vendor: OpenCV 5.0.0 snapshot at 40738fb16ceddb5fb3fea747585f7ce6abb0605b

2026-08-22 00:10:33 +08:00
commit f7f077da11
6933 changed files with 2335208 additions and 0 deletions
@@ -0,0 +1,121 @@
+// This file is part of OpenCV project.
+// 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
+// To download the onnx model, see: https://storage.googleapis.com/ailia-models/colorization/colorizer.onnx
+
+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include "common.hpp"
+#include <opencv2/highgui.hpp>
+#include <iostream>
+
+using namespace cv;
+using namespace std;
+using namespace cv::dnn;
+
+
+int main(int argc, char** argv) {
+    const string about =
+        "This sample demonstrates recoloring grayscale images with dnn.\n"
+        "This program is based on:\n"
+        "  http://richzhang.github.io/colorization\n"
+        "  https://github.com/richzhang/colorization\n"
+        "To download the onnx model:\n"
+        " https://storage.googleapis.com/ailia-models/colorization/colorizer.onnx\n";
+
+    const string param_keys =
+        "{ help h          |            | Print help message. }"
+        "{ input i         | baboon.jpg | Path to the input image }"
+        "{ onnx_model_path |            | Path to the ONNX model. Required. }";
+
+    const string backend_keys = format(
+        "{ backend         | 0 | Choose one of computation backends: "
+                                    "%d: automatically (by default), "
+                                    "%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
+                                    "%d: OpenCV implementation, "
+                                    "%d: VKCOM, "
+                                    "%d: CUDA, "
+                                    "%d: WebNN }",
+        cv::dnn::DNN_BACKEND_DEFAULT, cv::dnn::DNN_BACKEND_INFERENCE_ENGINE, cv::dnn::DNN_BACKEND_OPENCV,
+        cv::dnn::DNN_BACKEND_VKCOM, cv::dnn::DNN_BACKEND_CUDA, cv::dnn::DNN_BACKEND_WEBNN);
+    const string target_keys = format(
+        "{ target          | 0 | Choose one of target computation devices: "
+                              "%d: CPU target (by default), "
+                              "%d: OpenCL, "
+                              "%d: OpenCL fp16 (half-float precision), "
+                              "%d: VPU, "
+                              "%d: Vulkan, "
+                              "%d: CUDA, "
+                              "%d: CUDA fp16 (half-float preprocess) }",
+        cv::dnn::DNN_TARGET_CPU, cv::dnn::DNN_TARGET_OPENCL, cv::dnn::DNN_TARGET_OPENCL_FP16,
+        cv::dnn::DNN_TARGET_MYRIAD, cv::dnn::DNN_TARGET_VULKAN, cv::dnn::DNN_TARGET_CUDA,
+        cv::dnn::DNN_TARGET_CUDA_FP16);
+
+    const string keys = param_keys + backend_keys + target_keys;
+    CommandLineParser parser(argc, argv, keys);
+    parser.about(about);
+
+    if (parser.has("help")) {
+        parser.printMessage();
+        return 0;
+    }
+
+    string inputImagePath = parser.get<string>("input");
+    string onnxModelPath = parser.get<string>("onnx_model_path");
+    int backendId = parser.get<int>("backend");
+    int targetId = parser.get<int>("target");
+
+    if (onnxModelPath.empty()) {
+        cerr << "The path to the ONNX model is required!" << endl;
+        return -1;
+    }
+
+    Mat imgGray = imread(samples::findFile(inputImagePath), IMREAD_GRAYSCALE);
+    if (imgGray.empty()) {
+        cerr << "Could not read the image: " << inputImagePath << endl;
+        return -1;
+    }
+
+    Mat imgL = imgGray;
+    imgL.convertTo(imgL, CV_32F, 100.0/255.0);
+    Mat imgLResized;
+    resize(imgL, imgLResized, Size(256, 256), 0, 0, INTER_CUBIC);
+
+    // Prepare the model
+    EngineType engine = ENGINE_AUTO;
+    if (backendId != 0 || targetId != 0){
+        engine = ENGINE_CLASSIC;
+    }
+    dnn::Net net = dnn::readNetFromONNX(onnxModelPath, engine);
+    net.setPreferableBackend(backendId);
+    net.setPreferableTarget(targetId);
+    //! [Read and initialize network]
+
+    // Create blob from the image
+    Mat blob = dnn::blobFromImage(imgLResized, 1.0, Size(256, 256), Scalar(), false, false);
+
+    net.setInput(blob);
+
+    // Run inference
+    Mat result = net.forward();
+    Size siz(result.size[2], result.size[3]);
+    Mat a(siz, CV_32F, result.ptr(0,0));
+    Mat b(siz, CV_32F, result.ptr(0,1));
+    resize(a, a, imgGray.size());
+    resize(b, b, imgGray.size());
+
+    // merge, and convert back to BGR
+    Mat color, chn[] = {imgL, a, b};
+
+    // Proc
+    Mat lab;
+    merge(chn, 3, lab);
+    cvtColor(lab, color, COLOR_Lab2BGR);
+
+    imshow("input image", imgGray);
+    imshow("output image", color);
+    waitKey();
+
+    return 0;
+}