public Contour()
{
// cvContourArea, cvArcLength
// 輪郭によって区切られた領域の面積と，輪郭の長さを求める
const int SIZE = 500;
// (1)画像を確保し初期化する
using (CvMemStorage storage = new CvMemStorage())
using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 3))
{
img.Zero();
// (2)点列を生成する
CvSeq<CvPoint> points = new CvSeq<CvPoint>(SeqType.PolyLine, storage);
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
double scale = rng.RandReal() + 0.5;
CvPoint pt0 = new CvPoint
{
X = (int)(Math.Cos(0) * SIZE / 4 * scale + SIZE / 2),
Y = (int)(Math.Sin(0) * SIZE / 4 * scale + SIZE / 2)
};
img.Circle(pt0, 2, CvColor.Green);
points.Push(pt0);
for (int i = 1; i < 20; i++)
{
scale = rng.RandReal() + 0.5;
CvPoint pt1 = new CvPoint
{
X = (int)(Math.Cos(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2),
Y = (int)(Math.Sin(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2)
};
img.Line(pt0, pt1, CvColor.Green, 2);
pt0.X = pt1.X;
pt0.Y = pt1.Y;
img.Circle(pt0, 3, CvColor.Green, Cv.FILLED);
points.Push(pt0);
}
img.Line(pt0, points.GetSeqElem(0).Value, CvColor.Green, 2);
// (3)包含矩形，面積，長さを求める
CvRect rect = points.BoundingRect(false);
double area = points.ContourArea();
double length = points.ArcLength(CvSlice.WholeSeq, 1);
// (4)結果を画像に書き込む
img.Rectangle(new CvPoint(rect.X, rect.Y), new CvPoint(rect.X + rect.Width, rect.Y + rect.Height), CvColor.Red, 2);
string text_area = string.Format("Area: wrect={0}, contour={1}", rect.Width * rect.Height, area);
string text_length = string.Format("Length: rect={0}, contour={1}", 2 * (rect.Width + rect.Height), length);
using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.7, 0.7, 0, 1, LineType.AntiAlias))
{
img.PutText(text_area, new CvPoint(10, img.Height - 30), font, CvColor.White);
img.PutText(text_length, new CvPoint(10, img.Height - 10), font, CvColor.White);
}
// (5)画像を表示，キーが押されたときに終了
using (CvWindow window = new CvWindow("BoundingRect", WindowMode.AutoSize))
{
window.Image = img;
CvWindow.WaitKey(0);
}
}
}
public Contour()
{
// cvContourArea, cvArcLength
// 輪郭によって区切られた領域の面積と，輪郭の長さを求める
const int SIZE = 500;
// (1)画像を確保し初期化する
using (CvMemStorage storage = new CvMemStorage())
using (IplImage img = new IplImage(SIZE, SIZE, BitDepth.U8, 3))
{
img.Zero();
// (2)点列を生成する
CvSeq <CvPoint> points = new CvSeq <CvPoint>(SeqType.PolyLine, storage);
CvRNG rng = new CvRNG((ulong)DateTime.Now.Ticks);
double scale = rng.RandReal() + 0.5;
CvPoint pt0 = new CvPoint
{
X = (int)(Math.Cos(0) * SIZE / 4 * scale + SIZE / 2),
Y = (int)(Math.Sin(0) * SIZE / 4 * scale + SIZE / 2)
};
img.Circle(pt0, 2, CvColor.Green);
points.Push(pt0);
for (int i = 1; i < 20; i++)
{
scale = rng.RandReal() + 0.5;
CvPoint pt1 = new CvPoint
{
X = (int)(Math.Cos(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2),
Y = (int)(Math.Sin(i * 2 * Math.PI / 20) * SIZE / 4 * scale + SIZE / 2)
};
img.Line(pt0, pt1, CvColor.Green, 2);
pt0.X = pt1.X;
pt0.Y = pt1.Y;
img.Circle(pt0, 3, CvColor.Green, Cv.FILLED);
points.Push(pt0);
}
img.Line(pt0, points.GetSeqElem(0).Value, CvColor.Green, 2);
// (3)包含矩形，面積，長さを求める
CvRect rect = points.BoundingRect(false);
double area = points.ContourArea();
double length = points.ArcLength(CvSlice.WholeSeq, 1);
// (4)結果を画像に書き込む
img.Rectangle(new CvPoint(rect.X, rect.Y), new CvPoint(rect.X + rect.Width, rect.Y + rect.Height), CvColor.Red, 2);
string text_area = string.Format("Area: wrect={0}, contour={1}", rect.Width * rect.Height, area);
string text_length = string.Format("Length: rect={0}, contour={1}", 2 * (rect.Width + rect.Height), length);
using (CvFont font = new CvFont(FontFace.HersheySimplex, 0.7, 0.7, 0, 1, LineType.AntiAlias))
{
img.PutText(text_area, new CvPoint(10, img.Height - 30), font, CvColor.White);
