# plt.show()
numFrm0 = dstCorr.shape[0]
numFrm1 = dstCorr.shape[1]
dirOut = '%s/%s_%s_matches' % (wdir, dir0, dir1)
if not os.path.isdir(dirOut):
os.mkdir(dirOut)
for ii0 in xrange(numFrm0):
tdst = dstCorr[ii0, :]
idxMax = np.argmax(tdst)
ccMax = tdst[idxMax]
if ccMax > 0.5:
ii1 = idxMax
_, img0 = cap0.read(ii0)
_, img1 = cap1.read(ii1)
fkp0 = cap0.getKPByID(ii0, dscName)
kp0, kd0 = fp.file2kp(fkp0)
fkp1 = cap1.getKPByID(ii1, dscName)
kp1, kd1 = fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
H, status, mKP1, mKP0, listGoodMatches, scores = getHomography2(
kp0, kd0, kp1, kd1)
cc = calcCCHomography(img0, img1, H)
imgMatch = drawMatchesAndHomography(img1,
img0,
mKP0,
mKP1,
status,
H,
num=100)
imgMatch = resizeImageToMaxSize(imgMatch, 900)
winName = '[%d,%d] corr=%0.2f, Score=%d/%d (%0.1f%%)' % (
# kp1,kd1=fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
# cc=calcCCHomography(img0, img1, H)
# dstCorr[ii0,ii1]=cc
# dstCorr0[ii1]=cc
# if cc>0.2:
# print "Corr[%d,%d]=%0.2f !!!!" % (ii0,ii1,cc)
# else:
# print "Corr[%d,%d]=%0.2f" % (ii0,ii1,cc)
# np.savetxt(foutDstCorr0, dstCorr0, delimiter=',')
dstCorr=np.zeros( (numFrm0, numFrm1) )
for ii0 in xrange(numFrm0):
_,img0=cap0.read(ii0)
fkp0=cap0.getKPByID(ii0, dscName)
kp0,kd0=fp.file2kp(fkp0)
dstCorr0=np.zeros( (numFrm1,1) )
foutDstCorr0="%s/%s_match_to_%s_%d.csv" % (wdir,dir0,dir1, ii0)
if os.path.isfile(foutDstCorr0):
dstCorr0=np.genfromtxt(foutDstCorr0, delimiter=',')
dstCorr[ii0,:]=dstCorr0
# for ii1 in xrange(numFrm1):
# _,img1=cap1.read(ii1)
# fkp1=cap1.getKPByID(ii1, dscName)
# kp1,kd1=fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
# cc=calcCCHomography(img0, img1, H)
# dstCorr[ii0,ii1]=cc
# dstCorr0[ii1]=cc
# if cc>0.2:
# print "Corr[%d,%d]=%0.2f !!!!" % (ii0,ii1,cc)
# plt.show()
numFrm0=dstCorr.shape[0]
numFrm1=dstCorr.shape[1]
dirOut='%s/%s_%s_matches' % (wdir, dir0, dir1)
if not os.path.isdir(dirOut):
os.mkdir(dirOut)
for ii0 in xrange(numFrm0):
tdst=dstCorr[ii0,:]
idxMax=np.argmax(tdst)
ccMax=tdst[idxMax]
if ccMax>0.5:
ii1=idxMax
_,img0=cap0.read(ii0)
_,img1=cap1.read(ii1)
fkp0=cap0.getKPByID(ii0, dscName)
kp0,kd0=fp.file2kp(fkp0)
fkp1=cap1.getKPByID(ii1, dscName)
kp1,kd1=fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
H, status, mKP1, mKP0, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
cc=calcCCHomography(img0, img1, H)
imgMatch=drawMatchesAndHomography(img1, img0, mKP0, mKP1, status, H, num=100)
imgMatch=resizeImageToMaxSize(imgMatch, 900)
winName='[%d,%d] corr=%0.2f, Score=%d/%d (%0.1f%%)' % (ii0, ii1, cc, np.sum(status), len(status), 100.*float(np.sum(status))/float(len(status)))
fimgOut='%s/match_%d_to_%d_corr=%0.1f.png' % (dirOut, ii0, ii1, cc)
cv2.putText(imgMatch, winName, (30,30), cv2.FONT_HERSHEY_PLAIN,1.4,(0,0,0),6)
cv2.putText(imgMatch, winName, (30,30), cv2.FONT_HERSHEY_PLAIN,1.4,(255,255,255),2)
