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OpenCVPart.h
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OpenCVPart.h
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#pragma once
#include "stdafx.h"
void cropImg(int indInterfaceSize = 2) {
int x_left = 0, x_right = 0, y_top = 0, y_bottom = 0;
cv::Mat img = cv::imread("temp/screen.png");
if (img.size().height == 1080 && img.size().width == 1920) {
switch (indInterfaceSize) {
case 0:
x_left = 741;
x_right = 1488;
y_top = 133;
y_bottom = 880;
break;
case 1:
x_left = 780;
x_right = 1628;
y_top = 174;
y_bottom = 1022;
break;
case 2:
x_left = 825;
x_right = 1648;
y_top = 193;
y_bottom = 1016;
break;
case 3:
x_left = 866;
x_right = 1665;
y_top = 212;
y_bottom = 1011;
break;
case 4:
x_left = 954;
x_right = 1703;
y_top = 251;
y_bottom = 1000;
break;
default:
x_left = 866;
x_right = 1665;
y_top = 212;
y_bottom = 1011;
}
}
else if (img.size().height == 1440 && img.size().width == 2560) {
x_left = 1094;
x_right = 2203;
y_top = 251;
y_bottom = 1360;
}
else if (img.size().height == 1440 && img.size().width == 3440) {
x_left = 1534;
x_right = 2643;
y_top = 251;
y_bottom = 1360;
}
else if (img.size().height == 2160 && img.size().width == 3840) {
x_left = 1544;
x_right = 3274;
y_top = 330;
y_bottom = 2060;
}
cv::Mat imgCrop = img(cv::Range(y_top, y_bottom), cv::Range(x_left, x_right));
imwrite("temp/map.png", imgCrop);
}
std::vector<std::string> load_class_list()
{
std::vector<std::string> class_list;
std::ifstream ifs("model/names.txt");
std::string line;
while (getline(ifs, line))
{
class_list.push_back(line);
}
return class_list;
}
void load_net(cv::dnn::Net& net, bool is_cuda)
{
auto result = cv::dnn::readNet("model/best823.onnx");
if (is_cuda)
{
std::cout << "Attempty to use CUDA\n";
result.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
result.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA_FP16);
}
else
{
std::cout << "Running on CPU\n";
result.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
result.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
}
net = result;
}
const std::vector<cv::Scalar> colors = { cv::Scalar(255, 255, 0), cv::Scalar(0, 255, 0), cv::Scalar(0, 255, 255), cv::Scalar(255, 0, 0) };
const float INPUT_WIDTH = 832.0;
const float INPUT_HEIGHT = 832.0;
const float SCORE_THRESHOLD = 0.2;
const float NMS_THRESHOLD = 0.4;
const float CONFIDENCE_THRESHOLD = 0.4;
const int classCount = 1;
struct Detection
{
int class_id;
float confidence;
cv::Point center;
cv::Rect box;
bool operator<(const Detection& X) const {
if (confidence < X.confidence) return true;
return false;
}
};
cv::Mat format_yolov5(const cv::Mat& source) {
int col = source.cols;
int row = source.rows;
int _max = MAX(col, row);
cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
source.copyTo(result(cv::Rect(0, 0, col, row)));
return result;
}
void detect(cv::Mat& image, cv::dnn::Net& net, std::vector<Detection>& output, const std::vector<std::string>& className) {
cv::Mat blob;
auto input_image = format_yolov5(image);
cv::dnn::blobFromImage(input_image, blob, 1. / 255., cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(), true, false);
net.setInput(blob);
std::vector<cv::Mat> outputs;
net.forward(outputs, net.getUnconnectedOutLayersNames());
float x_factor = input_image.cols / INPUT_WIDTH;
float y_factor = input_image.rows / INPUT_HEIGHT;
float* data = (float*)outputs[0].data;
const int dimensions = 85;
const int rows = 25200;
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
std::vector<cv::Point> centers;
for (int i = 0; i < rows; ++i) {
float confidence = data[4];
if (confidence >= CONFIDENCE_THRESHOLD) {
float* classes_scores = data + 5;
cv::Mat scores(1, className.size(), CV_32FC1, classes_scores);
cv::Point class_id;
double max_class_score;
minMaxLoc(scores, 0, &max_class_score, 0, &class_id);
if (max_class_score > SCORE_THRESHOLD) {
confidences.push_back(confidence);
class_ids.push_back(class_id.x);
float x = data[0];
float y = data[1];
centers.push_back(cv::Point(x * x_factor, y * y_factor));
float w = data[2];
float h = data[3];
int left = std::round((x - 0.5 * w) * x_factor);
int top = std::round((y - 0.5 * h) * y_factor);
int width = std::round(w * x_factor);
int height = std::round(h * y_factor);
boxes.push_back(cv::Rect(left, top, width, height));
}
}
data += (5 + className.size());
}
std::vector<int> nms_result;
cv::dnn::NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, nms_result);
for (int i = 0; i < nms_result.size(); i++) {
int idx = nms_result[i];
Detection result;
result.class_id = class_ids[idx];
result.confidence = confidences[idx];
result.box = boxes[idx];
result.center = centers[idx];
output.push_back(result);
}
}
void initNet(cv::dnn::Net& net) {
bool is_cuda = false; // argc > 1 && strcmp(argv[1], "cuda") == 0;
load_net(net, is_cuda);
}
void yolo(cv::dnn::Net &net, std::vector<Detection> &myMark, std::vector<Detection> &yellMark, std::vector<Detection> &squadMark)
{
std::vector<std::string> class_list = load_class_list();
cv::Mat frame = cv::imread("temp/map.png");
std::vector<Detection> output;
detect(frame, net, output, class_list);
int detections = output.size();
for (int i = 0; i < detections; ++i)
{
auto detection = output[i];
auto box = detection.box;
auto classId = detection.class_id;
const auto color = colors[classId % colors.size()];
if (classId == 3)
yellMark.push_back(detection);
else if (classId == 0)
myMark.push_back(detection);
else if (classId == 5)
squadMark.push_back(detection);
cv::rectangle(frame, box, color, 1);
//cv::rectangle(frame, cv::Point(box.x, box.y - 20), cv::Point(box.x + box.width, box.y), color, cv::FILLED);
cv::putText(frame, (class_list[classId] + " " + std::to_string(detection.confidence)).c_str(), cv::Point(box.x, box.y - 5), cv::FONT_HERSHEY_COMPLEX, 0.4, cv::Scalar(0, 0, 0), 1.4);
}
cv::imwrite("temp/recogn.png", frame);
if(!yellMark.empty())
std::sort(yellMark.rbegin(), yellMark.rend());
if (!myMark.empty())
std::sort(myMark.rbegin(), myMark.rend());
if (!squadMark.empty())
std::sort(squadMark.rbegin(), squadMark.rend());
}