fourier_descriptors_demo.py 6.87 KB
Newer Older
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169
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<Point> c
    p = pRef;
#    vector<vector<Point> > 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]<minX):
                minX = pNew[0][0]
            if (pNew[0][0]>maxX):
                maxX = pNew[0][0]
            if (pNew[0][1]<minY):
                minY = pNew[0][1]
            if (pNew[0][1]>maxY):
                maxY = pNew[0][1]
            c = np.append(c,pNew,axis = 0)
    return c

#static vector<Point> NoisyPolygon(vector<Point> 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<Point> ctrRef;
#    vector<Point> 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);