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参考上海交通大学代码进行修改

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Liu Lida
2025-12-05 11:13:37 +08:00
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# ---> C++
# Prerequisites
*.d
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
*.smod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app
# BUILD
build/
.cache/
.vscode/

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cmake_minimum_required(VERSION 3.10)
project(armor_detector_mdv)
# Set C++ standard
set(CMAKE_CXX_STANDARD 23)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# Find OpenCV package
find_package(OpenCV REQUIRED)
# Include OpenCV headers
include_directories(${OpenCV_INCLUDE_DIRS})
include_directories(/usr/include)
include_directories(./inc)
# Add executable for MindVision version
add_executable(armor_detector_mdv
src/MindVisionMain.cpp
src/MindVisionCamera.cpp
src/ImagePreprocessor.cpp
src/ArmorDetector.cpp
src/KalmanFilter.cpp
src/Visualizer.cpp
src/BallisticPredictor.cpp
src/TTLCommunicator.cpp
)
# Link OpenCV libraries
target_link_libraries(armor_detector_mdv ${OpenCV_LIBS} -lutil -lpthread)
# Link MindVision SDK library
target_link_libraries(armor_detector_mdv MVSDK)
# Additional flags for better compilation
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -Wextra -O3 -pthread")
# For serial communication on Linux, you might need to link additional libraries:
# target_link_libraries(armor_detector_mdv ${OpenCV_LIBS} -lutil -lpthread)

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GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
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How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found.
Catalyst-MDVS
Copyright (C) 2025 VSAG
This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with this program. If not, see <http://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If your software can interact with users remotely through a computer network, you should also make sure that it provides a way for users to get its source. For example, if your program is a web application, its interface could display a "Source" link that leads users to an archive of the code. There are many ways you could offer source, and different solutions will be better for different programs; see section 13 for the specific requirements.
You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU AGPL, see <http://www.gnu.org/licenses/>.

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# MindVision相机驱动的装甲板检测系统
本项目是基于MindVision相机SDK的装甲板检测系统，工业相机替代原有的OpenCV相机接口。
## 项目结构
- `MindVisionCamera.h/cpp` - MindVision相机驱动实现
- `main.cpp` - 主程序入口（已修改为使用MindVision相机）
- 其他 `.cpp/.h` 文件 - 原有的图像处理、装甲板检测、卡尔曼滤波等功能模块
## 环境准备
此项目需要在WSL Ubuntu环境中编译和运行，因为MindVision SDK位于WSL系统中。
### 1. 确保MindVision SDK已安装
在WSL Ubuntu中确认SDK路径：
```bash
ls -la /home/lld/mdv/include/CameraApi.h
ls -la /home/lld/mdv/lib/x86/
```
### 2. 安装必要的依赖
```bash
sudo apt update
sudo apt install build-essential cmake libopencv-dev
```
## 编译项目
### 方法1：使用构建脚本
```bash
cd /home/lld/code/mdvs
chmod +x build.sh
./build.sh
```
### 方法2：手动编译
```bash
cd /home/lld/code/mdvs
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
```
## 运行程序
```bash
./build/armor_detector_mdv
```
## 配置说明
- 相机ID可在main函数中修改 `cam_id` 变量
- 默认分辨率：640x480，帧率：30fps
- 支持颜色切换（红色/蓝色）
- 支持TTL通信（默认关闭）
## 注意事项
1. 此项目使用MindVision相机SDK直接驱动相机，可以获得更好的性能和相机控制能力
2. 曝光、白平衡等参数可在`MindVisionCamera.cpp`中的`set_camera_parameters()`函数中调整
3. 如果遇到编译错误，请检查MindVision SDK的库文件名是否正确（可能是`libMVSDK.so`或`libMVSDK.a`）
## 键盘控制
- 按 'q' 键退出程序
- 按其他键继续运行
## 故障排除
如果相机无法打开：
1. 检查相机是否正确连接
2. 确认MindVision相机驱动是否正确安装
3. 验证相机ID是否正确
4. 检查权限（可能需要将用户添加到video组）
## MindVision-SDK
`Linux`: >**wget https://www.mindvision.com.cn/wp-content/uploads/2023/08/linuxSDK_V2.1.0.37.tar.gz**

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#ifndef ARMORDETECTOR_H
#define ARMORDETECTOR_H
#include <opencv2/opencv.hpp>
#include <vector>
#include <string>
#include "config.h"
struct LightBar {
cv::Point2f center;
cv::Size2f size;
float angle; // in degrees
float angle_rad; // in radians
float area;
std::vector<cv::Point2f> center_line; // [end1, end2]
float center_line_length;
std::vector<cv::Point2f> box;
};
struct ArmorPlate {
std::string color;
cv::Point2f center;
double confidence;
std::pair<LightBar, LightBar> pair;
std::vector<cv::Point2f> corners_2d; // Can be computed later for 3D pose
// Function to get bounding rectangle of the armor plate
cv::Rect2d getBoundingRect() const {
if (corners_2d.size() >= 4) {
cv::Point2f min_pt = corners_2d[0];
cv::Point2f max_pt = corners_2d[0];
for (const auto& pt : corners_2d) {
min_pt.x = std::min(min_pt.x, pt.x);
min_pt.y = std::min(min_pt.y, pt.y);
max_pt.x = std::max(max_pt.x, pt.x);
max_pt.y = std::max(max_pt.y, pt.y);
}
return cv::Rect2d(min_pt.x, min_pt.y, max_pt.x - min_pt.x, max_pt.y - min_pt.y);
}
// Fallback: use center and a fixed size
return cv::Rect2d(center.x - 30, center.y - 15, 60, 30);
}
};
class ArmorDetector {
public:
ArmorDetector();
// Main detection function
void detect(const cv::Mat& mask, const std::string& target_color,
std::vector<LightBar>& valid_light_bars,
std::vector<ArmorPlate>& armor_plates);
private:
float angle_threshold_rad;
// Helper methods
std::vector<LightBar> extract_initial_light_bars(const cv::Mat& mask);
std::vector<std::vector<cv::Point2f>> merge_nearby_light_bars(const std::vector<LightBar>& initial_light_bars);
std::vector<LightBar> filter_valid_light_bars(const std::vector<std::vector<cv::Point2f>>& processed_boxes);
std::vector<ArmorPlate> pair_light_bars_to_armor(const std::vector<LightBar>& light_bars, const std::string& target_color);
double calculate_iou(const cv::Rect& b1, const cv::Rect& b2);
};
#endif // ARMORDETECTOR_H

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#ifndef BALLISTICPREDICTOR_H
#define BALLISTICPREDICTOR_H
#include <opencv2/opencv.hpp>
class BallisticPredictor {
public:
BallisticPredictor(float bullet_speed = 16.0f);
~BallisticPredictor(); // Added destructor to properly manage memory
// Predict ballistic point considering target movement and flight time
cv::Point2f* predict_ballistic_point(const cv::Point2f* predicted_center,
const cv::Point2f& img_center,
const cv::Point2f& target_speed);
// Get last ballistic point
const cv::Point2f* get_last_ballistic_point() const { return last_ballistic_point; }
private:
float bullet_speed; // Bullet speed (m/s)
float armor_half_width; // Armor half width (converted to meters)
float armor_half_height; // Armor half height (converted to meters)
cv::Point2f* last_ballistic_point; // Last ballistic prediction
// Calculate distance to target
float calculate_distance(const cv::Point2f& armor_center, const cv::Point2f& img_center, float focal_length = 800.0f);
};
#endif // BALLISTICPREDICTOR_H

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#ifndef CAMERA_H
#define CAMERA_H
#include <opencv2/opencv.hpp>
#include <string>
class Camera {
public:
cv::VideoCapture cap;
bool is_opened;
std::string target_color;
Camera(int cam_id = 0, const std::string& target_color = "red");
~Camera();
void set_cam_params();
bool read_frame(cv::Mat& frame);
void release();
bool switch_color(const std::string& target_color);
private:
void set_camera_parameters();
};
#endif // CAMERA_H

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#ifndef IMAGEPREPROCESSOR_H
#define IMAGEPREPROCESSOR_H
#include <opencv2/opencv.hpp>
#include <string>
class ImagePreprocessor {
public:
ImagePreprocessor();
~ImagePreprocessor();
// Apply morphological operations to a mask
void apply_morphology(const cv::Mat& input_mask, cv::Mat& output_mask);
// Apply Gaussian blur to reduce noise
void apply_gaussian_blur(const cv::Mat& input, cv::Mat& output, int kernel_size = 6);
};
#endif // IMAGEPREPROCESSOR_H

