yolox in c++ (#177)

LaurentBerger · web-flow · commit d84d6d67073f · 2023-08-09T10:37:18.000+08:00
* yolox in c++

* review 1

* review 202306

* disable counter
diff --git a/models/object_detection_yolox/CMakeLists.txt b/models/object_detection_yolox/CMakeLists.txt
@@ -0,0 +1,29 @@
+cmake_minimum_required(VERSION 3.24)
+set(project_name "opencv_zoo_object_detection_yolox")
+
+PROJECT (${project_name})
+
+set(OPENCV_VERSION "4.7.0")
+set(OPENCV_INSTALLATION_PATH "" CACHE PATH "Where to look for OpenCV installation")
+find_package(OpenCV ${OPENCV_VERSION} REQUIRED HINTS ${OPENCV_INSTALLATION_PATH})
+# Find OpenCV, you may need to set OpenCV_DIR variable
+# to the absolute path to the directory containing OpenCVConfig.cmake file
+# via the command line or GUI
+
+file(GLOB SourceFile
+    "demo.cpp")
+# If the package has been found, several variables will
+# be set, you can find the full list with descriptions
+# in the OpenCVConfig.cmake file.
+# Print some message showing some of them
+message(STATUS "OpenCV library status:")
+message(STATUS "    config: ${OpenCV_DIR}")
+message(STATUS "    version: ${OpenCV_VERSION}")
+message(STATUS "    libraries: ${OpenCV_LIBS}")
+message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")
+
+# Declare the executable target built from your sources
+add_executable(${project_name} ${SourceFile})
+
+# Link your application with OpenCV libraries
+target_link_libraries(${project_name} PRIVATE ${OpenCV_LIBS})
diff --git a/models/object_detection_yolox/README.md b/models/object_detection_yolox/README.md
@@ -13,6 +13,8 @@ Note:
 
 ## Demo
 
+### Python
+
 Run the following command to try the demo: 
 ```shell
 # detect on camera input
@@ -24,6 +26,23 @@ Note:
 - image result saved as "result.jpg"
 - this model requires `opencv-python>=4.8.0`
 
+### C++
+
+Install latest OpenCV and CMake >= 3.24.0 to get started with:
+
+```shell
+# A typical and default installation path of OpenCV is /usr/local
+cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
+cmake --build build
+
+# detect on camera input
+./build/opencv_zoo_object_detection_yolox
+# detect on an image
+./build/opencv_zoo_object_detection_yolox -m=/path/to/model -i=/path/to/image -v
+# get help messages
+./build/opencv_zoo_object_detection_yolox -h
+```
+
 
 ## Results
 
diff --git a/models/object_detection_yolox/demo.cpp b/models/object_detection_yolox/demo.cpp
@@ -0,0 +1,315 @@
+#include <vector>
+#include <string>
+#include <utility>
+
+#include <opencv2/opencv.hpp>
+
+using namespace std;
+using namespace cv;
+using namespace dnn;
+
+vector< pair<dnn::Backend, dnn::Target> > backendTargetPairs = {
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_OPENCV, dnn::DNN_TARGET_CPU),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CUDA, dnn::DNN_TARGET_CUDA_FP16),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_TIMVX, dnn::DNN_TARGET_NPU),
+        std::make_pair<dnn::Backend, dnn::Target>(dnn::DNN_BACKEND_CANN, dnn::DNN_TARGET_NPU) };
+
+vector<string> labelYolox = {
+        "person", "bicycle", "car", "motorcycle", "airplane", "bus",
+        "train", "truck", "boat", "traffic light", "fire hydrant",
+        "stop sign", "parking meter", "bench", "bird", "cat", "dog",
+        "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
+        "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+        "skis", "snowboard", "sports ball", "kite", "baseball bat",
+        "baseball glove", "skateboard", "surfboard", "tennis racket",
+        "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
+        "banana", "apple", "sandwich", "orange", "broccoli", "carrot",
