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Merge pull request #25710 from gursimarsingh:improved_object_detection_sample
Merged yolo_detector and object detection sample #25710 Relates to #25006 This pull request merges the yolo_detector.cpp sample with the object_detector.cpp sample. It also beautifies the bounding box display on the output images ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Gursimar Singh
GitHub
+467
-247
@@ -1,68 +1,114 @@
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//![includes]
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#include <fstream>
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#include <sstream>
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#include <opencv2/dnn.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/imgcodecs.hpp>
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#include <opencv2/highgui.hpp>
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#if defined(HAVE_THREADS)
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#define USE_THREADS 1
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#endif
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#ifdef USE_THREADS
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#include <mutex>
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#include <thread>
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#include <queue>
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#endif
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#include "iostream"
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#include "common.hpp"
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std::string param_keys =
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"{ help h | | Print help message. }"
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"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
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"{ zoo | models.yml | An optional path to file with preprocessing parameters }"
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"{ device | 0 | camera device number. }"
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"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
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"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
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"{ classes | | Optional path to a text file with names of classes to label detected objects. }"
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"{ thr | .5 | Confidence threshold. }"
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"{ nms | .4 | Non-maximum suppression threshold. }"
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"{ async | 0 | Number of asynchronous forwards at the same time. "
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"Choose 0 for synchronous mode }";
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std::string backend_keys = cv::format(
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"{ backend | 0 | Choose one of computation backends: "
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"%d: automatically (by default), "
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"%d: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
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"%d: OpenCV implementation, "
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"%d: VKCOM, "
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"%d: CUDA }", 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);
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std::string target_keys = cv::format(
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"{ target | 0 | Choose one of target computation devices: "
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"%d: CPU target (by default), "
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"%d: OpenCL, "
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"%d: OpenCL fp16 (half-float precision), "
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"%d: VPU, "
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"%d: Vulkan, "
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"%d: CUDA, "
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"%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);
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std::string keys = param_keys + backend_keys + target_keys;
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//![includes]
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using namespace cv;
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using namespace dnn;
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using namespace std;
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float confThreshold, nmsThreshold;
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std::vector<std::string> classes;
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const string about =
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"Firstly, download required models using `download_models.py` (if not already done). Set environment variable OPENCV_DOWNLOAD_CACHE_DIR to specify where models should be downloaded. Also, point OPENCV_SAMPLES_DATA_PATH to opencv/samples/data.\n"
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"To run:\n"
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"\t ./example_dnn_object_detection model_name --input=path/to/your/input/image/or/video (don't give --input flag if want to use device camera)\n"
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"Sample command:\n"
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"\t ./example_dnn_object_detection yolov8 --input=$OPENCV_SAMPLES_DATA_PATH/baboon.jpg\n"
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inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
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const Scalar& mean, bool swapRB);
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"Model path can also be specified using --model argument. ";
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void postprocess(Mat& frame, const std::vector<Mat>& out, Net& net, int backend);
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const string param_keys =
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"{ help h | | Print help message. }"
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"{ @alias | | 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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"{ device | 0 | camera device number. }"
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"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
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"{ thr | .5 | Confidence threshold. }"
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"{ nms | .4 | Non-maximum suppression threshold. }"
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"{ async | 0 | Number of asynchronous forwards at the same time. "
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"Choose 0 for synchronous mode }"
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"{ padvalue | 114.0 | padding value. }"
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"{ paddingmode | 2 | Choose one of padding modes: "
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"0: resize to required input size without extra processing, "
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"1: Image will be cropped after resize, "
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"2: Resize image to the desired size while preserving the aspect ratio of original image }";
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void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
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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 (https://software.intel.com/openvino-toolkit), "
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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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void callback(int pos, void* userdata);
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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 preprocess) }");
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string keys = param_keys + backend_keys + target_keys;
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float confThreshold, nmsThreshold, scale, paddingValue;
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vector<string> labels;
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Scalar meanv;
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bool swapRB;
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int inpWidth, inpHeight;
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size_t asyncNumReq = 0;
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ImagePaddingMode paddingMode;
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string modelName, framework;
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static void preprocess(const Mat& frame, Net& net, Size inpSize);
