【影象輪廓與分割二】
阿新 • • 發佈:2019-01-12
一:內容介紹
本節主要介紹OpenCV的imgproc模組的影象輪廓與分割部分:
1. 查詢並繪製輪廓
2. 尋找物體的凸包
3. 使用多邊形將輪廓包圍
4. 影象的矩
5. 分水嶺演算法
6. 影象修補
二:學習筆記
1. findContours()函式查詢影象輪廓和canny檢測邊緣、hough檢測直線,這些都非常使用。但是關於opencv中findContours()的具體原理我也沒看,想深入研究的話可以看What is the algorithm that opencv uses for finding contours?
2. 尋找凸包和使用多邊形將輪廓包圍
3. 影象矩作為影象的一種統計特徵,滿足平移、伸縮、旋轉的不變性。同時,矩本身也有一定的物理含義,特殊地,輪廓的m00矩代表輪廓的面積。
4. 分水嶺演算法可以將影象的邊緣轉換為“山脈”,將均勻區域轉化為“山谷”,有助於影象分割。例程裡邊用的還有點複雜,得稍微理解一下。
5. 影象修補,不怎麼用感覺。
三:相關原始碼及解析
本章示例較多,示例列表:
1.查詢並繪製輪廓
2.尋找和繪製物體的凸包
3.使用多邊形將包圍輪廓
4.查詢並繪製圖像輪廓矩
5.分水嶺演算法
6.影象修補
1. 查詢並繪製輪廓
#include<opencv2\opencv.hpp> #include<vector> #include<iostream> using namespace std; using namespace cv; #define WINDOW_NAME1 "【原始圖視窗】" #define WINDOW_NAME2 "【輪廓圖】" Mat g_srcImage, g_grayImage; int g_nThresh = 80; int g_nThresh_max = 255; RNG g_rng; Mat g_cannyMat_output; vector<vector<point> > g_vContours; vector<vec4i> g_vHierarchy; void on_ThreshChange(int, void*); int main() { g_srcImage = imread("poster_cola.jpg"); //載入源影象 cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY); //轉換灰度圖 blur(g_grayImage, g_grayImage, Size(3, 3) ); //降噪 namedWindow(WINDOW_NAME1); imshow(WINDOW_NAME1, g_srcImage); createTrackbar("canny閾值", WINDOW_NAME1, &g_nThresh, g_nThresh_max, on_ThreshChange); on_ThreshChange(0, 0); while(waitKey(9)!=27); return 0; } void on_ThreshChange(int, void*) { Canny(g_grayImage, g_cannyMat_output, g_nThresh, g_nThresh*2); //用canny運算元檢測邊緣 Mat temp = g_cannyMat_output.clone(); findContours(g_cannyMat_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE); //此程式中對二值影象尋找輪廓是有點問題的 Mat drawing = Mat::zeros(g_cannyMat_output.size(), CV_8UC3); for (int i = 0; i < g_vContours.size(); i++) { Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)); //任意值 drawContours(drawing, g_vContours, i, color); } imshow(WINDOW_NAME2, drawing); }
官方samples裡帶的尋找輪廓的例子,更容易理解一點:
#include<opencv2\opencv.hpp> #include <math.h> #include <iostream> using namespace cv; using namespace std; const int w = 500; //影象的長和寬 int levels = 3; vector<vector<point> > contours; vector<vec4i> hierarchy; static void on_trackbar(int, void*) { Mat cnt_img = Mat::zeros(w, w, CV_8UC3); int _levels = levels - 3; drawContours(cnt_img, contours, _levels <= 0 ? 3 : -1, Scalar(128, 255, 255), //可嘗試更換此處的3試一下 3, LINE_AA, hierarchy, std::abs(_levels)); imshow("contours", cnt_img); } int main(int argc, char** argv) { Mat img = Mat::zeros(w, w, CV_8UC1); //Draw 6 faces for (int i = 0; i < 6; i++) { int dx = (i % 2) * 250 - 30; int dy = (i / 2) * 150; const Scalar white = Scalar(255); const Scalar black = Scalar(0); if (i == 0) { for (int j = 0; j <= 10; j++) { double angle = (j + 5)*CV_PI / 21; line(img, Point(cvRound(dx + 100 + j * 10 - 80 * cos(angle)), cvRound(dy + 100 - 90 * sin(angle))), Point(cvRound(dx + 100 + j * 10 - 30 * cos(angle)), cvRound(dy + 100 - 30 * sin(angle))), white, 1, 8, 0); } } ellipse(img, Point(dx + 150, dy + 100), Size(100, 70), 0, 0, 360, white, -1, 8, 0); ellipse(img, Point(dx + 115, dy + 70), Size(30, 20), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 185, dy + 70), Size(30, 20), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 115, dy + 70), Size(15, 15), 0, 0, 360, white, -1, 8, 0); ellipse(img, Point(dx + 185, dy + 70), Size(15, 15), 0, 0, 360, white, -1, 8, 0); ellipse(img, Point(dx + 115, dy + 70), Size(5, 5), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 185, dy + 70), Size(5, 5), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 150, dy + 100), Size(10, 5), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 150, dy + 150), Size(40, 10), 0, 0, 360, black, -1, 8, 0); ellipse(img, Point(dx + 27, dy + 100), Size(20, 35), 0, 0, 360, white, -1, 8, 0); ellipse(img, Point(dx + 273, dy + 100), Size(20, 35), 0, 0, 360, white, -1, 8, 0); } //show the faces namedWindow("image", 1); imshow("image", img); //Extract the contours so that vector<vector<point> > contours0; findContours(img, contours0, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE); contours.resize(contours0.size()); for (size_t k = 0; k < contours0.size(); k++) approxPolyDP(Mat(contours0[k]), contours[k], 3, true); namedWindow("contours", 1); createTrackbar("levels+3", "contours", &levels, 7, on_trackbar); on_trackbar(0, 0); waitKey(); return 0; }
2 . 尋找和繪製物體的凸包
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "【原始圖視窗】"
#define WINDOW_NAME2 "【效果圖視窗】"
Mat g_srcImage, g_grayImage;
int g_nThresh = 50;
int g_maxThresh = 255;
RNG g_rng;
Mat srcImage_copy = g_srcImage.clone();
Mat g_thresholdImage_output;
vector<vector<point> > g_vContours;
vector<vec4i> g_vHierarchy;
void on_ThreshChange(int, void*);
int main()
{
g_srcImage = imread("poster_cartoon_1.jpg");
cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
blur(g_grayImage, g_grayImage, Size(3, 3));
namedWindow(WINDOW_NAME1);
imshow(WINDOW_NAME1, g_srcImage);
createTrackbar("閾值:", WINDOW_NAME1, &g_nThresh, g_maxThresh, on_ThreshChange);
on_ThreshChange(0, 0); //呼叫一次進行初始化
while (waitKey(2) != 27);
return 0;
}
void on_ThreshChange(int, void*)
{
threshold(g_grayImage, g_thresholdImage_output, g_nThresh, 255, THRESH_BINARY); //二值化
findContours(g_thresholdImage_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
//遍歷每個輪廓,尋找其凸包
vector<vector<point> > hull(g_vContours.size());
for (unsigned int i = 0; i < g_vContours.size(); i++)
{
convexHull(Mat(g_vContours[i]), hull[i]);
}
//繪出輪廓及凸包
Mat drawing = Mat::zeros(g_thresholdImage_output.size(), CV_8UC3);
for (unsigned int i = 0; i < g_vContours.size(); i++)
{
Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255) );
drawContours(drawing, g_vContours, i, color); //畫輪廓
color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
drawContours(drawing, hull, i, color); //畫凸包圖
}
imshow(WINDOW_NAME2, drawing);
}
3 . 使用多邊形將包圍輪廓
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "【原始圖視窗】"
#define WINDOW_NAME2 "【效果圖視窗】"
Mat g_srcImage;
Mat g_grayImage;
int g_nThresh = 50; //閾值
int g_nMaxThresh = 255; //最大閾值
RNG g_rng;
void on_ContoursChange(int, void*);
int main()
{
g_srcImage = imread("poster_landscape_4.jpg");
cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY); //轉化為灰度圖
blur(g_grayImage, g_grayImage, Size(3, 3)); //平滑處理
namedWindow(WINDOW_NAME1);
imshow(WINDOW_NAME1, g_srcImage);
createTrackbar("閾值:", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ContoursChange);
on_ContoursChange(0, 0);
while (waitKey(3) != 27);
return 0;
}
void on_ContoursChange(int, void*)
{
Mat threshold_output;
vector<vector<point> > contours;
vector<vec4i> hierarchy;
threshold(g_grayImage, threshold_output, g_nThresh, 255, THRESH_BINARY); //Threshold檢測邊緣
findContours(threshold_output, contours, hierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE);
//多邊形逼近輪廓+獲取矩形和圓形邊界框
vector<vector<point> > contours_poly(contours.size());
vector<rect> boundRect(contours.size());
vector<point2f> center(contours.size());
vector<float> radius(contours.size());
// Mat tmp(contours[3]);
//一個迴圈,遍歷所有部分,進行本程式最核心的操作
