feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
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wangzhengyang
293
3rdparty/opencv-4.5.4/modules/gapi/samples/onevpl_infer_single_roi.cpp
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293
3rdparty/opencv-4.5.4/modules/gapi/samples/onevpl_infer_single_roi.cpp
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#include <algorithm>
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#include <fstream>
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#include <iostream>
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#include <cctype>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/gapi.hpp>
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#include <opencv2/gapi/core.hpp>
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#include <opencv2/gapi/cpu/gcpukernel.hpp>
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#include <opencv2/gapi/infer/ie.hpp>
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#include <opencv2/gapi/render.hpp>
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#include <opencv2/gapi/streaming/onevpl/source.hpp>
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#include <opencv2/highgui.hpp> // CommandLineParser
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const std::string about =
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"This is an OpenCV-based version of oneVPLSource decoder example";
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const std::string keys =
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"{ h help | | Print this help message }"
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"{ input | | Path to the input demultiplexed video file }"
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"{ output | | Path to the output RAW video file. Use .avi extension }"
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"{ facem | face-detection-adas-0001.xml | Path to OpenVINO IE face detection model (.xml) }"
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"{ cfg_params | <prop name>:<value>;<prop name>:<value> | Semicolon separated list of oneVPL mfxVariants which is used for configuring source (see `MFXSetConfigFilterProperty` by https://spec.oneapi.io/versions/latest/elements/oneVPL/source/index.html) }";
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namespace {
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std::string get_weights_path(const std::string &model_path) {
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const auto EXT_LEN = 4u;
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const auto sz = model_path.size();
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CV_Assert(sz > EXT_LEN);
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auto ext = model_path.substr(sz - EXT_LEN);
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std::transform(ext.begin(), ext.end(), ext.begin(), [](unsigned char c){
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return static_cast<unsigned char>(std::tolower(c));
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});
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CV_Assert(ext == ".xml");
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return model_path.substr(0u, sz - EXT_LEN) + ".bin";
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}
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} // anonymous namespace
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namespace custom {
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G_API_NET(FaceDetector, <cv::GMat(cv::GMat)>, "face-detector");
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using GDetections = cv::GArray<cv::Rect>;
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using GRect = cv::GOpaque<cv::Rect>;
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using GSize = cv::GOpaque<cv::Size>;
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using GPrims = cv::GArray<cv::gapi::wip::draw::Prim>;
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G_API_OP(LocateROI, <GRect(GSize)>, "sample.custom.locate-roi") {
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static cv::GOpaqueDesc outMeta(const cv::GOpaqueDesc &) {
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return cv::empty_gopaque_desc();
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}
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};
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G_API_OP(ParseSSD, <GDetections(cv::GMat, GRect, GSize)>, "sample.custom.parse-ssd") {
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static cv::GArrayDesc outMeta(const cv::GMatDesc &, const cv::GOpaqueDesc &, const cv::GOpaqueDesc &) {
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return cv::empty_array_desc();
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}
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};
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G_API_OP(BBoxes, <GPrims(GDetections, GRect)>, "sample.custom.b-boxes") {
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static cv::GArrayDesc outMeta(const cv::GArrayDesc &, const cv::GOpaqueDesc &) {
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return cv::empty_array_desc();
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}
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};
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GAPI_OCV_KERNEL(OCVLocateROI, LocateROI) {
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// This is the place where we can run extra analytics
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// on the input image frame and select the ROI (region
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// of interest) where we want to detect our objects (or
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// run any other inference).
