当我阅读ssd-caffe代码时有一些问题，我确实需要您的帮助。
注意:
最佳答案
数据层提供了异步读取硬盘上随机数据的可能性(它使用2个线程:一个线程读取数据，另一个线程将数据传送到神经网络)。您的顶部Blob由数据和标签组成。不幸的是，标签是一维的。为了解决此问题，可以以特殊顺序组织lmdb数据库。然后，当我们读取数据时，在将其传递到神经网络之前，我们先对其进行转换以使其适应我们的问题。在下面的示例中，我将进行演示:首先，我将编写一个LMDB数据库，其中包含10个不同的图像(它是同一张图像，但我们假定它们是不同的)，10个随机边界框和10个维度为3的随机标签。
注意:要重现以下代码，您必须安装caffe。如果只编译了caffe文件夹，则在 root_caffe / examples / new_folder 中创建该文件夹，将代码放入其中，然后进行编译 make 。
#include <caffe/caffe.hpp>
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/db.hpp"
#include "boost/scoped_ptr.hpp"
#include <opencv2/imgcodecs.hpp>
#include <iostream>
#include <stdlib.h>
using namespace caffe;
using boost::scoped_ptr;
std::vector<float> generate_random_boxes(const int max_num_bbx){
std::vector<float> bbx(4*max_num_bbx);
for(int i = 0; i < max_num_bbx; i++){
float scale = 500*static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
float x1 = static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
float y1 = static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
float x2 = x1 + static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
float y2 = x1 + static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
bbx[i*4] = scale*x1;
bbx[i*4 + 1] = scale*y1;
bbx[i*4 + 2] = scale*x2;
bbx[i*4 + 3] = scale*y2;
}
return bbx;
}
std::vector<float> generate_random_labels(const int dim_label, const int max_num_bbx){
std::vector<float> labels(dim_label*max_num_bbx);
for(int i = 0; i < max_num_bbx; i++){
for(int j = 0; j < dim_label; j++){
labels[dim_label*i + j] = static_cast <float> (rand()) / static_cast <float> (RAND_MAX);
}
}
return labels;
}
int main(){
const std::string root_path = "/path/for/test/";
const std::string path_lmdb = root_path + "lmdb";
std::string rm_lmdb = std::string("rm -rf ") + path_lmdb.c_str();
system(rm_lmdb.c_str());
scoped_ptr<db::DB> db(db::GetDB("lmdb"));
db->Open(path_lmdb, db::NEW);
scoped_ptr<db::Transaction> txn(db->NewTransaction());
int n = 10;
int max_num_bbx = 7;
int dim_label = 3;
cv::Mat aux_img = cv::imread(root_path + "image.jpg");
int rows = aux_img.rows;
int cols = aux_img.cols;
std::vector<cv::Mat> vec_img(n);
std::vector< std::vector<float> > vec_bbx(n);
std::vector< std::vector<float> > vec_label(n);
for(int i = 0; i < n; i++){
vec_img[i] = aux_img.clone();
vec_bbx[i] = generate_random_boxes(max_num_bbx);
vec_label[i] = generate_random_labels(dim_label, max_num_bbx);
}
for(int i = 0; i< n; i++){
int sz = 3*rows*cols + 4*max_num_bbx + dim_label*max_num_bbx;
Datum datum;
datum.set_label(0); //no used
datum.set_channels(1);
datum.set_height(1);
datum.set_width(sz);
google::protobuf::RepeatedField<float>* datumFloatData = datum.mutable_float_data();
//store images
cv::Mat img = vec_img[i];
for(int d = 0; d < 3; d++){ //BGR
for(int r = 0; r < rows; r++){
for(int c = 0; c < cols; c++){
cv::Vec3b pixel = img.at<cv::Vec3b>(r, c);
datumFloatData->Add(float(pixel[d]));
}
}
}
//store bounding-boxes
std::vector<float>& bbx = vec_bbx[i];
for(int j = 0; j < 4*max_num_bbx; j++)
datumFloatData->Add(bbx[j]);
//store labels
std::vector<float>& label = vec_label[i];
for(int j = 0; j < dim_label*max_num_bbx; j++)
datumFloatData->Add(label[j]);
//store lmdb
std::string key_str = caffe::format_int(i);
std::string out;
CHECK(datum.SerializeToString(&out));
txn->Put(key_str, out);
txn->Commit();
txn.reset(db->NewTransaction());
std::cout<<"save data: "<<i<<std::endl;
}
return 0;
}
然后在文件夹“/ path / for / test” 中，我们将有一个名为 lmdb 的文件夹，其中包含我们的数据库。现在，我们必须读取数据并按所需顺序对其进行组织。为此，我将使用 Slice 层，该层允许将输入的底部数据划分为多个顶部。因此，由图像批处理，边界框和标签组成的输入数据将被分为5个顶部Blob:img_b，img_g，img_r，bbx，标签。
#include <caffe/caffe.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "boost/scoped_ptr.hpp"
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
using namespace caffe;
using boost::scoped_ptr;
int main(){
