简单介绍
本篇解说使用opencv来測试，表示camera gamma參数的灰阶卡图片指标：YA Block、DynamicRange、Gray Scale。
详细实现
实现代码
#include <math.h>
#include <string.h>
#include <stdio.h>
#include <stdlib.h>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv/cv.h>
#include "opencv2/imgproc/imgproc.hpp"
#define barPic "barPic"
#define Pic_windosw "src"
using namespace cv;
char pic_name[20];
char pic_tmp[20] = "tmp.jpg";
char pic_windows[20] = "src";
Mat src, src2;
int src2_width = 400, src2_height = 400;
int width, height, roi_width, roi_height;
int rect[4];
bool mouse_flag = false, ROI_flag = false;
Mat imageROI;
double white=0, black=0, graySum = 0;
CvFont font;
double hScale=1;
double vScale=1;
int lineWidth=1;
char showWhite[20] = "White:0", showBlack[20]="Black:0", grayNumber[20]="grayNumber:0";
void doGammaForA_black(Mat image){
int i, j, k;
IplImage pI_1, pI_2;
CvScalar s;
roi_width = image.rows;
roi_height = image.cols;
Mat src = Mat(roi_width, roi_height, CV_8UC1, 1);
white = 0;
black = 0;
pI_1 = image;
pI_2 = src;
cvCvtColor(&pI_1, &pI_2, CV_BGR2GRAY);
for(i=0; i<roi_width; i++){
for(j=0; j<10; j++){
s = cvGet2D(&pI_2, i, j);
if(white==0){
white = s.val[0];
}else{
white = (white + s.val[0]) / 2;
}
}
for(k=roi_height-10; k< roi_height; k++){
s = cvGet2D(&pI_2, i, k);
if(black==0){
black = s.val[0];
}else{
black = (black + s.val[0]) / 2;
}
}
}
sprintf(showWhite, "White:%d", (int)white);
sprintf(showBlack, "Black:%d", (int)black);
}
void doGammaForGrayNumber(Mat image){
int i, j, k, num=0;
double tmp=0, cur_tmp=0;
IplImage pI_1, pI_2;
CvScalar s;
roi_width = image.rows;
roi_height = image.cols;
Mat src = Mat(roi_width, roi_height, CV_8UC1, 1);
graySum = 0;
pI_1 = image;
pI_2 = src;
cvCvtColor(&pI_1, &pI_2, CV_BGR2GRAY);
for(j=0; j<roi_height; j++){
for(i=0; i<roi_width; i++){
s = cvGet2D(&pI_2, i, j);
if(cur_tmp==0){
cur_tmp=s.val[0];
}else{
cur_tmp=(cur_tmp + s.val[0]) / 2;
}
}
if(num >= 3){
if(tmp - cur_tmp > 8){
graySum += 1;
}
tmp = cur_tmp;
cur_tmp = 0;
num = 0;
}
num += 1;
}
sprintf(grayNumber, "grayNumber:%d", (int)graySum);
}
void on_mouse( int event, int x, int y, int flags, void* ustc) {
switch(event){
case CV_EVENT_LBUTTONDOWN:
mouse_flag = true;
rect[0] = x;
rect[1] = y;
rect[2] = 0;
rect[3] = 0;
break;
case CV_EVENT_LBUTTONUP:
src = imread(pic_name, 1);
mouse_flag = false;
rect[2] = x;
rect[3] = y;
rectangle(src, Point(rect[0], rect[1]), Point(rect[2], rect[3]), Scalar(0,255,0), 2);
src = imread(pic_name, 1);
imageROI = src(cv::Rect(rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]));
ROI_flag = true;
break;
case CV_EVENT_MOUSEMOVE:
if(mouse_flag){
src = imread(pic_name, 1);
rectangle(src, Point(rect[0], rect[1]), Point(x, y), Scalar(0,255,0), 2);
cv::imshow(Pic_windosw, src);
}
break;
default:
break;
}
}
int main(int agrc, char* argv[]){
bool exit = false;
char c;
IplImage pI_barPic;
memcpy(pic_name,argv[1],sizeof(argv[1]));
src=cv::imread(pic_name,1);
width = src.rows;
height = src.cols;
src2 = cv::Mat(src2_width, src2_height, CV_8UC3, 1);
pI_barPic = src2;
cv::imshow(Pic_windosw, src);
cvInitFont(&font, CV_FONT_HERSHEY_PLAIN|CV_FONT_ITALIC, hScale, vScale, 0, lineWidth);
cvPutText(&pI_barPic, showWhite ,cvPoint(10, 15),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, showBlack ,cvPoint(10, 30),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, grayNumber ,cvPoint(10,45),&font,CV_RGB(255,0,0));
cv::imshow(barPic, src2);
cvSetMouseCallback(Pic_windosw, on_mouse, NULL);
while(!exit){
c = cv::waitKey(100);
if(c == ‘q‘){
exit = true;
}
if(ROI_flag){
cv::imshow("test", imageROI);
doGammaForA_black(imageROI); //A块亮度,动态范围
doGammaForGrayNumber(imageROI);//Gray Scale
cvZero(&pI_barPic);
cvPutText(&pI_barPic, showWhite ,cvPoint(10, 15),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, showBlack ,cvPoint(10, 30),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, grayNumber ,cvPoint(10,45),&font,CV_RGB(255,0,0));
cv::imshow(barPic, src2);
ROI_flag = false;
}
}
cvDestroyAllWindows();
return 0;
}
代码解说
1、首先也是进行各类初始化。导入了须要处理的灰阶卡照片src。生成一张空白照片src2用来显示结果。接着用cvInitFont初始化字体显示。 依次的三个cvPutText，将YA Block、DynamicRange、Gray Scale的显示。初始化到src2相应位置。然后给src显示窗体，绑定鼠标响应函数。最后是 一个循环，在循环中，首先等待键值，假设输入键值‘q‘，就直接退出程序。接着推断ROI_flag是否为true，是的话就进入相应操作。不是就直接跳过。
src=cv::imread(pic_name,1);
width = src.rows;
height = src.cols;
src2 = cv::Mat(src2_width, src2_height, CV_8UC3, 1);
pI_barPic = src2;
cv::imshow(Pic_windosw, src);
cvInitFont(&font, CV_FONT_HERSHEY_PLAIN|CV_FONT_ITALIC, hScale, vScale, 0, lineWidth);
cvPutText(&pI_barPic, showWhite ,cvPoint(10, 15),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, showBlack ,cvPoint(10, 30),&font,CV_RGB(255,0,0));
cvPutText(&pI_barPic, grayNumber ,cvPoint(10,45),&font,CV_RGB(255,0,0));
cv::imshow(barPic, src2);
cvSetMouseCallback(Pic_windosw, on_mouse, NULL);
while(!exit){
c = cv::waitKey(100);
if(c == ‘q‘){
exit = true;
}
if(ROI_flag){
.............
