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MIL转Mat失败版，优化opencv_onnx功能将输出结果按照原图片大小进行输出掩膜
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opencv_onnx.cpp
191
opencv_onnx.cpp
@ -1,36 +1,34 @@
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//
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// Created by zjc on 24-11-19.
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//
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#include <opencv2/opencv.hpp>
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#include <opencv2/dnn/dnn.hpp>
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#include <iostream>
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// 参数
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const float CONFIDENCE_THRESHOLD = 0.2; // 置信度阈值
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const float NMS_THRESHOLD = 0.2; // 非极大值抑制阈值
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const int INPUT_WIDTH = 640; // 模型输入宽度
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const int INPUT_HEIGHT = 640; // 模型输入高度
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// Parameters
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const float CONFIDENCE_THRESHOLD = 0.2; // Confidence threshold
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const float NMS_THRESHOLD = 0.2; // Non-maximum suppression threshold
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const int INPUT_WIDTH = 640; // Model input width
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const int INPUT_HEIGHT = 640; // Model input height
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// 检测结构体
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// Detection structure
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struct Detection {
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cv::Rect box;
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float confidence;
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};
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class Timer {
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public:
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Timer() : start_time(std::chrono::high_resolution_clock::now()) {}
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// 重新启动定时器
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// Restart the timer
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void restart() {
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start_time = std::chrono::high_resolution_clock::now();
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}
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// 获取并打印从上次启动到当前的时间差
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// Get and print the time elapsed since last start
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void printElapsedTime(const std::string& message) {
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auto end_time = std::chrono::high_resolution_clock::now();
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std::chrono::duration<double> elapsed = end_time - start_time;
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std::cout << message << ": " << elapsed.count() << " seconds" << std::endl;
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// 重新启动定时器以供下次测量
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// Restart the timer for the next measurement
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start_time = end_time;
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}
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@ -38,195 +36,164 @@ private:
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std::chrono::high_resolution_clock::time_point start_time;
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};
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// 在图像上绘制检测框
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void drawDetections(cv::Mat& inputImage, const std::vector<Detection>& detections) {
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for (const auto& detection : detections) {
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cv::rectangle(inputImage, detection.box, cv::Scalar(0, 255, 0), 2);
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std::string label = "Object: " + cv::format("%.2f", 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(inputImage, cv::Point(detection.box.x, detection.box.y - labelSize.height - baseLine),
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cv::Point(detection.box.x + labelSize.width, detection.box.y), cv::Scalar(0, 255, 0), cv::FILLED);
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cv::putText(inputImage, label, cv::Point(detection.box.x, detection.box.y - baseLine), cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
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}
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}
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// Function to resize and pad the input image
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cv::Mat resizeAndPad(const cv::Mat& image, int targetWidth, int targetHeight, int& padTop, int& padLeft, float& scale, const cv::Scalar& padColor) {
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int originalWidth = image.cols;
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int originalHeight = image.rows;
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// 计算缩放比例
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// Calculate scaling factor
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scale = std::min((float)targetWidth / originalWidth, (float)targetHeight / originalHeight);
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// 缩放后的新尺寸
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// New dimensions after scaling
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int newWidth = static_cast<int>(originalWidth * scale);
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int newHeight = static_cast<int>(originalHeight * scale);
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// 缩放图像