+        "hot dog", "pizza", "donut", "cake", "chair", "couch",
+        "potted plant", "bed", "dining table", "toilet", "tv", "laptop",
+        "mouse", "remote", "keyboard", "cell phone", "microwave",
+        "oven", "toaster", "sink", "refrigerator", "book", "clock",
+        "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };
+
+class YoloX {
+private:
+    Net net;
+    string modelPath;
+    Size inputSize;
+    float confThreshold;
+    float nmsThreshold;
+    float objThreshold;
+    dnn::Backend backendId;
+    dnn::Target targetId;
+    int num_classes;
+    vector<int> strides;
+    Mat expandedStrides;
+    Mat grids;
+
+public:
+    YoloX(string modPath, float confThresh = 0.35, float nmsThresh = 0.5, float objThresh = 0.5, dnn::Backend bId = DNN_BACKEND_DEFAULT, dnn::Target tId = DNN_TARGET_CPU) :
+        modelPath(modPath), confThreshold(confThresh),
+        nmsThreshold(nmsThresh), objThreshold(objThresh),
+        backendId(bId), targetId(tId)
+    {
+        this->num_classes = int(labelYolox.size());
+        this->net = readNet(modelPath);
+        this->inputSize = Size(640, 640);
+        this->strides = vector<int>{ 8, 16, 32 };
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+        this->generateAnchors();
+    }
+
+    void setBackendAndTarget(dnn::Backend bId, dnn::Target tId)
+    {
+        this->backendId = bId;
+        this->targetId = tId;
+        this->net.setPreferableBackend(this->backendId);
+        this->net.setPreferableTarget(this->targetId);
+    }
+
+    Mat preprocess(Mat img)
+    {
+        Mat blob;
+        Image2BlobParams paramYolox;
+        paramYolox.datalayout = DNN_LAYOUT_NCHW;
+        paramYolox.ddepth = CV_32F;
+        paramYolox.mean = Scalar::all(0);
+        paramYolox.scalefactor = Scalar::all(1);
+        paramYolox.size = Size(img.cols, img.rows);
+        paramYolox.swapRB = true;
+
+        blob = blobFromImageWithParams(img, paramYolox);
+        return blob;
+    }
+
+   Mat infer(Mat srcimg)
+    {
+        Mat inputBlob = this->preprocess(srcimg);
+
+        this->net.setInput(inputBlob);
+        vector<Mat> outs;
+        this->net.forward(outs, this->net.getUnconnectedOutLayersNames());
+
+        Mat predictions = this->postprocess(outs[0]);
+        return predictions;
+    }
+
+    Mat postprocess(Mat outputs)
+    {
+        Mat dets = outputs.reshape(0,outputs.size[1]);
+        Mat col01;
+        add(dets.colRange(0, 2), this->grids, col01);
+        Mat col23;
+        exp(dets.colRange(2, 4), col23);
+        vector<Mat> col = { col01, col23 };
+        Mat boxes;
+        hconcat(col, boxes);
+        float* ptr = this->expandedStrides.ptr<float>(0);
+        for (int r = 0; r < boxes.rows; r++, ptr++)
+        {
+            boxes.rowRange(r, r + 1) = *ptr * boxes.rowRange(r, r + 1);
+        }
+        // get boxes
+        Mat boxes_xyxy(boxes.rows, boxes.cols, CV_32FC1, Scalar(1));
+        Mat scores = dets.colRange(5, dets.cols).clone();
+        vector<float> maxScores(dets.rows);
+        vector<int> maxScoreIdx(dets.rows);
+        vector<Rect2d> boxesXYXY(dets.rows);
+
+        for (int r = 0; r < boxes_xyxy.rows; r++, ptr++)
+        {
+            boxes_xyxy.at<float>(r, 0) = boxes.at<float>(r, 0) - boxes.at<float>(r, 2) / 2.f;
+            boxes_xyxy.at<float>(r, 1) = boxes.at<float>(r, 1) - boxes.at<float>(r, 3) / 2.f;
+            boxes_xyxy.at<float>(r, 2) = boxes.at<float>(r, 0) + boxes.at<float>(r, 2) / 2.f;
+            boxes_xyxy.at<float>(r, 3) = boxes.at<float>(r, 1) + boxes.at<float>(r, 3) / 2.f;
+            // get scores and class indices
