init - 初始化项目

modules/gapi/samples/semantic_segmentation.cpp

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+#include <opencv2/imgproc.hpp>
+#include <opencv2/gapi/infer/ie.hpp>
+#include <opencv2/gapi/cpu/gcpukernel.hpp>
+#include <opencv2/gapi/streaming/cap.hpp>
+#include <opencv2/highgui.hpp>
+
+const std::string keys =
+    "{ h help |                                     | Print this help message }"
+    "{ input  |                                     | Path to the input video file }"
+    "{ output |                                     | Path to the output video file }"
+    "{ ssm    | semantic-segmentation-adas-0001.xml | Path to OpenVINO IE semantic segmentation model (.xml) }";
+
+// 20 colors for 20 classes of semantic-segmentation-adas-0001
+const std::vector<cv::Vec3b> colors = {
+    { 128, 64,  128 },
+    { 232, 35,  244 },
+    { 70,  70,  70 },
+    { 156, 102, 102 },
+    { 153, 153, 190 },
+    { 153, 153, 153 },
+    { 30,  170, 250 },
+    { 0,   220, 220 },
+    { 35,  142, 107 },
+    { 152, 251, 152 },
+    { 180, 130, 70 },
+    { 60,  20,  220 },
+    { 0,   0,   255 },
+    { 142, 0,   0 },
+    { 70,  0,   0 },
+    { 100, 60,  0 },
+    { 90,  0,   0 },
+    { 230, 0,   0 },
+    { 32,  11,  119 },
+    { 0,   74,  111 },
+};
+
+namespace {
+std::string get_weights_path(const std::string &model_path) {
+    const auto EXT_LEN = 4u;
+    const auto sz = model_path.size();
+    CV_Assert(sz > EXT_LEN);
+
+    auto ext = model_path.substr(sz - EXT_LEN);
+    std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){
+            return static_cast<unsigned char>(std::tolower(c));
+        });
+    CV_Assert(ext == ".xml");
+    return model_path.substr(0u, sz - EXT_LEN) + ".bin";
+}
+} // anonymous namespace
+
+namespace custom {
+G_API_OP(PostProcessing, <cv::GMat(cv::GMat, cv::GMat)>, "sample.custom.post_processing") {
+    static cv::GMatDesc outMeta(const cv::GMatDesc &in, const cv::GMatDesc &) {
+        return in;
+    }
+};
+
+GAPI_OCV_KERNEL(OCVPostProcessing, PostProcessing) {
+    static void run(const cv::Mat &in, const cv::Mat &detected_classes, cv::Mat &out) {
+        // This kernel constructs output image by class table and colors vector
+
+        // The semantic-segmentation-adas-0001 output a blob with the shape
+        // [B, C=1, H=1024, W=2048]
+        const int outHeight = 1024;
+        const int outWidth = 2048;
+        cv::Mat maskImg(outHeight, outWidth, CV_8UC3);
+        const int* const classes = detected_classes.ptr<int>();
+        for (int rowId = 0; rowId < outHeight; ++rowId) {
+            for (int colId = 0; colId < outWidth; ++colId) {
+                size_t classId = static_cast<size_t>(classes[rowId * outWidth + colId]);
+                maskImg.at<cv::Vec3b>(rowId, colId) =
+                    classId < colors.size()
+                        ? colors[classId]
+                        : cv::Vec3b{0, 0, 0}; // sample detects 20 classes
+            }
+        }
+        cv::resize(maskImg, out, in.size());
+        const float blending = 0.3f;
+        out = in * blending + out * (1 - blending);
+    }
+};
+} // namespace custom
+
+int main(int argc, char *argv[]) {
+    cv::CommandLineParser cmd(argc, argv, keys);
+    if (cmd.has("help")) {
+        cmd.printMessage();
+        return 0;
+    }
+
+    // Prepare parameters first
+    const std::string input  = cmd.get<std::string>("input");
+    const std::string output = cmd.get<std::string>("output");
+    const auto model_path    = cmd.get<std::string>("ssm");
+    const auto weights_path  = get_weights_path(model_path);
+    const auto device        = "CPU";
+    G_API_NET(SemSegmNet, <cv::GMat(cv::GMat)>, "semantic-segmentation");
+    const auto net = cv::gapi::ie::Params<SemSegmNet> {
+        model_path, weights_path, device
+    };
+    const auto kernels = cv::gapi::kernels<custom::OCVPostProcessing>();
+    const auto networks = cv::gapi::networks(net);
+
+    // Now build the graph
+    cv::GMat in;
+    cv::GMat detected_classes = cv::gapi::infer<SemSegmNet>(in);
+    cv::GMat out = custom::PostProcessing::on(in, detected_classes);
+
+    cv::GStreamingCompiled pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
+        .compileStreaming(cv::compile_args(kernels, networks));
+    auto inputs = cv::gin(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
+
+    // The execution part
+    pipeline.setSource(std::move(inputs));
+    pipeline.start();
+
+    cv::VideoWriter writer;
+    cv::Mat outMat;
+    while (pipeline.pull(cv::gout(outMat))) {
+        cv::imshow("Out", outMat);
+        cv::waitKey(1);
+        if (!output.empty()) {
+            if (!writer.isOpened()) {
+                const auto sz = cv::Size{outMat.cols, outMat.rows};
+                writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
+                CV_Assert(writer.isOpened());
+            }
+            writer << outMat;
+        }
+    }
+    return 0;
+}