mmdeploy/demo/csrc/cpp/segmentor.cxx

77 lines
2.0 KiB
C++

// Copyright (c) OpenMMLab. All rights reserved.
#include "mmdeploy/segmentor.hpp"
#include <fstream>
#include <numeric>
#include <opencv2/imgcodecs/imgcodecs.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <random>
#include <string>
#include <vector>
using namespace std;
vector<cv::Vec3b> gen_palette(int num_classes) {
std::mt19937 gen;
std::uniform_int_distribution<ushort> uniform_dist(0, 255);
vector<cv::Vec3b> palette;
palette.reserve(num_classes);
for (auto i = 0; i < num_classes; ++i) {
palette.emplace_back(uniform_dist(gen), uniform_dist(gen), uniform_dist(gen));
}
return palette;
}
int main(int argc, char* argv[]) {
if (argc != 4) {
fprintf(stderr, "usage:\n image_segmentation device_name model_path image_path\n");
return 1;
}
auto device_name = argv[1];
auto model_path = argv[2];
auto image_path = argv[3];
cv::Mat img = cv::imread(image_path);
if (!img.data) {
fprintf(stderr, "failed to load image: %s\n", image_path);
return 1;
}
using namespace mmdeploy;
Segmentor segmentor{Model{model_path}, Device{device_name}};
auto result = segmentor.Apply(img);
auto palette = gen_palette(result->classes + 1);
cv::Mat color_mask = cv::Mat::zeros(result->height, result->width, CV_8UC3);
int pos = 0;
int total = color_mask.rows * color_mask.cols;
std::vector<int> idxs(result->classes);
for (auto iter = color_mask.begin<cv::Vec3b>(); iter != color_mask.end<cv::Vec3b>(); ++iter) {
// output mask
if (result->mask) {
*iter = palette[result->mask[pos++]];
}
// output score
if (result->score) {
std::iota(idxs.begin(), idxs.end(), 0);
auto k =
std::max_element(idxs.begin(), idxs.end(),
[&](int i, int j) {
return result->score[pos + i * total] < result->score[pos + j * total];
}) -
idxs.begin();
*iter = palette[k];
pos += 1;
}
}
img = img * 0.5 + color_mask * 0.5;
cv::imwrite("output_segmentation.png", img);
return 0;
}