img.PutText(text_length, new CvPoint(10, img.Height - 10), font, CvColor.White);
}
// (5)画像を表示，キーが押されたときに終了
using (CvWindow window = new CvWindow("BoundingRect", WindowMode.AutoSize))
{
window.Image = img;
CvWindow.WaitKey(0);
}
}
}
/// <summary>
///
/// </summary>
public SeqPartition()
{
CvMemStorage storage = new CvMemStorage(0);
pointSeq = new CvSeq <CvPoint>(SeqType.EltypeS32C2, CvSeq.SizeOf, storage);
Random rand = new Random();
canvas = new IplImage(Width, Height, BitDepth.U8, 3);
colors = new CvScalar[Count];
for (int i = 0; i < Count; i++)
{
CvPoint pt = new CvPoint
{
X = rand.Next(Width),
Y = rand.Next(Height)
};
pointSeq.Push(pt);
int icolor = rand.Next() | 0x00404040;
colors[i] = Cv.RGB(icolor & 255, (icolor >> 8) & 255, (icolor >> 16) & 255);
}
using (window = new CvWindowEx()
{
Text = "points"
})
{
window.CreateTrackbar("threshold", 10, 50, OnTrack);
OnTrack(10);
CvWindowEx.WaitKey();
}
}
/// <summary>
///
/// </summary>
public SeqPartition()
{
CvMemStorage storage = new CvMemStorage(0);
pointSeq = new CvSeq<CvPoint>(SeqType.EltypeS32C2, CvSeq.SizeOf, storage);
Random rand = new Random();
canvas = new IplImage(Width, Height, BitDepth.U8, 3);
colors = new CvScalar[Count];
for (int i = 0; i < Count; i++)
{
CvPoint pt = new CvPoint
{
X = rand.Next(Width),
Y = rand.Next(Height)
};
pointSeq.Push(pt);
int icolor = rand.Next() | 0x00404040;
colors[i] = Cv.RGB(icolor & 255, (icolor >> 8) & 255, (icolor >> 16) & 255);
}
using (window = new CvWindowEx() { Text = "points" })
{
window.CreateTrackbar("threshold", 10, 50, OnTrack);
OnTrack(10);
CvWindowEx.WaitKey();
}
}
public unsafe SeqTest()
{
using (CvMemStorage storage = new CvMemStorage(0))
{
Random rand = new Random();
CvSeq <int> seq = new CvSeq <int>(SeqType.EltypeS32C1, storage);
// push
for (int i = 0; i < 10; i++)
{
int push = seq.Push(rand.Next(100));//seq.Push(i);
Console.WriteLine("{0} is pushed", push);
}
Console.WriteLine("----------");
// enumerate
Console.WriteLine("contents of seq");
foreach (int item in seq)
{
Console.Write("{0} ", item);
}
Console.WriteLine();
// sort
CvCmpFunc <int> func = delegate(int a, int b)
{
return(a.CompareTo(b));
};
seq.Sort(func);
// convert to array
int[] array = seq.ToArray();
Console.WriteLine("contents of sorted seq");
foreach (int item in array)
{
Console.Write("{0} ", item);
}
Console.WriteLine();
Console.WriteLine("----------");
// pop
for (int i = 0; i < 10; i++)
{
int pop = seq.Pop();
Console.WriteLine("{0} is popped", pop);
}
Console.ReadKey();
}
}
public unsafe SeqTest()
{
using (CvMemStorage storage = new CvMemStorage(0))
{
Random rand = new Random();
CvSeq<int> seq = new CvSeq<int>(SeqType.EltypeS32C1, storage);
// push
for (int i = 0; i < 10; i++)
{
int push = seq.Push(rand.Next(100));//seq.Push(i);
Console.WriteLine("{0} is pushed", push);
}
Console.WriteLine("----------");
// enumerate
Console.WriteLine("contents of seq");
foreach (int item in seq)
{
Console.Write("{0} ", item);
}
Console.WriteLine();
// sort
CvCmpFunc<int> func = delegate(int a, int b)
{
return a.CompareTo(b);
};
seq.Sort(func);
// convert to array
int[] array = seq.ToArray();
Console.WriteLine("contents of sorted seq");
foreach (int item in array)
{
Console.Write("{0} ", item);
}
Console.WriteLine();
Console.WriteLine("----------");
// pop
for (int i = 0; i < 10; i++)
{
int pop = seq.Pop();
Console.WriteLine("{0} is popped", pop);
}
Console.ReadKey();
}
}
static CvPoint[] FindSquares4(IplImage img, CvMemStorage storage)
{
const int N = 11;
CvSize sz = new CvSize(img.Width & -2, img.Height & -2);
IplImage timg = img.Clone(); // make a copy of input image
IplImage gray = new IplImage(sz, BitDepth.U8, 1);
IplImage pyr = new IplImage(sz.Width / 2, sz.Height / 2, BitDepth.U8, 3);