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#ifndef KALMANFILTER_H
#define KALMANFILTER_H
#include <opencv2/opencv.hpp>
class KalmanFilter {
public:
KalmanFilter();
void update(const cv::Point2f& measurement);
cv::Point2f predict();
// Method to update the process noise based on target movement characteristics
void updateProcessNoise(const cv::Point2f& current_measurement);
cv::Point2f get_last_measurement() const { return last_measurement; }
cv::Point2f get_last_prediction() const { return last_prediction; }
cv::Point2f get_current_velocity() const; // New method to get current velocity
bool is_initialized() const { return initialized; }
private:
cv::KalmanFilter kf;
bool initialized;
cv::Point2f last_measurement;
cv::Point2f last_prediction;
cv::Point2f prev_measurement;
bool has_prev_measurement;
// Store previous states for velocity and acceleration estimation
cv::Point2f prev_velocity;
double time_elapsed; // Time in seconds between measurements
bool has_prev_velocity;
void init_params();
cv::Point2f estimate_velocity(const cv::Point2f& current, const cv::Point2f& previous);
};
#endif // KALMANFILTER_H

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#ifndef MINDVISION_CAMERA_H
#define MINDVISION_CAMERA_H
#include <opencv2/opencv.hpp>
#include <string>
#include <memory>
// MindVision SDK 头文件 - 可能需要根据实际SDK文件调整
extern "C" {
#include "CameraApi.h" // 这是MindVision SDK的典型头文件
}
class MindVisionCamera {
public:
CameraHandle camera_handle; // MindVision SDk中的相机句柄
bool is_opened;
std::string target_color;
int width;
int height;
int fps;
unsigned char* g_pRgbBuffer; // 处理后数据缓存区
tSdkCameraCapbility capability; // 相机能力信息
tSdkImageResolution image_resolution; // 分辨率信息
MindVisionCamera(int cam_id = 0, const std::string& target_color = "red");
~MindVisionCamera();
bool set_cam_params();
bool read_frame(cv::Mat& frame);
bool read_frame_with_color_filter(cv::Mat& frame, cv::Mat& mask, const std::string& target_color);
void release();
bool switch_color(const std::string& target_color);
int get_width() const { return width; }
int get_height() const { return height; }
bool set_resolution(int width, int height);
private:
void set_camera_parameters();
bool initialize_camera(int cam_id);
};
#endif // MINDVISION_CAMERA_H

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#ifndef TTLCOMMUNICATOR_H
#define TTLCOMMUNICATOR_H
#include <string>
class TTLCommunicator {
public:
TTLCommunicator(const std::string& port_name = "/dev/ttyUSB0", int baudrate = 115200);
~TTLCommunicator();
bool connect();
void close();
bool send_data(const std::string& data);
bool is_connected() const { return connected; }
private:
std::string port_name;
int baudrate;
bool connected;
int serial_fd;
bool open_serial_port();
void close_serial_port();
};
#endif // TTLCOMMUNICATOR_H

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#ifndef VISUALIZER_H
#define VISUALIZER_H
#include <opencv2/opencv.hpp>
#include <vector>
#include <string>
#include "ArmorDetector.h"
class Visualizer {
public:
Visualizer();
~Visualizer();
// Draw light bars on frame
cv::Mat& draw_light_bars(cv::Mat& frame, const std::vector<LightBar>& light_bars, const std::string& target_color);
// Draw armor plate on frame
cv::Mat& draw_armor_plate(cv::Mat& frame, const ArmorPlate& armor_plate);
// Draw offset text
cv::Mat& draw_offset_text(cv::Mat& frame, const cv::Point2f* display_center, const std::string& target_color, bool is_predicted = false);
// Draw crosshair at center
cv::Mat& draw_crosshair(cv::Mat& frame, const cv::Point2f& center, const cv::Scalar& color = cv::Scalar(0, 255, 0), int size = 10);
// Draw ballistic point
cv::Mat& draw_ballistic_point(cv::Mat& frame, const cv::Point2f* ballistic_point);
};
#endif // VISUALIZER_H

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#ifndef CONFIG_H
#define CONFIG_H
#include <opencv2/opencv.hpp>
// Camera intrinsic parameters (need actual calibration)
const cv::Mat CAMERA_MATRIX = (cv::Mat_<float>(3, 3) <<
600, 0, 320,
0, 600, 240,
0, 0, 1);
const cv::Mat DIST_COEFFS = cv::Mat::zeros(5, 1, CV_32F); // Distortion coefficients (set to 0 for no distortion)
// Armor dimensions (in mm)
const float ARMOR_WIDTH = 135.0f;
const float ARMOR_HEIGHT = 125.0f;
const float ARMOR_THICKNESS = 20.0f;
const cv::Mat ARMOR_3D_POINTS = (cv::Mat_<float>(4, 3) <<
-ARMOR_WIDTH / 2, -ARMOR_HEIGHT / 2, 0,
ARMOR_WIDTH / 2, -ARMOR_HEIGHT / 2, 0,
ARMOR_WIDTH / 2, ARMOR_HEIGHT / 2, 0,
-ARMOR_WIDTH / 2, ARMOR_HEIGHT / 2, 0);
// Detection thresholds
const float HORIZONTAL_ANGLE_THRESHOLD = 25.0f; // Light bar horizontal angle threshold (degrees)
const float HORIZONTAL_ANGLE_THRESHOLD_RAD = HORIZONTAL_ANGLE_THRESHOLD * CV_PI / 180.0f;
const float NEARBY_LIGHT_BAR_THRESHOLD = 500.0f; // Light bar merging distance threshold (pixels)
const float LIGHT_BAR_IOU_THRESHOLD = 0.05f; // Light bar merging IOU threshold
const float ARMOR_DISTANCE_RATIO_MIN = 0.6f; // Armor light bar distance ratio range
const float ARMOR_DISTANCE_RATIO_MAX = 4.0f;
const float ARMOR_LENGTH_DIFF_RATIO = 0.5f; // Armor light bar length difference ratio
const float ARMOR_ANGLE_DIFF_THRESHOLD = 15.0f * CV_PI / 180.0f; // Armor light bar angle difference threshold (radians)
// Kalman filter parameters
const float KF_PROCESS_NOISE = 0.02f; // Process noise covariance
const float KF_MEASUREMENT_NOISE = 0.5f; // Measurement noise covariance
// Focal length in pixels (from camera calibration)
const double FOCAL_PIXAL = 600.0; // This should match your actual camera calibration
// TTL communication settings
const int TTL_BAUDRATE = 115200;
#endif // CONFIG_H

