我目前正在尝试为 PSPNet 的预测（最初在 Matlab 中）编写一个 c++ 包装器。PSPNet 在 Caffe 上运行。
情况：我有一个训练有素的 caffe 模型，并且想实现这个包装器以在给定输入时运行分割结果。在这种情况下，我的crop_size 小于它的原始大小。因此，它被手动裁剪为多个 425x425 “帧”，并在for-loop中的预处理后前馈到 caffe 网络。
问题：然而，尽管在 for 循环中，net 似乎只向前运行一次。受其处理时间和输出的支持，请参阅下文。
这是我目前正在尝试处理的不完整代码：
#define USE_OPENCV 1
#define trimapSize 1
#define Debug 0
#include <caffe/caffe.hpp>
#include "Header.h"
#include "caffe/data_reader.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/blob.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 <chrono> //Just for time measurement
#include <cmath>
#include <array>
#include <iostream>
#include <fstream>
#ifdef USE_OPENCV
using namespace caffe; // NOLINT(build/namespaces)
using std::string;
class Classifier {
public:
Classifier(const string& model_file,
const string& trained_file);
cv::Mat Predict(const cv::Mat& img);
private:
void SetMean(int weight, int heigh);
void WrapInputLayer(std::vector<cv::Mat>* input_channels);
cv::Mat Visualization(Blob<float>* output_layer);
cv::Mat Preprocess(const cv::Mat& img_scale, int ori_rows, int ori_cols, std::vector<cv::Mat>* input_channels);
private:
shared_ptr<Net<float> > net_;
cv::Size input_geometry_;
int num_channels_;
cv::Mat mean_;
};
Classifier::Classifier(const string& model_file,
const string& trained_file) {
Caffe::set_mode(Caffe::GPU);
/* 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(), 2) << "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());
}
/* Create the mean file in binaryproto format. */
void Classifier::SetMean(int weight, int heigh) {
mean_ = cv::Mat(heigh, weight, CV_32FC3);
mean_ = cv::Scalar(94.6744, 88.8887, 100.5404);//RGB
}
cv::Mat Classifier::Predict(const cv::Mat& img) {
cv::Mat originalTmp = img.clone();
Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(1, num_channels_,
input_geometry_.height, input_geometry_.width);
std::cout << "input_geometry_.height = " << input_geometry_.height << "input_geometry_.width = "<< input_geometry_.width << std::endl;
/* Forward dimension change to all layers. */
net_->Reshape();
std::vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels);
/*-----------------------------FOR MULTI-SCALE PROCESSING--------------------------*/
int base_size = 0;
int ori_rows = img.rows;
int ori_cols = img.cols;
float scale_array [1] = {1};
// float scale_array = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75]
std::cout << "ori_rows = " << ori_rows << "\t ori_cols = " << ori_cols << std::endl;
cv::Mat data_all = cv::Mat::zeros(cv::Size(425, 425), CV_32FC3);
if (ori_rows > ori_cols) {
base_size = ori_rows;
}
else base_size = ori_cols;
std::cout << "base_size = " << base_size << std::endl;
std::cout << "size of array = " << (sizeof(scale_array)/sizeof(*scale_array)) << std::endl;
for (int i=0; i < (sizeof(scale_array)/sizeof(*scale_array)); i++){
int long_size = base_size * scale_array[i] + 1;
int new_rows = long_size;
int new_cols = long_size;
std::cout << "BEFORE new rows = " << new_rows << "\t new cols = " << new_cols << std::endl;
if (ori_rows > ori_cols){
new_cols = round(long_size/ori_rows*ori_cols);
}
else {new_rows = round(long_size/ori_cols*ori_rows);}
std::cout << "AFTER new rows = " << new_rows << "\t new cols = " << new_cols << std::endl;
cv::Mat img_scale;
cv::resize(img, img_scale, cv::Size(new_cols, new_rows), 0, 0, CV_INTER_LINEAR);
