHT=0 RESIZE_WIDTH=0

echo "Creating train lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset \
    --resize_height=$RESIZE_HEIGHT \
    --resize_width=$RESIZE_WIDTH \
    --shuffle \
    $TRAIN_DATA_ROOT \
    $DATA/train.txt \
    $EXAMPLE/myself_train_lmdb      #把这里改成自己命名的数据库

echo "Creating val lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset \
    --resize_height=$RESIZE_HEIGHT \
    --resize_width=$RESIZE_WIDTH \
    --shuffle \
    $VAL_DATA_ROOT \
    $DATA/val.txt \
    $EXAMPLE/myself_val_lmdb    #这里也改一下

返回caffe根目录 运行 sh ./examples/myself/create_imagenet.sh

接下来就会在examples/myself 下生成 两个文件夹 myself_train_lmdb和 myself_train_lmdb

4. 训练数据

把caffe/models/bvlc_reference_caffenet中所有文件复制到caffe/examples/myself文件夹中

① 修改train_val.prototxt

 #data/myself文件夹下myimagenet_mean.binaryproto没有这个文件，把data/ilsvrc12下的imagenet_mean.binaryproto复制到该文件夹下，并重命名为myimagenet_mean.binaryproto

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "data/myself/myimagenet_mean.binaryproto"
  }

# mean pixel / channel-wise mean instead of mean image # transform_param { # crop_size: 227 # mean_value: 104 # mean_value: 117 # mean_value: 123 # mirror: true # } data_param { source: "examples/myself/myself_train_lmdb" batch_size: 256 backend: LMDB } } layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 227 mean_file: "data/myself/myimagenet_mean.binaryproto" } # mean pixel / channel-wise mean instead of mean image # transform_param { # crop_size: 227 # mean_value: 104 # mean_value: 117 # mean_value: 123 # mirror: false # } data_param { source: "examples/myself/myself_val_lmdb" batch_size: 50 backend: LMDB } } ------------------------------往后拉，在最后---------------------------------------------------- layer { name: "fc8" type: "InnerProduct" bottom: "fc7" top: "fc8" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 2 #改这里，图片有几个分类，就写几 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }

②修改solver.prototxt

test_iter: 1000是指测试的批次，我们就10张照片，设置10就可以了。 test_interval: 1000是指每1000次迭代测试一次，我改成了10。 base_lr: 0.01是基础学习率，因为数据量小，0.01就会下降太快了，因此改成0.001 lr_policy: “step”学习率变化 gamma: 0.1学习率变化的比率 stepsize: 100000每100000次迭代减少学习率 display: 20每20层显示一次 max_iter: 4000最大迭代次数， momentum: 0.9学习的参数，不用变 weight_decay: 0.0005学习的参数，不用变 snapshot: 10000每迭代10000次显示状态，这里改为1000次 solver_mode: GPU末尾加一行，代表用GPU进行

③ 图像均值

减去图像均值会获得更好的效果，所以我们使用tools/compute_image_mean.cpp实现，这个cpp是一个很好的例子去熟悉如何操作多个组建，例如协议的缓冲区，leveldbs，登录等。我们同样复制caffe-maester/examples/imagenet的./make_imagenet_mean到examples/myself中，将其改名为make_myimagenet_mean.sh,加以修改路径。

#!/usr/bin/env sh

# Compute the mean image from the imagenet training lmdb # N.B. this is available in data/ilsvrc12 EXAMPLE=/home/caffe/examples/myself DATA=/home/caffe/data/myself TOOLS=/home/caffe/build/tools $TOOLS/compute_image_mean$EXAMPLE/myself_train_lmdb \ $DATA/myimagenet_mean.binaryproto echo "Done."

④ 运行

拷贝examples/imagenet目录下的train_caffenet.sh文件到example/myself目录下。

#!/usr/bin/env sh

./build/tools/caffe train \
    --solver=examples/myself/solver.prototxt

在caffe的主目录下输入命令：./ examples/myself/train_caffenet.sh开始训练网络。

我电脑快，训练了十分钟左右就好啦（默默炫耀一下。。。）出现 【Restarting data prefetching from start.】的提示不要慌，因为图片太少，又从第一幅图片开始训练了。哈哈，我的精确率0.996 还是挺高的。

5 . 测试数据

① 找一个你要测试的图片，我找了一个想买但买不起的包包的图片。。

。。

②修改deploy.prototxt 并编写一个labels.txt

deploy.prototxt 修改一个地方

layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  inner_product_param {
    num_output: 2      #改成2
  }
}

labels.txt的内容如下：

bag
pants

② 打开data/myself 将myimagenet_mean.binaryproto 转换成 mymean.npy

打开jupyter写个python

import caffe
import numpy as np

proto_path='myimagenet_mean.binaryproto'
npy_path='mymean.npy'

blob=caffe.proto.caffe_pb2.BlobProto()
data=open(proto_path,'rb').read()
blob.ParseFromString(data)

array=np.array(caffe.io.blobproto_to_array(blob))
mean_npy=array[0]
np.save(npy_path,mean_npy)

把生成的mymean.npy复制到examples/myself下

③用Python写代码对 包包图片 分类

import caffe
import sys
import numpy as np

caffe_root='/home/caffe/'
sys.path.insert(0,caffe_root+'python')

caffe.set_mode_cpu()

deploy=caffe_root+'examples/myself/deploy.prototxt'
caffe_model=caffe_root+'examples/myself/mycaffenet_train_iter_1000.caffemodel'
img=caffe_root+'examples/myself/pinko.jpeg'
labels_name=caffe_root+'examples/myself/labels.txt'
mean_file=caffe_root+'examples/myself/mymean.npy'

net=caffe.Net(deploy,caffe_model,caffe.TEST)

transformer=caffe.io.Transformer({'data':net.blobs['data'].data.shape})
transformer.set_transpose('data',(2,0,1))
transformer.set_mean('data',np.load(mean_file).mean(1).mean(1))
transformer.set_raw_scale('data',255)
transformer.set_channel_swap('data',(2,1,0))

image=caffe.io.load_image(img)
net.blobs['data'].data[...]=transformer.preprocess('data',image)

out=net.forward()
labels=np.loadtxt(labels_name,str,delimiter='\t')

prob=net.blobs['prob'].data[0].flatten()
top_k=net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
for i in np.arange(top_k.size):
    print top_k[i],labels[top_k[i]],prob[top_k[i]]

下面是结果： 完美 O(∩_∩)O