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#include <ros/ros.h>
#include <image_transport/image_transport.h>
#include <cv_bridge/cv_bridge.h>
#include <sensor_msgs/image_encodings.h>
#include <initializer_list>
#include <torch/script.h> // One-stop header.
#include <opencv2/core/version.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/opencv_modules.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video/video.hpp>
// includes for OpenCV >= 3.x
#ifndef CV_VERSION_EPOCH
#include <opencv2/core/types.hpp>
#include <opencv2/videoio/videoio.hpp>
#include <opencv2/imgcodecs/imgcodecs.hpp>
#endif
// OpenCV includes for OpenCV 2.x
#ifdef CV_VERSION_EPOCH
#include <opencv2/highgui/highgui_c.h>
#include <opencv2/imgproc/imgproc_c.h>
#include <opencv2/core/types_c.h>
#include <opencv2/core/version.hpp>
#endif
static const std::string OPENCV_WINDOW = "Image window";
class Midas
{
ros::NodeHandle nh_;
image_transport::ImageTransport it_;
image_transport::Subscriber image_sub_;
image_transport::Publisher image_pub_;
torch::jit::script::Module module;
torch::Device device;
auto ToTensor(cv::Mat img, bool show_output = false, bool unsqueeze = false, int unsqueeze_dim = 0)
{
//std::cout << "image shape: " << img.size() << std::endl;
at::Tensor tensor_image = torch::from_blob(img.data, { img.rows, img.cols, 3 }, at::kByte);
if (unsqueeze)
{
tensor_image.unsqueeze_(unsqueeze_dim);
//std::cout << "tensors new shape: " << tensor_image.sizes() << std::endl;
}
if (show_output)
{
std::cout << tensor_image.slice(2, 0, 1) << std::endl;
}
//std::cout << "tenor shape: " << tensor_image.sizes() << std::endl;
return tensor_image;
}
auto ToInput(at::Tensor tensor_image)
{
// Create a vector of inputs.
return std::vector<torch::jit::IValue>{tensor_image};
}
auto ToCvImage(at::Tensor tensor, int cv_type = CV_8UC3)
{
int width = tensor.sizes()[0];
int height = tensor.sizes()[1];
try
{
cv::Mat output_mat;
if (cv_type == CV_8UC4 || cv_type == CV_8UC3 || cv_type == CV_8UC2 || cv_type == CV_8UC1) {
cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr<uchar>());
output_mat = cv_image;
}
else if (cv_type == CV_32FC4 || cv_type == CV_32FC3 || cv_type == CV_32FC2 || cv_type == CV_32FC1) {
cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr<float>());
output_mat = cv_image;
}
else if (cv_type == CV_64FC4 || cv_type == CV_64FC3 || cv_type == CV_64FC2 || cv_type == CV_64FC1) {
cv::Mat cv_image(cv::Size{ height, width }, cv_type, tensor.data_ptr<double>());
output_mat = cv_image;
}
//show_image(output_mat, "converted image from tensor");
return output_mat.clone();
}
catch (const c10::Error& e)
{
std::cout << "an error has occured : " << e.msg() << std::endl;
}
return cv::Mat(height, width, CV_8UC3);
}
std::string input_topic, output_topic, model_name;
bool out_orig_size;
int net_width, net_height;
torch::NoGradGuard guard;
at::Tensor mean, std;
at::Tensor output, tensor;
public:
Midas()
: nh_(), it_(nh_), device(torch::Device(torch::kCPU))
{
ros::param::param<std::string>("~input_topic", input_topic, "image_topic");
ros::param::param<std::string>("~output_topic", output_topic, "midas_topic");
ros::param::param<std::string>("~model_name", model_name, "model-small-traced.pt");
ros::param::param<bool>("~out_orig_size", out_orig_size, true);
ros::param::param<int>("~net_width", net_width, 256);
ros::param::param<int>("~net_height", net_height, 256);
std::cout << ", input_topic = " << input_topic <<
", output_topic = " << output_topic <<
", model_name = " << model_name <<
", out_orig_size = " << out_orig_size <<
", net_width = " << net_width <<
", net_height = " << net_height <<
std::endl;
// Subscrive to input video feed and publish output video feed
image_sub_ = it_.subscribe(input_topic, 1, &Midas::imageCb, this);
image_pub_ = it_.advertise(output_topic, 1);
std::cout << "Try to load torchscript model \n";
try {
// Deserialize the ScriptModule from a file using torch::jit::load().