std::cout << "img_scale height: " << img_scale.rows << "\t width = " << img_scale.cols << std::endl;
cv::imshow("img_scale",img_scale);
cv::waitKey(0);
data_all = data_all + Preprocess(img_scale, ori_rows, ori_cols, &input_channels);
std::cout << "ok! DONE PREPROCESS!" << std::endl;
}
return data_all;
}
cv::Mat Classifier::Preprocess(const cv::Mat& img_scale, int ori_rows, int ori_cols, std::vector<cv::Mat>* input_channels)
{
int crop_size = 425;
int new_rows = img_scale.rows;
int new_cols = img_scale.cols;
cv::Mat data_output = cv::Mat::zeros(cv::Size(ori_cols, ori_rows), CV_32FC3);
int long_size = new_rows;
cv::Mat img_processed;
if (new_cols > new_rows){
long_size = new_cols;
}
if (long_size <= crop_size){
// img_processed = Preprocess(img_scale, &input_channels);
//RUN CAFFE --- NOT YET DONE ---
std::cout << "OK!" << std::endl;
}
else {
float stride_rate = 2.0/3.0;
std::cout << "stride_rate = " << stride_rate << std::endl;
int stride = ceil(crop_size*stride_rate);
std::cout << "stride = " << stride << std::endl;
cv::Mat img_pad = img_scale;
int pad_rows = img_pad.rows;
int pad_cols = img_pad.cols;
int h_grid = ceil((pad_rows - crop_size)/stride) + 1;
int w_grid = ceil((pad_cols - crop_size)/stride) + 1;
cv::Mat img_sub;
cv::Mat data_scale = cv::Mat::zeros(cv::Size(pad_cols, pad_cols), CV_32FC3);
for(int grid_yidx = 1; grid_yidx <= h_grid; grid_yidx++){
for (int grid_xidx = 1; grid_xidx <= w_grid; grid_xidx++){
int s_x = (grid_xidx-1)*stride+1;
int s_y = (grid_yidx-1)*stride+1;
int e_x = std::min(s_x + crop_size -1, pad_cols);
int e_y = std::min(s_y + crop_size -1, pad_rows);
s_x = e_x - crop_size + 1;
s_y = e_y - crop_size + 1;
/* Cropping image */
img_pad(cv::Rect(s_x,s_y,crop_size,crop_size)).copyTo(img_sub);
cv::Mat sample;
if (img_sub.channels() == 3 && num_channels_ == 1)
cv::cvtColor(img_sub, sample, cv::COLOR_BGR2GRAY);
else if (img_sub.channels() == 4 && num_channels_ == 1)
cv::cvtColor(img_sub, sample, cv::COLOR_BGRA2GRAY);
else if (img_sub.channels() == 4 && num_channels_ == 3)
cv::cvtColor(img_sub, sample, cv::COLOR_BGRA2BGR);
else if (img_sub.channels() == 1 && num_channels_ == 3)
cv::cvtColor(img_sub, sample, cv::COLOR_GRAY2BGR);
else
sample = img_sub;
cv::Mat sample_float;
if (num_channels_ == 3)
sample.convertTo(sample_float, CV_32FC3);
else
sample.convertTo(sample_float, CV_32FC1);
SetMean(sample.rows, sample.cols);
cv::imshow("sample_float", sample_float);
cv::cvtColor(sample_float, sample_float, cv::COLOR_BGRA2RGB);
sample_float = sample_float.t();
cv::Mat sample_normalized(sample_float.size(),sample_float.type());
cv::subtract(sample_float.clone(), mean_, sample_normalized);
cv::Mat sample_temp;
sample_normalized.convertTo(sample_temp, CV_32FC3, 255);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/sample_normalized.png", sample_temp);
cv::imshow("sample_normalized", sample_normalized);
cv::waitKey(0);
/* 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. */
img_processed = sample_normalized.t();
cv::split(img_processed, *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.";
img_processed.convertTo(sample_temp, CV_32FC3, 255);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/img_processed.png", sample_temp);
cv::imshow("img_normalised",img_processed);
cv::waitKey();