for (unsigned int i = 0; i < contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), contours_poly[i], 3, true); //指定精度逼近多邊形曲線
boundRect[i] = boundingRect(Mat(contours_poly[i])); //計算點集的最外面矩形邊框
minEnclosingCircle(contours_poly[i], center[i], radius[i]); //對給定的2D點集,尋找最小面積的包圍圓形
}
//繪製多邊形輪廓+包圍的矩形框+圓形框
Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
for (unsigned int i = 0; i < contours.size(); i++)
{
Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)); //隨機設定顏色
drawContours(drawing, contours_poly, i, color);
rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color);
circle(drawing, center[i], (int)radius[i], color);
}
namedWindow(WINDOW_NAME2);
imshow(WINDOW_NAME2, drawing);
}
4 . 查詢並繪製圖像輪廓矩
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "【原始圖】"
#define WINDOW_NAME2 "【影象輪廓】"
Mat g_srcImage;
Mat g_grayImage;
int g_nThresh = 100;
int g_nMaxThresh = 255;
RNG g_rng;
Mat g_cannyMat_output;
vector<vector<point> > g_vContours;
vector<vec4i> g_vHierarchy;
void on_ThreshChange(int, void*);
int main()
{
g_srcImage = imread("poster_landscape_5.jpg");
cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);
blur(g_grayImage, g_grayImage, Size(3,3));
namedWindow(WINDOW_NAME1);
imshow(WINDOW_NAME1, g_srcImage);
createTrackbar("閾值", WINDOW_NAME1, &g_nThresh, g_nMaxThresh, on_ThreshChange);
on_ThreshChange(0, 0);
while (waitKey(5)!=27);
return 0;
}
void on_ThreshChange(int, void*)
{
Canny(g_grayImage, g_cannyMat_output, g_nThresh, g_nThresh*2); //使用canny檢測邊緣
findContours(g_cannyMat_output, g_vContours, g_vHierarchy, RETR_TREE, CHAIN_APPROX_SIMPLE); //找到輪廓
vector<moments> mu(g_vContours.size()); //計算矩
for (unsigned int i = 0; i < g_vContours.size(); i++)
mu[i] = moments(g_vContours[i], false);
vector<point2f> mc(g_vContours.size()); //計算中心矩
for (unsigned int i = 0; i < g_vContours.size(); i++)
mc[i] = Point2f(static_cast<float>(mu[i].m10/mu[i].m00), static_cast<float>(mu[i].m01 / mu[i].m00));
cout << "輸出內容:面積和輪廓長度" << endl;
Mat drawing = Mat::zeros(g_cannyMat_output.size(), CV_8UC3); //繪製輪廓
for (unsigned int i = 0; i < g_vContours.size(); i++) //通過m00計算輪廓面積並且和OpenCV函式比較
{
cout << "通過m00計算出輪廓" << i << "的面積,(M_00)=" << mu[i].m00 << endl
<< " OpenCV函式計算出的面積=" << contourArea(g_vContours[i]) << ", 長度: " << arcLength(g_vContours[i], true) << endl << endl;
Scalar color = Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255));
drawContours(drawing, g_vContours, i, color, 2, 8, g_vHierarchy, 0, Point()); //繪製外層和內層輪廓
circle(drawing, mc[i], 4, color, -1);
}
namedWindow(WINDOW_NAME2); //顯示到視窗
imshow(WINDOW_NAME2, drawing);
}
5 . 分水嶺演算法
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace std;
using namespace cv;
#define WINDOW_NAME "【程式視窗1】"
Mat g_maskImage, g_srcImage;
Point prevPt(-1, -1);
static void on_Mouse(int event, int x, int y, int flags, void*);
int main()
{
//載入原圖,初始化掩膜和灰度圖
g_srcImage = imread("poster_landscape_6.jpg");
imshow(WINDOW_NAME, g_srcImage);
Mat srcImage, grayImage;
g_srcImage.copyTo(srcImage);
cvtColor(g_srcImage, g_maskImage, COLOR_BGR2GRAY);
cvtColor(g_maskImage, grayImage, COLOR_GRAY2BGR);
g_maskImage = Scalar::all(0);
//設定滑鼠回撥函式
setMouseCallback(WINDOW_NAME, on_Mouse);
//輪詢按鍵
while (1)
{
int c = waitKey(0);
if ((char)c == 27) break;
if ((char)c == '2') { //按鍵‘2’, 恢復源圖
g_maskImage = Scalar::all(0);
srcImage.copyTo(g_srcImage);
imshow("image", g_srcImage);
}
if ((char)c=='1' || (char)c==' ' ) {
//定義一些引數
vector<vector<point> > contours;
vector<vec4i> hierarchy;
//尋找輪廓
