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https://gitcode.com/gh_mirrors/es/esp32-opencv.git
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initial commit
This commit is contained in:
Joachim
@ -0,0 +1,351 @@
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#include "opencv2/opencv_modules.hpp"
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#include <iostream>
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#if defined(HAVE_OPENCV_GAPI)
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#include <chrono>
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#include <iomanip>
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#include "opencv2/imgproc.hpp"
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#include "opencv2/imgcodecs.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/imgproc.hpp"
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#include "opencv2/gapi/infer.hpp"
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#include "opencv2/gapi/infer/ie.hpp"
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#include "opencv2/gapi/cpu/gcpukernel.hpp"
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#include "opencv2/gapi/streaming/cap.hpp"
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#include "opencv2/highgui.hpp"
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const std::string about =
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"This is an OpenCV-based version of Security Barrier Camera 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 an input video file }"
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"{ detm | | IE vehicle/license plate detection model IR }"
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"{ detw | | IE vehicle/license plate detection model weights }"
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"{ detd | | IE vehicle/license plate detection model device }"
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"{ vehm | | IE vehicle attributes model IR }"
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"{ vehw | | IE vehicle attributes model weights }"
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"{ vehd | | IE vehicle attributes model device }"
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"{ lprm | | IE license plate recognition model IR }"
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"{ lprw | | IE license plate recognition model weights }"
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"{ lprd | | IE license plate recognition model device }"
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"{ pure | | When set, no output is displayed. Useful for benchmarking }"
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"{ ser | | When set, runs a regular (serial) pipeline }";
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namespace {
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struct Avg {
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struct Elapsed {
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explicit Elapsed(double ms) : ss(ms/1000.), mm(static_cast<int>(ss)/60) {}
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const double ss;
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const int mm;
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};
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using MS = std::chrono::duration<double, std::ratio<1, 1000>>;
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using TS = std::chrono::time_point<std::chrono::high_resolution_clock>;
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TS started;
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void start() { started = now(); }
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TS now() const { return std::chrono::high_resolution_clock::now(); }
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double tick() const { return std::chrono::duration_cast<MS>(now() - started).count(); }
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Elapsed elapsed() const { return Elapsed{tick()}; }
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double fps(std::size_t n) const { return static_cast<double>(n) / (tick() / 1000.); }
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};
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std::ostream& operator<<(std::ostream &os, const Avg::Elapsed &e) {
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os << e.mm << ':' << (e.ss - 60*e.mm);
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return os;
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}
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} // namespace
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namespace custom {
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G_API_NET(VehicleLicenseDetector, <cv::GMat(cv::GMat)>, "vehicle-license-plate-detector");
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using Attrs = std::tuple<cv::GMat, cv::GMat>;
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G_API_NET(VehicleAttributes, <Attrs(cv::GMat)>, "vehicle-attributes");
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G_API_NET(LPR, <cv::GMat(cv::GMat)>, "license-plate-recognition");
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using GVehiclesPlates = std::tuple< cv::GArray<cv::Rect>
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, cv::GArray<cv::Rect> >;
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G_API_OP_M(ProcessDetections,
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<GVehiclesPlates(cv::GMat, cv::GMat)>,
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"custom.security_barrier.detector.postproc") {
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static std::tuple<cv::GArrayDesc,cv::GArrayDesc>
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outMeta(const cv::GMatDesc &, const cv::GMatDesc) {
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// FIXME: Need to get rid of this - literally there's nothing useful
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return std::make_tuple(cv::empty_array_desc(), cv::empty_array_desc());
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}
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};
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GAPI_OCV_KERNEL(OCVProcessDetections, ProcessDetections) {
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static void run(const cv::Mat &in_ssd_result,
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const cv::Mat &in_frame,
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std::vector<cv::Rect> &out_vehicles,
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std::vector<cv::Rect> &out_plates) {
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const int MAX_PROPOSALS = 200;
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const int OBJECT_SIZE = 7;
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const cv::Size upscale = in_frame.size();
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const cv::Rect surface({0,0}, upscale);
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out_vehicles.clear();
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out_plates.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]; // batch id
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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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if (image_id < 0.f) { // indicates end of detections
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break;
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}
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if (confidence < 0.5f) { // fixme: hard-coded snapshot
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continue;
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}
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cv::Rect rc;
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rc.x = static_cast<int>(rc_left * upscale.width);
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rc.y = static_cast<int>(rc_top * upscale.height);
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rc.width = static_cast<int>(rc_right * upscale.width) - rc.x;
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rc.height = static_cast<int>(rc_bottom * upscale.height) - rc.y;
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using PT = cv::Point;
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using SZ = cv::Size;
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switch (static_cast<int>(label)) {
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case 1: out_vehicles.push_back(rc & surface); break;
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case 2: out_plates.emplace_back((rc-PT(15,15)+SZ(30,30)) & surface); break;
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default: CV_Assert(false && "Unknown object class");
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}
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}
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}
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};
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} // namespace custom
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namespace labels {
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const std::string colors[] = {
