Gitea Mirror Bot
170 lines
6.9 KiB
Python
170 lines
6.9 KiB
Python
import numpy as np
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import cv2 as cv
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import math
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class ThParameters:
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def __init__(self):
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self.levelNoise=6
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self.angle=45
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self.scale10=5
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self.origin=10
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self.xg=150
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self.yg=150
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self.update=True
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def UpdateShape(x ):
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p.update = True
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def union(a,b):
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x = min(a[0], b[0])
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y = min(a[1], b[1])
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w = max(a[0]+a[2], b[0]+b[2]) - x
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h = max(a[1]+a[3], b[1]+b[3]) - y
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return (x, y, w, h)
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def intersection(a,b):
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x = max(a[0], b[0])
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y = max(a[1], b[1])
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w = min(a[0]+a[2], b[0]+b[2]) - x
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h = min(a[1]+a[3], b[1]+b[3]) - y
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if w<0 or h<0: return () # or (0,0,0,0) ?
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return (x, y, w, h)
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def NoisyPolygon(pRef,n):
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# vector<Point> c
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p = pRef;
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# vector<vector<Point> > contour;
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p = p+n*np.random.random_sample((p.shape[0],p.shape[1]))-n/2.0
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if (n==0):
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return p
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c = np.empty(shape=[0, 2])
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minX = p[0][0]
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maxX = p[0][0]
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minY = p[0][1]
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maxY = p[0][1]
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for i in range( 0,p.shape[0]):
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next = i + 1;
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if (next == p.shape[0]):
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next = 0;
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u = p[next] - p[i]
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d = int(cv.norm(u))
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a = np.arctan2(u[1], u[0])
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step = 1
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if (n != 0):
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step = d // n
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for j in range( 1,int(d),int(max(step, 1))):
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while True:
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pAct = (u*j) / (d)
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r = n*np.random.random_sample()
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theta = a + 2*math.pi*np.random.random_sample()
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# pNew = Point(Point2d(r*cos(theta) + pAct.x + p[i].x, r*sin(theta) + pAct.y + p[i].y));
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pNew = np.array([(r*np.cos(theta) + pAct[0] + p[i][0], r*np.sin(theta) + pAct[1] + p[i][1])])
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if (pNew[0][0]>=0 and pNew[0][1]>=0):
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break
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if (pNew[0][0]<minX):
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minX = pNew[0][0]
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if (pNew[0][0]>maxX):
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maxX = pNew[0][0]
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if (pNew[0][1]<minY):
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minY = pNew[0][1]
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if (pNew[0][1]>maxY):
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maxY = pNew[0][1]
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c = np.append(c,pNew,axis = 0)
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return c
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#static vector<Point> NoisyPolygon(vector<Point> pRef, double n);
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#static void UpdateShape(int , void *r);
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#static void AddSlider(String sliderName, String windowName, int minSlider, int maxSlider, int valDefault, int *valSlider, void(*f)(int, void *), void *r);
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def AddSlider(sliderName,windowName,minSlider,maxSlider,valDefault, update):
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cv.createTrackbar(sliderName, windowName, valDefault,maxSlider-minSlider+1, update)
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cv.setTrackbarMin(sliderName, windowName, minSlider)
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cv.setTrackbarMax(sliderName, windowName, maxSlider)
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cv.setTrackbarPos(sliderName, windowName, valDefault)
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# vector<Point> ctrRef;
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# vector<Point> ctrRotate, ctrNoisy, ctrNoisyRotate, ctrNoisyRotateShift;
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# // build a shape with 5 vertex
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ctrRef = np.array([(250,250),(400, 250),(400, 300),(250, 300),(180, 270)])
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cg = np.mean(ctrRef,axis=0)
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p=ThParameters()
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cv.namedWindow("FD Curve matching");
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# A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
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AddSlider("Noise", "FD Curve matching", 0, 20, p.levelNoise, UpdateShape)
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AddSlider("Angle", "FD Curve matching", 0, 359, p.angle, UpdateShape)
