vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b
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/*
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* This file is part of OpenCV project.
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* It is subject to the license terms in the LICENSE file found in the top-level directory of this distribution and at http://opencv.org/license.html.
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*
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* Copyright (C) 2025, Bigvision LLC.
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*
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* @file alpha_matting.cpp
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* @brief MODNet Alpha Matting using OpenCV DNN
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*
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* This sample demonstrates human portrait alpha matting using MODNet model.
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* MODNet is a trimap-free portrait matting method that can produce high-quality
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* alpha mattes for portrait images in real-time.
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*
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* Reference:
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* Github: https://github.com/ZHKKKe/MODNet
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*
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* Usage:
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* ./example_dnn_alpha_matting --input=image.jpg # Process image
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*
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* Requirements:
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* - OpenCV >= 5.0.0 with DNN module
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* - MODNet ONNX model
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*/
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/highgui.hpp>
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#include <iostream>
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#include <vector>
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#include <string>
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#include "common.hpp"
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using namespace cv;
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using namespace cv::dnn;
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using namespace std;
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const string about =
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"This sample demonstrates human portrait alpha matting using MODNet model.\n"
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"MODNet is a trimap-free portrait matting method that can produce high-quality\n"
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"alpha mattes for portrait images in real-time.\n\n"
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"Usage examples:\n"
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"\t./example_alpha_matting --input=image.jpg\n"
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"\t./example_alpha_matting modnet (using config alias)\n\n"
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"To download the MODNet model, run: python download_models.py modnet\n"
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"Press any key to exit \n";
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const string param_keys =
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"{ help h | | Print help message }"
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"{ @alias | modnet | An alias name of model to extract preprocessing parameters from models.yml file }"
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"{ zoo | ../dnn/models.yml | An optional path to file with preprocessing parameters }"
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"{ input i | messi5.jpg | Path to input image file }"
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"{ model | | Path to MODNet ONNX model file }";
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const string backend_keys = format(
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"{ backend | default | Choose one of computation backends: "
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"default: automatically (by default), "
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"openvino: Intel's Deep Learning Inference Engine, "
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"opencv: OpenCV implementation, "
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"vkcom: VKCOM, "
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"cuda: CUDA, "
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"webnn: WebNN }");
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const string target_keys = format(
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"{ target | cpu | Choose one of target computation devices: "
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"cpu: CPU target (by default), "
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"opencl: OpenCL, "
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"opencl_fp16: OpenCL fp16 (half-float precision), "
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"vpu: VPU, "
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"vulkan: Vulkan, "
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"cuda: CUDA, "
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"cuda_fp16: CUDA fp16 (half-float precision) }");
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string keys = param_keys + backend_keys + target_keys;
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static void loadModel(const string modelPath, String backend, String target, Net &net, EngineType engine)
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{
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net = readNetFromONNX(modelPath, engine);
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net.setPreferableBackend(getBackendID(backend));
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net.setPreferableTarget(getTargetID(target));
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}
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static void postprocess(const Mat &image, const Mat &alpha_output, Mat &alpha_mask)
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{
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int h = image.rows;
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int w = image.cols;
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Mat alpha;
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if (alpha_output.dims == 4 && alpha_output.size[0] == 1 && alpha_output.size[1] == 1)
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{
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alpha = alpha_output.reshape(0, {alpha_output.size[2], alpha_output.size[3]});
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}
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else
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{
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alpha = alpha_output.clone();
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}
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resize(alpha, alpha, Size(w, h));
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alpha = cv::min(cv::max(alpha, 0.0), 1.0);
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alpha.convertTo(alpha_mask, CV_8U, 255.0);
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}
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static void processImage(const Mat &image, Mat &alpha_mask, Mat &composite, Net &net,
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float scale, int width, int height, const Scalar &mean, bool swapRB)
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{
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if (image.empty())
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return;
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Mat blob = blobFromImage(image, scale, Size(width, height), mean, swapRB, false, CV_32F);
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net.setInput(blob);
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Mat output = net.forward();
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postprocess(image, output, alpha_mask);
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Mat alpha_3ch;
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cvtColor(alpha_mask, alpha_3ch, COLOR_GRAY2BGR);
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alpha_3ch.convertTo(alpha_3ch, CV_32F, 1.0 / 255.0);
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Mat image_f;
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image.convertTo(image_f, CV_32F);
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multiply(image_f, alpha_3ch, composite);
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composite.convertTo(composite, CV_8U);
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}
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static void setupWindows()
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{
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namedWindow("Original", WINDOW_AUTOSIZE);
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namedWindow("Alpha Mask", WINDOW_AUTOSIZE);
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namedWindow("Composite", WINDOW_AUTOSIZE);
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moveWindow("Alpha Mask", 200, 0);
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moveWindow("Composite", 400, 0);
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}
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int main(int argc, char **argv)
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{
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CommandLineParser parser(argc, argv, keys);
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if (parser.has("help"))
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{
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cout << about << endl;
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parser.printMessage();
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return 0;
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}
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string modelName = parser.get<String>("@alias");
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string zooFile = parser.get<String>("zoo");
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zooFile = findFile(zooFile);
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(argc, argv, keys);
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int input_width = parser.get<int>("width");
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int input_height = parser.get<int>("height");
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float scale_factor = parser.get<float>("scale");
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Scalar mean_values = parser.get<Scalar>("mean");
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bool swapRB = parser.get<bool>("rgb");
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String backend = parser.get<String>("backend");
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String target = parser.get<String>("target");
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String sha1 = parser.get<String>("sha1");
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string model = findModel(parser.get<String>("model"), sha1);
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parser.about(about);
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EngineType engine = ENGINE_AUTO;
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if (backend != "default" || target != "cpu")
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{
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engine = ENGINE_CLASSIC;
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}
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Net net;
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loadModel(model, backend, target, net, engine);
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string input_path = samples::findFile(parser.get<String>("input"));
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Mat image = imread(input_path);
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if (image.empty())
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{
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cout << "[ERROR] Cannot load input image: " << input_path << endl;
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return -1;
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}
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setupWindows();
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cout << "Processing image: " << input_path << endl;
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cout << "Press any key to exit" << endl;
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Mat alpha_mask, composite;
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processImage(image, alpha_mask, composite, net, scale_factor, input_width, input_height, mean_values, swapRB);
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imshow("Original", image);
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imshow("Alpha Mask", alpha_mask);
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imshow("Composite", composite);
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waitKey(0);
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destroyAllWindows();
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return 0;
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}
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