.............
.............
}
}
}
2、在鼠标响应函数中，操作非常easy，就是让用户框选中灰阶卡所在的位子。 当用户左键按下的时候，随着鼠标的拖动，会出现一个绿色的矩形框。 当鼠标左键抬起之后，当前矩形框的位置，就被觉得是灰阶卡所在的位置。 同一时候将标志位ROI_flag设置为true。
void on_mouse( int event, int x, int y, int flags, void* ustc) {
switch(event){
case CV_EVENT_LBUTTONDOWN:
mouse_flag = true;
rect[0] = x;
rect[1] = y;
rect[2] = 0;
rect[3] = 0;
break;
case CV_EVENT_LBUTTONUP:
src = imread(pic_name, 1);
mouse_flag = false;
rect[2] = x;
rect[3] = y;
rectangle(src, Point(rect[0], rect[1]), Point(rect[2], rect[3]), Scalar(0,255,0), 2);
src = imread(pic_name, 1);
imageROI = src(cv::Rect(rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]));
ROI_flag = true;
break;
case CV_EVENT_MOUSEMOVE:
if(mouse_flag){
src = imread(pic_name, 1);
rectangle(src, Point(rect[0], rect[1]), Point(x, y), Scalar(0,255,0), 2);
cv::imshow(Pic_windosw, src);
}
break;
default:
break;
}
}
3、之前有提到，当ROI_flag置为true之后。就会进入相应处理函数中，首先是使用doGammaForA_black(imageROI);对框选出来的灰阶卡进行
YA Block、DynamicRange的检測。
检測的方式是，首先将灰阶卡图片灰阶化，然后分别取出图片最左边的10列，计算它平均亮度作为YA Block；取出图片最右边10列，也是计算它们的
平均亮度最为最暗处亮度，当左边平均亮度减去右边平均亮度。得到的就是DynamicRange。
void doGammaForA_black(Mat image){
int i, j, k;
IplImage pI_1, pI_2;
CvScalar s;
roi_width = image.rows;
roi_height = image.cols;
Mat src = Mat(roi_width, roi_height, CV_8UC1, 1);
white = 0;
black = 0;
pI_1 = image;
pI_2 = src;
cvCvtColor(&pI_1, &pI_2, CV_BGR2GRAY);
for(i=0; i<roi_width; i++){
for(j=0; j<10; j++){
s = cvGet2D(&pI_2, i, j);
if(white==0){
white = s.val[0];
}else{
white = (white + s.val[0]) / 2;
}
}
for(k=roi_height-10; k< roi_height; k++){
s = cvGet2D(&pI_2, i, k);
if(black==0){
black = s.val[0];
}else{
black = (black + s.val[0]) / 2;
}
}
}
sprintf(showWhite, "White:%d", (int)white);
sprintf(showBlack, "Black:%d", (int)black);
}
4、最后是使用doGammaForGrayNumber(imageROI)，进行Gray Scale的计算。计算的方式是：首先将灰阶卡图片灰阶化，然后从左往右遍历整个图片。 以3列为一组的计算出它们的平均亮度。当相邻两块的亮度差值大于8的时候，表示Gray Scale加1.
void doGammaForGrayNumber(Mat image){
int i, j, k, num=0;
double tmp=0, cur_tmp=0;
IplImage pI_1, pI_2;
CvScalar s;
roi_width = image.rows;
roi_height = image.cols;
Mat src = Mat(roi_width, roi_height, CV_8UC1, 1);
graySum = 0;
pI_1 = image;
pI_2 = src;
cvCvtColor(&pI_1, &pI_2, CV_BGR2GRAY);
for(j=0; j<roi_height; j++){
for(i=0; i<roi_width; i++){
s = cvGet2D(&pI_2, i, j);
if(cur_tmp==0){
cur_tmp=s.val[0];
}else{
cur_tmp=(cur_tmp + s.val[0]) / 2;
}
}
if(num >= 3){
if(tmp - cur_tmp > 8){
graySum += 1;
}
tmp = cur_tmp;
cur_tmp = 0;
num = 0;
}
num += 1;
}
sprintf(grayNumber, "grayNumber:%d", (int)graySum);
}
效果演示
相应的效果演演示样例如以下:
时间： 2024-10-07 08:42:16