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static void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name);
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static void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness);
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static void callback(int pos, void* userdata);
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static Scalar getColor(int classId);
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static void yoloPostProcessing(
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const vector<Mat>& outs,
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vector<int>& keep_classIds,
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vector<float>& keep_confidences,
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vector<Rect2d>& keep_boxes,
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float conf_threshold,
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float iou_threshold,
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const string& yolo_name);
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static void printAliases(string& zooFile){
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vector<string> aliases = findAliases(zooFile, "object_detection");
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cout<<"Alias choices: [ ";
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for (auto it: aliases){
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cout<<"'"<<it<<"' ";
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}
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cout<<"]"<<endl;
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}
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static Scalar getTextColor(Scalar bgColor) {
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double luminance = 0.299 * bgColor[2] + 0.587 * bgColor[1] + 0.114 * bgColor[0];
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return luminance > 128 ? Scalar(0, 0, 0) : Scalar(255, 255, 255);
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}
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#ifdef USE_THREADS
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template <typename T>
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class QueueFPS : public std::queue<T>
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{
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@@ -112,233 +158,362 @@ private:
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TickMeter tm;
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std::mutex mutex;
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};
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#endif // USE_THREADS
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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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const std::string modelName = parser.get<String>("@alias");
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const std::string zooFile = parser.get<String>("zoo");
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string zooFile = parser.get<String>("zoo");
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if (!parser.has("@alias") || parser.has("help"))
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{
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cout << about << endl;
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parser.printMessage();
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printAliases(zooFile);
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return -1;
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}
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zooFile = findFile(zooFile);
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modelName = parser.get<String>("@alias");
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keys += genPreprocArguments(modelName, zooFile);
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parser = CommandLineParser(argc, argv, keys);
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parser.about("Use this script to run object detection deep learning networks using OpenCV.");
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if (argc == 1 || parser.has("help"))
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if (!parser.has("model"))
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{
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parser.printMessage();
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return 0;
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cout << "Path to model is not provided in command line or model alias is not correct" << endl;
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printAliases(zooFile);
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return -1;
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}
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confThreshold = parser.get<float>("thr");
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nmsThreshold = parser.get<float>("nms");
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float scale = parser.get<float>("scale");
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Scalar mean = parser.get<Scalar>("mean");
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bool swapRB = parser.get<bool>("rgb");
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int inpWidth = parser.get<int>("width");
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int inpHeight = parser.get<int>("height");
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size_t asyncNumReq = parser.get<int>("async");
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CV_Assert(parser.has("model"));
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std::string modelPath = findFile(parser.get<String>("model"));
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std::string configPath = findFile(parser.get<String>("config"));
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//![preprocess_params]
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scale = parser.get<float>("scale");
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meanv = parser.get<Scalar>("mean");
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swapRB = parser.get<bool>("rgb");
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inpWidth = parser.get<int>("width");
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inpHeight = parser.get<int>("height");
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int async = parser.get<int>("async");
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paddingValue = parser.get<float>("padvalue");
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const string yolo_name = parser.get<String>("postprocessing");
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paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
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//![preprocess_params]
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String sha1 = parser.get<String>("sha1");
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const string modelPath = findModel(parser.get<String>("model"), sha1);
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const string configPath = findFile(parser.get<String>("config"));
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framework = modelPath.substr(modelPath.rfind('.') + 1);
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// Open file with classes names.
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if (parser.has("classes"))
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if (parser.has("labels"))
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{
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std::string file = parser.get<String>("classes");
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std::ifstream ifs(file.c_str());
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const string file = findFile(parser.get<String>("labels"));
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ifstream ifs(file.c_str());
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if (!ifs.is_open())
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CV_Error(Error::StsError, "File " + file + " not found");
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std::string line;
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while (std::getline(ifs, line))
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string line;
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while (getline(ifs, line))
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{
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classes.push_back(line);
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labels.push_back(line);
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}
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}
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// Load a model.
