diff --git a/CMakeLists.txt b/CMakeLists.txt index df8239d..389c023 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -71,9 +71,9 @@ add_subdirectory(tests) #add_executable(onnx src/Matrox/onnx_running.cpp) #target_link_libraries(onnx Qt6::Widgets ${MIL_LIBS}) # -#add_executable(opencv_onnx opencv_onnx.cpp) -## 链接 OpenCV 和 Qt 库 -#target_link_libraries(opencv_onnx Qt6::Widgets ${OpenCV_LIBS} comdlg32) +add_executable(opencv_onnx opencv_onnx.cpp) +# 链接 OpenCV 和 Qt 库 +target_link_libraries(opencv_onnx Qt6::Widgets ${OpenCV_LIBS} comdlg32) # # #add_executable(create_mask src/Matrox/mask.cpp) diff --git a/opencv_onnx.cpp b/opencv_onnx.cpp index 10484d5..d82fe57 100644 --- a/opencv_onnx.cpp +++ b/opencv_onnx.cpp @@ -85,8 +85,8 @@ int main() { Timer timer1; // 加载模型 cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath); - // net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); // 设置为使用 CUDA 后端 - // net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); // 设置为在 GPU 上运行 + net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); // 设置为使用 CUDA 后端 + net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); // 设置为在 GPU 上运行 timer1.printElapsedTime("Time to load the model"); // 读取输入图像 @@ -112,85 +112,86 @@ int main() { timer1.printElapsedTime("Time to preprocessing"); timer1.restart(); - // 推理模型 - cv::Mat output = net.forward(); + for(int j = 0; j <30; j++) { + // 推理模型 + cv::Mat output = net.forward(); - // 处理输出数据 - std::vector detections; - float* data = (float*)output.data; - for (int i = 0; i < 25200; ++i) { - float confidence = data[i * 6 + 4]; // 置信度 - if (confidence >= CONFIDENCE_THRESHOLD) { - // 获取检测框并映射到图像坐标 - // Remove the unnecessary multiplication - float cx = data[i * 6]; - float cy = data[i * 6 + 1]; - float w = data[i * 6 + 2]; - float h = data[i * 6 + 3]; + // 处理输出数据 + std::vector detections; + float* data = (float*)output.data; + for (int i = 0; i < 25200; ++i) { + float confidence = data[i * 6 + 4]; // 置信度 + if (confidence >= CONFIDENCE_THRESHOLD) { + // 获取检测框并映射到图像坐标 + // Remove the unnecessary multiplication + float cx = data[i * 6]; + float cy = data[i * 6 + 1]; + float w = data[i * 6 + 2]; + float h = data[i * 6 + 3]; - // If needed, adjust for differences between input image size and model input size - // Since they are the same in your case, this step can be omitted or kept as is - cx = cx * inputImage.cols / INPUT_WIDTH; - cy = cy * inputImage.rows / INPUT_HEIGHT; - w = w * inputImage.cols / INPUT_WIDTH; - h = h * inputImage.rows / INPUT_HEIGHT; + // If needed, adjust for differences between input image size and model input size + // Since they are the same in your case, this step can be omitted or kept as is + cx = cx * inputImage.cols / INPUT_WIDTH; + cy = cy * inputImage.rows / INPUT_HEIGHT; + w = w * inputImage.cols / INPUT_WIDTH; + h = h * inputImage.rows / INPUT_HEIGHT; - // Proceed with the rest of your code - int left = static_cast(cx - w / 2); - int top = static_cast(cy - h / 2); - int width = static_cast(w); - int height = static_cast(h); + // Proceed with the rest of your code + int left = static_cast(cx - w / 2); + int top = static_cast(cy - h / 2); + int width = static_cast(w); + int height = static_cast(h); - // Ensure coordinates are within image bounds - left = std::max(0, std::min(left, inputImage.cols - 1)); - top = std::max(0, std::min(top, inputImage.rows - 1)); - width = std::min(width, inputImage.cols - left); - height = std::min(height, inputImage.rows - top); + // Ensure coordinates are within image bounds + left = std::max(0, std::min(left, inputImage.cols - 1)); + top = std::max(0, std::min(top, inputImage.rows - 1)); + width = std::min(width, inputImage.cols - left); + height = std::min(height, inputImage.rows - top); - // Add detection - detections.push_back({cv::Rect(left, top, width, height), confidence}); + // Add detection + detections.push_back({cv::Rect(left, top, width, height), confidence}); + } } - } - // 非极大值抑制 - std::vector indices; - std::vector boxes; - std::vector scores; - for (const auto& detection : detections) { - boxes.push_back(detection.box); + // 非极大值抑制 + std::vector indices; + std::vector boxes; + std::vector scores; + for (const auto& detection : detections) { + boxes.push_back(detection.box); - scores.push_back(detection.confidence); - } - cv::dnn::NMSBoxes(boxes, scores, CONFIDENCE_THRESHOLD, NMS_THRESHOLD, indices); - std::cout << "Number of detections after NMS: " << indices.size() << std::endl; - if (indices.empty()) { - std::cout << "No boxes passed NMS." << std::endl; - } - for (int idx : indices) { - Detection detection = detections[idx]; - std::cout << "Drawing box at: (" << detection.box.x << ", " << detection.box.y - << "), width: " << detection.box.width << ", height: " << detection.box.height << std::endl; - drawDetections(inputImage, {detection}); - } - - std::vector finalDetections; - for (int idx : indices) { - finalDetections.push_back(detections[idx]); - } - for (int i = 0; i < 25200; ++i) { - float confidence = data[i * 6 + 4]; - if (confidence >= CONFIDENCE_THRESHOLD) { - std::cout << "Detection " << i << ": confidence=" << confidence << std::endl; + scores.push_back(detection.confidence); } + cv::dnn::NMSBoxes(boxes, scores, CONFIDENCE_THRESHOLD, NMS_THRESHOLD, indices); + std::cout << "Number of detections after NMS: " << indices.size() << std::endl; + if (indices.empty()) { + std::cout << "No boxes passed NMS." << std::endl; + } + for (int idx : indices) { + Detection detection = detections[idx]; + std::cout << "Drawing box at: (" << detection.box.x << ", " << detection.box.y + << "), width: " << detection.box.width << ", height: " << detection.box.height << std::endl; + drawDetections(inputImage, {detection}); + } + + std::vector finalDetections; + for (int idx : indices) { + finalDetections.push_back(detections[idx]); + } + for (int i = 0; i < 25200; ++i) { + float confidence = data[i * 6 + 4]; + if (confidence >= CONFIDENCE_THRESHOLD) { + // std::cout << "Detection " << i << ": confidence=" << confidence << std::endl; + } + } + + // 绘制检测框并显示图像 + drawDetections(image, finalDetections); + timer1.printElapsedTime("Time to run inference"); } - - // 绘制检测框并显示图像 - drawDetections(image, finalDetections); - timer1.printElapsedTime("Time to run inference"); - cv::imshow("Detections", inputImage); cv::waitKey(0); diff --git a/src/Matrox/template_matching.cpp b/src/Matrox/template_matching.cpp index 7b16eef..c1b2d13 100644 --- a/src/Matrox/template_matching.cpp +++ b/src/Matrox/template_matching.cpp @@ -198,10 +198,10 @@ void TemplateMatcher::LoadTemplate(TemplateMatcher& matcher, std::map兼容 -// 配置文件当中加入是否显示参数，能调控加载模板的过程是否显示。已 -//TODO: 3修改当前的代码使模板匹配不出错 已 -//TODO: 4成立模板文件夹，能够加载文件夹下的全部模板并实现检测 已 +//TODO: 1加入加载多个模板的功能 已 + 加入配置文件 + //TODO: 5制作标准结构的函数，例如：matcher.findModels(MIL_ID inputImage, MIL_ID output_image, map); ////未实现，因为加载和寻找分开后，要对加载和寻找函数传入类成员，无法统一，其余可用到的参数统一，加一个类成员即可。 //TODO: 6完善相应部分的手册 已 diff --git a/tests/test_template_matching.cpp b/tests/test_template_matching.cpp index b7653ca..61f39a4 100644 --- a/tests/test_template_matching.cpp +++ b/tests/test_template_matching.cpp @@ -8,7 +8,7 @@ #include "Matrox/utils.h" #include "Matrox/template_matching.h" -#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\8.bmp") +#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\cotton_image_new\\357.bmp") #define SAVE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\suspect.png")