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//
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// Currently it doesn't do anything intelligent,
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// but only crops the input image to square (this is
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// the most convenient aspect ratio for detectors to use)
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static void run(const cv::Size& in_size, cv::Rect &out_rect) {
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// Identify the central point & square size (- some padding)
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const auto center = cv::Point{in_size.width/2, in_size.height/2};
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auto sqside = std::min(in_size.width, in_size.height);
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// Now build the central square ROI
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out_rect = cv::Rect{ center.x - sqside/2
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, center.y - sqside/2
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, sqside
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, sqside
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};
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}
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};
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GAPI_OCV_KERNEL(OCVParseSSD, ParseSSD) {
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static void run(const cv::Mat &in_ssd_result,
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const cv::Rect &in_roi,
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const cv::Size &in_parent_size,
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std::vector<cv::Rect> &out_objects) {
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const auto &in_ssd_dims = in_ssd_result.size;
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CV_Assert(in_ssd_dims.dims() == 4u);
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const int MAX_PROPOSALS = in_ssd_dims[2];
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const int OBJECT_SIZE = in_ssd_dims[3];
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CV_Assert(OBJECT_SIZE == 7); // fixed SSD object size
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const cv::Size up_roi = in_roi.size();
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const cv::Rect surface({0,0}, in_parent_size);
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out_objects.clear();
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const float *data = in_ssd_result.ptr<float>();
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for (int i = 0; i < MAX_PROPOSALS; i++) {
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const float image_id = data[i * OBJECT_SIZE + 0];
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const float label = data[i * OBJECT_SIZE + 1];
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const float confidence = data[i * OBJECT_SIZE + 2];
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const float rc_left = data[i * OBJECT_SIZE + 3];
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const float rc_top = data[i * OBJECT_SIZE + 4];
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const float rc_right = data[i * OBJECT_SIZE + 5];
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const float rc_bottom = data[i * OBJECT_SIZE + 6];
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(void) label; // unused
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if (image_id < 0.f) {
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break; // marks end-of-detections
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}
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if (confidence < 0.5f) {
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continue; // skip objects with low confidence
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}
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// map relative coordinates to the original image scale
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// taking the ROI into account
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cv::Rect rc;
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rc.x = static_cast<int>(rc_left * up_roi.width);
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rc.y = static_cast<int>(rc_top * up_roi.height);
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rc.width = static_cast<int>(rc_right * up_roi.width) - rc.x;
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rc.height = static_cast<int>(rc_bottom * up_roi.height) - rc.y;
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rc.x += in_roi.x;
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rc.y += in_roi.y;
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out_objects.emplace_back(rc & surface);
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}
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}
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};
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GAPI_OCV_KERNEL(OCVBBoxes, BBoxes) {
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// This kernel converts the rectangles into G-API's
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// rendering primitives
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static void run(const std::vector<cv::Rect> &in_face_rcs,
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const cv::Rect &in_roi,
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std::vector<cv::gapi::wip::draw::Prim> &out_prims) {
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out_prims.clear();
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const auto cvt = [](const cv::Rect &rc, const cv::Scalar &clr) {
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return cv::gapi::wip::draw::Rect(rc, clr, 2);
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};
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out_prims.emplace_back(cvt(in_roi, CV_RGB(0,255,255))); // cyan
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for (auto &&rc : in_face_rcs) {
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out_prims.emplace_back(cvt(rc, CV_RGB(0,255,0))); // green
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}
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}
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};
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} // namespace custom
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namespace cfg {
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typename cv::gapi::wip::onevpl::CfgParam create_from_string(const std::string &line);
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}
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int main(int argc, char *argv[]) {
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cv::CommandLineParser cmd(argc, argv, keys);
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cmd.about(about);
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if (cmd.has("help")) {
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cmd.printMessage();
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return 0;
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}
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// get file name
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std::string file_path = cmd.get<std::string>("input");
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const std::string output = cmd.get<std::string>("output");
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const auto face_model_path = cmd.get<std::string>("facem");
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// check ouput file extension
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if (!output.empty()) {
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auto ext = output.find_last_of(".");
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if (ext == std::string::npos || (output.substr(ext + 1) != "avi")) {
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std::cerr << "Output file should have *.avi extension for output video" << std::endl;
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return -1;