module = torch::jit::load(model_name);
}
catch (const c10::Error& e) {
std::cerr << "error loading the model\n";
exit(0);
}
std::cout << "ok\n";
try {
module.eval();
torch::jit::getProfilingMode() = false;
torch::jit::setGraphExecutorOptimize(true);
mean = torch::tensor({ 0.485, 0.456, 0.406 });
std = torch::tensor({ 0.229, 0.224, 0.225 });
if (torch::hasCUDA()) {
std::cout << "cuda is available" << std::endl;
at::globalContext().setBenchmarkCuDNN(true);
device = torch::Device(torch::kCUDA);
module.to(device);
mean = mean.to(device);
std = std.to(device);
}
}
catch (const c10::Error& e)
{
std::cerr << " module initialization: " << e.msg() << std::endl;
}
}
~Midas()
{
}
void imageCb(const sensor_msgs::ImageConstPtr& msg)
{
cv_bridge::CvImagePtr cv_ptr;
try
{
// sensor_msgs::Image to cv::Mat
cv_ptr = cv_bridge::toCvCopy(msg, sensor_msgs::image_encodings::RGB8);
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
// pre-processing
auto tensor_cpu = ToTensor(cv_ptr->image); // OpenCV-image -> Libtorch-tensor
try {
tensor = tensor_cpu.to(device); // move to device (CPU or GPU)
tensor = tensor.toType(c10::kFloat);
tensor = tensor.permute({ 2, 0, 1 }); // HWC -> CHW
tensor = tensor.unsqueeze(0);
tensor = at::upsample_bilinear2d(tensor, { net_height, net_width }, true); // resize
tensor = tensor.squeeze(0);
tensor = tensor.permute({ 1, 2, 0 }); // CHW -> HWC
tensor = tensor.div(255).sub(mean).div(std); // normalization
tensor = tensor.permute({ 2, 0, 1 }); // HWC -> CHW
tensor.unsqueeze_(0); // CHW -> NCHW
}
catch (const c10::Error& e)
{
std::cerr << " pre-processing exception: " << e.msg() << std::endl;
return;
}
auto input_to_net = ToInput(tensor); // input to the network
// inference
output;
try {
output = module.forward(input_to_net).toTensor(); // run inference
}
catch (const c10::Error& e)
{
std::cerr << " module.forward() exception: " << e.msg() << std::endl;
return;
}
output = output.detach().to(torch::kF32);
// move to CPU temporary
at::Tensor output_tmp = output;
output_tmp = output_tmp.to(torch::kCPU);
// normalization
float min_val = std::numeric_limits<float>::max();
float max_val = std::numeric_limits<float>::min();
for (int i = 0; i < net_width * net_height; ++i) {
float val = output_tmp.data_ptr<float>()[i];
if (min_val > val) min_val = val;
if (max_val < val) max_val = val;
}
float range_val = max_val - min_val;
output = output.sub(min_val).div(range_val).mul(255.0F).clamp(0, 255).to(torch::kF32); // .to(torch::kU8);
// resize to the original size if required
if (out_orig_size) {
try {
output = at::upsample_bilinear2d(output.unsqueeze(0), { cv_ptr->image.size().height, cv_ptr->image.size().width }, true);
output = output.squeeze(0);
}
catch (const c10::Error& e)
{
std::cout << " upsample_bilinear2d() exception: " << e.msg() << std::endl;
return;
}
}
output = output.permute({ 1, 2, 0 }).to(torch::kCPU);
int cv_type = CV_32FC1; // CV_8UC1;
auto cv_img = ToCvImage(output, cv_type);
sensor_msgs::Image img_msg;
try {
// cv::Mat -> sensor_msgs::Image
std_msgs::Header header; // empty header
header.seq = 0; // user defined counter
header.stamp = ros::Time::now();// time
//cv_bridge::CvImage img_bridge = cv_bridge::CvImage(header, sensor_msgs::image_encodings::MONO8, cv_img);
cv_bridge::CvImage img_bridge = cv_bridge::CvImage(header, sensor_msgs::image_encodings::TYPE_32FC1, cv_img);
img_bridge.toImageMsg(img_msg); // cv_bridge -> sensor_msgs::Image
}
catch (cv_bridge::Exception& e)
{
ROS_ERROR("cv_bridge exception: %s", e.what());
return;
}
// Output modified video stream
image_pub_.publish(img_msg);
}
};
int main(int argc, char** argv)
{
ros::init(argc, argv, "midas", ros::init_options::AnonymousName);
Midas ic;
ros::spin();
return 0;
} |