133 lines
4.1 KiB
C++
133 lines
4.1 KiB
C++
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#include "net.h"
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#if defined(USE_NCNN_SIMPLEOCV)
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#include "simpleocv.h"
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#else
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#endif
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#include <stdlib.h>
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#include <float.h>
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#include <stdio.h>
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#include <vector>
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static void draw_objects(const cv::Mat& bgr, const cv::Mat& fgr, const cv::Mat& pha)
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{
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cv::Mat fgr8U;
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fgr.convertTo(fgr8U, CV_8UC3, 255.0, 0);
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cv::Mat pha8U;
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pha.convertTo(pha8U, CV_8UC1, 255.0, 0);
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cv::Mat comp;
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cv::resize(bgr, comp, pha.size(), 0, 0, 1);
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for (int i = 0; i < pha8U.rows; i++)
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{
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for (int j = 0; j < pha8U.cols; j++)
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{
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uchar data = pha8U.at<uchar>(i, j);
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float alpha = (float)data / 255;
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comp.at<cv::Vec3b>(i, j)[0] = fgr8U.at<cv::Vec3b>(i, j)[0] * alpha + (1 - alpha) * 155;
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comp.at<cv::Vec3b>(i, j)[1] = fgr8U.at<cv::Vec3b>(i, j)[1] * alpha + (1 - alpha) * 255;
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comp.at<cv::Vec3b>(i, j)[2] = fgr8U.at<cv::Vec3b>(i, j)[2] * alpha + (1 - alpha) * 120;
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}
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}
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cv::imshow("pha", pha8U);
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cv::imshow("fgr", fgr8U);
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cv::imshow("comp", comp);
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cv::waitKey(0);
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}
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static int detect_rvm(const cv::Mat& bgr, cv::Mat& pha, cv::Mat& fgr)
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{
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const float downsample_ratio = 0.5f;
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const int target_width = 512;
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const int target_height = 512;
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ncnn::Net net;
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net.opt.use_vulkan_compute = false;
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//original pretrained model from https://github.com/PeterL1n/RobustVideoMatting
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//ncnn model https://pan.baidu.com/s/11iEY2RGfzWFtce8ue7T3JQ password: d9t6
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net.load_param("rvm_512.param");
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net.load_model("rvm_512.bin");
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//if you use another input size,pleaze change input shape
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ncnn::Mat r1i = ncnn::Mat(128, 128, 16);
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ncnn::Mat r2i = ncnn::Mat(64, 64, 20);
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ncnn::Mat r3i = ncnn::Mat(32, 32, 40);
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ncnn::Mat r4i = ncnn::Mat(16, 16, 64);
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r1i.fill(0.0f);
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r2i.fill(0.0f);
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r3i.fill(0.0f);
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r4i.fill(0.0f);
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ncnn::Extractor ex = net.create_extractor();
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const float mean_vals1[3] = {123.675f, 116.28f, 103.53f};
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const float norm_vals1[3] = {0.01712475f, 0.0175f, 0.01742919f};
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const float mean_vals2[3] = {0, 0, 0};
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const float norm_vals2[3] = {1 / 255.0, 1 / 255.0, 1 / 255.0};
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ncnn::Mat ncnn_in2 = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, bgr.cols, bgr.rows, target_width, target_height);
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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);
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ncnn_in1.substract_mean_normalize(mean_vals1, norm_vals1);
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ncnn_in2.substract_mean_normalize(mean_vals2, norm_vals2);
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ex.input("src1", ncnn_in1);
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ex.input("src2", ncnn_in2);
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ex.input("r1i", r1i);
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ex.input("r2i", r2i);
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ex.input("r3i", r3i);
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ex.input("r4i", r4i);
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//if use video matting,these output will be input of next infer
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ex.extract("r4o", r4i);
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ex.extract("r3o", r3i);
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ex.extract("r2o", r2i);
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ex.extract("r1o", r1i);
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ncnn::Mat pha_;
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ex.extract("pha", pha_);
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ncnn::Mat fgr_;
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ex.extract("fgr", fgr_);
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cv::Mat cv_pha = cv::Mat(pha_.h, pha_.w, CV_32FC1, (float*)pha_.data);
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cv::Mat cv_fgr = cv::Mat(fgr_.h, fgr_.w, CV_32FC3);
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float* fgr_data = (float*)fgr_.data;
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for (int i = 0; i < fgr_.h; i++)
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{
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for (int j = 0; j < fgr_.w; j++)
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{
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cv_fgr.at<cv::Vec3f>(i, j)[2] = fgr_data[0 * fgr_.h * fgr_.w + i * fgr_.w + j];
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cv_fgr.at<cv::Vec3f>(i, j)[1] = fgr_data[1 * fgr_.h * fgr_.w + i * fgr_.w + j];
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cv_fgr.at<cv::Vec3f>(i, j)[0] = fgr_data[2 * fgr_.h * fgr_.w + i * fgr_.w + j];
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}
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}
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cv_pha.copyTo(pha);
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cv_fgr.copyTo(fgr);
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return 0;
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}
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int main(int argc, char** argv)
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{
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if (argc != 2)
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{
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fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
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return -1;
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}
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const char* imagepath = argv[1];
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cv::Mat m = cv::imread(imagepath, 1);
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if (m.empty())
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{
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fprintf(stderr, "cv::imread %s failed\n", imagepath);
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return -1;
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}
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cv::Mat fgr, pha;
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detect_rvm(m, pha, fgr);
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draw_objects(m, fgr, pha);
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return 0;
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}
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