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#include "ArmorDetector.h"
#include <iostream>
#include <cmath>
#include <algorithm>
ArmorDetector::ArmorDetector() {
angle_threshold_rad = HORIZONTAL_ANGLE_THRESHOLD_RAD;
}
void ArmorDetector::detect(const cv::Mat& mask, const std::string& target_color,
std::vector<LightBar>& valid_light_bars,
std::vector<ArmorPlate>& armor_plates) {
std::vector<LightBar> initial_light_bars = extract_initial_light_bars(mask);
if (initial_light_bars.empty()) {
valid_light_bars.clear();
armor_plates.clear();
return;
}
std::vector<std::vector<cv::Point2f>> processed_boxes = merge_nearby_light_bars(initial_light_bars);
if (processed_boxes.empty()) {
valid_light_bars.clear();
armor_plates.clear();
return;
}
valid_light_bars = filter_valid_light_bars(processed_boxes);
if (valid_light_bars.size() < 2) {
armor_plates.clear();
return;
}
armor_plates = pair_light_bars_to_armor(valid_light_bars, target_color);
if (armor_plates.empty()) {
return;
}
}
std::vector<LightBar> ArmorDetector::extract_initial_light_bars(const cv::Mat& mask) {
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(mask, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
std::vector<LightBar> initial_light_bars;
for (size_t i = 0; i < contours.size(); i++) {
if (contours[i].size() < 5) continue; // Need at least 5 points to fit ellipse
cv::RotatedRect rect = cv::minAreaRect(contours[i]);
cv::Point2f vertices[4];
rect.points(vertices);
std::vector<cv::Point2f> box;
for (int j = 0; j < 4; j++) {
box.push_back(vertices[j]);
}
float contour_area = cv::contourArea(contours[i]);
float rect_area = rect.size.width * rect.size.height;
// Filter: sufficient area + high fill ratio
if (rect_area > 30 && rect_area > 0 && (contour_area / rect_area) > 0.4) {
LightBar light_bar;
light_bar.center = rect.center;
light_bar.size = rect.size;
light_bar.angle = rect.angle;
light_bar.area = rect_area;
light_bar.box = box;
initial_light_bars.push_back(light_bar);
}
}
return initial_light_bars;
}
std::vector<std::vector<cv::Point2f>> ArmorDetector::merge_nearby_light_bars(const std::vector<LightBar>& initial_light_bars) {
std::vector<std::vector<cv::Point2f>> processed_boxes;
std::vector<bool> visited(initial_light_bars.size(), false);
for (size_t i = 0; i < initial_light_bars.size(); i++) {
if (visited[i]) continue;
std::vector<int> nearby_indices = {static_cast<int>(i)};
cv::Point2f center1 = initial_light_bars[i].center;
for (size_t j = i + 1; j < initial_light_bars.size(); j++) {
if (visited[j]) continue;
cv::Point2f center2 = initial_light_bars[j].center;
float distance = std::sqrt(std::pow(center2.x - center1.x, 2) + std::pow(center2.y - center1.y, 2));
// Distance + IOU filtering
if (distance < NEARBY_LIGHT_BAR_THRESHOLD) {
cv::Rect b1 = cv::boundingRect(initial_light_bars[i].box);
cv::Rect b2 = cv::boundingRect(initial_light_bars[j].box);
double iou = calculate_iou(b1, b2);
if (iou > LIGHT_BAR_IOU_THRESHOLD) {
nearby_indices.push_back(static_cast<int>(j));
visited[j] = true;
}
}
}
// Merge nearby light bar vertices to generate new rectangle
std::vector<cv::Point2f> all_points;
for (int k : nearby_indices) {
for (const auto& point : initial_light_bars[k].box) {
all_points.push_back(point);
}
}
if (!all_points.empty()) {
cv::RotatedRect merged_rect = cv::minAreaRect(all_points);
cv::Point2f vertices[4];
merged_rect.points(vertices);
std::vector<cv::Point2f> merged_box;
for (int k = 0; k < 4; k++) {
merged_box.push_back(vertices[k]);
}
processed_boxes.push_back(merged_box);
visited[i] = true;
}
}
return processed_boxes;
}
std::vector<LightBar> ArmorDetector::filter_valid_light_bars(const std::vector<std::vector<cv::Point2f>>& processed_boxes) {
std::vector<LightBar> valid_light_bars;
for (const auto& box : processed_boxes) {
if (box.size() < 4) continue;
cv::RotatedRect rect = cv::minAreaRect(box);
cv::Point2f center = rect.center;
float width = rect.size.width;
float height = rect.size.height;
float length = width > height ? width : height;
float bar_width = width > height ? height : width;
float main_angle = rect.angle;
if (width > height) {
// 保持原始角度不变
} else {
main_angle += 90;
}
// Normalize angle to [-90, 90]
main_angle = std::fmod(main_angle, 180);
if (main_angle > 90) main_angle -= 180;
if (main_angle < -90) main_angle += 180;
float main_angle_rad = main_angle * CV_PI / 180.0f;
cv::Point2f end1(
center.x + (length / 2) * std::cos(main_angle_rad),
center.y + (length / 2) * std::sin(main_angle_rad)
);
cv::Point2f end2(
center.x - (length / 2) * std::cos(main_angle_rad),
center.y - (length / 2) * std::sin(main_angle_rad)
);
float rect_area = length * bar_width;
float contour_area = cv::contourArea(box);
float area_ratio = rect_area > 0 ? contour_area / rect_area : 0;
// Filter: sufficient area + high fill ratio + non-horizontal
if (rect_area > 40 && area_ratio > 0.4 && std::abs(main_angle_rad) > angle_threshold_rad) {
LightBar light_bar;
light_bar.center = center;
light_bar.size = cv::Size2f(length, bar_width);
light_bar.angle = main_angle;
light_bar.angle_rad = main_angle_rad;
light_bar.area = rect_area;
light_bar.center_line = {end1, end2};
light_bar.center_line_length = length;
light_bar.box = box;
valid_light_bars.push_back(light_bar);
}
}
return valid_light_bars;
}
std::vector<ArmorPlate> ArmorDetector::pair_light_bars_to_armor(const std::vector<LightBar>& light_bars, const std::string& target_color) {
std::vector<ArmorPlate> armor_plates;
std::vector<bool> used(light_bars.size(), false);
for (size_t i = 0; i < light_bars.size(); i++) {
if (used[i]) continue;
const LightBar& lb1 = light_bars[i];
for (size_t j = i + 1; j < light_bars.size(); j++) {
if (used[j]) continue;
const LightBar& lb2 = light_bars[j];
// Angle difference filtering
float angle_diff = std::abs(lb1.angle_rad - lb2.angle_rad);
float angle_diff_2 = 2.0f * CV_PI - angle_diff;
angle_diff = std::min(angle_diff, angle_diff_2);
if (angle_diff > ARMOR_ANGLE_DIFF_THRESHOLD) {
continue;
}
// Distance ratio filtering
float dx = lb2.center.x - lb1.center.x;
float dy = lb2.center.y - lb1.center.y;
float distance = std::sqrt(dx * dx + dy * dy);
float avg_length = (lb1.center_line_length + lb2.center_line_length) / 2;
float distance_ratio = avg_length > 0 ? distance / avg_length : 0;
if (distance_ratio <= ARMOR_DISTANCE_RATIO_MIN || distance_ratio >= ARMOR_DISTANCE_RATIO_MAX) {
continue;
}
// Length difference filtering
float length_diff_ratio = avg_length > 0 ?
std::abs(lb1.center_line_length - lb2.center_line_length) / avg_length : 0;
if (length_diff_ratio > ARMOR_LENGTH_DIFF_RATIO) {
continue;
}
cv::Point2f armor_center(
(lb1.center.x + lb2.center.x) / 2,
(lb1.center.y + lb2.center.y) / 2
);
double confidence = (lb1.area + lb2.area) / (distance + 1);
ArmorPlate armor_plate;
armor_plate.color = target_color;
armor_plate.center = armor_center;
armor_plate.confidence = confidence;
armor_plate.pair = std::make_pair(lb1, lb2);
armor_plate.corners_2d = std::vector<cv::Point2f>(); // Will be computed later if needed
armor_plates.push_back(armor_plate);
used[i] = true;
used[j] = true;
break;
}
}
// Sort by confidence in descending order
std::sort(armor_plates.begin(), armor_plates.end(),
[](const ArmorPlate& a, const ArmorPlate& b) {
return a.confidence > b.confidence;
});
return armor_plates;
}
double ArmorDetector::calculate_iou(const cv::Rect& b1, const cv::Rect& b2) {
cv::Point2f inter_tl(std::max(b1.x, b2.x), std::max(b1.y, b2.y));
cv::Point2f inter_br(std::min(b1.x + b1.width, b2.x + b2.width),
std::min(b1.y + b1.height, b2.y + b2.height));
if (inter_br.x <= inter_tl.x || inter_br.y <= inter_tl.y) {
return 0.0;
}
float inter_area = (inter_br.x - inter_tl.x) * (inter_br.y - inter_tl.y);
float b1_area = b1.width * b1.height;
float b2_area = b2.width * b2.height;
float union_area = b1_area + b2_area - inter_area;
return union_area > 0 ? inter_area / union_area : 0.0;
}

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#include "BallisticPredictor.h"
#include "config.h"
#include <cmath>
BallisticPredictor::BallisticPredictor(float bullet_speed)
: bullet_speed(bullet_speed), last_ballistic_point(nullptr) {
armor_half_width = ARMOR_WIDTH / 2000.0f; // Convert to meters
armor_half_height = ARMOR_HEIGHT / 2000.0f; // Convert to meters
}
BallisticPredictor::~BallisticPredictor() {
if (last_ballistic_point != nullptr) {
delete last_ballistic_point;
last_ballistic_point = nullptr;
}
}
cv::Point2f* BallisticPredictor::predict_ballistic_point(const cv::Point2f* predicted_center,
const cv::Point2f& img_center,
const cv::Point2f& target_speed) {
// Clean up the last ballistic point before creating a new one
if (last_ballistic_point != nullptr) {
delete last_ballistic_point;
last_ballistic_point = nullptr;
}
if (predicted_center == nullptr) {
return nullptr;
}
// 1. Estimate target distance (meters)
float distance = calculate_distance(*predicted_center, img_center);
// 2. Calculate bullet flight time (seconds): distance / bullet speed
float flight_time = bullet_speed > 0 ? distance / bullet_speed : 0.0f;
// 3. Calculate target movement in flight time (pixels)
// target speed (pixels/s) * flight time (s) = predicted movement in pixels
float delta_x = target_speed.x * flight_time;
float delta_y = target_speed.y * flight_time;
// 4. Ballistic prediction point = Kalman prediction center + target movement compensation
float ballistic_x = predicted_center->x + delta_x;
float ballistic_y = predicted_center->y + delta_y;
cv::Point2f ballistic_point(ballistic_x, ballistic_y);
// Update last ballistic point
last_ballistic_point = new cv::Point2f(ballistic_point);
return last_ballistic_point;
}
float BallisticPredictor::calculate_distance(const cv::Point2f& armor_center, const cv::Point2f& img_center, float focal_length) {
// Calculate the pixel distance from armor center to image center
float dx = std::abs(armor_center.x - img_center.x);
float dy = std::abs(armor_center.y - img_center.y);
float pixel_distance = std::sqrt(dx * dx + dy * dy);
// If target is near the center, avoid division by zero
if (pixel_distance < 10) {
return 5.0f; // Default 5 meters (adjust based on actual scenario)
}
// Distance estimation formula: (actual size * focal length) / pixel size
float distance = (armor_half_width * focal_length) / (pixel_distance * 0.5f);
return std::max(0.5f, distance); // Limit minimum distance to 0.5 meters
}