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Net net = readNet(modelPath, configPath, parser.get<String>("framework"));
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int backend = parser.get<int>("backend");
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//![read_net]
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Net net = readNet(modelPath, configPath);
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int backend = getBackendID(parser.get<String>("backend"));
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net.setPreferableBackend(backend);
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net.setPreferableTarget(parser.get<int>("target"));
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std::vector<String> outNames = net.getUnconnectedOutLayersNames();
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net.setPreferableTarget(getTargetID(parser.get<String>("target")));
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//![read_net]
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// Create a window
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static const std::string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_NORMAL);
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static const string kWinName = "Deep learning object detection in OpenCV";
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namedWindow(kWinName, WINDOW_AUTOSIZE);
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int initialConf = (int)(confThreshold * 100);
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createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback);
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createTrackbar("Confidence threshold, %", kWinName, &initialConf, 99, callback, &net);
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// Open a video file or an image file or a camera stream.
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VideoCapture cap;
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if (parser.has("input"))
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cap.open(parser.get<String>("input"));
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else
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cap.open(parser.get<int>("device"));
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bool openSuccess = parser.has("input") ? cap.open(parser.get<String>("input")) : cap.open(parser.get<int>("device"));
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if (!openSuccess){
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cout << "Could not open input file or camera device" << endl;
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return 0;
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}
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#ifdef USE_THREADS
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bool process = true;
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FontFace sans("sans");
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// Frames capturing thread
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QueueFPS<Mat> framesQueue;
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std::thread framesThread([&](){
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Mat frame;
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while (process)
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{
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cap >> frame;
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if (!frame.empty())
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framesQueue.push(frame.clone());
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else
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break;
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}
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});
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int stdSize = 15;
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int stdWeight = 150;
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int stdImgSize = 512;
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int stdThickness = 2;
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vector<int> classIds;
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vector<float> confidences;
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vector<Rect> boxes;
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// Frames processing thread
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QueueFPS<Mat> processedFramesQueue;
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QueueFPS<std::vector<Mat> > predictionsQueue;
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std::thread processingThread([&](){
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std::queue<AsyncArray> futureOutputs;
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Mat blob;
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while (process)
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{
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// Get a next frame
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if (async > 0 && backend == DNN_BACKEND_INFERENCE_ENGINE){
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asyncNumReq = async;
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}
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if (async != 0) {
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// Threading is enabled
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bool process = true;
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// Frames capturing thread
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QueueFPS<Mat> framesQueue;
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std::thread framesThread([&]() {
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Mat frame;
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{
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if (!framesQueue.empty())
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{
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frame = framesQueue.get();
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if (asyncNumReq)
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{
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if (futureOutputs.size() == asyncNumReq)
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frame = Mat();
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}
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else
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framesQueue.clear(); // Skip the rest of frames
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}
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}
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// Process the frame
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if (!frame.empty())
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{
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preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
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processedFramesQueue.push(frame);
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if (asyncNumReq)