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AddSlider("Scale", "FD Curve matching", 5, 100, p.scale10, UpdateShape)
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AddSlider("Origin", "FD Curve matching", 0, 100, p.origin, UpdateShape)
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AddSlider("Xg", "FD Curve matching", 150, 450, p.xg, UpdateShape)
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AddSlider("Yg", "FD Curve matching", 150, 450, p.yg, UpdateShape)
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code = 0
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img = np.zeros((300,512,3), np.uint8)
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print ("******************** PRESS g TO MATCH CURVES *************\n")
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while (code!=27):
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code = cv.waitKey(60)
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if p.update:
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p.levelNoise=cv.getTrackbarPos('Noise','FD Curve matching')
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p.angle=cv.getTrackbarPos('Angle','FD Curve matching')
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p.scale10=cv.getTrackbarPos('Scale','FD Curve matching')
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p.origin=cv.getTrackbarPos('Origin','FD Curve matching')
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p.xg=cv.getTrackbarPos('Xg','FD Curve matching')
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p.yg=cv.getTrackbarPos('Yg','FD Curve matching')
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r = cv.getRotationMatrix2D((p.xg, p.yg), angle=p.angle, scale=10.0/ p.scale10);
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ctrNoisy= NoisyPolygon(ctrRef,p.levelNoise)
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ctrNoisy1 = np.reshape(ctrNoisy,(ctrNoisy.shape[0],1,2))
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ctrNoisyRotate = cv.transform(ctrNoisy1,r)
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ctrNoisyRotateShift = np.empty([ctrNoisyRotate.shape[0],1,2],dtype=np.int32)
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for i in range(0,ctrNoisy.shape[0]):
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k=(i+(p.origin*ctrNoisy.shape[0])//100)% ctrNoisyRotate.shape[0]
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ctrNoisyRotateShift[i] = ctrNoisyRotate[k]
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# To draw contour using drawcontours
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cc= np.reshape(ctrNoisyRotateShift,[ctrNoisyRotateShift.shape[0],2])
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c = [ ctrRef,cc]
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p.update = False;
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rglobal =(0,0,0,0)
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for i in range(0,2):
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r = cv.boundingRect(c[i])
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rglobal = union(rglobal,r)
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r = list(rglobal)
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r[2] = r[2]+10
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r[3] = r[3]+10
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rglobal = tuple(r)
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img = np.zeros((2 * rglobal[3], 2 * rglobal[2], 3), np.uint8)
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cv.drawContours(img, c, 0, (255,0,0),1);
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cv.drawContours(img, c, 1, (0, 255, 0),1);
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cv.circle(img, tuple(c[0][0]), 5, (255, 0, 0),3);
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cv.circle(img, tuple(c[1][0]), 5, (0, 255, 0),3);
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cv.imshow("FD Curve matching", img);
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if code == ord('d') :
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cv.destroyWindow("FD Curve matching");
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cv.namedWindow("FD Curve matching");
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# A rotation with center at (150,150) of angle 45 degrees and a scaling of 5/10
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AddSlider("Noise", "FD Curve matching", 0, 20, p.levelNoise, UpdateShape)
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AddSlider("Angle", "FD Curve matching", 0, 359, p.angle, UpdateShape)
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AddSlider("Scale", "FD Curve matching", 5, 100, p.scale10, UpdateShape)
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AddSlider("Origin%%", "FD Curve matching", 0, 100, p.origin, UpdateShape)
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AddSlider("Xg", "FD Curve matching", 150, 450, p.xg, UpdateShape)
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AddSlider("Yg", "FD Curve matching", 150, 450, p.yg, UpdateShape)
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if code == ord('g'):
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fit = cv.ximgproc.createContourFitting(1024,16);
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# sampling contour we want 256 points
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cn= np.reshape(ctrRef,[ctrRef.shape[0],1,2])
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ctrRef2d = cv.ximgproc.contourSampling(cn, 256)
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ctrRot2d = cv.ximgproc.contourSampling(ctrNoisyRotateShift, 256)
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fit.setFDSize(16)
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c1 = ctrRef2d
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c2 = ctrRot2d
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alphaPhiST, dist = fit.estimateTransformation(ctrRot2d, ctrRef2d)
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print( "Transform *********\n Origin = ", 1-alphaPhiST[0,0] ," expected ", p.origin / 100. ,"\n")
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print( "Angle = ", alphaPhiST[0,1] * 180 / math.pi ," expected " , p.angle,"\n")
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print( "Scale = " ,alphaPhiST[0,2] ," expected " , p.scale10 / 10.0 , "\n")
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dst = cv.ximgproc.transformFD(ctrRot2d, alphaPhiST,cn, False);
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ctmp= np.reshape(dst,[dst.shape[0],2])
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cdst=ctmp.astype(int)
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c = [ ctrRef,cc,cdst]
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cv.drawContours(img, c, 2, (0,0,255),1);
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cv.circle(img, (int(c[2][0][0]),int(c[2][0][1])), 5, (0, 0, 255),5);
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cv.imshow("FD Curve matching", img);
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