C++遍历获取文件夹下面所有文件名

#include <iostream>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#ifdef linux
#include <unistd.h>
#include <dirent.h>
#endif
#ifdef WIN32
#include <direct.h>
#include <io.h>
#endif
using namespace std;

vector<string> getFiles(string cate_dir)
{
vector<string> files;//存放文件名

#ifdef WIN32
_finddata_t file;
long lf;
//输入文件夹路径
if ((lf=_findfirst(cate_dir.c_str(), &file)) == -1) {
cout<<cate_dir<<" not found!!!"<<endl;
} else {
while(_findnext(lf, &file) == 0) {
//输出文件名
//cout<<file.name<<endl;
if (strcmp(file.name, ".") == 0 || strcmp(file.name, "..") == 0)
continue;
files.push_back(file.name);
}
}
_findclose(lf);
#endif

#ifdef linux
DIR *dir;
struct dirent *ptr;
char base[1000];

if ((dir=opendir(cate_dir.c_str())) == NULL)
{
perror("Open dir error...");
exit(1);
}

while ((ptr=readdir(dir)) != NULL)
{
if(strcmp(ptr->d_name,".")==0 || strcmp(ptr->d_name,"..")==0)    ///current dir OR parrent dir
continue;
else if(ptr->d_type == 8)    ///file
//printf("d_name:%s/%s\n",basePath,ptr->d_name);
files.push_back(ptr->d_name);
else if(ptr->d_type == 10)    ///link file
//printf("d_name:%s/%s\n",basePath,ptr->d_name);
continue;
else if(ptr->d_type == 4)    ///dir
{
files.push_back(ptr->d_name);
/*
memset(base,'\0',sizeof(base));
strcpy(base,basePath);
strcat(base,"/");
strcat(base,ptr->d_nSame);
readFileList(base);
*/
}
}
closedir(dir);
#endif

//排序，按从小到大排序
sort(files.begin(), files.end());
return files;
}

返回的是一个vector，调用的话就是files[i]

比如zqh文件夹下面有1.jpg,2.jpg,那么files[0]=”1.jpg”,files[1]=”2.jpg”

以下是做图像分类的时候，调用caffe的classification.bin（改动以后的），遍历文件夹下的所有图片，并将每张图片属于每一类的概率写入csv文件。

#include <caffe/caffe.hpp>
#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif  // USE_OPENCV
#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include <fstream>
#include <unistd.h>
#include <dirent.h>
#include <stdlib.h>
#ifdef USE_OPENCV
using namespace caffe;  // NOLINT(build/namespaces)
using std::string;
using namespace std;
ofstream outFile;

/* Pair (label, confidence) representing a prediction. */
typedef std::pair<string, float> Prediction;

class Classifier {
public:
Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file);

std::vector<Prediction> Classify(const cv::Mat& img, const string file,int N = 5);

private:
void SetMean(const string& mean_file);

std::vector<float> Predict(const cv::Mat& img);

void WrapInputLayer(std::vector<cv::Mat>* input_channels);

void Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels);

private:
shared_ptr<Net<float> > net_;
cv::Size input_geometry_;
int num_channels_;
cv::Mat mean_;
std::vector<string> labels_;
};

Classifier::Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file) {
#ifdef CPU_ONLY
Caffe::set_mode(Caffe::CPU);
#else
Caffe::set_mode(Caffe::GPU);
#endif

/* Load the network. */
net_.reset(new Net<float>(model_file, TEST));
net_->CopyTrainedLayersFrom(trained_file);

CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";

Blob<float>* input_layer = net_->input_blobs()[0];
num_channels_ = input_layer->channels();
CHECK(num_channels_ == 3 || num_channels_ == 1)
<< "Input layer should have 1 or 3 channels.";
input_geometry_ = cv::Size(input_layer->width(), input_layer->height());

/* Load the binaryproto mean file. */
SetMean(mean_file);

/* Load labels. */
std::ifstream labels(label_file.c_str());
CHECK(labels) << "Unable to open labels file " << label_file;
string line;
while (std::getline(labels, line))
labels_.push_back(string(line));

Blob<float>* output_layer = net_->output_blobs()[0];
CHECK_EQ(labels_.size(), output_layer->channels())
<< "Number of labels is different from the output layer dimension.";
}

static bool PairCompare(const std::pair<float, int>& lhs,
const std::pair<float, int>& rhs) {
return lhs.first > rhs.first;
}

/* Return the indices of the top N values of vector v. */
static std::vector<int> Argmax(const std::vector<float>& v, int N) {
std::vector<std::pair<float, int> > pairs;
for (size_t i = 0; i < v.size(); ++i)
pairs.push_back(std::make_pair(v[i], i));
std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);

std::vector<int> result;
for (int i = 0; i < N; ++i)
result.push_back(pairs[i].second);
return result;
}

/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, const string file,int N) {
std::vector<float> output = Predict(img);
int i;
double sum=0;
int cnt=0;
int b=file.find('.');
for(i=0;i<output.size();i++)
{
sum+=output[i];
if(i)
{
outFile << file.substr(0,b) << ',' << i << ',' << output[i] << endl;
}
if(i==29)
{
outFile << file.substr(0,b) << ',' << 30 << ',' << output[0] << endl;
}
}
if(sum > 1)
{
cnt++;
cout << cnt << endl;
}
N = std::min<int>(labels_.size(), N);
std::vector<int> maxN = Argmax(output, N);
std::vector<Prediction> predictions;
for (int i = 0; i < N; ++i) {
int idx = maxN[i];
predictions.push_back(std::make_pair(labels_[idx], output[idx]));
}

return predictions;
}

/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);