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ZoneId=3

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Catalyst-MDVS2/src/Camera.cpp Normal file
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#include "Camera.h"
#include <iostream>
Camera::Camera(int cam_id, const std::string& target_color) {
// 先设置为 MJPEG 模式再打开（某些驱动要求）
cap.open(cam_id, cv::CAP_V4L2);
if (!cap.isOpened()) {
throw std::runtime_error("Cannot open camera!");
}
// 必须先设 FOURCC，再设分辨率和帧率！顺序很重要
cap.set(cv::CAP_PROP_FOURCC, cv::VideoWriter::fourcc('M', 'J', 'P', 'G'));
cap.set(cv::CAP_PROP_FRAME_WIDTH, 640);
cap.set(cv::CAP_PROP_FRAME_HEIGHT, 480);
cap.set(cv::CAP_PROP_FPS, 100);
is_opened = true;
this->target_color = target_color;
if (this->target_color != "red" && this->target_color != "blue") {
throw std::invalid_argument("Only 'red' or 'blue' colors are supported");
}
set_cam_params(); // 再次应用参数（包括曝光等）
}
Camera::~Camera() {
release();
}
void Camera::set_cam_params() {
// 先设 FOURCC（顺序很重要！）
cap.set(cv::CAP_PROP_FOURCC, cv::VideoWriter::fourcc('M', 'J', 'P', 'G'));
const int width = 640;
const int height = 480;
const int fps = 60; // 从 80 降到 60
cap.set(cv::CAP_PROP_FRAME_WIDTH, width);
cap.set(cv::CAP_PROP_FRAME_HEIGHT, height);
cap.set(cv::CAP_PROP_FPS, fps);
cap.set(cv::CAP_PROP_AUTOFOCUS, 1);
cap.set(cv::CAP_PROP_AUTO_WB, 1);
cap.set(cv::CAP_PROP_AUTO_EXPOSURE, 0); // 手动曝光
if (target_color == "red") {
cap.set(cv::CAP_PROP_EXPOSURE, -50); // 可微调
//cap.set(cv::CAP_PROP_CONTRAST,50);
} else if (target_color == "blue") {
cap.set(cv::CAP_PROP_EXPOSURE, -8);
}
cap.set(cv::CAP_PROP_WB_TEMPERATURE, 3200);
// 验证
double w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
double h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
double f = cap.get(cv::CAP_PROP_FPS);
double fourcc = cap.get(cv::CAP_PROP_FOURCC);
char str[5] = {0};
memcpy(str, &fourcc, 4);
std::cout << "Actual: " << w << "x" << h << " @ " << f << "fps, FOURCC='" << str << "'" << std::endl;
}
bool Camera::read_frame(cv::Mat& frame) {
if (!is_opened) {
return false;
}
return cap.read(frame);
}
void Camera::release() {
if (is_opened) {
cap.release();
is_opened = false;
}
}
bool Camera::switch_color(const std::string& new_color) {
std::string lower_color = new_color;
// Convert to lowercase manually
for (auto& c : lower_color) {
c = std::tolower(c);
}
if ((lower_color == "red" || lower_color == "blue") && lower_color != target_color) {
target_color = lower_color;
set_cam_params();
return true;
}
return false;
}

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ZoneId=3

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Catalyst-MDVS2/src/ImagePreprocessor.cpp Normal file
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#include "ImagePreprocessor.h"
#include <algorithm>
#include <cctype>
ImagePreprocessor::ImagePreprocessor() {
// Constructor implementation
}
ImagePreprocessor::~ImagePreprocessor() {
// Destructor implementation
}
void ImagePreprocessor::apply_morphology(const cv::Mat& input_mask, cv::Mat& output_mask) {
// Apply morphological operations to reduce noise
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3));
cv::morphologyEx(input_mask, output_mask, cv::MORPH_OPEN, kernel);
cv::morphologyEx(output_mask, output_mask, cv::MORPH_CLOSE, kernel);
}
void ImagePreprocessor::apply_gaussian_blur(const cv::Mat& input, cv::Mat& output, int kernel_size) {
// Apply Gaussian blur to reduce noise
// Ensure kernel size is odd for Gaussian blur
int adjusted_kernel_size = kernel_size % 2 == 0 ? kernel_size + 1 : kernel_size;
cv::GaussianBlur(input, output, cv::Size(adjusted_kernel_size, adjusted_kernel_size), 0);
}

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ZoneId=3

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Catalyst-MDVS2/src/KalmanFilter.cpp Normal file
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#include "KalmanFilter.h"
#include "config.h"
#include <iostream>
#include <chrono>
KalmanFilter::KalmanFilter() {
// 6 state variables (x, y, vx, vy, ax, ay), 2 measurements (x, y)
kf.init(6, 2, 0);
init_params();
initialized = false;
has_prev_measurement = false;
has_prev_velocity = false;
time_elapsed = 1.0 / 100.0; // Assuming 100 FPS as default
last_measurement = cv::Point2f(0, 0);
last_prediction = cv::Point2f(0, 0);
prev_measurement = cv::Point2f(0, 0);
prev_velocity = cv::Point2f(0, 0);
}
void KalmanFilter::init_params() {
// Transition matrix for constant acceleration model
// x(k+1) = x(k) + vx(k)*dt + 0.5*ax(k)*dt^2
// y(k+1) = y(k) + vy(k)*dt + 0.5*ay(k)*dt^2
// vx(k+1) = vx(k) + ax(k)*dt
// vy(k+1) = vy(k) + ay(k)*dt
// ax(k+1) = ax(k)
// ay(k+1) = ay(k)
double dt = time_elapsed;
kf.transitionMatrix = (cv::Mat_<float>(6, 6) <<
1, 0, dt, 0, 0.5*dt*dt, 0,
0, 1, 0, dt, 0, 0.5*dt*dt,
0, 0, 1, 0, dt, 0,
0, 0, 0, 1, 0, dt,
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1
);
// Measurement matrix: measuring x and y only
kf.measurementMatrix = (cv::Mat_<float>(2, 6) <<
1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0
);
// Process noise covariance - higher for acceleration components
kf.processNoiseCov = cv::Mat::zeros(6, 6, CV_32F);
kf.processNoiseCov.at<float>(0, 0) = 1.0f; // x position noise
kf.processNoiseCov.at<float>(1, 1) = 1.0f; // y position noise
kf.processNoiseCov.at<float>(2, 2) = 4.0f; // x velocity noise
kf.processNoiseCov.at<float>(3, 3) = 4.0f; // y velocity noise
kf.processNoiseCov.at<float>(4, 4) = 8.0f; // x acceleration noise
kf.processNoiseCov.at<float>(5, 5) = 8.0f; // y acceleration noise
// Measurement noise covariance
kf.measurementNoiseCov = cv::Mat::eye(2, 2, CV_32F) * 0.1f; // Lower measurement noise
// Error covariance post
kf.errorCovPost = cv::Mat::eye(6, 6, CV_32F) * 0.1f;
}
cv::Point2f KalmanFilter::estimate_velocity(const cv::Point2f& current, const cv::Point2f& previous) {
// Calculate velocity based on position difference
cv::Point2f velocity = current - previous;
velocity.x /= (float)time_elapsed;
velocity.y /= (float)time_elapsed;
return velocity;
}
void KalmanFilter::updateProcessNoise(const cv::Point2f& current_measurement) {
if (has_prev_measurement && has_prev_velocity) {
// Estimate current velocity
cv::Point2f current_velocity = estimate_velocity(current_measurement, prev_measurement);
// Calculate acceleration based on change in velocity
cv::Point2f acceleration = current_velocity - prev_velocity;
acceleration.x /= (float)time_elapsed;
acceleration.y /= (float)time_elapsed;
// Adjust process noise based on estimated acceleration (higher acceleration = higher uncertainty)
float acc_magnitude = std::sqrt(acceleration.x * acceleration.x + acceleration.y * acceleration.y);
// Increase process noise for rapid maneuvers
float noise_factor = 1.0f + 0.5f * acc_magnitude;
kf.processNoiseCov.at<float>(4, 4) = 8.0f * noise_factor; // ax noise
kf.processNoiseCov.at<float>(5, 5) = 8.0f * noise_factor; // ay noise
}
}
void KalmanFilter::update(const cv::Point2f& measurement) {
cv::Mat measurement_mat = (cv::Mat_<float>(2, 1) << measurement.x, measurement.y);
if (!initialized) {
// Initialize state if not done yet
kf.statePost = (cv::Mat_<float>(6, 1) <<
measurement.x, // x position
measurement.y, // y position
0.0f, // x velocity
0.0f, // y velocity
0.0f, // x acceleration
0.0f // y acceleration
);
initialized = true;
} else {
// Update process noise based on target movement characteristics
updateProcessNoise(measurement);
// Perform correction step
kf.correct(measurement_mat);
}
// Update previous state for next iteration
if (has_prev_measurement) {
// Calculate velocity based on position change
cv::Point2f current_velocity = estimate_velocity(measurement, prev_measurement);
if (has_prev_velocity) {
// Estimate acceleration based on velocity change
cv::Point2f acceleration = current_velocity - prev_velocity;
acceleration.x /= (float)time_elapsed;
acceleration.y /= (float)time_elapsed;
// Update state with estimated acceleration
kf.statePost.at<float>(4) = acceleration.x; // x acceleration
kf.statePost.at<float>(5) = acceleration.y; // y acceleration
}
// Update previous velocity
prev_velocity = current_velocity;
has_prev_velocity = true;
} else {
// Initialize previous velocity as zero
prev_velocity = cv::Point2f(0, 0);
has_prev_velocity = true;
}
// Update stored measurements
prev_measurement = last_measurement;
has_prev_measurement = true;
last_measurement = measurement;
}
cv::Point2f KalmanFilter::get_current_velocity() const {
if (!initialized) {
return cv::Point2f(0.0f, 0.0f);
}
// Return the velocity components from the state vector
float vx = kf.statePost.at<float>(2);
float vy = kf.statePost.at<float>(3);
return cv::Point2f(vx, vy);
}
cv::Point2f KalmanFilter::predict() {
if (!initialized) {
return cv::Point2f(0, 0);
}
cv::Mat prediction = kf.predict();
cv::Point2f result(prediction.at<float>(0), prediction.at<float>(1));
last_prediction = result;
return result;
}

