这里是c++测试版本的boxDetect.cpp，来源应该是SSD
// This is a demo code for using a SSD model to do detection.
// The code is modified from examples/cpp_classification/classification.cpp.
// Usage:
// ssd_detect [FLAGS] model_file weights_file list_file
//
// where model_file is the .prototxt file defining the network architecture, and
// weights_file is the .caffemodel file containing the network parameters, and
// list_file contains a list of image files with the format as follows:
// folder/img1.JPEG
// folder/img2.JPEG
// list_file can also contain a list of video files with the format as follows:
// folder/video1.mp4
// folder/video2.mp4
//
#include <stdio.h>
#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 <iomanip>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "caffe/boxDetect.h"
#ifdef USE_OPENCV
using namespace caffe; // NOLINT(build/namespaces)
using namespace std;
Detector::Detector(const string& model_file,
const string& weights_file,
const string& mean_file,
const string& mean_value) {
#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));//重新构建网络，调用Net的构造方法
net_->CopyTrainedLayersFrom(weights_file);//载入模型参数
CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
//输入层
Blob<float>* input_layer = net_->input_blobs()[0];
num_channels_ = input_layer->channels();
//输入层一般是彩色图像、或灰度图像，因此需要进行判断
CHECK(num_channels_ == 3 || num_channels_ == 1)
<< "Input layer should have 1 or 3 channels.";
//输入图像的尺寸
input_geometry_.push_back(cv::Size(768, 768));
/* Load the binaryproto mean file. */
SetMean(mean_file, mean_value);
}
std::vector<vector<float> > Detector::Detect(const cv::Mat& img) {
vector<vector<float> > detections;
for(int i=0;i< input_geometry_.size();i++)
{
int img_height = input_geometry_[i].height;
int img_width = input_geometry_[i].width;
Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(1, num_channels_,
img_height, img_width);
/* Forward dimension change to all layers. */
net_->Reshape();
std::vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels, i);
Preprocess(img, &input_channels, i);
net_->Forward();
/* Copy the output layer to a std::vector */
Blob<float>* result_blob = net_->output_blobs()[0];
const float* result = result_blob->cpu_data();
//const int num_det = result_blob->height();
// vector<vector<float> > detections;
int j=0;
//get datas for textboxes++
while(true)
{
if(result[j]==0&&result[j+1]==0&&result[j+2]==0)
{
break;
}
vector<float> detection;
detection.push_back(result[j+2]);
detection.push_back(result[j+7]);
detection.push_back(result[j+8]);
detection.push_back(result[j+9]);
detection.push_back(result[j+10]);
detection.push_back(result[j+11]);
detection.push_back(result[j+12]);
detection.push_back(result[j+13]);
detection.push_back(result[j+14]);
detections.push_back(detection);
j=j+15;
}
}
return detections;
}
/* Load the mean file in binaryproto format. */
void Detector::SetMean(const string& mean_file, const string& mean_value) {
// cv::Scalar channel_mean;
// if (!mean_file.empty()) {
// CHECK(mean_value.empty()) <<
// "Cannot specify mean_file and mean_value at the same time";
// 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. */
// channel_mean = cv::mean(mean);
// mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
// }
for (int m = 0; m < input_geometry_.size(); ++m)
{
if (!mean_value.empty()) {
CHECK(mean_file.empty()) <<
"Cannot specify mean_file and mean_value at the same time";
stringstream ss(mean_value);
vector<float> values;
string item;
while (getline(ss, item, ',')) {
float value = std::atof(item.c_str());
values.push_back(value);
}
CHECK(values.size() == 1 || values.size() == num_channels_) <<
"Specify either 1 mean_value or as many as channels: " << num_channels_;
std::vector<cv::Mat> channels;
cv::Mat mean;
for (int i = 0; i < num_channels_; ++i) {
/* Extract an individual channel. */
cv::Mat channel(input_geometry_[m].height, input_geometry_[m].width, CV_32FC1,
cv::Scalar(values[i]));
channels.push_back(channel);
}
cv::merge(channels, mean);
mean_.push_back(mean);
}
}
}
/* 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. */
/* 这个其实是为了获得net_网络的输入层数据的指针，然后后面我们直接把输入图片数据拷贝到这个指针里面*/
void Detector::WrapInputLayer(std::vector<cv::Mat>* input_channels, int n) {
Blob<float>* input_layer = net_->input_blobs()[0];
int width = input_geometry_[n].width;
int height = input_geometry_[n].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;
}
}
//图片预处理函数，包括图片缩放、归一化、3通道图片分开存储
//对于三通道输入CNN，经过该函数返回的是std::vector<cv::Mat>因为是三通道数据，索引用了vector
void Detector::Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels, int n) {
/* Convert the input image to the input image format of the network. */
cv::Mat sample;
//如果输入图片是一张彩色图片，但是CNN的输入是一张灰度图像，那么我们需要把彩色图片转换成灰度图片
//如果输入图片是灰度图片，或者是4通道图片，而CNN的输入要求是彩色图片，因此我们也需要把它转化成三通道彩色图片
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;
/*2、缩放处理，因为我们输入的一张图片如果是任意大小的图片，那么我们就应该把它缩放到227×227*/
cv::Mat sample_resized;
if (sample.size() != input_geometry_[n])
cv::resize(sample, sample_resized, input_geometry_[n]);
else
sample_resized = sample;
/*3、数据类型处理，因为我们的图片是uchar类型，我们需要把数据转换成float类型*/
cv::Mat sample_float;
if (num_channels_ == 3)
sample_resized.convertTo(sample_float, CV_32FC3);
else
sample_resized.convertTo(sample_float, CV_32FC1);
//均值归一化，为什么没有大小归一化？
cv::Mat sample_normalized;
cv::subtract(sample_float, mean_[n], 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. */
/* 3通道数据分开存储 */
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.";
}
DEFINE_string(mean_file, "",
"The mean file used to subtract from the input image.");
DEFINE_string(mean_value, "104,117,123",
"If specified, can be one value or can be same as image channels"
" - would subtract from the corresponding channel). Separated by ','."
"Either mean_file or mean_value should be provided, not both.");
DEFINE_string(file_type, "image",
"The file type in the list_file. Currently support image and video.");
DEFINE_string(out_file, "",
"If provided, store the detection results in the out_file.");
DEFINE_double(confidence_threshold, 0.01,
"Only store detections with score higher than the threshold.");
#endif // USE_OPENCV
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