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vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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Gitea Mirror Bot
2026-08-22 00:11:13 +08:00
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modules/ximgproc/samples/fourier_descriptors_demo.py
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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);