cv2.imwrite(fimgOut, imgMatch)
# cv2.imshow(winName, imgMatch)
cv2.imshow('corr-map', imgMatch)
cv2.waitKey(20)
#
# fimg='/home/ar/img/lena.png'
# img=cv2.imread(fimg, 0)
# det1=cv2.SIFT()
# det2=cv2.SURF()
# kp1,kd1=det1.detectAndCompute(img,None)
# kp2,kd2=det2.detectAndCompute(img,None)
# arrkp1=kp2arr(kp1)
# arrkp2=kp2arr(kp2)
# print 'test'
lstfn = np.recfromtxt(fidx)
cnt = 0
for ff in lstfn:
for tt in listDet:
fkp = '%s_%s.kp' % (ff, tt)
fimgpOut = '%s_%s_kp_best.png' % (ff, tt)
imgc = cv2.imread(ff, 1)
kp, kd = fp.file2kp(fkp)
# arrkp=kp2arr(kp)
kpBest = getBestKp(kp)
# fp.plotFPoints(imgc, kpBest, color=(0,0,255))
imgc = cv2.drawKeypoints(
imgc, kpBest, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("win-%s" % tt, imgc)
# cv2.imwrite(fimgpOut, imgc)
cv2.waitKey(0)
print ff
cnt += 1
# if cnt>3:
# break
frm = cv2.cvtColor(frm, cv2.COLOR_BGR2GRAY)
if parMaxFrameSize > 1:
frm = ph.resizeToMaxSize(frm, parMaxFrameSize)
frmOut = '%s/frame_%04d.png' % (wdirOut, cnt)
frmOutPano = '%s_pano.png' % frmOut
cv2.imwrite(frmOut, frm)
if isFirstFrame:
fsiz = np.array(frm.shape[0:2][::-1], np.float)
frmPrev = frm
frmPrevName = frmName
isFirstFrame = False
continue
fkpPrev = '%s_%s.kp' % (frmPrevName, dscName)
fkp = '%s_%s.kp' % (frmName, dscName)
kpPrev = fp.file2kp(fkpPrev)
kp = fp.file2kp(fkp)
H, Q, Pano = imregFeaturePoins(frmPrev,
frm, (kpPrev, kp),
isDebug=True,
detector=detector)
cv2.imwrite(frmOutPano, Pano)
if Q < parThresholdQ:
if len(listH) == 0:
H = np.eye(3, 3)
else:
H = listH[-1].copy()
if len(listH) > 0:
H0 = listH[-1]
listH.append(np.dot(H0, H))
else:
cap=ph.VideoCSVReader(fvideo)
cap.printInfo()
lstKP=[]
lstKD=[]
lstKPData=[]
numFrm=cap.getNumFrames()
numFrm=6
totH=None
homoBuff=HomoBuff(pcFrm)
curlDefData=np.zeros((numFrm,2))
arrCoords=[]
imgOld=None
imgCurr=None
for ii in xrange(0, numFrm):
fkp=cap.getKPByID(ii, dscName)
kpData=fp.file2kp(fkp)
kpScore=calcFrameKPScore(kpData[0])
# print kpScore
# lstKP.append(kpData[0])
# lstKD.append(kpData[1])
tmpH=np.eye(3)
if ii==0:
# totH=np.eye(3)
totH=tmpH.copy()
imgOld=cv2.imread(cap.listFImg[ii], 0)
else:
oldKPData=lstKPData[-1]
tmpH,_,_=getHomography(kpData[0], kpData[1], oldKPData[0], oldKPData[1])
# tmpH,_,_=getHomography(oldKPData[0], oldKPData[1], kpData[0], kpData[1])
totH=np.dot(totH, tmpH)
imgCurr=cv2.imread(cap.listFImg[ii], 0)
cap = ph.VideoCSVReader(fvideo)
cap.printInfo()
lstKP = []
lstKD = []
lstKPData = []
numFrm = cap.getNumFrames()
numFrm = 6
totH = None
homoBuff = HomoBuff(pcFrm)
curlDefData = np.zeros((numFrm, 2))
arrCoords = []
imgOld = None
imgCurr = None
for ii in xrange(0, numFrm):
fkp = cap.getKPByID(ii, dscName)
kpData = fp.file2kp(fkp)
kpScore = calcFrameKPScore(kpData[0])
# print kpScore
# lstKP.append(kpData[0])
# lstKD.append(kpData[1])
tmpH = np.eye(3)
if ii == 0:
