mirror of
https://github.com/azaion/autopilot.git
synced 2026-04-22 12:36:35 +00:00
Changed directory structure and renamed applications
- autopilot -> drone_controller - rtsp_ai_player -> ai_controller - added top level qmake project file - updated documentation - moved small demo applications from tmp/ to misc/
This commit is contained in:
Tuomas Järvinen
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#include <QDebug>
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#include <QThread>
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#include "aiengineinferenceopencvonnx.h"
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AiEngineInferenceOpencvOnnx::AiEngineInferenceOpencvOnnx(QString modelPath, QObject *parent)
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: AiEngineInference{modelPath, parent},
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mInference(modelPath.toStdString(), cv::Size(640, 640), "classes.txt")
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{
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//qDebug() << "TUOMAS test mModelPath=" << mModelPath;
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//mEngine = new InferenceEngine(modelPath.toStdString());
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//mInference = new Inference(modelPath.toStdString(), cv::Size(INFERENCE_SQUARE_WIDTH, INFERENCE_SQUARE_HEIGHT), "classes.txt");
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}
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void AiEngineInferenceOpencvOnnx::performInferenceSlot(cv::Mat frame)
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{
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try {
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qDebug() << "performInferenceSlot() in thread: " << QThread::currentThreadId();
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mActive = true;
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cv::Mat scaledImage = resizeAndPad(frame);
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//cv::imwrite("/tmp/frame.png", scaledImage);
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std::vector<Detection> detections = mInference.runInference(scaledImage);
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AiEngineInferenceResult result;
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qDebug() << "performInferenceSlot() found " << detections.size() << " objects";
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for (uint i = 0; i < detections.size(); ++i) {
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const Detection &detection = detections[i];
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// Add detected objects to the results
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AiEngineObject object;
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object.classId = detection.class_id;
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object.propability = detection.confidence;
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object.rectangle.top = detection.box.y;
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object.rectangle.left = detection.box.x;
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object.rectangle.bottom = detection.box.y + detection.box.height;
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object.rectangle.right = detection.box.x + detection.box.width;
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result.objects.append(object);
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//qDebug() << "performInferenceSlot()" << object.rectangle.top << object.rectangle.left << "and" << object.rectangle.bottom << object.rectangle.right;
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}
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auto end = std::remove_if(detections.begin(), detections.end(), [](const Detection& detection) {
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return detection.class_id != 0;
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});
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detections.erase(end, detections.end());
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if (result.objects.empty() == false) {
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result.frame = mInference.drawLabels(scaledImage, detections);
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emit resultsReady(result);
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}
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mActive = false;
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}
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catch (const cv::Exception& e) {
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std::cerr << "performInferenceSlot() Error: " << e.what() << std::endl;
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}
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}
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void AiEngineInferenceOpencvOnnx::initialize(int number)
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{
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(void)number;
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}
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#pragma once
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#include <QObject>
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#include "aiengineinference.h"
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#include "src-opencv-onnx/inference.h"
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class AiEngineInferenceOpencvOnnx : public AiEngineInference
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{
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Q_OBJECT
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public:
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explicit AiEngineInferenceOpencvOnnx(QString modelPath, QObject *parent = nullptr);
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void initialize(int number);
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public slots:
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void performInferenceSlot(cv::Mat frame) override;
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private:
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Inference mInference;
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};
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#include "inference.h"
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Inference::Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape, const std::string &classesTxtFile, const bool &runWithCuda)
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{
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modelPath = onnxModelPath;
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modelShape = modelInputShape;
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classesPath = classesTxtFile;
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cudaEnabled = runWithCuda;
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std::cout << "SIZE = " << modelInputShape.width << "x" << modelInputShape.height << std::endl;
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loadOnnxNetwork();
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// loadClassesFromFile(); The classes are hard-coded for this example
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}
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std::vector<Detection> Inference::runInference(const cv::Mat &input)
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{
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cv::Mat modelInput = input;
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if (letterBoxForSquare && modelShape.width == modelShape.height)
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modelInput = formatToSquare(modelInput);
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cv::Mat blob;
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cv::dnn::blobFromImage(modelInput, blob, 1.0/255.0, modelShape, cv::Scalar(), true, false);
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net.setInput(blob);
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std::vector<cv::Mat> outputs;
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net.forward(outputs, net.getUnconnectedOutLayersNames());
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int rows = outputs[0].size[1];
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int dimensions = outputs[0].size[2];
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bool yolov8 = true;
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// yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
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// yolov8 has an output of shape (batchSize, 84, 8400) (Num classes + box[x,y,w,h])
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/*
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if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
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{
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std::cout << "yolov8 = " << yolov8 << std::endl;
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yolov8 = true;
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rows = outputs[0].size[2];
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dimensions = outputs[0].size[1];
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outputs[0] = outputs[0].reshape(1, dimensions);
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cv::transpose(outputs[0], outputs[0]);
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}
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*/
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float *data = (float *)outputs[0].data;
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float x_factor = modelInput.cols / modelShape.width;
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float y_factor = modelInput.rows / modelShape.height;
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std::vector<int> class_ids;
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std::vector<float> confidences;
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std::vector<cv::Rect> boxes;
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for (int i = 0; i < rows; ++i)
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{
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if (yolov8)
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{
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float *classes_scores = data+4;
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cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
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cv::Point class_id;
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double maxClassScore;
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minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);
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if (maxClassScore > modelScoreThreshold)
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{
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confidences.push_back(maxClassScore);
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class_ids.push_back(class_id.x);
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float x = data[0];
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float y = data[1];
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float w = data[2];
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float h = data[3];
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int left = int((x - 0.5 * w) * x_factor);
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int top = int((y - 0.5 * h) * y_factor);
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int width = int(w * x_factor);
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int height = int(h * y_factor);
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boxes.push_back(cv::Rect(left, top, width, height));