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Catalyst-MDVS2/src/MindVisionCamera.cpp Normal file
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#include "MindVisionCamera.h"
#include <iostream>
#include <memory>
MindVisionCamera::MindVisionCamera(int cam_id, const std::string& target_color)
: camera_handle(-1), is_opened(false), width(640), height(480), fps(100), g_pRgbBuffer(nullptr) {
if (!initialize_camera(cam_id)) {
// 即使初始化失败，也要清理可能已分配的资源
release();
throw std::runtime_error("Cannot initialize MindVision camera!");
}
is_opened = true;
this->target_color = target_color;
if (this->target_color != "red" && this->target_color != "blue") {
release(); // 清理已初始化的资源
throw std::invalid_argument("Only 'red' or 'blue' colors are supported");
}
if (!set_cam_params()) { // 修改：set_cam_params现在返回bool值
release(); // 清理已初始化的资源
throw std::runtime_error("Failed to set camera parameters!");
}
}
bool MindVisionCamera::initialize_camera(int cam_id) {
// 初始化SDK
CameraSdkInit(1);
int iCameraCounts = 10;
tSdkCameraDevInfo tCameraEnumList[10]; // 声明为数组而不是指针
// 枚举设备，并建立设备列表
int iStatus = CameraEnumerateDevice(tCameraEnumList, &iCameraCounts);
std::cout << "CameraEnumerateDevice returned: " << iStatus << std::endl;
std::cout << "Found " << iCameraCounts << " cameras" << std::endl;
if (iCameraCounts <= 0) {
std::cerr << "No MindVision cameras found!" << std::endl;
return false;
}
// 选择指定ID的相机
if (cam_id >= iCameraCounts) {
std::cerr << "Camera ID " << cam_id << " not found! Only " << iCameraCounts << " cameras detected." << std::endl;
return false;
}
std::cout << "Initializing camera " << cam_id << ": " << tCameraEnumList[cam_id].acFriendlyName << std::endl;
// 相机初始化
iStatus = CameraInit(&tCameraEnumList[cam_id], -1, -1, &camera_handle);
std::cout << "CameraInit returned: " << iStatus << std::endl;
if (iStatus != CAMERA_STATUS_SUCCESS) {
std::cerr << "Failed to initialize camera! Error code: " << iStatus << std::endl;
return false;
}
// 获取相机能力
iStatus = CameraGetCapability(camera_handle, &capability);
if (iStatus != CAMERA_STATUS_SUCCESS) {
std::cerr << "Failed to get camera capability! Error code: " << iStatus << std::endl;
return false;
}
// 设置输出格式 - 保持与原始工作项目一致，直接设置为BGR8格式
CameraSetIspOutFormat(camera_handle, CAMERA_MEDIA_TYPE_BGR8);
// 让SDK进入工作模式 - 根据原始OpenCv项目，应该在设置格式前调用
CameraPlay(camera_handle);
// 获取并设置分辨率为 640x480
int status = CameraGetImageResolution(camera_handle, &image_resolution);
std::cout << "Default resolution query returned: " << status << std::endl;
// 设置分辨率为 640x480
image_resolution.iIndex = 0xFF; // Use custom resolution (0xFF typically indicates custom resolution)
image_resolution.iWidth = 640;
image_resolution.iHeight = 480;
image_resolution.iWidthFOV = image_resolution.iWidth;
image_resolution.iHeightFOV = image_resolution.iHeight;
image_resolution.iHOffsetFOV = 0;
image_resolution.iVOffsetFOV = 0;
status = CameraSetImageResolution(camera_handle, &image_resolution);
if (status != CAMERA_STATUS_SUCCESS) {
std::cout << "Failed to set resolution to 640x480, using default settings" << std::endl;
// Try to get the current resolution after failed set
CameraGetImageResolution(camera_handle, &image_resolution);
} else {
std::cout << "Successfully set camera resolution to: " << image_resolution.iWidth << "x" << image_resolution.iHeight << std::endl;
}
width = image_resolution.iWidth;
height = image_resolution.iHeight;
// 分配处理后的数据缓存区 - 使用最大分辨率，与原始项目一致
g_pRgbBuffer = (unsigned char *)malloc(capability.sResolutionRange.iHeightMax *
capability.sResolutionRange.iWidthMax * 3);
// 设置帧率（虽然稍后 set_cam_params 会再次设置，但这里先设定）
CameraSetFrameSpeed(camera_handle, 2); // 设置为较高帧率
std::cout << "Initialized camera with resolution: " << width << "x" << height << std::endl;
if (!g_pRgbBuffer) {
std::cerr << "Failed to allocate global RGB buffer" << std::endl;
return false;
}
return true;
}
MindVisionCamera::~MindVisionCamera() {
try {
if (g_pRgbBuffer) {
free(g_pRgbBuffer);
g_pRgbBuffer = nullptr;
}
release();
} catch (...) {
// 析构函数中捕获异常但不重新抛出
// 确保即使在异常情况下也能正确清理资源
}
}
void MindVisionCamera::set_camera_parameters() {
// 先设置为手动曝光模式
CameraSetAeState(camera_handle, false);
// 根据颜色设置曝光参数，以优化装甲板检测效果
if (target_color == "red") {
CameraSetExposureTime(camera_handle, 5000); // 红色目标，曝光时间5ms (5000微秒)
} else if (target_color == "blue") {
CameraSetExposureTime(camera_handle, 8000); // 蓝色目标，曝光时间8ms (8000微秒)
}
// 设置白平衡
CameraSetWbMode(camera_handle, false); // 关闭自动白平衡
CameraSetOnceWB(camera_handle); // 一次性白平衡
// 设置其他参数
CameraSetTriggerMode(camera_handle, 0); // 连续采集模式
// 设置帧率为100FPS
CameraSetFrameSpeed(camera_handle, 2); // 2代表较高帧率，具体值可能需要调整
}
bool MindVisionCamera::set_cam_params() {
// 只设置相机参数（如曝光、白平衡等），不重新设置分辨率
set_camera_parameters();
return true;
}
bool MindVisionCamera::read_frame(cv::Mat& frame) {