std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now(); //Just for time measurement
// float loss = 0.0;
// net_->Forward(&loss);
net_->Forward();
std::chrono::steady_clock::time_point end= std::chrono::steady_clock::now();
std::cout << "Processing time = " << (std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count())/1000000.0 << " sec" <<std::endl; //Just for time measurement
/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
cv::Mat segment = Visualization(output_layer);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/segment.png", segment);
}
}
}
return (img_processed);
}
struct RGB {
int R;
int G;
int B;
};
vector<RGB> get_palette(int nClass)
{
vector<RGB> listPlalette;
RGB rgb0;
rgb0.R = 0;
rgb0.G = 0;
rgb0.B = 0;
listPlalette.push_back(rgb0);
for (int i = 1; i < nClass; i++)
{
RGB rgb;
rgb.R = i*50;
rgb.G = i*50 + i;
rgb.B = 255-i*20;
listPlalette.push_back(rgb);
}
return listPlalette;
}
cv::Mat Classifier::Visualization(Blob<float>* output_layer) {
std::vector<cv::Mat> input_channels;
int H = output_layer->height();
int W = output_layer->width();
// int N = output_layer->num(); //Batch Size
int C = output_layer->channels(); //Number of classes
int index = 0;
#ifdef CPU_ONLY
const float* output_data = output_layer->cpu_data();
#else
const float* output_data = output_layer->cpu_data();
#endif // !CPU_ONLY
cv::Mat class_each_row(C, W*H, CV_32F);
for (int i = 0; i < C; i++) {
for (int j = 0; j < (W*H); j++) {
class_each_row.at<float>(i, j) = output_data[index];
index = index + 1;
}
}
class_each_row = class_each_row.t();
//==================================CONVERT INTO LABELS==================================//
float maxValue = 0;
int* labelIndex = (int*)malloc(W*H * sizeof(int));
int indexX = 0;
for (int i = 0; i < class_each_row.rows; i++) {
maxValue = -999999999999;
indexX = 0;
for (int k = 0; k < C; k++)
{
float dataM = class_each_row.at<float>(i, k);
if (dataM > maxValue) {
maxValue = dataM;
indexX = k;
}
}
labelIndex[i] = indexX;
}
cv::Mat labelTmp(W, H, CV_8UC3);
uchar* dataLabelTmp = labelTmp.data;
vector<RGB> listPalette = get_palette(21);
for (int i = 0; i < H; i++)
{
for (int j = 0; j < W; j++)
{
RGB rgb = listPalette[labelIndex[(i*W + j)]];
dataLabelTmp[3 * (i*W + j)] = rgb.B;
dataLabelTmp[3 * (i*W + j) + 1] = rgb.G;
dataLabelTmp[3 * (i*W + j) + 2] = rgb.R;
}
}
cv::imshow( "Display window", labelTmp);
cv::waitKey(0);
free(labelIndex);
labelIndex = NULL;
return labelTmp;
}
/* 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;
}
}
int main(int argc, char** argv) {
if (argc != 4) {
std::cerr << "Usage: " << argv[0]
<< " \ndeploy.prototxt \nnetwork.caffemodel"
<< " \nimg.jpg" << " \ncamvid12.png (for example: /SegNet-Tutorial/Scripts/camvid12.png)" << std::endl;
return 1;
}
::google::InitGoogleLogging(argv[0]);
string model_file = argv[1];
string trained_file = argv[2]; //for visualization
Classifier classifier(model_file, trained_file);
string file = argv[3];
std::cout << "---------- Semantic Segmentation for "
<< file << " ----------" << std::endl;
cv::Mat img = cv::imread(file, 1);
CHECK(!img.empty()) << "Unable to decode image " << file;
cv::Mat prediction;
classifier.Predict(img);
}
#else
int main(int argc, char** argv) {
LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif //USE_OPENCV
澄清一下：for循环是指预处理中的循环：特别是这部分：
