退购1.1定位算法

examples/YOLOv8-CPP-Inference/inference.cpp

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+#include "inference.h"
+
+Inference::Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape, const std::string &classesTxtFile, const bool &runWithCuda)
+{
+    modelPath = onnxModelPath;
+    modelShape = modelInputShape;
+    classesPath = classesTxtFile;
+    cudaEnabled = runWithCuda;
+
+    loadOnnxNetwork();
+    // loadClassesFromFile(); The classes are hard-coded for this example
+}
+
+std::vector<Detection> Inference::runInference(const cv::Mat &input)
+{
+    cv::Mat modelInput = input;
+    if (letterBoxForSquare && modelShape.width == modelShape.height)
+        modelInput = formatToSquare(modelInput);
+
+    cv::Mat blob;
+    cv::dnn::blobFromImage(modelInput, blob, 1.0/255.0, modelShape, cv::Scalar(), true, false);
+    net.setInput(blob);
+
+    std::vector<cv::Mat> outputs;
+    net.forward(outputs, net.getUnconnectedOutLayersNames());
+
+    int rows = outputs[0].size[1];
+    int dimensions = outputs[0].size[2];
+
+    bool yolov8 = false;
+    // yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
+    // yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
+    if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
+    {
+        yolov8 = true;
+        rows = outputs[0].size[2];
+        dimensions = outputs[0].size[1];
+
+        outputs[0] = outputs[0].reshape(1, dimensions);
+        cv::transpose(outputs[0], outputs[0]);
+    }
+    float *data = (float *)outputs[0].data;
+
+    float x_factor = modelInput.cols / modelShape.width;
+    float y_factor = modelInput.rows / modelShape.height;
+
+    std::vector<int> class_ids;
+    std::vector<float> confidences;
+    std::vector<cv::Rect> boxes;
+
+    for (int i = 0; i < rows; ++i)
+    {
+        if (yolov8)
+        {
+            float *classes_scores = data+4;
+
+            cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
+            cv::Point class_id;
+            double maxClassScore;
+
+            minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
+
+            if (maxClassScore > modelScoreThreshold)
+            {
+                confidences.push_back(maxClassScore);
+                class_ids.push_back(class_id.x);
+
+                float x = data[0];
+                float y = data[1];
+                float w = data[2];
+                float h = data[3];
+
+                int left = int((x - 0.5 * w) * x_factor);
+                int top = int((y - 0.5 * h) * y_factor);
+
+                int width = int(w * x_factor);
+                int height = int(h * y_factor);
+
+                boxes.push_back(cv::Rect(left, top, width, height));
+            }
+        }
+        else // yolov5
+        {
+            float confidence = data[4];
+
+            if (confidence >= modelConfidenceThreshold)
+            {
+                float *classes_scores = data+5;
+
+                cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
+                cv::Point class_id;
+                double max_class_score;
+
+                minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
+
+                if (max_class_score > modelScoreThreshold)
+                {
+                    confidences.push_back(confidence);
+                    class_ids.push_back(class_id.x);
+
+                    float x = data[0];
+                    float y = data[1];
+                    float w = data[2];
+                    float h = data[3];
+
+                    int left = int((x - 0.5 * w) * x_factor);
+                    int top = int((y - 0.5 * h) * y_factor);
+
+                    int width = int(w * x_factor);
+                    int height = int(h * y_factor);
+
+                    boxes.push_back(cv::Rect(left, top, width, height));
+                }
+            }
+        }
+
+        data += dimensions;
+    }
+
+    std::vector<int> nms_result;
+    cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);
+
+    std::vector<Detection> detections{};
+    for (unsigned long i = 0; i < nms_result.size(); ++i)
+    {
+        int idx = nms_result[i];
+
+        Detection result;
+        result.class_id = class_ids[idx];
+        result.confidence = confidences[idx];
+
+        std::random_device rd;
+        std::mt19937 gen(rd());
+        std::uniform_int_distribution<int> dis(100, 255);
+        result.color = cv::Scalar(dis(gen),
+                                  dis(gen),
+                                  dis(gen));
+
+        result.className = classes[result.class_id];
+        result.box = boxes[idx];
+
+        detections.push_back(result);
+    }
+
+    return detections;
+}
+
+void Inference::loadClassesFromFile()
+{
+    std::ifstream inputFile(classesPath);
+    if (inputFile.is_open())
+    {
+        std::string classLine;
+        while (std::getline(inputFile, classLine))
+            classes.push_back(classLine);
+        inputFile.close();
+    }
+}
+
+void Inference::loadOnnxNetwork()
+{
+    net = cv::dnn::readNetFromONNX(modelPath);
+    if (cudaEnabled)
+    {
+        std::cout << "\nRunning on CUDA" << std::endl;
+        net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
+        net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
+    }
+    else
+    {
+        std::cout << "\nRunning on CPU" << std::endl;
+        net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
+        net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
+    }
+}
+
+cv::Mat Inference::formatToSquare(const cv::Mat &source)
+{
+    int col = source.cols;
+    int row = source.rows;
+    int _max = MAX(col, row);
+    cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
+    source.copyTo(result(cv::Rect(0, 0, col, row)));
+    return result;
+}