if (!is_opened) {
return false;
}
tSdkFrameHead sFrameInfo;
BYTE* pbyBuffer;
// 获取一帧数据
if (CameraGetImageBuffer(camera_handle, &sFrameInfo, &pbyBuffer, 1000) == CAMERA_STATUS_SUCCESS) {
// 直接使用CameraGetImageBufferEx获取处理后的RGB数据，提高效率
INT width, height;
unsigned char* pData = CameraGetImageBufferEx(camera_handle, &width, &height, 2000);
if(pData != NULL) {
// 创建OpenCV Mat对象，直接使用获取的数据
cv::Mat temp_mat(height, width, CV_8UC3, pData);
frame = temp_mat.clone(); // clone()确保数据被复制，而不是共享指针
// CameraGetImageBufferEx方式不需要手动释放缓冲区
return true;
}
// 使用全局缓冲区处理图像数据 - 与原始项目一致
int status = CameraImageProcess(camera_handle, pbyBuffer, g_pRgbBuffer, &sFrameInfo);
if (status != CAMERA_STATUS_SUCCESS) {
std::cerr << "Failed to process image: error code " << status << std::endl;
CameraReleaseImageBuffer(camera_handle, pbyBuffer);
return false;
}
// 创建OpenCV Mat对象 - 使用全局缓冲区，固定为CV_8UC3格式
cv::Mat temp_mat(sFrameInfo.iHeight, sFrameInfo.iWidth, CV_8UC3, g_pRgbBuffer);
frame = temp_mat.clone(); // clone()确保数据被复制，而不是共享指针
// 释放原始缓冲区
CameraReleaseImageBuffer(camera_handle, pbyBuffer);
return true;
}
return false;
}
bool MindVisionCamera::read_frame_with_color_filter(cv::Mat& frame, cv::Mat& raw_mask, const std::string& target_color) {
if (!is_opened) {
return false;
}
// 尝试使用CameraGetImageBufferEx方式获取图像
INT width, height;
unsigned char* pData = CameraGetImageBufferEx(camera_handle, &width, &height, 2000);
if(pData != NULL) {
// 创建OpenCV Mat对象，直接使用获取的数据
cv::Mat original_img(height, width, CV_8UC3, pData);
frame = original_img.clone(); // clone()确保数据被复制，而不是共享指针
// 创建HSV图像用于颜色过滤
cv::Mat hsv_img;
cv::cvtColor(frame, hsv_img, cv::COLOR_BGR2HSV);
// 根据颜色创建原始掩码
if (target_color == "red") {
// Red color range in HSV
cv::Mat mask1, mask2;
cv::inRange(hsv_img, cv::Scalar(0, 43, 49), cv::Scalar(25, 255, 255), mask1);
cv::inRange(hsv_img, cv::Scalar(156, 46, 49), cv::Scalar(180, 255, 255), mask2);
raw_mask = mask1 | mask2;
} else if (target_color == "blue") {
// Blue color range in HSV
cv::inRange(hsv_img, cv::Scalar(83, 200, 44), cv::Scalar(130, 255, 255), raw_mask);
} else {
raw_mask = cv::Mat::zeros(hsv_img.size(), CV_8UC1);
}
// CameraGetImageBufferEx方式不需要手动释放缓冲区
return true;
}
// 如果CameraGetImageBufferEx失败，回退到原来的方式
tSdkFrameHead sFrameInfo;
BYTE* pbyBuffer;
// 获取一帧数据
if (CameraGetImageBuffer(camera_handle, &sFrameInfo, &pbyBuffer, 1000) == CAMERA_STATUS_SUCCESS) {
// 使用全局缓冲区处理图像数据 - 与原始项目一致
int status = CameraImageProcess(camera_handle, pbyBuffer, g_pRgbBuffer, &sFrameInfo);
if (status != CAMERA_STATUS_SUCCESS) {
std::cerr << "Failed to process image: error code " << status << std::endl;
CameraReleaseImageBuffer(camera_handle, pbyBuffer);
return false;
}
// 创建原始RGB图像 - 使用全局缓冲区，固定为CV_8UC3格式
cv::Mat original_img(sFrameInfo.iHeight, sFrameInfo.iWidth, CV_8UC3, g_pRgbBuffer);
// 复制图像数据，确保原始数据不会被后续操作影响
frame = original_img.clone();
// 创建HSV图像用于颜色过滤
cv::Mat hsv_img;
cv::cvtColor(frame, hsv_img, cv::COLOR_BGR2HSV);
// 根据颜色创建原始掩码
if (target_color == "red") {
// Red color range in HSV
cv::Mat mask1, mask2;
cv::inRange(hsv_img, cv::Scalar(0, 43, 49), cv::Scalar(25, 255, 255), mask1);
cv::inRange(hsv_img, cv::Scalar(156, 46, 49), cv::Scalar(180, 255, 255), mask2);
raw_mask = mask1 | mask2;
} else if (target_color == "blue") {
// Blue color range in HSV
cv::inRange(hsv_img, cv::Scalar(83, 200, 44), cv::Scalar(130, 255, 255), raw_mask);
} else {
raw_mask = cv::Mat::zeros(hsv_img.size(), CV_8UC1);
}
// 释放相机缓冲区
CameraReleaseImageBuffer(camera_handle, pbyBuffer);
return true;
}
return false;
}
void MindVisionCamera::release() {
if (camera_handle != 0) {
// 停止采集，不管is_opened状态如何都尝试释放
CameraPause(camera_handle);
CameraUnInit(camera_handle);
camera_handle = -1;
}
if(g_pRgbBuffer) {
free(g_pRgbBuffer);
g_pRgbBuffer = nullptr;
}
is_opened = false;
}
bool MindVisionCamera::switch_color(const std::string& new_color) {
std::string lower_color = new_color;
// Convert to lowercase manually
for (auto& c : lower_color) {
c = std::tolower(c);
}
if ((lower_color == "red" || lower_color == "blue") && lower_color != target_color) {
target_color = lower_color;
return set_cam_params(); // 返回set_cam_params的结果
}
return false;
}
bool MindVisionCamera::set_resolution(int width, int height){
if (!is_opened) {
return false;
}
tSdkImageResolution res;
int status = CameraGetImageResolution(camera_handle, &res);
res.iIndex = 0xFF;
res.iWidth = width;
res.iHeight = height;
res.iWidthFOV = capability.sResolutionRange.iWidthMax;
res.iHeightFOV = capability.sResolutionRange.iHeightMax;
res.iHOffsetFOV = 0;
res.iVOffsetFOV = 0;
status = CameraSetImageResolution(camera_handle, &res);
if (status == CAMERA_STATUS_SUCCESS) {
this->width = width;
this->height = height;
image_resolution = res;
return true;
}
return false;
}