findContours(g_maskImage, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE);
//輪廓為空時的處理
if (contours.empty()) continue;
//複製掩膜
Mat maskImage(g_maskImage.size(), CV_32S);
maskImage = Scalar::all(0);
//迴圈繪製出輪廓
int compCount = 0;
for (int index = 0; index >= 0; index = hierarchy[index][0], compCount++)
drawContours(maskImage, contours, index, Scalar::all(compCount+1), -1, LINE_8, hierarchy);
//compCount為零時的處理
if (compCount == 0)
continue;
//生成隨機顏色
vector<vec3b> colorTab;
for (unsigned int i = 0; i < compCount; i++) {
int b = theRNG().uniform(0, 255);
int g = theRNG().uniform(0, 255);
int r = theRNG().uniform(0, 255);
colorTab.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r));
}
//計算處理時間並輸出到視窗中
double dTime = (double)getTickCount();
watershed(srcImage, maskImage);
dTime = (double)getTickCount() - dTime;
printf("處理時間=%gms\n", dTime*1000./getTickFrequency());
//雙層迴圈,將分水嶺影象遍歷存入watershedImage中
Mat watershedImage(maskImage.size(), CV_8UC3);
for (unsigned int i = 0; i < maskImage.rows; i++)
for (unsigned int j = 0; j < maskImage.cols; j++)
{
int index = maskImage.at<int>(i, j);
if (index == -1)
watershedImage.at<vec3b>(i, j) = Vec3b(255, 255, 255);
else if (index <= 0 || index > compCount)
watershedImage.at<vec3b>(i, j) = Vec3b(0, 0, 0);
else
watershedImage.at<vec3b>(i, j) = colorTab[index - 1];
}
//混合灰度圖和分水嶺效果圖並顯示最終的視窗
watershedImage = watershedImage*0.5 + grayImage*0.5;
imshow("watershed transform", watershedImage);
}
}
return 0;
}
static void on_Mouse(int event, int x, int y, int flags, void*)
{
//處理滑鼠不在視窗中的情況
if (x < 0 || x >= g_srcImage.cols || y < 0 || y >= g_srcImage.rows) return;
//處理滑鼠左鍵相關訊息
if (event == EVENT_LBUTTONUP || !(flags & EVENT_FLAG_LBUTTON)) //左鍵擡起動作或處於沒有按下狀態
prevPt = Point(-1, -1);
else if (event == EVENT_LBUTTONDOWN) //左鍵按下動作
prevPt = Point(x, y);
//滑鼠左鍵按下並移動,繪製出白色線條
else if (event == EVENT_MOUSEMOVE && (flags & EVENT_FLAG_LBUTTON))
{
Point pt(x, y);
if (prevPt.x < 0) prevPt = pt;
line(g_maskImage, prevPt, pt, Scalar::all(255), 5);
line(g_srcImage, prevPt, pt, Scalar::all(255), 5);
prevPt = pt;
imshow(WINDOW_NAME, g_srcImage);
}
}
6 . 影象修補
#include<opencv2\opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
#define WINDOW_NAME1 "【原始圖】"
#define WINDOW_NAME2 "【修補後的效果圖】"
Mat srcImage1, inpaintMask;
Point previousPoint(-1, -1); //原來的點座標
static void on_Mouse(int event, int x, int y, int flags, void*)
{
if (event == EVENT_LBUTTONUP || !(flags & EVENT_FLAG_LBUTTON)) //滑鼠左鍵擡起或沒有按鍵按下
previousPoint = Point(-1, -1);
else if (event == EVENT_LBUTTONDOWN) //滑鼠左鍵按下訊息
previousPoint = Point(x, y);
else if (event==EVENT_MOUSEMOVE && (flags & EVENT_FLAG_LBUTTON)) //滑鼠左鍵按下並移動,進行繪製
{
Point pt(x, y);
if (previousPoint.x < 0) previousPoint = pt;
//繪製白色線條
line(inpaintMask, previousPoint, pt, Scalar::all(255), 5);
line(srcImage1, previousPoint, pt, Scalar::all(255), 5);
previousPoint = pt;
imshow(WINDOW_NAME1, srcImage1);
}
}
int main()
{
Mat srcImage = imread("poster_landscape_7.jpg");
srcImage1 = srcImage.clone();
inpaintMask = Mat::zeros(srcImage1.size(), CV_8U);
imshow(WINDOW_NAME1, srcImage1); //顯示原始圖
cvSetMouseCallback(WINDOW_NAME1, on_Mouse); //設定滑鼠回撥訊息
while (1) //輪詢按鍵,根據不同的按鍵進行處理
{
char c = (char)waitKey();
if (c == 27) break;
if (c == '2') { //鍵值為2, 恢復成原始影象
inpaintMask = Scalar::all(0);
srcImage.copyTo(srcImage1);
imshow(WINDOW_NAME1, srcImage1);
}
if (c=='1' || c==' ') //鍵值為1或者空格,進行影象修補操作
{
Mat inpaintedImage;
inpaint(srcImage1, inpaintMask, inpaintedImage, 3, INPAINT_TELEA);
imshow(WINDOW_NAME2, inpaintedImage);
}
}
return 0;
}