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"white", "gray", "yellow", "red", "green", "blue", "black"
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};
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const std::string types[] = {
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"car", "van", "truck", "bus"
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};
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const std::vector<std::string> license_text = {
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"0", "1", "2", "3", "4", "5", "6", "7", "8", "9",
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"<Anhui>", "<Beijing>", "<Chongqing>", "<Fujian>",
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"<Gansu>", "<Guangdong>", "<Guangxi>", "<Guizhou>",
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"<Hainan>", "<Hebei>", "<Heilongjiang>", "<Henan>",
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"<HongKong>", "<Hubei>", "<Hunan>", "<InnerMongolia>",
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"<Jiangsu>", "<Jiangxi>", "<Jilin>", "<Liaoning>",
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"<Macau>", "<Ningxia>", "<Qinghai>", "<Shaanxi>",
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"<Shandong>", "<Shanghai>", "<Shanxi>", "<Sichuan>",
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"<Tianjin>", "<Tibet>", "<Xinjiang>", "<Yunnan>",
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"<Zhejiang>", "<police>",
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"A", "B", "C", "D", "E", "F", "G", "H", "I", "J",
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"K", "L", "M", "N", "O", "P", "Q", "R", "S", "T",
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"U", "V", "W", "X", "Y", "Z"
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};
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namespace {
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void DrawResults(cv::Mat &frame,
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const std::vector<cv::Rect> &vehicles,
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const std::vector<cv::Mat> &out_colors,
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const std::vector<cv::Mat> &out_types,
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const std::vector<cv::Rect> &plates,
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const std::vector<cv::Mat> &out_numbers) {
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CV_Assert(vehicles.size() == out_colors.size());
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CV_Assert(vehicles.size() == out_types.size());
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CV_Assert(plates.size() == out_numbers.size());
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for (auto it = vehicles.begin(); it != vehicles.end(); ++it) {
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const auto idx = std::distance(vehicles.begin(), it);
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const auto &rc = *it;
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const float *colors_data = out_colors[idx].ptr<float>();
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const float *types_data = out_types [idx].ptr<float>();
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const auto color_id = std::max_element(colors_data, colors_data + 7) - colors_data;
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const auto type_id = std::max_element(types_data, types_data + 4) - types_data;
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const int ATTRIB_OFFSET = 25;
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cv::rectangle(frame, rc, {0, 255, 0}, 4);
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cv::putText(frame, labels::colors[color_id],
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cv::Point(rc.x + 5, rc.y + ATTRIB_OFFSET),
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cv::FONT_HERSHEY_COMPLEX_SMALL,
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1,
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cv::Scalar(255, 0, 0));
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cv::putText(frame, labels::types[type_id],
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cv::Point(rc.x + 5, rc.y + ATTRIB_OFFSET * 2),
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cv::FONT_HERSHEY_COMPLEX_SMALL,
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1,
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cv::Scalar(255, 0, 0));
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}
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for (auto it = plates.begin(); it != plates.end(); ++it) {
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const int MAX_LICENSE = 88;
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const int LPR_OFFSET = 50;
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const auto &rc = *it;
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const auto idx = std::distance(plates.begin(), it);
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std::string result;
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const auto *lpr_data = out_numbers[idx].ptr<float>();
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for (int i = 0; i < MAX_LICENSE; i++) {
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if (lpr_data[i] == -1) break;
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result += labels::license_text[static_cast<size_t>(lpr_data[i])];
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}
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const int y_pos = std::max(0, rc.y + rc.height - LPR_OFFSET);
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cv::rectangle(frame, rc, {0, 0, 255}, 4);
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cv::putText(frame, result,
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cv::Point(rc.x, y_pos),
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cv::FONT_HERSHEY_COMPLEX_SMALL,
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1,
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cv::Scalar(0, 0, 255));
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}
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}
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void DrawFPS(cv::Mat &frame, std::size_t n, double fps) {
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std::ostringstream out;
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out << "FRAME " << n << ": "
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<< std::fixed << std::setprecision(2) << fps
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<< " FPS (AVG)";
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cv::putText(frame, out.str(),
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cv::Point(0, frame.rows),
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cv::FONT_HERSHEY_SIMPLEX,
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1,
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cv::Scalar(0, 0, 0),
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2);
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}
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} // anonymous namespace
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} // namespace labels
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int main(int argc, char *argv[])
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{
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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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const std::string input = cmd.get<std::string>("input");
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const bool no_show = cmd.get<bool>("pure");
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cv::GComputation pp([]() {
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cv::GMat in;
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cv::GMat detections = cv::gapi::infer<custom::VehicleLicenseDetector>(in);
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cv::GArray<cv::Rect> vehicles;
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cv::GArray<cv::Rect> plates;
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std::tie(vehicles, plates) = custom::ProcessDetections::on(detections, in);
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cv::GArray<cv::GMat> colors;
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cv::GArray<cv::GMat> types;
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std::tie(colors, types) = cv::gapi::infer<custom::VehicleAttributes>(vehicles, in);
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cv::GArray<cv::GMat> numbers = cv::gapi::infer<custom::LPR>(plates, in);
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cv::GMat frame = cv::gapi::copy(in); // pass-through the input frame
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return cv::GComputation(cv::GIn(in),
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cv::GOut(frame, vehicles, colors, types, plates, numbers));
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});
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// Note: it might be very useful to have dimensions loaded at this point!