+            scores.rowRange(r, r + 1) = scores.rowRange(r, r + 1) * dets.at<float>(r, 4);
+            double minVal, maxVal;
+            Point maxIdx;
+            minMaxLoc(scores.rowRange(r, r+1), &minVal, &maxVal, nullptr, &maxIdx);
+            maxScoreIdx[r] = maxIdx.x;
+            maxScores[r] = float(maxVal);
+            boxesXYXY[r].x = boxes_xyxy.at<float>(r, 0);
+            boxesXYXY[r].y = boxes_xyxy.at<float>(r, 1);
+            boxesXYXY[r].width = boxes_xyxy.at<float>(r, 2);
+            boxesXYXY[r].height = boxes_xyxy.at<float>(r, 3);
+        }
+
+        vector< int > keep;
+        NMSBoxesBatched(boxesXYXY, maxScores, maxScoreIdx, this->confThreshold, this->nmsThreshold, keep);
+        Mat candidates(int(keep.size()), 6, CV_32FC1);
+        int row = 0;
+        for (auto idx : keep)
+        {
+            boxes_xyxy.rowRange(idx, idx + 1).copyTo(candidates(Rect(0, row, 4, 1)));
+            candidates.at<float>(row, 4) = maxScores[idx];
+            candidates.at<float>(row, 5) = float(maxScoreIdx[idx]);
+            row++;
+        }
+        if (keep.size() == 0)
+            return Mat();
+        return candidates;
+        
+    }
+
+
+    void generateAnchors()
+    {
+        vector< tuple<int, int, int> > nb;
+        int total = 0;
+
+        for (auto v : this->strides)
+        {
+            int w = this->inputSize.width / v;
+            int h = this->inputSize.height / v;
+            nb.push_back(tuple<int, int, int>(w * h, w, v));
+            total += w * h;
+        }
+        this->grids = Mat(total, 2, CV_32FC1);
+        this->expandedStrides = Mat(total, 1, CV_32FC1);
+        float* ptrGrids = this->grids.ptr<float>(0);
+        float* ptrStrides = this->expandedStrides.ptr<float>(0);
+        int pos = 0;
+        for (auto le : nb)
+        {
+            int r = get<1>(le);
+            for (int i = 0; i < get<0>(le); i++, pos++)
+            {
+                *ptrGrids++ = float(i % r);
+                *ptrGrids++ = float(i / r);
+                *ptrStrides++ = float((get<2>(le)));
+            }
+        }
+    }
+};
+
+std::string keys =
+"{ help  h          |                                               | Print help message. }"
+"{ model m          | object_detection_yolox_2022nov.onnx           | Usage: Path to the model, defaults to object_detection_yolox_2022nov.onnx  }"
+"{ input i          |                                               | Path to input image or video file. Skip this argument to capture frames from a camera.}"
+"{ confidence       | 0.5                                           | Class confidence }"
+"{ obj              | 0.5                                           | Enter object threshold }"
+"{ nms              | 0.5                                           | Enter nms IOU threshold }"
+"{ save s           | true                                          | Specify to save results. This flag is invalid when using camera. }"
+"{ vis v            | 1                                             | Specify to open a window for result visualization. This flag is invalid when using camera. }"
+"{ backend bt       | 0                                             | Choose one of computation backends: "
+"0: (default) OpenCV implementation + CPU, "
+"1: CUDA + GPU (CUDA), "
+"2: CUDA + GPU (CUDA FP16), "
+"3: TIM-VX + NPU, "
+"4: CANN + NPU}";
+
+pair<Mat, double> letterBox(Mat srcimg, Size targetSize = Size(640, 640))
+{
+    Mat paddedImg(targetSize.height, targetSize.width, CV_32FC3, Scalar::all(114.0));
+    Mat resizeImg;
+
+    double ratio = min(targetSize.height / double(srcimg.rows), targetSize.width / double(srcimg.cols));
