Merge pull request #1634 from LaurentBerger:FD_bug
* remove bug when src is vector Point2i in FourierDescriptors * remove unused code and commentspull/1646/head
LaurentBerger
7 years ago
committed by
Alexander Alekhin
parent
beef60bab5
commit
a798127837
4 changed files with 266 additions and 8 deletions
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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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@ -0,0 +1,90 @@ |
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp" |
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namespace opencv_test { namespace { |
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TEST(ximpgroc_fourierdescriptors,test_FD_AND_FIT) |
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{ |
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Mat fd; |
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vector<Point2f> ctr(16); |
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float Rx = 100, Ry = 100; |
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Point2f g(0, 0); |
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float angleOri = 0; |
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for (int i = 0; i < static_cast<int>(ctr.size()); i++) |
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{ |
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float theta = static_cast<float>(2 * CV_PI / static_cast<int>(ctr.size()) * i + angleOri); |
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ctr[i] = Point2f(Rx * cos(theta) + g.x, Ry * sin(theta) + g.y); |
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} |
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ximgproc::fourierDescriptor(ctr, fd); |
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CV_Assert(cv::norm(fd.at<Vec2f>(0, 0)) < ctr.size() * FLT_EPSILON && cv::norm(fd.at<Vec2f>(0, 1) - Vec2f(Rx, 0)) < ctr.size() * FLT_EPSILON); |
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Rx = 100, Ry = 50; |
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g = Point2f(50, 20); |
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for (int i = 0; i < static_cast<int>(ctr.size()); i++) |
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{ |
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float theta = static_cast<float>(2 * CV_PI / static_cast<int>(ctr.size()) * i + angleOri); |
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ctr[i] = Point2f(Rx * cos(theta) + g.x, Ry * sin(theta) + g.y); |
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} |
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ximgproc::fourierDescriptor(ctr, fd); |
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CV_Assert(cv::norm(fd.at<Vec2f>(0, 0) - Vec2f(g)) < 1 && |
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fabs(fd.at<Vec2f>(0, 1)[0] + fd.at<Vec2f>(0, static_cast<int>(ctr.size()) - 1)[0] - Rx) < 1 && |
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fabs(fd.at<Vec2f>(0, 1)[0] - fd.at<Vec2f>(0, static_cast<int>(ctr.size()) - 1)[0] - Ry) < 1); |
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Rx = 70, Ry = 100; |
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g = Point2f(30, 100); |
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angleOri = static_cast<float>(CV_PI / 4); |
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for (int i = 0; i < static_cast<int>(ctr.size()); i++) |
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{ |
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float theta = static_cast<float>(2 * CV_PI / static_cast<int>(ctr.size()) * i + CV_PI / 4); |
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ctr[i] = Point2f(Rx * cos(theta) + g.x, Ry * sin(theta) + g.y); |
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} |
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ximgproc::fourierDescriptor(ctr, fd); |
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CV_Assert(cv::norm(fd.at<Vec2f>(0, 0) - Vec2f(g)) < 1); |
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CV_Assert(cv::norm(Vec2f((Rx + Ry)*cos(angleOri) / 2, (Rx + Ry)*sin(angleOri) / 2) - fd.at<Vec2f>(0, 1)) < 1); |
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CV_Assert(cv::norm(Vec2f((Rx - Ry)*cos(angleOri) / 2, -(Rx - Ry)*sin(angleOri) / 2) - fd.at<Vec2f>(0, static_cast<int>(ctr.size()) - 1)) < 1); |
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RNG rAlea; |
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g.x = 0; g.y = 0; |
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ctr.resize(256); |
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for (int i = 0; i < static_cast<int>(ctr.size()); i++) |
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{ |
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ctr[i] = Point2f(rAlea.uniform(0.0F, 1.0F), rAlea.uniform(0.0F, 1.0F)); |
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g += ctr[i]; |
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} |
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g.x = g.x / ctr.size(); |
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g.y = g.y / ctr.size(); |
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double rotAngle = 35; |
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double s = 0.1515; |
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Mat r = getRotationMatrix2D(g, rotAngle, 0.1515); |
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vector<Point2f> unknownCtr; |
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vector<Point2f> ctrShift; |
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int valShift = 170; |
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for (int i = 0; i < static_cast<int>(ctr.size()); i++) |
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ctrShift.push_back(ctr[(i + valShift) % ctr.size()]); |
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cv::transform(ctrShift, unknownCtr, r); |
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ximgproc::ContourFitting fit; |
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fit.setFDSize(16); |
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Mat t; |
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double dist; |
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fit.estimateTransformation(unknownCtr, ctr, t, &dist, false); |
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CV_Assert(fabs(t.at<double>(0, 0)*ctr.size() + valShift) < 10 || fabs((1 - t.at<double>(0, 0))*ctr.size() - valShift) < 10); |
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CV_Assert(fabs(t.at<double>(0, 1) - rotAngle / 180.*CV_PI) < 0.1); |
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CV_Assert(fabs(t.at<double>(0, 2) - 1 / s) < 0.1); |
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ctr.resize(4); |
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ctr[0] = Point2f(0, 0); |
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ctr[1] = Point2f(16, 0); |
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ctr[2] = Point2f(16, 16); |
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ctr[3] = Point2f(0, 16); |
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double squareArea = contourArea(ctr), lengthSquare = arcLength(ctr, true); |
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Mat ctrs; |
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ximgproc::contourSampling(ctr, ctrs, 64); |
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CV_Assert(fabs(squareArea - contourArea(ctrs)) < FLT_EPSILON); |
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CV_Assert(fabs(lengthSquare - arcLength(ctrs, true)) < FLT_EPSILON); |
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} |
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}} // namespace
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