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// Resize the image
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cv::Mat resizedImage;
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cv::resize(image, resizedImage, cv::Size(newWidth, newHeight));
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// 计算填充值
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// Padding calculations
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padTop = (targetHeight - newHeight) / 2;
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int padBottom = targetHeight - newHeight - padTop;
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padLeft = (targetWidth - newWidth) / 2;
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int padRight = targetWidth - newWidth - padLeft;
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// 在图像周围添加填充，使用灰色 (128, 128, 128) 填充
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// Add padding around the image (using gray color)
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cv::Mat paddedImage;
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cv::copyMakeBorder(resizedImage, paddedImage, padTop, padBottom, padLeft, padRight, cv::BORDER_CONSTANT, padColor);
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return paddedImage;
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}
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// Function to create an image with only detected regions filled as white (rest black) in the original image size
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cv::Mat createDetectionMask(const cv::Mat& originalImage, const std::vector<Detection>& detections, float scale, int padTop, int padLeft) {
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// Create a black image with the same size as the original image
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cv::Mat mask = cv::Mat::zeros(originalImage.size(), CV_8UC1); // Single channel for black and white mask
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// Fill the detected regions with white
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for (const auto& detection : detections) {
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// Rescale the coordinates from the padded image back to the original image
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int x = static_cast<int>((detection.box.x - padLeft) / scale);
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int y = static_cast<int>((detection.box.y - padTop) / scale);
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int w = static_cast<int>(detection.box.width / scale);
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int h = static_cast<int>(detection.box.height / scale);
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// Ensure coordinates are within the bounds of the original image
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x = std::max(0, std::min(x, originalImage.cols - 1));
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y = std::max(0, std::min(y, originalImage.rows - 1));
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w = std::min(w, originalImage.cols - x);
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h = std::min(h, originalImage.rows - y);
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cv::rectangle(mask, cv::Rect(x, y, w, h), cv::Scalar(255), cv::FILLED); // White color for detections
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}
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return mask;
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}
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int main() {
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// 模型路径和图片路径
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// Model and image paths
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std::string modelPath = "C:\\Users\\zjc\\Desktop\\dimo_11.14.onnx";
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std::string imagePath = "C:\\Users\\zjc\\Desktop\\dimo.bmp";
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Timer timer1;
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// 加载模型
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cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); // 设置为使用 CUDA 后端
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); // 设置为在 GPU 上运行
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timer1.printElapsedTime("Time to load the model");
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// 读取输入图像
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// Load the model
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cv::dnn::Net net = cv::dnn::readNetFromONNX(modelPath);
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net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA); // Use CUDA backend
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net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA); // Run on GPU
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timer1.printElapsedTime("Time to load the model");
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// Read the input image
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timer1.restart();
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cv::Mat image = cv::imread(imagePath);
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if (image.empty()) {
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std::cerr << "Could not read the image: " << imagePath << std::endl;
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return -1;
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}
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// 设置填充颜色为灰色
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// Set padding color (gray)
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cv::Scalar padColor(128, 128, 128);
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// 预处理图像并添加填充
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// Preprocess image and add padding
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int padTop, padLeft;
