#include "net.h" #if defined(USE_NCNN_SIMPLEOCV) #include "simpleocv.h" #else #include #include #include #endif #include #include #include #include static void draw_objects(const cv::Mat& bgr, const cv::Mat& fgr, const cv::Mat& pha) { cv::Mat fgr8U; fgr.convertTo(fgr8U, CV_8UC3, 255.0, 0); cv::Mat pha8U; pha.convertTo(pha8U, CV_8UC1, 255.0, 0); cv::Mat comp; cv::resize(bgr, comp, pha.size(), 0, 0, 1); for (int i = 0; i < pha8U.rows; i++) { for (int j = 0; j < pha8U.cols; j++) { uchar data = pha8U.at(i, j); float alpha = (float)data / 255; comp.at(i, j)[0] = fgr8U.at(i, j)[0] * alpha + (1 - alpha) * 155; comp.at(i, j)[1] = fgr8U.at(i, j)[1] * alpha + (1 - alpha) * 255; comp.at(i, j)[2] = fgr8U.at(i, j)[2] * alpha + (1 - alpha) * 120; } } cv::imshow("pha", pha8U); cv::imshow("fgr", fgr8U); cv::imshow("comp", comp); cv::waitKey(0); } static int detect_rvm(const cv::Mat& bgr, cv::Mat& pha, cv::Mat& fgr) { const float downsample_ratio = 0.5f; const int target_width = 512; const int target_height = 512; ncnn::Net net; net.opt.use_vulkan_compute = false; //original pretrained model from https://github.com/PeterL1n/RobustVideoMatting //ncnn model https://pan.baidu.com/s/11iEY2RGfzWFtce8ue7T3JQ password: d9t6 net.load_param("rvm_512.param"); net.load_model("rvm_512.bin"); //if you use another input size,pleaze change input shape ncnn::Mat r1i = ncnn::Mat(128, 128, 16); ncnn::Mat r2i = ncnn::Mat(64, 64, 20); ncnn::Mat r3i = ncnn::Mat(32, 32, 40); ncnn::Mat r4i = ncnn::Mat(16, 16, 64); r1i.fill(0.0f); r2i.fill(0.0f); r3i.fill(0.0f); r4i.fill(0.0f); ncnn::Extractor ex = net.create_extractor(); const float mean_vals1[3] = {123.675f, 116.28f, 103.53f}; const float norm_vals1[3] = {0.01712475f, 0.0175f, 0.01742919f}; const float mean_vals2[3] = {0, 0, 0}; const float norm_vals2[3] = {1 / 255.0, 1 / 255.0, 1 / 255.0}; ncnn::Mat ncnn_in2 = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_width, target_height); ncnn::Mat ncnn_in1 = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_width * downsample_ratio, target_height * downsample_ratio); ncnn_in1.substract_mean_normalize(mean_vals1, norm_vals1); ncnn_in2.substract_mean_normalize(mean_vals2, norm_vals2); ex.input("src1", ncnn_in1); ex.input("src2", ncnn_in2); ex.input("r1i", r1i); ex.input("r2i", r2i); ex.input("r3i", r3i); ex.input("r4i", r4i); //if use video matting,these output will be input of next infer ex.extract("r4o", r4i); ex.extract("r3o", r3i); ex.extract("r2o", r2i); ex.extract("r1o", r1i); ncnn::Mat pha_; ex.extract("pha", pha_); ncnn::Mat fgr_; ex.extract("fgr", fgr_); cv::Mat cv_pha = cv::Mat(pha_.h, pha_.w, CV_32FC1, (float*)pha_.data); cv::Mat cv_fgr = cv::Mat(fgr_.h, fgr_.w, CV_32FC3); float* fgr_data = (float*)fgr_.data; for (int i = 0; i < fgr_.h; i++) { for (int j = 0; j < fgr_.w; j++) { cv_fgr.at(i, j)[2] = fgr_data[0 * fgr_.h * fgr_.w + i * fgr_.w + j]; cv_fgr.at(i, j)[1] = fgr_data[1 * fgr_.h * fgr_.w + i * fgr_.w + j]; cv_fgr.at(i, j)[0] = fgr_data[2 * fgr_.h * fgr_.w + i * fgr_.w + j]; } } cv_pha.copyTo(pha); cv_fgr.copyTo(fgr); return 0; } int main(int argc, char** argv) { if (argc != 2) { fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]); return -1; } const char* imagepath = argv[1]; cv::Mat m = cv::imread(imagepath, 1); if (m.empty()) { fprintf(stderr, "cv::imread %s failed\n", imagepath); return -1; } cv::Mat fgr, pha; detect_rvm(m, pha, fgr); draw_objects(m, fgr, pha); return 0; }