mg_matting.h
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//
// Created by DefTruth on 2021/12/5.
//
#ifndef LITE_AI_TOOLKIT_ORT_CV_MG_MATTING_H
#define LITE_AI_TOOLKIT_ORT_CV_MG_MATTING_H
#include "lite/ort/core/ort_core.h"
namespace ortcv
{
class LITE_EXPORTS MGMatting
{
private:
Ort::Env ort_env;
Ort::Session *ort_session = nullptr;
// CPU MemoryInfo
Ort::AllocatorWithDefaultOptions allocator;
Ort::MemoryInfo memory_info_handler = Ort::MemoryInfo::CreateCpu(
OrtArenaAllocator, OrtMemTypeDefault);
// hardcode input node names
const unsigned int num_inputs = 2;
std::vector<const char *> input_node_names = {"image", "mask"};
// hardcode output node names
const unsigned int num_outputs = 3;
std::vector<const char *> output_node_names = {
"alpha_os1", "alpha_os4", "alpha_os8"
};
const LITEORT_CHAR *onnx_path = nullptr;
const char *log_id = nullptr;
// input values handler
unsigned int dynamic_input_height = 512; // init only, will change according to input mat.
unsigned int dynamic_input_width = 512; // init only, will change according to input mat.
unsigned int dynamic_input_image_size = 1 * 3 * 512 * 512; // init only, will change according to input mat.
unsigned int dynamic_input_mask_size = 1 * 1 * 512 * 512; // init only, will change according to input mat.
std::vector<int64_t> dynamic_input_image_dims = {1, 3, 512, 512}; // init only, will change according to input mat.
std::vector<int64_t> dynamic_input_mask_dims = {1, 1, 512, 512}; // init only, will change according to input mat.
std::vector<float> dynamic_image_values_handler;
std::vector<float> dynamic_mask_values_handler;
private:
const unsigned int num_threads; // initialize at runtime.
const float mean_vals[3] = {0.485f, 0.456f, 0.406f};
const float scale_vals[3] = {1.f / 0.229f, 1.f / 0.224f, 1.f / 0.225f}; // RGB
static constexpr const unsigned int align_val = 32;
public:
explicit MGMatting(const std::string &_onnx_path, unsigned int _num_threads = 1);
~MGMatting();
protected:
MGMatting(const MGMatting &) = delete; //
MGMatting(MGMatting &&) = delete; //
MGMatting &operator=(const MGMatting &) = delete; //
MGMatting &operator=(MGMatting &&) = delete; //
private:
// return normalized image,mask Tensors
std::vector<Ort::Value> transform(const cv::Mat &mat, const cv::Mat &mask);
void print_debug_string();
cv::Mat padding(const cv::Mat &unpad_mat);
void update_guidance_mask(cv::Mat &mask, unsigned int guidance_threshold = 128);
void update_dynamic_shape(unsigned int img_height, unsigned int img_width);
void update_alpha_pred(cv::Mat &alpha_pred, const cv::Mat &weight, const cv::Mat &other_alpha_pred);
cv::Mat get_unknown_tensor_from_pred(const cv::Mat &alpha_pred, unsigned int rand_width = 30);
void remove_small_connected_area(cv::Mat &alpha_pred);
void generate_matting(std::vector<Ort::Value> &output_tensors,
const cv::Mat &mat, types::MattingContent &content,
bool remove_noise = false);
public:
/**
* Image Matting Using MGMatting(https://github.com/yucornetto/MGMatting)
* @param mat: cv::Mat BGR HWC, source image
* @param mask: cv::Mat Gray, guidance mask.
* @param guidance_threshold: int, guidance threshold..
* @param content: types::MattingContent to catch the detected results.
*/
void detect(const cv::Mat &mat, cv::Mat &mask, types::MattingContent &content,
bool remove_noise = false, unsigned int guidance_threshold = 128);
};
}
#endif //LITE_AI_TOOLKIT_ORT_CV_MG_MATTING_H