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float scale;
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cv::Mat inputImage = resizeAndPad(image, INPUT_WIDTH, INPUT_HEIGHT, padTop, padLeft, scale, padColor);
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// 显示调整和填充后的图像
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// cv::imshow("Resized and Padded Image", inputImage);
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// 预处理图像
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// Prepare image for model input
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cv::Mat blob = cv::dnn::blobFromImage(inputImage, 1 / 255.0, cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(0, 0, 0), true, false);
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net.setInput(blob);
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timer1.printElapsedTime("Time to preprocessing");
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timer1.restart();
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for(int j = 0; j <1; j++) {
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// 推理模型
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timer1.printElapsedTime("Time to preprocess image");
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for (int j = 0; j < 1; j++) {
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// Run inference
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cv::Mat output = net.forward();
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// 处理输出数据
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// Process output data
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std::vector<Detection> detections;
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float* data = (float*)output.data;
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for (int i = 0; i < 25200; ++i) {
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float confidence = data[i * 6 + 4]; // 置信度
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float confidence = data[i * 6 + 4]; // Confidence score
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if (confidence >= CONFIDENCE_THRESHOLD) {
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// 获取检测框并映射到图像坐标
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// Remove the unnecessary multiplication
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// Get bounding box coordinates
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float cx = data[i * 6];
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float cy = data[i * 6 + 1];
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float w = data[i * 6 + 2];
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float h = data[i * 6 + 3];
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// If needed, adjust for differences between input image size and model input size
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// Since they are the same in your case, this step can be omitted or kept as is
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// Map to image coordinates
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cx = cx * inputImage.cols / INPUT_WIDTH;
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cy = cy * inputImage.rows / INPUT_HEIGHT;
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w = w * inputImage.cols / INPUT_WIDTH;
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h = h * inputImage.rows / INPUT_HEIGHT;
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// Proceed with the rest of your code
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int left = static_cast<int>(cx - w / 2);
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int top = static_cast<int>(cy - h / 2);
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int width = static_cast<int>(w);
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int height = static_cast<int>(h);
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// Ensure coordinates are within image bounds
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left = std::max(0, std::min(left, inputImage.cols - 1));
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top = std::max(0, std::min(top, inputImage.rows - 1));
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width = std::min(width, inputImage.cols - left);
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height = std::min(height, inputImage.rows - top);
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// Add detection
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// Add detection to vector
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detections.push_back({cv::Rect(left, top, width, height), confidence});
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}
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}
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// 非极大值抑制
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// Non-Maximum Suppression
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std::vector<int> indices;
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std::vector<cv::Rect> boxes;
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std::vector<float> scores;
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for (const auto& detection : detections) {
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boxes.push_back(detection.box);
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scores.push_back(detection.confidence);
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}
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cv::dnn::NMSBoxes(boxes, scores, CONFIDENCE_THRESHOLD, NMS_THRESHOLD, indices);
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std::cout << "Number of detections after NMS: " << indices.size() << std::endl;
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if (indices.empty()) {
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std::cout << "No boxes passed NMS." << std::endl;
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}
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for (int idx : indices) {