+    resize(srcimg, resizeImg, Size(int(srcimg.cols * ratio), int(srcimg.rows * ratio)), INTER_LINEAR);
+    resizeImg.copyTo(paddedImg(Rect(0, 0, int(srcimg.cols * ratio), int(srcimg.rows * ratio))));
+    return pair<Mat, double>(paddedImg, ratio);
+}
+
+Mat unLetterBox(Mat bbox, double letterboxScale)
+{
+    return bbox / letterboxScale;
+}
+
+Mat visualize(Mat dets, Mat srcimg, double letterbox_scale, double fps = -1)
+{
+    Mat resImg = srcimg.clone();
+
+    if (fps > 0)
+        putText(resImg, format("FPS: %.2f", fps), Size(10, 25), FONT_HERSHEY_SIMPLEX, 1, Scalar(0, 0, 255), 2);
+
+    for (int row = 0; row < dets.rows; row++)
+    {
+        Mat boxF = unLetterBox(dets(Rect(0, row, 4, 1)), letterbox_scale);
+        Mat box;
+        boxF.convertTo(box, CV_32S);
+        float score = dets.at<float>(row, 4);
+        int clsId = int(dets.at<float>(row, 5));
+
+        int x0 = box.at<int>(0, 0);
+        int y0 = box.at<int>(0, 1);
+        int x1 = box.at<int>(0, 2);
+        int y1 = box.at<int>(0, 3);
+
+        string text = format("%s : %f", labelYolox[clsId].c_str(), score * 100);
+        int font = FONT_HERSHEY_SIMPLEX;
+        int baseLine = 0;
+        Size txtSize = getTextSize(text, font, 0.4, 1, &baseLine);
+        rectangle(resImg, Point(x0, y0), Point(x1, y1), Scalar(0, 255, 0), 2);
+        rectangle(resImg, Point(x0, y0 + 1), Point(x0 + txtSize.width + 1, y0 + int(1.5 * txtSize.height)), Scalar(255, 255, 255), -1);
+        putText(resImg, text, Point(x0, y0 + txtSize.height), font, 0.4, Scalar(0, 0, 0), 1);
+    }
+
+    return resImg;
+}
+
+int main(int argc, char** argv)
+{
+    CommandLineParser parser(argc, argv, keys);
+
+    parser.about("Use this script to run Yolox deep learning networks in opencv_zoo using OpenCV.");
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    string model = parser.get<String>("model");
+    float confThreshold = parser.get<float>("confidence");
+    float objThreshold = parser.get<float>("obj");
+    float nmsThreshold = parser.get<float>("nms");
+    bool vis = parser.get<bool>("vis");
+    bool save = parser.get<bool>("save");
+    int backendTargetid = parser.get<int>("backend");
+
+    if (model.empty())
+    {
+        CV_Error(Error::StsError, "Model file " + model + " not found");
+    }
+
+    YoloX modelNet(model, confThreshold, nmsThreshold, objThreshold,
+        backendTargetPairs[backendTargetid].first, backendTargetPairs[backendTargetid].second);
+    //! [Open a video file or an image file or a camera stream]
+    VideoCapture cap;
+    if (parser.has("input"))
+        cap.open(samples::findFile(parser.get<String>("input")));
+    else
+        cap.open(0);
+    if (!cap.isOpened())
+        CV_Error(Error::StsError, "Cannot opend video or file");
+    Mat frame, inputBlob;
+    double letterboxScale;
+
+    static const std::string kWinName = model;
+    int nbInference = 0;
+    while (waitKey(1) < 0)
+    {
+        cap >> frame;
+        if (frame.empty())
+        {
+            cout << "Frame is empty" << endl;
+            waitKey();
+            break;
+        }
+        pair<Mat, double> w = letterBox(frame);
+        inputBlob = get<0>(w);
+        letterboxScale  = get<1>(w);
+        TickMeter tm;
+        tm.start();
+        Mat predictions = modelNet.infer(inputBlob);
+        tm.stop();
+        cout << "Inference time: " << tm.getTimeMilli() << " ms\n";
+        Mat img = visualize(predictions, frame, letterboxScale, tm.getFPS());
+        if (vis)
+        {
+            imshow(kWinName, img);
+        }
+    }
+    return 0;
+}
