From cc3bef23a2155a0dedfe7e837db86dd6c2eb3c61 Mon Sep 17 00:00:00 2001 From: eazzy <1044745821@qq.com> Date: Fri, 5 Jul 2024 02:47:15 +0800 Subject: [PATCH] =?UTF-8?q?=E9=A2=9C=E8=89=B2=E8=AF=86=E5=88=AB=E7=AE=97?= =?UTF-8?q?=E6=B3=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/Detect/CMakeLists.txt | 6 + src/Detect/ColorDetect.py | 80 +++++++++++++ src/Detect/HSV.py | 37 ++++++ src/Detect/ImageDetect.py | 41 +++++++ src/Detect/Makefile | 148 +++++++++++++++++++++++ src/Detect/RGB | Bin 0 -> 95080 bytes src/Detect/RGBDetect.cpp | 239 ++++++++++++++++++++++++++++++++++++++ 7 files changed, 551 insertions(+) create mode 100644 src/Detect/CMakeLists.txt create mode 100644 src/Detect/ColorDetect.py create mode 100644 src/Detect/HSV.py create mode 100644 src/Detect/ImageDetect.py create mode 100644 src/Detect/Makefile create mode 100644 src/Detect/RGB create mode 100644 src/Detect/RGBDetect.cpp diff --git a/src/Detect/CMakeLists.txt b/src/Detect/CMakeLists.txt new file mode 100644 index 0000000..1c4bb67 --- /dev/null +++ b/src/Detect/CMakeLists.txt @@ -0,0 +1,6 @@ +cmake_minimum_required(VERSION 3.10) +project(RGB) +find_package(OpenCV REQUIRED) +include_directories(${OpenCV_INCLUDE_DIRS}) +add_executable(RGB RGB.cpp) +target_link_libraries(RGB ${OpenCV_LIBS}) diff --git a/src/Detect/ColorDetect.py b/src/Detect/ColorDetect.py new file mode 100644 index 0000000..9c4d039 --- /dev/null +++ b/src/Detect/ColorDetect.py @@ -0,0 +1,80 @@ +import cv2 +import numpy as np + +def detect_and_draw_color(frame): + # 将BGR图像转换为HSV格式 + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + + # 定义红色和黄色的HSV范围 + # 这里的范围可能需要根据实际情况进行调整 + red_lower = np.array([170, 150, 90]) + red_upper = np.array([180, 230, 200]) + yellow_lower = np.array([40,80, 90]) + yellow_upper = np.array([110, 170, 220]) + + # 创建红色和黄色的掩码 + red_mask = cv2.inRange(hsv, red_lower, red_upper) + yellow_mask = cv2.inRange(hsv, yellow_lower, yellow_upper) + + # 应用形态学操作去除噪声 + kernel = np.ones((5, 5), np.uint8) + red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_OPEN, kernel) + yellow_mask = cv2.morphologyEx(yellow_mask, cv2.MORPH_OPEN, kernel) + + # 找到掩码中的轮廓 + red_contours, _ = cv2.findContours(red_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + yellow_contours, _ = cv2.findContours(yellow_mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) + + # 初始化伤员等级 + patient_level = "" + + # 绘制红色边框和标注 + if red_contours: + # 找到最大的红色轮廓 + max_red_contour = max(red_contours, key=cv2.contourArea) + if cv2.contourArea(max_red_contour) > 500: # 设置一个面积阈值 + x, y, w, h = cv2.boundingRect(max_red_contour) + cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2) + cv2.putText(frame, 'Urgent', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2) + patient_level = "严重" + + # 绘制黄色边框和标注 + if yellow_contours: + # 找到最大的黄色轮廓 + max_yellow_contour = max(yellow_contours, key=cv2.contourArea) + if cv2.contourArea(max_yellow_contour) > 500: # 设置一个面积阈值 + x, y, w, h = cv2.boundingRect(max_yellow_contour) + cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 255), 2) + cv2.putText(frame, 'Medium', (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 255), 2) + patient_level = "中等" + + # 如果检测到颜色，打印伤员等级 + if patient_level: + print(f"根据颜色识别确定伤员等级为：{patient_level}!") + + return frame + +# 打开摄像头 +cap = cv2.VideoCapture(0) + +while True: + # 读取一帧视频 + ret, frame = cap.read() + + if not ret: + print("无法捕获视频流，请检查摄像头是否正确连接。") + break + + # 检测颜色并绘制边框和标注 + result_frame = detect_and_draw_color(frame) + + # 显示结果 + cv2.imshow('Color Detection', result_frame) + + # 按 'q' 退出循环 + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# 释放摄像头和销毁所有窗口 +cap.release() +cv2.destroyAllWindows() diff --git a/src/Detect/HSV.py b/src/Detect/HSV.py new file mode 100644 index 0000000..63f8159 --- /dev/null +++ b/src/Detect/HSV.py @@ -0,0 +1,37 @@ +import cv2 +import numpy as np + +def mouse_callback(event, x, y, flags, param): + if event == cv2.EVENT_LBUTTONDOWN: + pixel = frame[y, x] + hue, sat, val = cv2.split(cv2.cvtColor(np.uint8([[pixel]]), cv2.COLOR_BGR2HSV)) + print(f'H: {hue[0, 0]}, S: {sat[0, 0]}, V: {val[0, 0]}') + +# 打开摄像头 +cap = cv2.VideoCapture(0) + +while True: + # 读取一帧视频 + ret, frame = cap.read() + + if not ret: + print("无法捕获视频流，请检查摄像头是否正确连接。") + break + + # 将BGR图像转换为HSV格式 + hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) + + # 设置鼠标回调函数 + cv2.namedWindow('HSV Image') + cv2.setMouseCallback('HSV Image', mouse_callback) + + # 显示HSV图像 + cv2.imshow('HSV Image', hsv) + + # 按 'q' 退出循环 + if cv2.waitKey(1) & 0xFF == ord('q'): + break + +# 释放摄像头和销毁所有窗口 +cap.release() +cv2.destroyAllWindows() diff --git a/src/Detect/ImageDetect.py b/src/Detect/ImageDetect.py new file mode 100644 index 0000000..f55c416 --- /dev/null +++ b/src/Detect/ImageDetect.py @@ -0,0 +1,41 @@ +import cv2 +import numpy as np + +def detect_color(image): + # 将BGR图像转换为HSV格式 + hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + # 定义红色和黄色的HSV范围 + # 这里的范围可能需要根据实际情况进行调整 + red_lower = np.array([0, 50, 50]) + red_upper = np.array([10, 255, 255]) + yellow_lower = np.array([20, 100, 100]) + yellow_upper = np.array([30, 255, 255]) + + # 创建红色和黄色的掩码 + red_mask = cv2.inRange(hsv, red_lower, red_upper) + yellow_mask = cv2.inRange(hsv, yellow_lower, yellow_upper) + + # 计算掩码区域内的像素数量 + red_pixels = np.sum(red_mask) + yellow_pixels = np.sum(yellow_mask) + + # 根据像素数量判断等级 + if red_pixels > yellow_pixels: + return "严重" + elif yellow_pixels > red_pixels: + return "中等" + else: + return "健康" + +# 读取图像 +image = cv2.imread('9cde8539c524cab6c62f22ed188074e.jpg') + +# 检测颜色并输出等级 +level = detect_color(image) +print(level) + +# 