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Detection detection = detections[idx];
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std::cout << "Drawing box at: (" << detection.box.x << ", " << detection.box.y
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<< "), width: " << detection.box.width << ", height: " << detection.box.height << std::endl;
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drawDetections(inputImage, {detection});
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}
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std::vector<Detection> finalDetections;
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for (int idx : indices) {
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finalDetections.push_back(detections[idx]);
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}
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for (int i = 0; i < 25200; ++i) {
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float confidence = data[i * 6 + 4];
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if (confidence >= CONFIDENCE_THRESHOLD) {
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// std::cout << "Detection " << i << ": confidence=" << confidence << std::endl;
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}
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}
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// 绘制检测框并显示图像
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drawDetections(image, finalDetections);
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// Create the mask for the detected regions (matching original image size)
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cv::Mat detectionMask = createDetectionMask(image, finalDetections, scale, padTop, padLeft);
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// Show the mask
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cv::imshow("Detection Mask", detectionMask);
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// Save the result as an image
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std::string savepath = "C:\\Users\\zjc\\Desktop\\suspect_mask.png";
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cv::imwrite(savepath, detectionMask);
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timer1.printElapsedTime("Time to run inference");
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}
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int depth = inputImage.depth(); // 图像数据类型
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int channels = inputImage.channels(); // 通道数
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// 判断图像深度和通道数，打印类型
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std::string depthStr;
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switch (depth) {
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case CV_8U:
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depthStr = "8-bit unsigned integer";
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break;
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case CV_8S:
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depthStr = "8-bit signed integer";
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break;
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case CV_16U:
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depthStr = "16-bit unsigned integer";
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break;
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case CV_16S:
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depthStr = "16-bit signed integer";
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break;
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case CV_32S:
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depthStr = "32-bit signed integer";
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break;
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case CV_32F:
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depthStr = "32-bit floating point";
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break;
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case CV_64F:
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depthStr = "64-bit floating point";
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break;
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default:
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depthStr = "Unknown depth";
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break;
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}
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std::cout << "Image Depth: " << depthStr << std::endl;
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std::cout << "Number of Channels: " << channels << std::endl;
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cv::imshow("Detections", inputImage);
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cv::waitKey(0);
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return 0;
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}
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//
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}
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@ -5,9 +5,18 @@ add_library(Matrox
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Matrox/template_matching.cpp
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Matrox/mask.cpp
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)
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# 头文件路径
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target_include_directories(Matrox PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
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# 链接依赖库
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target_link_libraries(Matrox PUBLIC Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
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#定义CVDL模块的库
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add_library(CVDL
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CVDL/OnnxRunner.cpp
