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