const std::string root_path = "/path/for/test/";
const std::string path_lmdb = root_path + "lmdb";
//parameters used to store lmdb data base
int n = 10;
int max_num_bbx = 7;
int dim_label = 3;
cv::Mat aux_img = cv::imread(root_path + "image.jpg");
int rows = aux_img.rows;
int cols = aux_img.cols;
//here we build the network input
NetParameter net_param;
LayerParameter* db_layer_param = net_param.add_layer();
db_layer_param->set_name("data");
db_layer_param->set_type("Data");
DataParameter* db_data_param = db_layer_param->mutable_data_param();
db_data_param->set_batch_size(2);
db_data_param->set_prefetch(3);
db_data_param->set_source(path_lmdb);
db_data_param->set_backend(DataParameter_DB_LMDB);
db_layer_param->add_top("data");
LayerParameter* slice_layer_param = net_param.add_layer();
slice_layer_param->set_name("slice");
slice_layer_param->set_type("Slice");
slice_layer_param->mutable_slice_param()->set_axis(3);//starting B
slice_layer_param->mutable_slice_param()->add_slice_point(rows*cols);//starting G
slice_layer_param->mutable_slice_param()->add_slice_point(2*rows*cols);//starting R
slice_layer_param->mutable_slice_param()->add_slice_point(3*rows*cols);//starting bbx
slice_layer_param->mutable_slice_param()->add_slice_point(3*rows*cols + 4*max_num_bbx);//starting labels
slice_layer_param->add_bottom("data");
slice_layer_param->add_top("img_b");
slice_layer_param->add_top("img_g");
slice_layer_param->add_top("img_r");
slice_layer_param->add_top("bbx");
slice_layer_param->add_top("labels");
//NOTE: you must add the additional layers of your model
/*
.
.
.
.
*/
//here we store and load the model
//NOTE:In this example is not necessary to store the model in prototxt file
const std::string net_file = root_path + "model.prototxt";
Net<float> net(net_param);
WriteProtoToTextFile(net_param,net_file);
//here we make forward in order to read our data
net.Forward();
/*Note that in this example we read 2 images, but then we will only show the first*/
//read first image
boost::shared_ptr< Blob< float > > img_b = net.blob_by_name("img_b");
boost::shared_ptr< Blob< float > > img_g = net.blob_by_name("img_g");
boost::shared_ptr< Blob< float > > img_r = net.blob_by_name("img_r");
cv::Mat img(rows,cols,CV_8UC3);
for(int r = 0; r < rows; r++){
for(int c = 0; c < cols; c++){
img.at<cv::Vec3b>(r,c)[0] = (uchar) img_b->cpu_data()[r*cols + c];
img.at<cv::Vec3b>(r,c)[1] = (uchar) img_g->cpu_data()[r*cols + c];
img.at<cv::Vec3b>(r,c)[2] = (uchar) img_r->cpu_data()[r*cols + c];
}
}
//read bounding boxes
boost::shared_ptr< Blob< float > > bbx = net.blob_by_name("bbx");
for(int i = 0; i < max_num_bbx; i++){
float x1 = bbx->cpu_data()[4*i];
float y1 = bbx->cpu_data()[4*i + 1];
float x2 = bbx->cpu_data()[4*i + 2];
float y2 = bbx->cpu_data()[4*i + 3];
cv::Point pt1(y1, x1);
cv::Point pt2(y2, x2);
cv::rectangle(img, pt1, pt2, cv::Scalar(0, 255, 0));
}
//read labels
boost::shared_ptr< Blob< float > > labels = net.blob_by_name("labels");
std::cout<<"labels: "<<std::endl;
for(int i = 0; i < max_num_bbx; i++){
for(int j = 0; j < dim_label; j++){
std::cout<<labels->cpu_data()[i*dim_label + j]<<" ";
}
std::cout<<std::endl;
}
cv::imshow("img", img);
cv::waitKey(0);
return 0;
}
生成的输出如下:
数据和 Slice层使用WriteProtoToTextFile(net_param，net_file)生成的原型(prototype)文件如下:
layer {
name: "data"
type: "Data"
top: "data"
data_param {
source: "/path/for/test/lmdb"
batch_size: 2
backend: LMDB
prefetch: 3
}
}
layer {
name: "slice"
type: "Slice"
bottom: "data"
top: "img_b"
top: "img_g"
top: "img_r"
top: "bbx"
top: "labels"
slice_param {
slice_point: 344000
slice_point: 688000
slice_point: 1032000
slice_point: 1032028
axis: 3
}
}
在 Slice图层之后，您可能需要添加其他 Reshape图层以使数据适应后续图层。
关于c++ - 如何在ssd-caffe中加载图像而不是LMDB，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/62307905/