/* Convert from BlobProto to Blob<float> */
Blob<float> mean_blob;
mean_blob.FromProto(blob_proto);
CHECK_EQ(mean_blob.channels(), num_channels_)
<< "Number of channels of mean file doesn't match input layer.";

/* The format of the mean file is planar 32-bit float BGR or grayscale. */
std::vector<cv::Mat> channels;
float* data = mean_blob.mutable_cpu_data();
for (int i = 0; i < num_channels_; ++i) {
/* Extract an individual channel. */
cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
channels.push_back(channel);
data += mean_blob.height() * mean_blob.width();
}

/* Merge the separate channels into a single image. */
cv::Mat mean;
cv::merge(channels, mean);

/* Compute the global mean pixel value and create a mean image
* filled with this value. */
cv::Scalar channel_mean = cv::mean(mean);
mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
}

std::vector<float> Classifier::Predict(const cv::Mat& img) {
Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(1, num_channels_,
input_geometry_.height, input_geometry_.width);
/* Forward dimension change to all layers. */
net_->Reshape();

std::vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels);
double mul=0.00390625;
cv::Mat out_img=img*mul;
Preprocess(out_img, &input_channels);

net_->Forward();

/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
const float* begin = output_layer->cpu_data();
const float* end = begin + output_layer->channels();
return std::vector<float>(begin, end);
}

/* Wrap the input layer of the network in separate cv::Mat objects
* (one per channel). This way we save one memcpy operation and we
* don't need to rely on cudaMemcpy2D. The last preprocessing
* operation will write the separate channels directly to the input
* layer. */
void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
Blob<float>* input_layer = net_->input_blobs()[0];

int width = input_layer->width();
int height = input_layer->height();
float* input_data = input_layer->mutable_cpu_data();
for (int i = 0; i < input_layer->channels(); ++i) {
cv::Mat channel(height, width, CV_32FC1, input_data);
input_channels->push_back(channel);
input_data += width * height;
}
}

void Classifier::Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels) {
/* Convert the input image to the input image format of the network. */
cv::Mat sample;
if (img.channels() == 3 && num_channels_ == 1)
cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
else if (img.channels() == 4 && num_channels_ == 1)
cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
else if (img.channels() == 4 && num_channels_ == 3)
cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
else if (img.channels() == 1 && num_channels_ == 3)
cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
else
sample = img;

cv::Mat sample_resized;
if (sample.size() != input_geometry_)
cv::resize(sample, sample_resized, input_geometry_);
else
sample_resized = sample;

cv::Mat sample_float;
if (num_channels_ == 3)
sample_resized.convertTo(sample_float, CV_32FC3);
else
sample_resized.convertTo(sample_float, CV_32FC1);

cv::Mat sample_normalized;
cv::subtract(sample_float, mean_, sample_normalized);

/* This operation will write the separate BGR planes directly to the
* input layer of the network because it is wrapped by the cv::Mat
* objects in input_channels. */
cv::split(sample_normalized, *input_channels);

CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
== net_->input_blobs()[0]->cpu_data())
<< "Input channels are not wrapping the input layer of the network.";
}
vector<string> getFiles(string cate_dir)
{
vector<string> files;
DIR *dir;
struct dirent *ptr;

if ((dir=opendir(cate_dir.c_str())) == NULL)
{
perror("Open dir error...");
exit(1);
}

while ((ptr=readdir(dir)) != NULL)
{
if(strcmp(ptr->d_name,".")==0 || strcmp(ptr->d_name,"..")==0)    ///current dir OR parrent dir
continue;
else if(ptr->d_type == 8)    ///file
//printf("d_name:%s/%s\n",basePath,ptr->d_name);
files.push_back(ptr->d_name);
else if(ptr->d_type == 10)    ///link file
//printf("d_name:%s/%s\n",basePath,ptr->d_name);
continue;
else if(ptr->d_type == 4)    ///dir
{
files.push_back(ptr->d_name);
/*
memset(base,'\0',sizeof(base));
strcpy(base,basePath);
strcat(base,"/");
strcat(base,ptr->d_nSame);
readFileList(base);
*/
}
}
closedir(dir);
sort(files.begin(), files.end());
return files;
}
int main(int argc, char** argv) {
if (argc != 5) {
std::cerr << "Usage: " << argv[0]
<< " deploy.prototxt network.caffemodel"
<< " mean.binaryproto labels.txt img.jpg" << std::endl;
return 1;
}
outFile.open("/home/zq/test.csv", ios::out);
::google::InitGoogleLogging(argv[0]);

string model_file   = argv[1];
string trained_file = argv[2];
string mean_file    = argv[3];
string label_file   = argv[4];
Classifier classifier(model_file, trained_file, mean_file, label_file);
string filepath = "data/pigface/test/";
vector<string> files;
files=getFiles(filepath);
int size = files.size();
cout << size << endl;
int j;
for(j=0;j<size;j++)
{
cv::Mat img = cv::imread(filepath+files[j], -1);
CHECK(!img.empty()) << "Unable to decode image " << files[j];
std::vector<Prediction> predictions = classifier.Classify(img,files[j]);

/* Print the top N predictions.
for (size_t i = 0; i < predictions.size(); ++i) {
Prediction p = predictions[i];
std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
<< p.first << "\"" << std::endl;
}
*/
}
outFile.close();
}
#else
int main(int argc, char** argv) {
LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif  // USE_OPENCV