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{
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futureOutputs.push(net.forwardAsync());
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}
|
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while (process) {
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cap >> frame;
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if (!frame.empty())
|
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framesQueue.push(frame.clone());
|
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else
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break;
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}
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});
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// Frames processing thread
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QueueFPS<Mat> processedFramesQueue;
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QueueFPS<std::vector<Mat>> predictionsQueue;
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std::thread processingThread([&]() {
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std::queue<AsyncArray> futureOutputs;
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Mat blob;
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while (process) {
|
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// Get the next frame
|
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Mat frame;
|
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{
|
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std::vector<Mat> outs;
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net.forward(outs, outNames);
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predictionsQueue.push(outs);
|
||||
if (!framesQueue.empty()) {
|
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frame = framesQueue.get();
|
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if (asyncNumReq) {
|
||||
if (futureOutputs.size() == asyncNumReq)
|
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frame = Mat();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
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// Process the frame
|
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if (!frame.empty()) {
|
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preprocess(frame, net, Size(inpWidth, inpHeight));
|
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processedFramesQueue.push(frame);
|
||||
|
||||
if (asyncNumReq) {
|
||||
futureOutputs.push(net.forwardAsync());
|
||||
} else {
|
||||
vector<Mat> outs;
|
||||
net.forward(outs, net.getUnconnectedOutLayersNames());
|
||||
predictionsQueue.push(outs);
|
||||
}
|
||||
}
|
||||
|
||||
while (!futureOutputs.empty() &&
|
||||
futureOutputs.front().wait_for(std::chrono::seconds(0))) {
|
||||
AsyncArray async_out = futureOutputs.front();
|
||||
futureOutputs.pop();
|
||||
Mat out;
|
||||
async_out.get(out);
|
||||
predictionsQueue.push({out});
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
while (!futureOutputs.empty() &&
|
||||
futureOutputs.front().wait_for(std::chrono::seconds(0)))
|
||||
{
|
||||
AsyncArray async_out = futureOutputs.front();
|
||||
futureOutputs.pop();
|
||||
Mat out;
|
||||
async_out.get(out);
|
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predictionsQueue.push({out});
|
||||
// Postprocessing and rendering loop
|
||||
while (waitKey(100) < 0) {
|
||||
if (predictionsQueue.empty())
|
||||
continue;
|
||||
|
||||
vector<Mat> outs = predictionsQueue.get();
|
||||
Mat frame = processedFramesQueue.get();
|
||||
|
||||
classIds.clear();
|
||||
confidences.clear();
|
||||
boxes.clear();
|
||||
postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
|
||||
|
||||
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
|
||||
|
||||
int imgWidth = max(frame.rows, frame.cols);
|
||||
int size = static_cast<int>((stdSize * imgWidth) / (stdImgSize * 1.5));
|
||||
int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
|
||||
|
||||
if (predictionsQueue.counter > 1) {
|
||||
string label = format("Camera: %.2f FPS", framesQueue.getFPS());
|
||||
rectangle(frame, Point(0, 0), Point(10 * size, 3 * size + size / 4), Scalar::all(255), FILLED);
|
||||
putText(frame, label, Point(0, size), Scalar::all(0), sans, size, weight);
|
||||
|
||||
label = format("Network: %.2f FPS", predictionsQueue.getFPS());
|
||||
putText(frame, label, Point(0, 2 * size), Scalar::all(0), sans, size, weight);
|
||||
|
||||
label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
|
||||
putText(frame, label, Point(0, 3 * size), Scalar::all(0), sans, size, weight);
|
||||
}
|
||||
imshow(kWinName, frame);
|
||||
}
|
||||
});
|
||||
|
||||
// Postprocessing and rendering loop
|
||||
while (waitKey(1) < 0)
|
||||
{
|
||||
if (predictionsQueue.empty())
|
||||
continue;
|
||||
process = false;
|
||||
framesThread.join();
|
||||
processingThread.join();
|
||||
} else {
|
||||
if (asyncNumReq)
|
||||
CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
|
||||
// Threading is disabled, run synchronously
|
||||
Mat frame, blob;
|
||||
while (waitKey(100) < 0) {
|
||||
cap >> frame;
|
||||
if (frame.empty()) {
|
||||
waitKey();
|
||||
break;
|
||||
}
|
||||
preprocess(frame, net, Size(inpWidth, inpHeight));
|
||||
|
||||
std::vector<Mat> outs = predictionsQueue.get();
|
||||
Mat frame = processedFramesQueue.get();
|
||||
vector<Mat> outs;
|
||||
net.forward(outs, net.getUnconnectedOutLayersNames());
|
||||
|
||||
postprocess(frame, outs, net, backend);
|
||||
classIds.clear();
|
||||
confidences.clear();
|
||||
boxes.clear();
|
||||
|
||||
if (predictionsQueue.counter > 1)
|
||||
{
|
||||
std::string label = format("Camera: %.2f FPS", framesQueue.getFPS());
|
||||
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
postprocess(frame, outs, net, backend, classIds, confidences, boxes, yolo_name);
|
||||
|
||||
label = format("Network: %.2f FPS", predictionsQueue.getFPS());
|
||||
putText(frame, label, Point(0, 30), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
drawPred(classIds, confidences, boxes, frame, sans, stdSize, stdWeight, stdImgSize, stdThickness);
|
||||
|
||||
label = format("Skipped frames: %d", framesQueue.counter - predictionsQueue.counter);
|
||||
putText(frame, label, Point(0, 45), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
vector<double> layersTimes;
|
||||
int imgWidth = max(frame.rows, frame.cols);
|
||||
int size = static_cast<int>((stdSize * imgWidth) / (stdImgSize * 1.5));
|
||||
int weight = static_cast<int>((stdWeight * imgWidth) / (stdImgSize * 1.5));
|
||||
double freq = getTickFrequency() / 1000;
|
||||
double t = net.getPerfProfile(layersTimes) / freq;
|
||||
string label = format("Inference time: %.2f ms", t);
|
||||
putText(frame, label, Point(0, size), Scalar(0, 255, 0), sans, size, weight);
|
||||
imshow(kWinName, frame);
|
||||
}
|
||||
imshow(kWinName, frame);
|
||||
}
|
||||
|
||||
process = false;
|
||||
framesThread.join();
|
||||
processingThread.join();
|
||||
|
||||
#else // USE_THREADS
|
||||
if (asyncNumReq)
|
||||
CV_Error(Error::StsNotImplemented, "Asynchronous forward is supported only with Inference Engine backend.");
|
||||
|
||||
// Process frames.