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[ZoneTransfer]
ZoneId=3

354
Catalyst-MDVS2/src/MindVisionMain.cpp Normal file
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#include <cstdlib>
#include <iostream>
#include <string>
#include <vector>
#include <chrono>
#include <thread>
#include <memory>
#include <unistd.h>
#include <opencv2/opencv.hpp>
#include <opencv2/tracking.hpp>
#include "config.h"
#include "MindVisionCamera.h" // 使用MindVision相机
#include "ImagePreprocessor.h"
#include "ArmorDetector.h"
#include "KalmanFilter.h"
#include "Visualizer.h"
#include "BallisticPredictor.h"
#include "TTLCommunicator.h"
// Function to output control data to TTL device (with enable control)
void output_control_data(const cv::Point2f* ballistic_point,
const std::string& target_color,
TTLCommunicator* ttl_communicator,
const cv::Point2f& img_center,
bool use_ttl) {
// Only send data when TTL is enabled and has valid target
if (use_ttl && ballistic_point != nullptr) {
std::ostringstream send_str;
// Calculate offset (based on actual image center)
int ballistic_offset_x = -static_cast<int>(ballistic_point->x - img_center.x);
if ( abs(ballistic_offset_x) > 320){
ballistic_offset_x = ( ballistic_offset_x / abs( ballistic_offset_x ) ) * 320 ;
}
int ballistic_offset_y = -static_cast<int>(img_center.y - ballistic_point->y);
if ( abs(ballistic_offset_y) > 180 ) {
ballistic_offset_y = ( ballistic_offset_x / abs( ballistic_offset_x ) ) * 180 ;
}
// Color simplification mapping
std::string simplified_color = target_color;
if (target_color == "red") simplified_color = "r";
else if (target_color == "blue") simplified_color = "b";
// Construct send string (original format)
send_str << "#s " << simplified_color << " " << std::to_string(ballistic_offset_x) << " " << std::to_string(ballistic_offset_y) << "*\n";
// Send data
if (ttl_communicator != nullptr) {
ttl_communicator->send_data(send_str.str());
}else{
std::cerr << "Error: TTLCommunicator is a null pointer!" << std::endl;
}
}
}
void set_camera_resolution(MindVisionCamera& camera, int width, int height) {
// The resolution is set during camera initialization in MindVision
// We need to implement a method in MindVisionCamera to change resolution
// For now, we'll just log the intended change
if (camera.set_resolution(width, height)){
std::cout << "Successfully set camera resolution to: " << width << "x" << height << std::endl;
} else {
std::cerr << "Failed to set camera resolution to: " << width << "x" << height << std::endl;
}
}
int main(int /*argc*/, char* /*argv*/[]) {
static int Numbe = 0;
std::string target_color = "red";
int cam_id = 0;
cv::Size default_resolution(640, 480); // Changed to 640x480 for consistency with SJTU project
bool use_ttl = false; // Set to false to disable TTL communication
if (Numbe == 0) {
// 执行 shell 命令（注意安全风险！）
std::system("sudo chmod 777 /dev/tty*");
Numbe++;
}
// Define optional resolution list (adjust based on camera support)
std::vector<cv::Size> resolutions = {
cv::Size(320, 240), // Low resolution, high frame rate
cv::Size(640, 480), // Standard resolution
cv::Size(1280, 720), // HD resolution
cv::Size(1920, 1080) // Full HD resolution
};
// Find the index of default resolution
int res_index = 1; // Default to index 1 (640x480)
for (size_t i = 0; i < resolutions.size(); i++) {
if (resolutions[i] == default_resolution) {
res_index = static_cast<int>(i);
break;
}
}
// Initialize TTL communication (only when enabled)
TTLCommunicator* ttl = nullptr;
if (use_ttl) {
// On Linux, the port would typically be /dev/ttyUSB0, /dev/ttyACM0, etc.
ttl = new TTLCommunicator("/dev/ttyUSB0", TTL_BAUDRATE);
if (ttl != nullptr) {
if (!ttl->connect()) {
std::cout << "Warning: Cannot establish TTL connection, will continue running but not send data" << std::endl;
}
} else {
std::cout << "Error: Failed to create TTL communicator instance" << std::endl;
}
} else {
std::cout << "TTL communication disabled" << std::endl;
}
// Initialize visual processing modules with MindVision camera
MindVisionCamera camera(cam_id, target_color);
if (!camera.is_opened) {
std::cerr << "Cannot open MindVision camera!" << std::endl;
return -1;
}
// Initialize ballistic predictor (adjustable bullet speed, e.g., 16m/s)
BallisticPredictor ballistic_predictor(16.0f);
// Record target speed (obtained from Kalman filter)
cv::Point2f target_speed(0.0f, 0.0f);
// Set initial resolution
set_camera_resolution(camera, resolutions[res_index].width, resolutions[res_index].height);
std::cout << "Initial camera resolution: " << resolutions[res_index].width << "x" << resolutions[res_index].height << std::endl;
ImagePreprocessor preprocessor;
ArmorDetector detector;
KalmanFilter kalman_tracker;
Visualizer visualizer;
// Initialize KCF tracker
cv::Ptr<cv::Tracker> tracker = cv::TrackerKCF::create();
bool is_tracking = false;
cv::Rect2d tracking_roi;
int tracking_frame_count = 0;
const int MAX_TRACKING_FRAMES = 100; // Maximum frames to track before returning to search
int frame_counter = 0; // Counter to control output frequency
int max_consecutive_predicts = 20; // Maximum consecutive prediction times
int consecutive_predicts = 0; // Current consecutive prediction count
cv::Mat frame;
try {
while (true) {
// 使用新的颜色过滤方法同时获取图像和原始掩码
cv::Mat raw_mask;
if (!camera.read_frame_with_color_filter(frame, raw_mask, target_color)) {
std::cout << "Cannot read from MindVision camera, exiting!，HERERER" << std::endl;
break;
}
// Get actual image size and calculate center
cv::Point2f img_center(frame.cols / 2.0f, frame.rows / 2.0f);
// 使用OpenCV进行形态学处理
cv::Mat mask;
preprocessor.apply_morphology(raw_mask, mask);
// 生成彩色图像（仅显示目标颜色）
cv::Mat color_only_frame;
frame.copyTo(color_only_frame, raw_mask);
// Initialize tracking center
cv::Point2f* tracking_center = nullptr;
std::unique_ptr<cv::Point2f> tracking_point = nullptr;
if (is_tracking) {
// Update tracker
bool success = tracker->update(frame, tracking_roi);
if (success && tracking_roi.area() > 0) {
// Calculate center of tracked ROI
tracking_point = std::make_unique<cv::Point2f>(
tracking_roi.x + tracking_roi.width / 2.0f,
tracking_roi.y + tracking_roi.height / 2.0f
);
tracking_center = tracking_point.get();
tracking_frame_count++;
// If tracking for too long or detection is available, search for armor again
if (tracking_frame_count > MAX_TRACKING_FRAMES) {
is_tracking = false;
tracking_frame_count = 0;
}
} else {
// Tracking failed, return to detection mode
is_tracking = false;
tracking_frame_count = 0;
}
}
// Armor plate detection - only when not tracking or need to verify tracking
std::vector<LightBar> valid_light_bars;
std::vector<ArmorPlate> armor_plates;
if (!is_tracking || tracking_frame_count % 10 == 0) { // Detect every 10 frames when tracking
detector.detect(mask, target_color, valid_light_bars, armor_plates);
}
// Kalman filter tracking (modified part: get target speed)
cv::Point2f* detection_center = nullptr;
if (!armor_plates.empty()) {
detection_center = &(armor_plates[0].center);
}
// Use smart pointer for safer memory management
std::unique_ptr<cv::Point2f> predicted_center = nullptr;
// Priority: detection -> tracking -> Kalman prediction
if (detection_center != nullptr) {
// Has detection result: update Kalman filter, reset consecutive prediction count
kalman_tracker.update(*detection_center);
predicted_center = std::make_unique<cv::Point2f>(kalman_tracker.predict());
// Start tracking if successfully detected
if (!is_tracking && !armor_plates.empty()) {
cv::Rect2d armor_rect = cv::boundingRect(std::vector<cv::Point2f>{
armor_plates[0].corners_2d[0],
armor_plates[0].corners_2d[1],
armor_plates[0].corners_2d[2],
armor_plates[0].corners_2d[3]
});
// Expand the bounding box slightly for better tracking
cv::Rect2d expanded_rect = cv::Rect2d(
armor_rect.x - armor_rect.width * 0.1,
armor_rect.y - armor_rect.height * 0.1,
armor_rect.width * 1.2,
armor_rect.height * 1.2
);
// Ensure the rectangle is within frame bounds
expanded_rect = expanded_rect & cv::Rect2d(0, 0, frame.cols, frame.rows);
tracker = cv::TrackerKCF::create(); // Create new tracker instance
tracker->init(frame, expanded_rect);
tracking_roi = expanded_rect;
is_tracking = true;
tracking_frame_count = 0;
}
consecutive_predicts = 0;
} else if (is_tracking && tracking_center != nullptr) {
// Use tracking result
kalman_tracker.update(*tracking_center);
predicted_center = std::make_unique<cv::Point2f>(kalman_tracker.predict());
consecutive_predicts = 0;
} else {
// No detection or tracking result: only use Kalman prediction, limit consecutive predictions
consecutive_predicts++;
if (consecutive_predicts < max_consecutive_predicts) {
cv::Point2f temp_pred = kalman_tracker.predict();
if (temp_pred.x != 0 || temp_pred.y != 0) { // Check if prediction is valid
predicted_center = std::make_unique<cv::Point2f>(temp_pred);
}
} else {
predicted_center = nullptr;
target_speed = cv::Point2f(0.0f, 0.0f);
}
}
// Determine display center
cv::Point2f* display_center = detection_center; // Give priority to detection results
if (display_center == nullptr && is_tracking && tracking_center != nullptr) {
display_center = tracking_center;
}
if (display_center == nullptr && predicted_center != nullptr) {
display_center = predicted_center.get();
}
bool is_predicted = (display_center != nullptr) && (detection_center == nullptr && (!is_tracking || tracking_center == nullptr));
// Calculate ballistic prediction point
cv::Point2f* ballistic_point = ballistic_predictor.predict_ballistic_point(
predicted_center.get(), img_center, target_speed
);
auto current_time = std::chrono::high_resolution_clock::now();
// Visualization
visualizer.draw_light_bars(frame, valid_light_bars, target_color);
if (!armor_plates.empty()) {
visualizer.draw_armor_plate(frame, armor_plates[0]);
}
// Draw tracking rectangle if tracking
if (is_tracking) {
cv::rectangle(frame, tracking_roi, cv::Scalar(0, 255, 0), 2);
}
visualizer.draw_offset_text(frame, display_center, target_color, is_predicted);
visualizer.draw_ballistic_point(frame, ballistic_point);
// Output control data to TTL (passing use_ttl to control whether to send)
// Now sending on every frame for smoother control
output_control_data(display_center, target_color, ttl, img_center, use_ttl);
// Display windows
cv::imshow("Armor Detection", frame);
cv::imshow(target_color + " Mask", mask);
cv::imshow(target_color + " Only", color_only_frame);
// Exit on 'q' key press
if (cv::waitKey(1) == 'q') {
break;
}
frame_counter++;
// Control max frame rate (100 FPS)
auto end_time = std::chrono::high_resolution_clock::now();
auto elapsed = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - current_time).count();
if (elapsed < 10) { // 100 FPS = 10ms per frame
std::this_thread::sleep_for(std::chrono::milliseconds(10 - elapsed));
}
// Smart pointers automatically handle memory cleanup
}
}
catch (const std::exception& e) {
std::cerr << "Error in main loop: " << e.what() << std::endl;
}
catch (...) {
std::cerr << "Unknown error occurred in main loop" << std::endl;
}
// Cleanup
try {
camera.release();
} catch (...) {
std::cerr << "Error during camera release" << std::endl;
}
try {
cv::destroyAllWindows();
} catch (...) {
std::cerr << "Error during window destruction" << std::endl;
}
// Only close TTL connection when enabled and initialized
if (use_ttl && ttl != nullptr) {
try {
ttl->close();
} catch (...) {
std::cerr << "Error during TTL close" << std::endl;
}
delete ttl;
ttl = nullptr;
}
return 0;
}