显示图像（可选） +cv2.imshow('Image', image) +cv2.waitKey(0) +cv2.destroyAllWindows() diff --git a/src/Detect/Makefile b/src/Detect/Makefile new file mode 100644 index 0000000..f02fa3a --- /dev/null +++ b/src/Detect/Makefile @@ -0,0 +1,148 @@ +# CMAKE generated file: DO NOT EDIT! +# Generated by "Unix Makefiles" Generator, CMake Version 3.16 + +# Default target executed when no arguments are 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zxS;kG{-00yiKfpH;d#Hf{U7nnhhO-d3v0TqAH0w{lzz0zUnBTi-|`bpf1}F1Uvyji zjc1SHPsp^m{RPvLJ_KOO)Ba!kgP&~ga{7JL{vv0W|F@<6cA3K$3V$fd*XsOKY1c1$ zbC>VF5PnAA!=Z1!X#bV}J&ZiWtY literal 0 HcmV?d00001 diff --git a/src/Detect/RGBDetect.cpp b/src/Detect/RGBDetect.cpp new file mode 100644 index 0000000..8490bae --- /dev/null +++ b/src/Detect/RGBDetect.cpp @@ -0,0 +1,239 @@ +#include +#include +#include +#define LINEAR_X 0 + +#define SIZE 3 +#define PI 3.1415926 +#define HMaxValue 255 +#define SMaxValue 255 +#define VMaxValue 255 +#define oo 1e9+7 +using namespace std; +using namespace cv; +int H_min=100, H_max=124, S_min=43, S_max=255, V_min=46, V_max=255; +Mat img_in; +void Gaussian(Mat input, Mat output, double sigma){ + double weight;//权重 + double sum = 0; + double Gaussian_Temp[SIZE][SIZE] = {0};//模板 + weight = (2*PI*sigma*sigma); + for(int i =0;i =rows||y>=cols)continue; + double m=Gaussian_Temp[k][t]; + if(channels == 1)sum1+=m*input.at(x,y); + else if(channels == 3) + { + Vec3b rgb = input.at(x,y); + sum3[0]+=m*rgb[0]; + sum3[1]+=m*rgb[1]; + sum3[2]+=m*rgb[2]; + + } + } + if(channels == 3){ + for(int k=0;k<3;k++){ + if(sum3[k]>255)sum3[k]=255; + if(sum3[k]<0)sum3[k]=0; + output.at(i,j)[k]=sum3[k]; + } + } + else { + if(sum1>255)sum1=255; + if(sum1<0)sum1=0; + output.at(i,j)=sum1; + } + } + } +} +void RBG2HSV(Mat input,Mat output){ + int rows=input.rows; + int cols=input.cols; + //cout<(i,j);//012:BGR + float b=1.0*pix[0]/255; + float g=1.0*pix[1]/255; + float r=1.0*pix[2]/255; + float maxrgb=max(r,max(g,b)); + float minrgb=min(r,min(g,b)); + float diff=maxrgb-minrgb; + float v=maxrgb; + float s=(diff/v); + float h; + if(maxrgb-minrgb<1e-5)h=0; + else if(maxrgb==r)h=60*(g-b)/diff; + else if(maxrgb==g)h=60*(b-r)/diff+120; + else if(maxrgb==b)h=60*(r-g)/diff+240; + if(h<0)h+=360; + else if(h>359)h-=360; + output.at(i,j)[0]=(int)(h*180/360); + output.at(i,j)[1]=(int)(s*255); + output.at(i,j)[2]=(int)(v*255); + + } + } +} +/* +画颜色统计图 +*/ +void Statistical(Mat input){ + int weigth=210,height=300; + Mat output=Mat::zeros(height,weigth,CV_8UC3); + int rows=input.rows; + int cols=input.cols; + int colorNum[7]={0}; + int sum=rows*cols; + for(int i=0;i(i,j);//012:HSV + if(pix[1]<43||pix[2]<46)continue; + //pix[0]=pix[0]*180/255; + int color=0; + if((pix[0]>=0&&pix[0]<=10)||(pix[0]>=156&&pix[0]<=180))color=0; + else