|
||||
Mat frame, blob;
|
||||
while (waitKey(1) < 0)
|
||||
{
|
||||
cap >> frame;
|
||||
if (frame.empty())
|
||||
{
|
||||
waitKey();
|
||||
break;
|
||||
}
|
||||
|
||||
preprocess(frame, net, Size(inpWidth, inpHeight), scale, mean, swapRB);
|
||||
|
||||
std::vector<Mat> outs;
|
||||
net.forward(outs, outNames);
|
||||
|
||||
postprocess(frame, outs, net, backend);
|
||||
|
||||
// Put efficiency information.
|
||||
std::vector<double> layersTimes;
|
||||
double freq = getTickFrequency() / 1000;
|
||||
double t = net.getPerfProfile(layersTimes) / freq;
|
||||
std::string label = format("Inference time: %.2f ms", t);
|
||||
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
|
||||
|
||||
imshow(kWinName, frame);
|
||||
}
|
||||
#endif // USE_THREADS
|
||||
return 0;
|
||||
}
|
||||
|
||||
inline void preprocess(const Mat& frame, Net& net, Size inpSize, float scale,
|
||||
const Scalar& mean, bool swapRB)
|
||||
void preprocess(const Mat& frame, Net& net, Size inpSize)
|
||||
{
|
||||
static Mat blob;
|
||||
// Create a 4D blob from a frame.
|
||||
if (inpSize.width <= 0) inpSize.width = frame.cols;
|
||||
if (inpSize.height <= 0) inpSize.height = frame.rows;
|
||||
blobFromImage(frame, blob, 1.0, inpSize, Scalar(), swapRB, false, CV_8U);
|
||||
Size size(inpSize.width <= 0 ? frame.cols : inpSize.width, inpSize.height <= 0 ? frame.rows : inpSize.height);
|
||||
|
||||
// Run a model.