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auto det_net = cv::gapi::ie::Params<custom::VehicleLicenseDetector> {
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cmd.get<std::string>("detm"), // path to topology IR
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cmd.get<std::string>("detw"), // path to weights
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cmd.get<std::string>("detd"), // device specifier
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};
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auto attr_net = cv::gapi::ie::Params<custom::VehicleAttributes> {
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cmd.get<std::string>("vehm"), // path to topology IR
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cmd.get<std::string>("vehw"), // path to weights
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cmd.get<std::string>("vehd"), // device specifier
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}.cfgOutputLayers({ "color", "type" });
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// Fill a special LPR input (seq_ind) with a predefined value
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// First element is 0.f, the rest 87 are 1.f
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const std::vector<int> lpr_seq_dims = {88,1};
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cv::Mat lpr_seq(lpr_seq_dims, CV_32F, cv::Scalar(1.f));
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lpr_seq.ptr<float>()[0] = 0.f;
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auto lpr_net = cv::gapi::ie::Params<custom::LPR> {
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cmd.get<std::string>("lprm"), // path to topology IR
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cmd.get<std::string>("lprw"), // path to weights
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cmd.get<std::string>("lprd"), // device specifier
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}.constInput("seq_ind", lpr_seq);
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auto kernels = cv::gapi::kernels<custom::OCVProcessDetections>();
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auto networks = cv::gapi::networks(det_net, attr_net, lpr_net);
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Avg avg;
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cv::Mat frame;
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std::vector<cv::Rect> vehicles, plates;
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std::vector<cv::Mat> out_colors;
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std::vector<cv::Mat> out_types;
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std::vector<cv::Mat> out_numbers;
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std::size_t frames = 0u;
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std::cout << "Reading " << input << std::endl;
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if (cmd.get<bool>("ser")) {
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std::cout << "Going serial..." << std::endl;
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cv::VideoCapture cap(input);
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auto cc = pp.compile(cv::GMatDesc{CV_8U,3,cv::Size(1920,1080)},
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cv::compile_args(kernels, networks));
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avg.start();
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while (cv::waitKey(1) < 0) {
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cap >> frame;
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if (frame.empty()) break;
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cc(cv::gin(frame),
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cv::gout(frame, vehicles, out_colors, out_types, plates, out_numbers));
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frames++;
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labels::DrawResults(frame, vehicles, out_colors, out_types, plates, out_numbers);
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labels::DrawFPS(frame, frames, avg.fps(frames));
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if (!no_show) cv::imshow("Out", frame);
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}
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} else {
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std::cout << "Going pipelined..." << std::endl;
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auto cc = pp.compileStreaming(cv::GMatDesc{CV_8U,3,cv::Size(1920,1080)},
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cv::compile_args(kernels, networks));
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cc.setSource(cv::gapi::wip::make_src<cv::gapi::wip::GCaptureSource>(input));
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avg.start();
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cc.start();
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// Implement different execution policies depending on the display option
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// for the best performance.
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while (cc.running()) {
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auto out_vector = cv::gout(frame, vehicles, out_colors, out_types, plates, out_numbers);
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if (no_show) {
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// This is purely a video processing. No need to balance with UI rendering.
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// Use a blocking pull() to obtain data. Break the loop if the stream is over.
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if (!cc.pull(std::move(out_vector)))
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break;
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} else if (!cc.try_pull(std::move(out_vector))) {
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// Use a non-blocking try_pull() to obtain data.
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// If there's no data, let UI refresh (and handle keypress)
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if (cv::waitKey(1) >= 0) break;
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else continue;
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}
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// At this point we have data for sure (obtained in either blocking or non-blocking way).
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frames++;
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labels::DrawResults(frame, vehicles, out_colors, out_types, plates, out_numbers);
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labels::DrawFPS(frame, frames, avg.fps(frames));
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if (!no_show) cv::imshow("Out", frame);
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}
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cc.stop();
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}
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std::cout << "Processed " << frames << " frames in " << avg.elapsed() << std::endl;
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return 0;
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}
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#else
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int main()
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{
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std::cerr << "This tutorial code requires G-API module "
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"with Inference Engine backend to run"
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<< std::endl;
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return 1;
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}
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#endif // HAVE_OPECV_GAPI
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