if(pix[0]>=11&&pix[0]<=25)color=1; + else if(pix[0]>=26&&pix[0]<=34)color=2; + else if(pix[0]>=35&&pix[0]<=77)color=3; + else if(pix[0]>=78&&pix[0]<=99)color=4; + else if(pix[0]>=100&&pix[0]<=124)color=5; + else if(pix[0]>=125&&pix[0]<=155)color=6; + colorNum[color]++; + } + } + + Scalar Color[7]={Scalar(0,0,255),Scalar(0,125,255),Scalar(0,255,255),Scalar(0,255,0),Scalar(255,255,0),Scalar(255,0,0),Scalar(255,0,255)}; + for(int i=0;i<7;i++){ + int h=colorNum[i]*height/sum; + rectangle(output,Point(i*30,height),Point((i+1)*30-1, height-h),Color[i],-1); + } + imshow("color",output); +} +void colorDetection(Mat input){ + vector > contours; + vector hierarchy; + findContours( input, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_NONE, Point(0, 0) ); + if(contours.size()==0)return; + int area=0,x=0,y=0,a=0,b=0,c=0,d=0; + for(int k=0;k point[i].x)min_x=(int)point[i].x; + if (min_y > point[i].y)min_y=(int)point[i].y; + + } + if(min_x<0)min_x=0; + if(min_y<0)min_y=0; + if(max_x>=input.cols)max_x=input.cols-1; + if(max_y>=input.rows)max_y=input.rows-1; + if((max_x-min_x)*(max_y-min_y)>area){ + area=(max_x-min_x)*(max_y-min_y); + x=min_x; + y=min_y; + a=max_x; + b=max_y; + c=max_x-min_x; + d=max_y-min_y; + } + } + + if(c==0||d==0)return; + //截取ROI区域 + Mat output=img_in(Rect(x,y,c,d)); + imshow("ROI",output); + Mat hsv=Mat::zeros(output.size(),CV_8UC3); + RBG2HSV(output,hsv); + //统计截取之后的图像各种颜色的含量 + Statistical(hsv); +} +/* +颜色分割 +*/ +void thresholdSeq(int var,void* usrdata){ + Mat input = *(static_cast (usrdata)); + Mat output=Mat::zeros(input.size(),CV_8UC1); + int rows=input.rows; + int cols=input.cols; + for(int i=0;i(i,j);//012:HSV + if(pix[0]H_max)continue; + if(pix[1]S_max)continue; + if(pix[2]V_max)continue; + output.at(i,j)=255; + } + } + imshow("thresholdSeq",output); + //目标颜色检测 + colorDetection(output); +} +int main(int argc, char **argv) +{ + //读取原始图像 + img_in=imread(argv[1],IMREAD_UNCHANGED); + //检查是否读取图像 + if(img_in.empty()){ + cout<<"Error! Input image cannot be read...\n"; + return -1; + } + + imshow("src",img_in); + Mat frOut1=Mat::zeros(img_in.size(),CV_8UC3); + Mat frOut2=Mat::zeros(img_in.size(),CV_8UC1); + // 空域滤波函数 + Gaussian(img_in,frOut1,1); + // 色度空间转换 + RBG2HSV(frOut1,frOut1); + //阈值分割 + namedWindow("thresholdSeq"); + createTrackbar("H_min", "thresholdSeq", &H_min, HMaxValue, thresholdSeq,&frOut1); + createTrackbar("H_max", "thresholdSeq", &H_max, HMaxValue, thresholdSeq,&frOut1); + createTrackbar("S_min", "thresholdSeq", &S_min, SMaxValue, thresholdSeq,&frOut1); + createTrackbar("S_max", "thresholdSeq", &S_max, SMaxValue, thresholdSeq,&frOut1); + createTrackbar("V_min", "thresholdSeq", &V_min, VMaxValue, thresholdSeq,&frOut1); + createTrackbar("V_max", "thresholdSeq", &V_max, VMaxValue, thresholdSeq,&frOut1); + waitKey(); + return 0; +}