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}
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}
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else // yolov5
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{
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float confidence = data[4];
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if (confidence >= modelConfidenceThreshold)
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{
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float *classes_scores = data+5;
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cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
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cv::Point class_id;
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double max_class_score;
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minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
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if (max_class_score > modelScoreThreshold)
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{
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confidences.push_back(confidence);
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class_ids.push_back(class_id.x);
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float x = data[0];
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float y = data[1];
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float w = data[2];
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float h = data[3];
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int left = int((x - 0.5 * w) * x_factor);
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int top = int((y - 0.5 * h) * y_factor);
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int width = int(w * x_factor);
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int height = int(h * y_factor);
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boxes.push_back(cv::Rect(left, top, width, height));
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}
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}
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}
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data += dimensions;
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}
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std::vector<int> nms_result;
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cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);
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std::vector<Detection> detections{};
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for (unsigned long i = 0; i < nms_result.size(); ++i)
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{
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int idx = nms_result[i];
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Detection result;
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result.class_id = class_ids[idx];
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result.confidence = confidences[idx];
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std::random_device rd;
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std::mt19937 gen(rd());
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std::uniform_int_distribution<int> dis(100, 255);
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result.color = cv::Scalar(dis(gen),
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dis(gen),
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dis(gen));
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result.className = classes[result.class_id];
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result.box = boxes[idx];
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detections.push_back(result);
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}
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return detections;
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}
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void Inference::loadClassesFromFile()
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{
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std::ifstream inputFile(classesPath);
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if (inputFile.is_open())
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{
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std::string classLine;
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while (std::getline(inputFile, classLine))
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classes.push_back(classLine);
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inputFile.close();
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}
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}
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void Inference::loadOnnxNetwork()
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{
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printf("loadOnnxNetwork() starts\n");
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net = cv::dnn::readNetFromONNX(modelPath);
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if (cudaEnabled)
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{
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std::cout << "\nRunning on CUDA" << std::endl;
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
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}
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else
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{
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std::cout << "\nRunning on CPU" << std::endl;
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
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}
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}
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cv::Mat Inference::formatToSquare(const cv::Mat &source)
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{
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int col = source.cols;
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int row = source.rows;
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int _max = MAX(col, row);
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cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
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source.copyTo(result(cv::Rect(0, 0, col, row)));
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return result;
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}
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cv::Mat Inference::drawLabels(const cv::Mat &image, const std::vector<Detection> &detections)
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{
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cv::Mat result = image.clone();
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for (const auto &detection : detections)
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{
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cv::rectangle(result, detection.box, cv::Scalar(0, 255, 0), 2);
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std::string label = detection.className + ": " + std::to_string(detection.confidence);
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int baseLine;
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cv::Size labelSize = cv::getTextSize(label, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
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cv::rectangle(
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result,
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cv::Point(detection.box.x, detection.box.y - labelSize.height),
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cv::Point(detection.box.x + labelSize.width, detection.box.y + baseLine),
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cv::Scalar(255, 255, 255),
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cv::FILLED);
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cv::putText(
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result,
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label,
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cv::Point(
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detection.box.x,
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detection.box.y),
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cv::FONT_HERSHEY_SIMPLEX,
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0.5,
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cv::Scalar(0, 0, 0),
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1);
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}
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return result;
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}
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@@ -0,0 +1,51 @@
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#pragma once
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// Cpp native
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#include <fstream>
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#include <vector>
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#include <string>
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#include <random>
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// OpenCV / DNN / Inference
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#include <opencv2/imgproc.hpp>
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#include <opencv2/opencv.hpp>
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#include <opencv2/dnn.hpp>
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struct Detection
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{
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int class_id{0};
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std::string className{};
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float confidence{0.0};
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cv::Scalar color{};
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cv::Rect box{};
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};
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class Inference
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{
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public:
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Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape = {640, 640}, const std::string &classesTxtFile = "", const bool &runWithCuda = false);
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std::vector<Detection> runInference(const cv::Mat &input);
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cv::Mat drawLabels(const cv::Mat &image, const std::vector<Detection> &detections);
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private:
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void loadClassesFromFile();
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void loadOnnxNetwork();
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cv::Mat formatToSquare(const cv::Mat &source);
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std::string modelPath{};
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std::string classesPath{};
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bool cudaEnabled{};
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std::vector<std::string> classes{"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"};
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cv::Size2f modelShape{};
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float modelConfidenceThreshold {0.25};
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float modelScoreThreshold {0.45};
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float modelNMSThreshold {0.50};
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bool letterBoxForSquare = false;
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cv::dnn::Net net;
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};
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