|
||||
net.setInput(blob, "", scale, mean);
|
||||
if (net.getLayer(0)->outputNameToIndex("im_info") != -1) // Faster-RCNN or R-FCN
|
||||
// Prepare the blob from the image
|
||||
Mat inp;
|
||||
if(framework == "weights"){ // checks whether model is darknet
|
||||
blobFromImage(frame, inp, scale, size, meanv, swapRB, false, CV_32F);
|
||||
}
|
||||
else{
|
||||
//![preprocess_call]
|
||||
Image2BlobParams imgParams(
|
||||
scale,
|
||||
size,
|
||||
meanv,
|
||||
swapRB,
|
||||
CV_32F,
|
||||
DNN_LAYOUT_NCHW,
|
||||
paddingMode,
|
||||
paddingValue);
|
||||
|
||||
inp = blobFromImageWithParams(frame, imgParams);
|
||||
//![preprocess_call]
|
||||
}
|
||||
|
||||
// Set the blob as the network input
|
||||
net.setInput(inp);
|
||||
|
||||
// Check if the model is Faster-RCNN or R-FCN
|
||||
if (net.getLayer(0)->outputNameToIndex("im_info") != -1)
|
||||
{
|
||||
resize(frame, frame, inpSize);
|
||||
Mat imInfo = (Mat_<float>(1, 3) << inpSize.height, inpSize.width, 1.6f);
|
||||
// Resize the frame and prepare imInfo
|
||||
resize(frame, frame, size);
|
||||
Mat imInfo = (Mat_<float>(1, 3) << size.height, size.width, 1.6f);
|
||||
net.setInput(imInfo, "im_info");
|
||||
}
|
||||
}
|
||||
|
||||
void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend)
|
||||
void yoloPostProcessing(
|
||||
const vector<Mat>& outs,
|
||||
vector<int>& keep_classIds,
|
||||
vector<float>& keep_confidences,
|
||||
vector<Rect2d>& keep_boxes,
|
||||
float conf_threshold,
|
||||
float iou_threshold,
|
||||
const string& yolo_name)
|
||||
{
|
||||
static std::vector<int> outLayers = net.getUnconnectedOutLayers();
|
||||
static std::string outLayerType = net.getLayer(outLayers[0])->type;
|
||||
// Retrieve
|
||||
vector<int> classIds;
|
||||
vector<float> confidences;
|
||||
vector<Rect2d> boxes;
|
||||
|
||||
vector<Mat> outs_copy = outs;
|
||||
|
||||
if (yolo_name == "yolov8")
|
||||
{
|
||||
transposeND(outs_copy[0], {0, 2, 1}, outs_copy[0]);
|
||||
}
|
||||
|
||||
if (yolo_name == "yolonas")
|
||||
{
|
||||
// outs contains 2 elements of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
|
||||
Mat concat_out;
|
||||
// squeeze the first dimension
|
||||
outs_copy[0] = outs_copy[0].reshape(1, outs_copy[0].size[1]);
|
||||
outs_copy[1] = outs_copy[1].reshape(1, outs_copy[1].size[1]);
|
||||
hconcat(outs_copy[1], outs_copy[0], concat_out);
|
||||
outs_copy[0] = concat_out;
|
||||
// remove the second element
|
||||
outs_copy.pop_back();
|
||||
// unsqueeze the first dimension
|
||||
outs_copy[0] = outs_copy[0].reshape(0, vector<int>{1, 8400, 84});
|
||||
}
|
||||
|
||||
for (auto preds : outs_copy)
|
||||
{
|
||||
preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85]
|
||||
for (int i = 0; i < preds.rows; ++i)
|
||||
{
|
||||
// filter out non-object
|
||||
float obj_conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? 1.0f : preds.at<float>(i, 4);
|
||||
if (obj_conf < conf_threshold)
|
||||
continue;
|
||||
|
||||
Mat scores = preds.row(i).colRange((yolo_name == "yolov8" || yolo_name == "yolonas") ? 4 : 5, preds.cols);
|
||||
double conf;
|
||||
Point maxLoc;
|
||||
minMaxLoc(scores, 0, &conf, 0, &maxLoc);
|
||||
|
||||
conf = (yolo_name == "yolov8" || yolo_name == "yolonas") ? conf : conf * obj_conf;
|
||||
if (conf < conf_threshold)
|
||||
continue;
|
||||
|
||||
// get bbox coords
|
||||
float* det = preds.ptr<float>(i);
|
||||
double cx = det[0];
|
||||
double cy = det[1];
|
||||
double w = det[2];
|
||||
double h = det[3];
|
||||
|
||||
// [x1, y1, x2, y2]
|
||||
if (yolo_name == "yolonas") {
|
||||
boxes.push_back(Rect2d(cx, cy, w, h));
|
||||
} else {
|
||||
boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