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Catalyst-MDVS2/src/TTLCommunicator.cpp Normal file
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#include "TTLCommunicator.h"
#include <iostream>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <termios.h>
#include <unistd.h>
#include <errno.h>
#include <string.h>
TTLCommunicator::TTLCommunicator(const std::string& port, int baudrate)
: port_name(port), baudrate(baudrate), connected(false) {
serial_fd = -1;
}
TTLCommunicator::~TTLCommunicator() {
close();
}
bool TTLCommunicator::connect() {
std::cout << "Attempting to connect to TTL device on port: " << port_name << " at baud rate: " << baudrate << std::endl;
connected = open_serial_port();
if (connected) {
std::cout << "TTL connection established." << std::endl;
} else {
std::cout << "Warning: Failed to establish TTL connection." << std::endl;
}
return connected;
}
void TTLCommunicator::close() {
if (connected) {
close_serial_port();
connected = false;
std::cout << "TTL connection closed." << std::endl;
}
}
bool TTLCommunicator::send_data(const std::string& data) {
if (!connected) {
std::cerr << "Error: Cannot send data, TTL not connected." << std::endl;
return false;
}
if (serial_fd != -1) {
ssize_t bytes_written = write(serial_fd, data.c_str(), data.length());
if (bytes_written == -1) {
std::cerr << "Error writing to serial port: " << strerror(errno) << std::endl;
return false;
} else {
std::cout << "Sent " << bytes_written << " bytes: " << data;
fsync(serial_fd); // Ensure data is sent
return true;
}
}
return false;
}
bool TTLCommunicator::open_serial_port() {
// Open the serial port
serial_fd = open(port_name.c_str(), O_RDWR | O_NOCTTY);
if (serial_fd == -1) {
std::cerr << "Error opening serial port " << port_name << std::endl;
return false;
}
struct termios tty;
// Get current attributes
if (tcgetattr(serial_fd, &tty) != 0) {
std::cerr << "Error getting serial attributes" << std::endl;
::close(serial_fd);
return false;
}
// Set baud rate
speed_t baud_rate;
switch(baudrate) {
case 9600: baud_rate = B9600; break;
case 19200: baud_rate = B19200; break;
case 38400: baud_rate = B38400; break;
case 57600: baud_rate = B57600; break;
case 115200: baud_rate = B115200; break;
case 230400: baud_rate = B230400; break;
case 460800: baud_rate = B460800; break;
case 921600: baud_rate = B921600; break;
default:
std::cerr << "Unsupported baud rate: " << baudrate << std::endl;
::close(serial_fd);
return false;
}
cfsetispeed(&tty, baud_rate);
cfsetospeed(&tty, baud_rate);
// 8N1: 8 data bits, no parity, 1 stop bit
tty.c_cflag &= ~PARENB; // No parity
tty.c_cflag &= ~CSTOPB; // 1 stop bit
tty.c_cflag &= ~CSIZE; // Clear data bits
tty.c_cflag |= CS8; // 8 data bits
tty.c_cflag &= ~CRTSCTS; // No flow control
tty.c_cflag |= CREAD | CLOCAL; // Enable reading, ignore control lines
// Non-canonical mode
tty.c_lflag &= ~ICANON; // Non-canonical mode
tty.c_lflag &= ~ECHO; // No echo
tty.c_lflag &= ~ECHOE; // No erasure
tty.c_lflag &= ~ISIG; // No signal handling
// No software flow control
tty.c_iflag &= ~(IXON | IXOFF | IXANY);
// No output processing
tty.c_oflag &= ~OPOST;
// Timeouts
tty.c_cc[VMIN] = 0; // Non-blocking read
tty.c_cc[VTIME] = 5; // 0.5 second timeout
// Apply settings
if (tcsetattr(serial_fd, TCSANOW, &tty) != 0) {
std::cerr << "Error setting serial attributes" << std::endl;
::close(serial_fd);
return false;
}
// Flush port
tcflush(serial_fd, TCIOFLUSH);
return true;
}
void TTLCommunicator::close_serial_port() {
if (serial_fd != -1) {
::close(serial_fd);
serial_fd = -1;
}
connected = false;
}

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Catalyst-MDVS2/src/TTLCommunicator.cpp:Zone.Identifier Normal file
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[ZoneTransfer]
ZoneId=3

93
Catalyst-MDVS2/src/Visualizer.cpp Normal file
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#include "Visualizer.h"
#include <sstream>
Visualizer::Visualizer() {
// Constructor implementation
}
Visualizer::~Visualizer() {
// Destructor implementation
}
cv::Mat& Visualizer::draw_light_bars(cv::Mat& frame, const std::vector<LightBar>& light_bars, const std::string& target_color) {
cv::Scalar color = (target_color == "red") ? cv::Scalar(0, 0, 255) : cv::Scalar(255, 0, 0); // Red or Blue in BGR
for (const auto& light_bar : light_bars) {
// Draw rotated rectangle for each light bar
for (size_t i = 0; i < light_bar.box.size(); i++) {
cv::Point2f pt1 = light_bar.box[i];
cv::Point2f pt2 = light_bar.box[(i + 1) % light_bar.box.size()];
cv::line(frame, pt1, pt2, color, 2);
}
}
return frame;
}
cv::Mat& Visualizer::draw_armor_plate(cv::Mat& frame, const ArmorPlate& armor_plate) {
cv::circle(frame, armor_plate.center, 10, cv::Scalar(0, 255, 0), 2); // Green circle around armor center
// Draw confidence text
std::stringstream ss;
ss << "Conf: " << std::fixed << std::setprecision(2) << armor_plate.confidence;
cv::putText(frame, ss.str(), cv::Point(armor_plate.center.x + 15, armor_plate.center.y - 15),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 1);
return frame;
}
cv::Mat& Visualizer::draw_offset_text(cv::Mat& frame, const cv::Point2f* display_center, const std::string& /*target_color*/, bool is_predicted) {
if (display_center == nullptr) {
return frame;
}
cv::Point2f img_center(frame.cols / 2.0f, frame.rows / 2.0f);
int offset_x = static_cast<int>(display_center->x - img_center.x);
int offset_y = static_cast<int>(img_center.y - display_center->y);
std::stringstream ss;
ss << "Offset: (" << offset_x << ", " << offset_y << ")";
// Use different color based on whether it's predicted or actual
cv::Scalar color = is_predicted ? cv::Scalar(0, 255, 255) : cv::Scalar(0, 255, 0); // Yellow for predicted, Green for actual
cv::putText(frame, ss.str(), cv::Point(20, 30), cv::FONT_HERSHEY_SIMPLEX, 0.7, color, 2);
return frame;
}
cv::Mat& Visualizer::draw_crosshair(cv::Mat& frame, const cv::Point2f& center, const cv::Scalar& color, int size) {
// Draw horizontal line
cv::line(frame,
cv::Point(static_cast<int>(center.x - size), static_cast<int>(center.y)),
cv::Point(static_cast<int>(center.x + size), static_cast<int>(center.y)),
color, 2);
// Draw vertical line
cv::line(frame,
cv::Point(static_cast<int>(center.x), static_cast<int>(center.y - size)),
cv::Point(static_cast<int>(center.x), static_cast<int>(center.y + size)),
color, 2);
return frame;
}
cv::Mat& Visualizer::draw_ballistic_point(cv::Mat& frame, const cv::Point2f* ballistic_point) {
if (ballistic_point != nullptr) {
// Draw a red crosshair at the ballistic prediction point
cv::circle(frame, *ballistic_point, 8, cv::Scalar(0, 0, 255), 2); // Red circle
cv::line(frame,
cv::Point(static_cast<int>(ballistic_point->x - 10), static_cast<int>(ballistic_point->y)),
cv::Point(static_cast<int>(ballistic_point->x + 10), static_cast<int>(ballistic_point->y)),
cv::Scalar(0, 0, 255), 2); // Red horizontal line
cv::line(frame,
cv::Point(static_cast<int>(ballistic_point->x), static_cast<int>(ballistic_point->y - 10)),
cv::Point(static_cast<int>(ballistic_point->x), static_cast<int>(ballistic_point->y + 10)),
cv::Scalar(0, 0, 255), 2); // Red vertical line
// Label as ballistic point
cv::putText(frame, "BP", cv::Point(static_cast<int>(ballistic_point->x + 15), static_cast<int>(ballistic_point->y - 15)),
cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 255), 1);
}
return frame;
}

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Catalyst-MDVS2/src/Visualizer.cpp:Zone.Identifier Normal file
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[ZoneTransfer]
ZoneId=3