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)
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# 头文件路径
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target_include_directories(CVDL PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
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# 链接依赖库
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target_link_libraries(CVDL PUBLIC Qt6::Widgets ${OpenCV_LIBS} ${MIL_LIBS})
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@ -265,7 +265,7 @@ void TemplateMatcher::FindTemplates( const MIL_ID& inputImage, MIL_ID& outputIma
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// TODO: Opencv ONNX runner,
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// 1. 构建相应的模型加载和模型运行函数
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// 1. 构建相应的模型加载和模型运行函数-
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// 2. 在src里头添加另一个cvdl库，专用于视觉深度学习
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// 3. 添加一个类OnnxRunner
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@ -9,7 +9,8 @@
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#include <vector>
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#include <cmath>
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#include <opencv2/core/mat.hpp>
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#include "Mil.h"
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#include <opencv2/opencv.hpp>
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using namespace std;
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/**
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@ -121,4 +122,106 @@ void read_params_from_file(const std::string& filename, std::map<std::string, in
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}
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// 图片转换函数，输入4096*1024*3的图片，输出为(4096 / n_valves) * (1024 / n_merge_vertical) * 1
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// Mat Mil2cvImage(MIL_ID &input_image,Mat) {}
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#include "mil.h" // 包含 MIL 库的头文件
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// cv::Mat milToMat(MIL_ID milImage, MIL_ID MilSystem) {
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// // 获取图像的宽度、高度和像素格式
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// int width, height, channels;
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// MbufInquire(milImage, M_SIZE_X, &width); // 获取图像宽度
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// MbufInquire(milImage, M_SIZE_Y, &height); // 获取图像高度
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// MbufInquire(milImage, M_SIZE_BAND, &channels); // 获取图像通道数
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//
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// // 为图像数据分配内存
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// byte* buf = new byte[width * height](); // 初始化内存
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//
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// // 如果是彩色图像，则转换为灰度图
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// if (channels > 1) {
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// MIL_ID grayscaleImage;
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// // 分配一个新的灰度图像缓冲区
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// MbufAlloc2d(MilSystem, width, height, 8 + M_UNSIGNED, M_IMAGE + M_PROC + M_DISP, &grayscaleImage);
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// // 将彩色图像转换为灰度图
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// MimConvert(milImage, grayscaleImage, M_RGB_TO_L);
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// // 获取转换后的灰度图像数据
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// MbufGet(grayscaleImage, buf);
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// MbufFree(grayscaleImage); // 释放灰度图像缓冲区
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// } else {
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// // 如果本身是灰度图像，直接获取数据
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// MbufGet(milImage, buf);
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// }
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//
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// // 将数据封装成OpenCV Mat对象
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// cv::Mat cvMat(height, width, CV_8UC1, buf);
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// return cvMat;
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// }
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cv::Mat milToMat(MIL_ID milImage) {
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// 获取图像的宽度、高度和像素格式
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int width, height;
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MIL_INT pixelFormat;
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MbufInquire(milImage, M_SIZE_X, &width); // 获取图像宽度
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MbufInquire(milImage, M_SIZE_Y, &height); // 获取图像高度
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MbufInquire(milImage, M_TYPE, &pixelFormat); // 获取图像像素格式
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// 判断像素格式并选择适当的数据类型
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// MIL_INT 和 M_UINT8 是典型的像素格式，OpenCV 处理不同的类型会有不同的实现
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cv::Mat matImage;
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if (pixelFormat == M_RGB)
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{
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// 如果是RGB图像
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matImage = cv::Mat(height, width, CV_8UC3);
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}
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else
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{// 如果是其他类型的图像，你需要根据实际格式进行调整
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std::cerr << "Unsupported MIL image format!" << std::endl;
|
||||
return matImage;
|
||||
}
|
||||
|
||||
// 获取图像数据并复制到 OpenCV Mat
|
||||
MbufGet(milImage, matImage.data); // 获取 MIL 图像数据并拷贝到 Mat 对象
|