|
||||
cx + 0.5 * w, cy + 0.5 * h));
|
||||
}
|
||||
classIds.push_back(maxLoc.x);
|
||||
confidences.push_back(static_cast<float>(conf));
|
||||
}
|
||||
}
|
||||
|
||||
// NMS
|
||||
vector<int> keep_idx;
|
||||
NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx);
|
||||
|
||||
for (auto i : keep_idx)
|
||||
{
|
||||
keep_classIds.push_back(classIds[i]);
|
||||
keep_confidences.push_back(confidences[i]);
|
||||
keep_boxes.push_back(boxes[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void postprocess(Mat& frame, const vector<Mat>& outs, Net& net, int backend, vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, const string yolo_name)
|
||||
{
|
||||
static vector<int> outLayers = net.getUnconnectedOutLayers();
|
||||
static string outLayerType = net.getLayer(outLayers[0])->type;
|
||||
|
||||
std::vector<int> classIds;
|
||||
std::vector<float> confidences;
|
||||
std::vector<Rect> boxes;
|
||||
if (outLayerType == "DetectionOutput")
|
||||
{
|
||||
// Network produces output blob with a shape 1x1xNx7 where N is a number of
|
||||
@@ -405,14 +580,46 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
|
||||
else if (outLayerType == "Identity")
|
||||
{
|
||||
//![forward_buffers]
|
||||
vector<int> keep_classIds;
|
||||
vector<float> keep_confidences;
|
||||
vector<Rect2d> keep_boxes;
|
||||
//![forward_buffers]
|
||||
|
||||
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for another backends we need NMS in sample
|
||||
// or NMS is required if number of outputs > 1
|
||||
//![postprocess]
|
||||
yoloPostProcessing(outs, keep_classIds, keep_confidences, keep_boxes, confThreshold, nmsThreshold, yolo_name);
|
||||
//![postprocess]
|
||||
|
||||
for (size_t i = 0; i < keep_classIds.size(); ++i)
|
||||
{
|
||||
classIds.push_back(keep_classIds[i]);
|
||||
confidences.push_back(keep_confidences[i]);
|
||||
Rect2d box = keep_boxes[i];
|
||||
boxes.push_back(Rect(cvFloor(box.x), cvFloor(box.y), cvFloor(box.width-box.x), cvFloor(box.height-box.y)));
|
||||
}
|
||||
if (framework == "onnx"){
|
||||
Image2BlobParams paramNet;
|
||||
paramNet.scalefactor = scale;
|
||||
paramNet.size = Size(inpWidth, inpHeight);
|
||||
paramNet.mean = meanv;
|
||||
paramNet.swapRB = swapRB;
|
||||
paramNet.paddingmode = paddingMode;
|
||||
|
||||
paramNet.blobRectsToImageRects(boxes, boxes, frame.size());
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
CV_Error(Error::StsNotImplemented, "Unknown output layer type: " + outLayerType);
|
||||
}
|
||||
|
||||
// NMS is used inside Region layer only on DNN_BACKEND_OPENCV for other backends we need NMS in sample
|
||||
// or NMS is required if the number of outputs > 1
|
||||
if (outLayers.size() > 1 || (outLayerType == "Region" && backend != DNN_BACKEND_OPENCV))
|
||||
{
|
||||
std::map<int, std::vector<size_t> > class2indices;
|
||||
map<int, vector<size_t> > class2indices;
|
||||
for (size_t i = 0; i < classIds.size(); i++)
|
||||
{
|
||||
if (confidences[i] >= confThreshold)
|
||||
@@ -420,20 +627,20 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend
|
||||
class2indices[classIds[i]].push_back(i);
|
||||
}
|
||||
}
|
||||
std::vector<Rect> nmsBoxes;
|
||||
std::vector<float> nmsConfidences;
|
||||
std::vector<int> nmsClassIds;
|
||||
for (std::map<int, std::vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
|
||||
vector<Rect> nmsBoxes;
|
||||
vector<float> nmsConfidences;
|
||||
vector<int> nmsClassIds;
|
||||
for (map<int, vector<size_t> >::iterator it = class2indices.begin(); it != class2indices.end(); ++it)
|
||||
{
|
||||
std::vector<Rect> localBoxes;
|
||||
std::vector<float> localConfidences;
|
||||