// create empty sequence that will contain points -
// 4 points per square (the square's vertices)
CvSeq <CvPoint> squares = new CvSeq <CvPoint>(SeqType.Zero, CvSeq.SizeOf, storage);
// select the maximum ROI in the image
// with the width and height divisible by 2
timg.ROI = new CvRect(0, 0, sz.Width, sz.Height);
// down-scale and upscale the image to filter out the noise
Cv.PyrDown(timg, pyr, CvFilter.Gaussian5x5);
Cv.PyrUp(pyr, timg, CvFilter.Gaussian5x5);
IplImage tgray = new IplImage(sz, BitDepth.U8, 1);
// find squares in every color plane of the image
for (int c = 0; c < 3; c++)
{
// extract the c-th color plane
timg.COI = c + 1;
Cv.Copy(timg, tgray, null);
// try several threshold levels
for (int l = 0; l < N; l++)
{
// hack: use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading
if (l == 0)
{
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
Cv.Canny(tgray, gray, 0, Thresh, ApertureSize.Size5);
// dilate canny output to remove potential
// holes between edge segments
Cv.Dilate(gray, gray, null, 1);
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
Cv.Threshold(tgray, gray, (l + 1) * 255.0 / N, 255, ThresholdType.Binary);
}
// find contours and store them all as a list
CvSeq <CvPoint> contours;
Cv.FindContours(gray, storage, out contours, CvContour.SizeOf, ContourRetrieval.List, ContourChain.ApproxSimple, new CvPoint(0, 0));
// test each contour
while (contours != null)
{
// approximate contour with accuracy proportional
// to the contour perimeter
CvSeq <CvPoint> result = Cv.ApproxPoly(contours, CvContour.SizeOf, storage, ApproxPolyMethod.DP, contours.ContourPerimeter() * 0.02, false);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (result.Total == 4 && Math.Abs(result.ContourArea(CvSlice.WholeSeq)) > 1000 && result.CheckContourConvexity())
{
double s = 0;
for (int i = 0; i < 5; i++)
{
// find minimum Angle between joint
// edges (maximum of cosine)
if (i >= 2)
{
double t = Math.Abs(Angle(result[i].Value, result[i - 2].Value, result[i - 1].Value));
s = s > t ? s : t;
}
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if (s < 0.3)
{
for (int i = 0; i < 4; i++)
{
//Console.WriteLine(result[i]);
squares.Push(result[i].Value);
}
}
}
// take the next contour
contours = contours.HNext;
}
}
}
// release all the temporary images
gray.Dispose();
pyr.Dispose();
tgray.Dispose();
timg.Dispose();
return(squares.ToArray());
}
/// <summary>
/// returns sequence of squares detected on the image.
/// the sequence is stored in the specified memory storage
/// </summary>
/// <param name="img"></param>
/// <param name="storage"></param>
/// <returns></returns>
static CvPoint[] FindSquares4(IplImage img, CvMemStorage storage)
{
const int N = 11;
CvSize sz = new CvSize(img.Width & -2, img.Height & -2);
IplImage timg = img.Clone(); // make a copy of input image
IplImage gray = new IplImage(sz, BitDepth.U8, 1);
IplImage pyr = new IplImage(sz.Width / 2, sz.Height / 2, BitDepth.U8, 3);
// create empty sequence that will contain points -
// 4 points per square (the square's vertices)
CvSeq<CvPoint> squares = new CvSeq<CvPoint>(SeqType.Zero, CvSeq.SizeOf, storage);
// select the maximum ROI in the image
// with the width and height divisible by 2
timg.ROI = new CvRect(0, 0, sz.Width, sz.Height);
// down-Scale and upscale the image to filter out the noise
Cv.PyrDown(timg, pyr, CvFilter.Gaussian5x5);
Cv.PyrUp(pyr, timg, CvFilter.Gaussian5x5);
IplImage tgray = new IplImage(sz, BitDepth.U8, 1);
// find squares in every color plane of the image
for (int c = 0; c < 3; c++)
{
// extract the c-th color plane
timg.COI = c + 1;
Cv.Copy(timg, tgray, null);
// try several threshold levels
for (int l = 0; l < N; l++)
{
// hack: use Canny instead of zero threshold level.