for(int grid_yidx = 1; grid_yidx <= h_grid; grid_yidx++){
for (int grid_xidx = 1; grid_xidx <= w_grid; grid_xidx++){
int s_x = (grid_xidx-1)*stride+1;
int s_y = (grid_yidx-1)*stride+1;
int e_x = std::min(s_x + crop_size -1, pad_cols);
int e_y = std::min(s_y + crop_size -1, pad_rows);
s_x = e_x - crop_size + 1;
s_y = e_y - crop_size + 1;
/* Cropping image */
img_pad(cv::Rect(s_x,s_y,crop_size,crop_size)).copyTo(img_sub);
cv::Mat sample;
if (img_sub.channels() == 3 && num_channels_ == 1)
cv::cvtColor(img_sub, sample, cv::COLOR_BGR2GRAY);
else if (img_sub.channels() == 4 && num_channels_ == 1)
cv::cvtColor(img_sub, sample, cv::COLOR_BGRA2GRAY);
else if (img_sub.channels() == 4 && num_channels_ == 3)
cv::cvtColor(img_sub, sample, cv::COLOR_BGRA2BGR);
else if (img_sub.channels() == 1 && num_channels_ == 3)
cv::cvtColor(img_sub, sample, cv::COLOR_GRAY2BGR);
else
sample = img_sub;
cv::Mat sample_float;
if (num_channels_ == 3)
sample.convertTo(sample_float, CV_32FC3);
else
sample.convertTo(sample_float, CV_32FC1);
SetMean(sample.rows, sample.cols);
cv::imshow("sample_float", sample_float);
cv::cvtColor(sample_float, sample_float, cv::COLOR_BGRA2RGB);
sample_float = sample_float.t();
cv::Mat sample_normalized(sample_float.size(),sample_float.type());
cv::subtract(sample_float.clone(), mean_, sample_normalized);
cv::Mat sample_temp;
sample_normalized.convertTo(sample_temp, CV_32FC3, 255);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/sample_normalized.png", sample_temp);
cv::imshow("sample_normalized", sample_normalized);
cv::waitKey(0);
/* 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. */
img_processed = sample_normalized.t();
cv::split(img_processed, *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.";
img_processed.convertTo(sample_temp, CV_32FC3, 255);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/img_processed.png", sample_temp);
cv::imshow("img_normalised",img_processed);
cv::waitKey();
std::chrono::steady_clock::time_point begin = std::chrono::steady_clock::now(); //Just for time measurement
// float loss = 0.0;
// net_->Forward(&loss);
net_->Forward();
std::chrono::steady_clock::time_point end= std::chrono::steady_clock::now();
std::cout << "Processing time = " << (std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count())/1000000.0 << " sec" <<std::endl; //Just for time measurement
/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
cv::Mat segment = Visualization(output_layer);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/segment.png", segment);
}
}
原始图像：原始图像（未经预处理）
输入：输入（第一个裁剪的帧）
输出：第一个裁剪帧的输出
转发所用时间：所用时间
裁剪后的帧通过 out 给出相同的输出。
P/s：如果我将下面的代码移到预测函数的末尾并返回段，它将运行良好。但只有最后一个裁剪的帧会被分割。
std::chrono::steady_clock::time_point begin =
std::chrono::steady_clock::now(); //Just for time measurement
// float loss = 0.0;
// net_->Forward(&loss);
net_->Forward();
std::chrono::steady_clock::time_point end = std::chrono::steady_clock::now();
std::cout << "Processing time = " << (std::chrono::duration_cast<std::chrono::microseconds>(end - begin).count())/1000000.0 << " sec" <<std::endl; //Just for time measurement
/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
cv::Mat segment = Visualization(output_layer);
cv::imwrite("/home/sgp1053c/Desktop/PSPNET-cudnn5_wrapper/wrapper/segment.png", segment);`
output：最后一个裁剪帧的输出
任何帮助将不胜感激，谢谢你！