std::vector<size_t> classIndices = it->second;
|
||||
vector<Rect> localBoxes;
|
||||
vector<float> localConfidences;
|
||||
vector<size_t> classIndices = it->second;
|
||||
for (size_t i = 0; i < classIndices.size(); i++)
|
||||
{
|
||||
localBoxes.push_back(boxes[classIndices[i]]);
|
||||
localConfidences.push_back(confidences[classIndices[i]]);
|
||||
}
|
||||
std::vector<int> nmsIndices;
|
||||
vector<int> nmsIndices;
|
||||
NMSBoxes(localBoxes, localConfidences, confThreshold, nmsThreshold, nmsIndices);
|
||||
for (size_t i = 0; i < nmsIndices.size(); i++)
|
||||
{
|
||||
@@ -447,36 +654,49 @@ void postprocess(Mat& frame, const std::vector<Mat>& outs, Net& net, int backend
|
||||
classIds = nmsClassIds;
|
||||
confidences = nmsConfidences;
|
||||
}
|
||||
|
||||
for (size_t idx = 0; idx < boxes.size(); ++idx)
|
||||
{
|
||||
Rect box = boxes[idx];
|
||||
drawPred(classIds[idx], confidences[idx], box.x, box.y,
|
||||
box.x + box.width, box.y + box.height, frame);
|
||||
}
|
||||
}
|
||||
|
||||
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
|
||||
void drawPred(vector<int>& classIds, vector<float>& confidences, vector<Rect>& boxes, Mat& frame, FontFace& sans, int stdSize, int stdWeight, int stdImgSize, int stdThickness)
|
||||
{
|
||||
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
|
||||
int imgWidth = max(frame.rows, frame.cols);
|
||||
int size = (stdSize*imgWidth)/stdImgSize;
|
||||
int weight = (stdWeight*imgWidth)/stdImgSize;
|
||||
int thickness = (stdThickness*imgWidth)/stdImgSize;
|
||||
|
||||
std::string label = format("%.2f", conf);
|
||||
if (!classes.empty())
|
||||
{
|
||||
CV_Assert(classId < (int)classes.size());
|
||||
label = classes[classId] + ": " + label;
|
||||
for (size_t idx = 0; idx < boxes.size(); ++idx){
|
||||
Scalar boxColor = getColor(classIds[idx]);
|
||||
int left = boxes[idx].x;
|
||||
int top = boxes[idx].y;
|
||||
int right = boxes[idx].x + boxes[idx].width;
|
||||
int bottom = boxes[idx].y + boxes[idx].height;
|
||||
rectangle(frame, Point(left, top), Point(right, bottom), boxColor, thickness);
|
||||
|
||||
string label = format("%.2f", confidences[idx]);
|
||||
if (!labels.empty())
|
||||
{
|
||||
CV_Assert(classIds[idx] < (int)labels.size());
|
||||
label = labels[classIds[idx]] + ": " + label;
|
||||
}
|
||||
|
||||
Rect r = getTextSize(Size(), label, Point(), sans, size, weight);
|
||||
int baseline = r.y + r.height;
|
||||
Size labelSize = Size(r.width, r.height + size/4 - baseline);
|
||||
|
||||
top = max(top-thickness/2, labelSize.height);
|
||||
rectangle(frame, Point(left-thickness/2, top-(labelSize.height)),
|
||||
Point(left + labelSize.width, top), boxColor, FILLED);
|
||||
putText(frame, label, Point(left, top-size/4), getTextColor(boxColor), sans, size, weight);
|
||||
}
|
||||
|
||||
int baseLine;
|
||||
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
|
||||
|
||||
top = max(top, labelSize.height);
|
||||
rectangle(frame, Point(left, top - labelSize.height),
|
||||
Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
|
||||
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
|
||||
}
|
||||
|
||||
void callback(int pos, void*)
|
||||
{
|
||||
confThreshold = pos * 0.01f;
|
||||
}
|
||||
|
||||
Scalar getColor(int classId) {
|
||||
int r = min((classId >> 0 & 1) * 128 + (classId >> 3 & 1) * 64 + (classId >> 6 & 1) * 32 + 80, 255);
|
||||
int g = min((classId >> 1 & 1) * 128 + (classId >> 4 & 1) * 64 + (classId >> 7 & 1) * 32 + 40, 255);
|
||||
int b = min((classId >> 2 & 1) * 128 + (classId >> 5 & 1) * 64 + (classId >> 8 & 1) * 32 + 40, 255);
|
||||
return Scalar(b, g, r);
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user