// Canny helps to catch squares with gradient shading
if (l == 0)
{
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
Cv.Canny(tgray, gray, 0, Thresh, ApertureSize.Size5);
// dilate canny output to remove potential
// holes between edge segments
Cv.Dilate(gray, gray, null, 1);
}
else
{
// apply threshold if l!=0:
// tgray(x,y) = gray(x,y) < (l+1)*255/N ? 255 : 0
Cv.Threshold(tgray, gray, (l + 1) * 255.0 / N, 255, ThresholdType.Binary);
}
// find contours and store them all as a list
CvSeq<CvPoint> contours;
Cv.FindContours(gray, storage, out contours, CvContour.SizeOf, ContourRetrieval.List, ContourChain.ApproxSimple, new CvPoint(0, 0));
// test each contour
while (contours != null)
{
// approximate contour with accuracy proportional
// to the contour perimeter
CvSeq<CvPoint> result = Cv.ApproxPoly(contours, CvContour.SizeOf, storage, ApproxPolyMethod.DP, contours.ContourPerimeter() * 0.02, false);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (result.Total == 4 && Math.Abs(result.ContourArea(CvSlice.WholeSeq)) > 1000 && result.CheckContourConvexity())
{
double s = 0;
for (int i = 0; i < 5; i++)
{
// find minimum Angle between joint
// edges (maximum of cosine)
if (i >= 2)
{
double t = Math.Abs(Angle(result[i].Value, result[i - 2].Value, result[i - 1].Value));
s = s > t ? s : t;
}
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if (s < 0.3)
{
for (int i = 0; i < 4; i++)
{
//Console.WriteLine(result[i]);
squares.Push(result[i].Value);
}
}
}
// take the next contour
contours = contours.HNext;
}
}
}
// release all the temporary images
gray.Dispose();
pyr.Dispose();
tgray.Dispose();
timg.Dispose();
return squares.ToArray();
}
/// <summary>
/// Detect the square in the image using contours
/// </summary>
/// <param name="img">Image</param>
/// <param name="modifiedImg">Modified image to be return</param>
/// <param name="storage">Memory storage</param>
/// <returns></returns>
public static CvPoint[] DetectSquares(IplImage img)
{
// Debug
//System.Diagnostics.Stopwatch stopWatch = new System.Diagnostics.Stopwatch();
//stopWatch.Start();
using (CvMemStorage storage = new CvMemStorage())
{
// create empty sequence that will contain points -
// 4 points per square (the square's vertices)
CvSeq<CvPoint> squares = new CvSeq<CvPoint>(SeqType.Zero, CvSeq.SizeOf, storage);
using (IplImage timg = img.Clone())
using (IplImage gray = new IplImage(timg.Size, BitDepth.U8, 1))
using (IplImage dstCanny = new IplImage(timg.Size, BitDepth.U8, 1))
{
// Get gray scale
timg.CvtColor(gray, ColorConversion.BgrToGray);
// Canny
Cv.Canny(gray, dstCanny, 70, 300);
// dilate canny output to remove potential
// holes between edge segments
Cv.Dilate(dstCanny, dstCanny, null, 2);
// find contours and store them all as a list
CvSeq<CvPoint> contours;
dstCanny.FindContours(storage, out contours);
// Debug
//Cv.ShowImage("Edge", dstCanny);
//if (contours != null) Console.WriteLine(contours.Count());
// Test each contour
while (contours != null)
{
// Debug
//if (stopWatch.ElapsedMilliseconds > 100)
//{
// Console.WriteLine("ROI detection is taking too long and is skipped.");
//}
// approximate contour with accuracy proportional
// to the contour perimeter
CvSeq<CvPoint> result = Cv.ApproxPoly(contours, CvContour.SizeOf, storage, ApproxPolyMethod.DP, contours.ContourPerimeter() * 0.02, false);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (result.Total == 4 &&
Math.Abs(result.ContourArea(CvSlice.WholeSeq)) > 250 &&
result.CheckContourConvexity())
{
double s = 0;
for (int i = 0; i < 5; i++)
{
// find minimum Angle between joint
// edges (maximum of cosine)
if (i >= 2)
{
double t = Math.Abs(Angle(result[i].Value, result[i - 2].Value, result[i - 1].Value));
s = s > t ? s : t;
}
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if (s < 0.3)
{
//Console.WriteLine("ROI found!"); // Debug
for (int i = 0; i < 4; i++)
{
//Console.WriteLine(result[i]);
squares.Push(result[i].Value);
}
}
}
// Take the next contour
contours = contours.HNext;
}
}
//stopWatch.Stop();
//Console.WriteLine("ROI Detection : {0} ms", stopWatch.ElapsedMilliseconds); // Debug
return squares.ToArray();
}
}