||||
|
||||
return matImage;
|
||||
}
|
||||
void processImage(cv::Mat& img) {
|
||||
// 1. 将图像从BGR转换到HSV色彩空间
|
||||
cv::Mat hsv;
|
||||
cv::cvtColor(img, hsv, cv::COLOR_BGR2HSV);
|
||||
|
||||
// 2. 定义绿色的HSV色彩范围
|
||||
cv::Scalar lower_green(35, 40, 40); // 低阈值 (H, S, V)
|
||||
cv::Scalar upper_green(85, 255, 255); // 高阈值 (H, S, V)
|
||||
|
||||
// 3. 根据绿色范围创建掩膜
|
||||
cv::Mat green_mask;
|
||||
cv::inRange(hsv, lower_green, upper_green, green_mask);
|
||||
|
||||
// 4. 使用形态学操作去除噪点，增强绿色区域
|
||||
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(5, 5));
|
||||
cv::morphologyEx(green_mask, green_mask, cv::MORPH_CLOSE, kernel);
|
||||
|
||||
// 5. 查找轮廓
|
||||
std::vector<std::vector<cv::Point>> contours;
|
||||
cv::findContours(green_mask, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
|
||||
|
||||
// 6. 创建一个黑色背景图像
|
||||
cv::Mat result = cv::Mat::zeros(img.size(), img.type());
|
||||
|
||||
// 7. 遍历轮廓，找到矩形框并将其内部填充为白色
|
||||
for (size_t i = 0; i < contours.size(); i++) {
|
||||
// 通过近似多边形来检测矩形
|
||||
std::vector<cv::Point> approx;
|
||||
cv::approxPolyDP(contours[i], approx, cv::arcLength(contours[i], true) * 0.02, true);
|
||||
|
||||
// 如果是四边形，认为是矩形
|
||||
if (approx.size() == 4) {
|
||||
// 计算矩形的bounding box
|
||||
cv::Rect rect = cv::boundingRect(approx);
|
||||
|
||||
// 仅在绿色区域内将矩形框填充为白色
|
||||
cv::rectangle(result, rect, cv::Scalar(255, 255, 255), cv::FILLED);
|
||||
}
|
||||
}
|
||||
|
||||
// 8. 显示结果图像
|
||||
cv::imshow("Processed Image", result);
|
||||
cv::waitKey(0);
|
||||
}
|
||||
@ -12,6 +12,7 @@
|
||||
#include <fstream>
|
||||
#include <string>
|
||||
#include <map>
|
||||
#include"opencv2/opencv.hpp"
|
||||
#include <sstream>
|
||||
|
||||
// 声明全局变量（注意：这里只是声明，不是定义）
|
||||
@ -38,5 +39,6 @@ MIL_ID convert_to_uint8(MIL_ID input_img);
|
||||
|
||||
std::wstring convert_to_wstring(const std::string& str);
|
||||
void read_params_from_file(const std::string& filename, std::map<std::string, int>& params) ;
|
||||
|
||||
cv::Mat milToMat(MIL_ID MilImage);
|
||||
void processImage(cv::Mat& img);
|
||||
#endif //UTILS_H
|
||||
|
||||
@ -9,16 +9,20 @@ target_link_libraries(test_color_range Matrox ${OpenCV_LIBS} ${MIL_LIBS})
|
||||
|
||||
# 测试用例 2: template_template_matching
|
||||
add_executable(test_template_matching
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/test_template_matching.cpp
|
||||
)
|
||||
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/test_template_matching.cpp)
|
||||
# 链接 Matrox 模块和依赖库
|
||||
target_link_libraries(test_template_matching Matrox ${OpenCV_LIBS} ${MIL_LIBS})
|
||||
|
||||
|
||||
add_executable(test_mask
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/test_mask.cpp
|
||||
)
|
||||
|
||||
#测试用例 3：test_mask
|
||||
add_executable(test_mask
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/test_mask.cpp)
|
||||
# 链接 Matrox 模块和依赖库
|
||||
target_link_libraries(test_mask Matrox ${OpenCV_LIBS} ${MIL_LIBS})
|
||||
target_link_libraries(test_mask Matrox ${OpenCV_LIBS} ${MIL_LIBS})
|
||||
|
||||
#测试用例 4：test_onnx
|
||||
add_executable(test_onnx
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/test_onnx.cpp)
|
||||
# 链接 Matrox 模块和依赖库
|
||||
target_link_libraries(test_onnx Matrox ${OpenCV_LIBS} ${MIL_LIBS})
|
||||
66
tests/test_onnx.cpp
Normal file
66
tests/test_onnx.cpp
Normal file
@ -0,0 +1,66 @@
|
||||
#include "vector"
|
||||
#include"iostream"
|
||||
#include"string"
|
||||
#include"Matrox/utils.h"
|
||||
#include"opencv2/opencv.hpp"
|
||||
|
||||
#define IMAGE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\8.bmp")
|
||||
#define SAVE_PATH MIL_TEXT("C:\\Users\\zjc\\Desktop\\suspect.png")
|
||||
|
||||
// int main() {
|
||||
// MIL_ID MilImage = M_NULL;
|
||||
// MIL_ID MilApplication = M_NULL, MilSystem = M_NULL, MilDisplay = M_NULL;
|
||||
//
|
||||
// MappAllocDefault(M_DEFAULT, &MilApplication, &MilSystem, &MilDisplay, M_NULL,
|
||||
// M_NULL);
|
||||
// MbufRestore(IMAGE_PATH, MilSystem, &MilImage);
|
||||
// cv::Mat opencvImage = milToMat(MilImage);
|
||||
// imshow("opencv", opencvImage);
|
||||
// // MosGetch();
|
||||
//
|
||||
// return 0;
|
||||
// }
|
||||
int main() {
|
||||
MIL_ID MilApplication = M_NULL, MilSystem = M_NULL, MilDisplay = M_NULL;
|
||||
MIL_ID MilImage = M_NULL;
|
||||
|
||||
// 初始化 MIL 应用程序、系统和显示
|
||||
MappAllocDefault(M_DEFAULT, &MilApplication, &MilSystem, &MilDisplay, M_NULL, M_NULL);
|
||||
if (MilApplication == M_NULL || MilSystem == M_NULL || MilDisplay == M_NULL) {
|
||||
std::cerr << "MIL Initialization failed!" << std::endl;
|
||||
return -1;
|
||||
}
|
||||
|
||||
// 加载图像
|
||||
MbufRestore(IMAGE_PATH, MilSystem, &MilImage);
|
||||
if (MilImage == M_NULL) {
|
||||
std::cerr << "Failed to load MIL image!" << std::endl;
|
||||
MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL,M_NULL);
|
||||
return -1;
|
||||
}
|
||||
|
||||
// 转换并显示
|
||||
cv::Mat opencvImage = milToMat(MilImage);
|
||||
if (!opencvImage.empty()) {
|
||||
cv::imshow("opencv", opencvImage);
|
||||
cv::waitKey(0);
|
||||
}
|
||||
std:: string savepath="C:\\Users\\zjc\\Desktop\\suspect.png";
|
||||
cv::imwrite(savepath,opencvImage);
|
||||
// 释放资源
|
||||
MbufFree(MilImage);
|
||||
MappFreeDefault(MilApplication, MilSystem, MilDisplay, M_NULL,M_NULL); // 使用 MappFreeDefault 代替 MappFree
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
// int main() {
|
||||
// cv::Mat img = cv::imread("C:\\Users\\zjc\\Desktop\\suspect.png");
|
||||
// if (img.empty()) {
|
||||
// std::cout << "图像加载失败!" << std::endl;
|
||||
// return -1;
|
||||
// }
|
||||
//
|
||||
// // 处理图像
|
||||
// processImage(img);
|
||||
// }
|
||||
@ -19,8 +19,6 @@ MIL_ID MilApplication = M_NULL, MilSystem = M_NULL, MilDisplay = M_NULL;
|
||||
int main() {
|
||||
using namespace std;
|
||||
|
||||
|
||||
|
||||
std::map<std::string, int> params;
|
||||
read_params_from_file("C:\\Users\\zjc\\Desktop\\config\\template_color_config.txt", params);
|
||||
// Initialize MIL application
|
||||
@ -61,6 +59,5 @@ int main() {
|
||||
|
||||
MbufFree(detection_result);
|
||||
MbufFree(MilImage);
|
||||
|
||||
return 0;
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user