# totH=np.eye(3)
totH = tmpH.copy()
imgOld = cv2.imread(cap.listFImg[ii], 0)
else:
oldKPData = lstKPData[-1]
tmpH, _, _ = getHomography(kpData[0], kpData[1], oldKPData[0],
oldKPData[1])
# tmpH,_,_=getHomography(oldKPData[0], oldKPData[1], kpData[0], kpData[1])
totH = np.dot(totH, tmpH)
frm=cv2.cvtColor(frm, cv2.COLOR_BGR2GRAY)
if parMaxFrameSize>1:
frm=ph.resizeToMaxSize(frm, parMaxFrameSize)
frmOut='%s/frame_%04d.png' % (wdirOut, cnt)
frmOutPano='%s_pano.png' % frmOut
cv2.imwrite(frmOut, frm)
if isFirstFrame:
fsiz=np.array(frm.shape[0:2][::-1], np.float)
frmPrev=frm
frmPrevName=frmName
isFirstFrame=False
continue
fkpPrev='%s_%s.kp' % (frmPrevName, dscName)
fkp ='%s_%s.kp' % (frmName, dscName)
kpPrev=fp.file2kp(fkpPrev)
kp =fp.file2kp(fkp)
H,Q,Pano=imregFeaturePoins(frmPrev, frm, (kpPrev, kp), isDebug=True, detector=detector)
cv2.imwrite(frmOutPano, Pano)
if Q<parThresholdQ:
if len(listH)==0:
H=np.eye(3,3)
else:
H=listH[-1].copy()
if len(listH)>0:
H0=listH[-1]
listH.append(np.dot(H0,H))
else:
listH.append(H)
#
tdxyas=calcShiftAngleScale2(listH[-1],np.array(frm.shape[:2][::-1])/2.0)
# kp1,kd1=fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
# cc=calcCCHomography(img0, img1, H)
# dstCorr[ii0,ii1]=cc
# dstCorr0[ii1]=cc
# if cc>0.2:
# print "Corr[%d,%d]=%0.2f !!!!" % (ii0,ii1,cc)
# else:
# print "Corr[%d,%d]=%0.2f" % (ii0,ii1,cc)
# np.savetxt(foutDstCorr0, dstCorr0, delimiter=',')
dstCorr = np.zeros((numFrm0, numFrm1))
for ii0 in xrange(numFrm0):
_, img0 = cap0.read(ii0)
fkp0 = cap0.getKPByID(ii0, dscName)
kp0, kd0 = fp.file2kp(fkp0)
dstCorr0 = np.zeros((numFrm1, 1))
foutDstCorr0 = "%s/%s_match_to_%s_%d.csv" % (wdir, dir0, dir1, ii0)
if os.path.isfile(foutDstCorr0):
dstCorr0 = np.genfromtxt(foutDstCorr0, delimiter=',')
dstCorr[ii0, :] = dstCorr0
# for ii1 in xrange(numFrm1):
# _,img1=cap1.read(ii1)
# fkp1=cap1.getKPByID(ii1, dscName)
# kp1,kd1=fp.file2kp(fkp1)
# H, status, mKP0, mKP1, listGoodMatches, scores= getHomography2(kp0, kd0, kp1, kd1)
# cc=calcCCHomography(img0, img1, H)
# dstCorr[ii0,ii1]=cc
# dstCorr0[ii1]=cc
# if cc>0.2:
# print "Corr[%d,%d]=%0.2f !!!!" % (ii0,ii1,cc)
if __name__=='__main__':
#
# fimg='/home/ar/img/lena.png'
# img=cv2.imread(fimg, 0)
# det1=cv2.SIFT()
# det2=cv2.SURF()
# kp1,kd1=det1.detectAndCompute(img,None)
# kp2,kd2=det2.detectAndCompute(img,None)
# arrkp1=kp2arr(kp1)
# arrkp2=kp2arr(kp2)
# print 'test'
lstfn=np.recfromtxt(fidx)
cnt=0
for ff in lstfn:
for tt in listDet:
fkp='%s_%s.kp' % (ff, tt)
fimgpOut='%s_%s_kp_best.png' % (ff,tt)
imgc=cv2.imread(ff, 1)
kp,kd=fp.file2kp(fkp)
# arrkp=kp2arr(kp)
kpBest=getBestKp(kp)
# fp.plotFPoints(imgc, kpBest, color=(0,0,255))
imgc=cv2.drawKeypoints(imgc, kpBest, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow("win-%s" % tt, imgc)
# cv2.imwrite(fimgpOut, imgc)
cv2.waitKey(0)
print ff
cnt+=1
# if cnt>3:
# break