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}
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}
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// get oneVPL cfg params from cmd
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std::stringstream params_list(cmd.get<std::string>("cfg_params"));
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std::vector<cv::gapi::wip::onevpl::CfgParam> source_cfgs;
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try {
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std::string line;
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while (std::getline(params_list, line, ';')) {
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source_cfgs.push_back(cfg::create_from_string(line));
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}
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} catch (const std::exception& ex) {
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std::cerr << "Invalid cfg parameter: " << ex.what() << std::endl;
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return -1;
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}
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auto face_net = cv::gapi::ie::Params<custom::FaceDetector> {
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face_model_path, // path to topology IR
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get_weights_path(face_model_path) // path to weights
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};
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auto kernels = cv::gapi::kernels
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< custom::OCVLocateROI
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, custom::OCVParseSSD
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, custom::OCVBBoxes>();
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auto networks = cv::gapi::networks(face_net);
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// Create source
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cv::Ptr<cv::gapi::wip::IStreamSource> cap;
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try {
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cap = cv::gapi::wip::make_onevpl_src(file_path, source_cfgs);
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std::cout << "oneVPL source desription: " << cap->descr_of() << std::endl;
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} catch (const std::exception& ex) {
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std::cerr << "Cannot create source: " << ex.what() << std::endl;
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return -1;
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}
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cv::GMetaArg descr = cap->descr_of();
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auto frame_descr = cv::util::get<cv::GFrameDesc>(descr);
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// Now build the graph
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cv::GFrame in;
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auto size = cv::gapi::streaming::size(in);
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auto roi = custom::LocateROI::on(size);
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auto blob = cv::gapi::infer<custom::FaceDetector>(roi, in);
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auto rcs = custom::ParseSSD::on(blob, roi, size);
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auto out_frame = cv::gapi::wip::draw::renderFrame(in, custom::BBoxes::on(rcs, roi));
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auto out = cv::gapi::streaming::BGR(out_frame);
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cv::GStreamingCompiled pipeline;
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try {
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pipeline = cv::GComputation(cv::GIn(in), cv::GOut(out))
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.compileStreaming(cv::compile_args(kernels, networks));
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} catch (const std::exception& ex) {
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std::cerr << "Exception occured during pipeline construction: " << ex.what() << std::endl;
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return -1;
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}
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// The execution part
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// TODO USE may set pool size from outside and set queue_capacity size,
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// compile arg: cv::gapi::streaming::queue_capacity
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pipeline.setSource(std::move(cap));
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pipeline.start();
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int framesCount = 0;
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cv::TickMeter t;
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cv::VideoWriter writer;
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if (!output.empty() && !writer.isOpened()) {
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const auto sz = cv::Size{frame_descr.size.width, frame_descr.size.height};
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writer.open(output, cv::VideoWriter::fourcc('M','J','P','G'), 25.0, sz);
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CV_Assert(writer.isOpened());
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}
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cv::Mat outMat;
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t.start();
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while (pipeline.pull(cv::gout(outMat))) {
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cv::imshow("Out", outMat);
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cv::waitKey(1);
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if (!output.empty()) {
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writer << outMat;
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}
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framesCount++;
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}
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t.stop();
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std::cout << "Elapsed time: " << t.getTimeSec() << std::endl;
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std::cout << "FPS: " << framesCount / t.getTimeSec() << std::endl;
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std::cout << "framesCount: " << framesCount << std::endl;
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return 0;
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}
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namespace cfg {
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typename cv::gapi::wip::onevpl::CfgParam create_from_string(const std::string &line) {
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using namespace cv::gapi::wip;
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if (line.empty()) {
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throw std::runtime_error("Cannot parse CfgParam from emply line");
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}
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std::string::size_type name_endline_pos = line.find(':');
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if (name_endline_pos == std::string::npos) {
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throw std::runtime_error("Cannot parse CfgParam from: " + line +
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"\nExpected separator \":\"");
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}
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std::string name = line.substr(0, name_endline_pos);
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std::string value = line.substr(name_endline_pos + 1);
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return cv::gapi::wip::onevpl::CfgParam::create(name, value);
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}
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}
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