def detectWrap(a):
t=time.time()
i=a[0]
imname=a[1]
bbox=a[2]
m=a[3]
cfg=a[4]
if cfg.show:
img=util.myimread(imname)
pylab.figure(10)
pylab.ioff()
pylab.clf()
pylab.axis("off")
pylab.imshow(img, interpolation="nearest", animated=True)
if bbox!=None:
gtbbox=[{"bbox":x} for x in bbox]
else:
gtbbox=None
img=util.myimread(imname,resize=RSZ)
f=pyrHOG2.pyrHOG(img,interv=10,savedir=cfg.savedir+"/hog/",notload=not(cfg.loadfeat),notsave=not(cfg.savefeat),hallucinate=cfg.hallucinate,cformat=True)
res=pyrHOG2.detect(f,m,gtbbox,hallucinate=cfg.hallucinate,initr=cfg.initr,ratio=cfg.ratio,deform=cfg.deform,posovr=cfg.posovr,bottomup=cfg.bottomup,usemrf=cfg.usemrf,numneg=cfg.numneg,thr=cfg.thr,inclusion=cfg.inclusion,small=cfg.small,show=cfg.show,usefather=cfg.usefather,useprior=cfg.useprior,nms=cfg.ovr,K=cfg.k)
if cfg.show:
pylab.show()
# raw_input()
print "Detect Wrap:",time.time()-t
return res
def detectWrap(a):
t = time.time()
i = a[0]
imname = a[1]
bbox = a[2]
m = a[3]
cfg = a[4]
if cfg.show:
img = util.myimread(imname)
pylab.figure(10)
pylab.ioff()
pylab.clf()
pylab.axis("off")
pylab.imshow(img, interpolation="nearest", animated=True)
if bbox != None:
gtbbox = [{"bbox": x} for x in bbox]
else:
gtbbox = None
img = util.myimread(imname, resize=RSZ)
f = pyrHOG2.pyrHOG(img,
interv=10,
savedir=cfg.savedir + "/hog/",
notload=not (cfg.loadfeat),
notsave=not (cfg.savefeat),
hallucinate=cfg.hallucinate,
cformat=True)
res = pyrHOG2.detect(f,
m,
gtbbox,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
posovr=cfg.posovr,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
numneg=cfg.numneg,
thr=cfg.thr,
inclusion=cfg.inclusion,
small=cfg.small,
show=cfg.show,
usefather=cfg.usefather,
useprior=cfg.useprior,
nms=cfg.ovr,
K=cfg.k)
if cfg.show:
pylab.show()
# raw_input()
print "Detect Wrap:", time.time() - t
return res
def loadDetectionsRamanan(fn):
f = util.loadmat(fn)
ids = f['ids']
scores = []
if f.has_key('sc'):
scores = f['sc']
boxes = f['BB']
n = len(ids)
det = []
for i in range(n):
this_id = ids[i][0][0].split(".")[0]
if not scores == []:
this_score = scores[i][0]
else:
this_score = 1.0
box = boxes[:, i]
x1 = float(box[0])
y1 = float(box[1])
x2 = float(box[2])
y2 = float(box[3])
det.append([this_id, this_score, x1, y1, x2, y2])
if 0:
im = util.myimread(
"/users/visics/mpederso/databases/afw/testimages/" + this_id +
".jpg")
pylab.clf()
pylab.imshow(im)
util.box([y1, x1, y2, x2])
pylab.draw()
pylab.show()
raw_input()
dets = sorted(det, key=itemgetter(1), reverse=True)
return dets
def detectWrap(a):
i=a[0]
imname=a[1]
bbox=a[2]
models=a[3]
cfg=a[4]
if cfg.show:
img=util.myimread(imname)
pylab.figure(10)
pylab.ioff()
pylab.clf()
pylab.axis("off")
pylab.imshow(img,interpolation="nearest",animated=True)
if bbox!=None:
gtbbox=[{"bbox":x} for x in bbox]
else:
gtbbox=None
notsave=False
#if cfg.__dict__.has_key("test"):
# notsave=cfg.test
f=pyrHOG2.pyrHOG(imname,interv=10,savedir=cfg.auxdir+"/hog/",notsave=not(cfg.savefeat),notload=not(cfg.loadfeat),hallucinate=cfg.hallucinate,cformat=True)
res=[]
for clm,m in enumerate(models):
res.append(pyrHOG2.detect(f,m,gtbbox,hallucinate=cfg.hallucinate,initr=cfg.initr,ratio=cfg.ratio,deform=cfg.deform,bottomup=cfg.bottomup,usemrf=cfg.usemrf,numneg=cfg.numneg,thr=cfg.thr,posovr=cfg.posovr,minnegincl=cfg.minnegincl,small=cfg.small,show=cfg.show,cl=clm,mythr=cfg.mythr,mpos=cfg.mpos,usefather=cfg.usefather,useprior=cfg.useprior,K=cfg.k))
if cfg.show:
pylab.show()
return res
def detectWrap(a):
i=a[0]
imname=a[1]
bbox=a[2]
models=a[3]
cfg=a[4]
if cfg.show:
img=util.myimread(imname)
pylab.figure(10)
pylab.ioff()
pylab.clf()
pylab.axis("off")
pylab.imshow(img,interpolation="nearest",animated=True)
if bbox!=None:
gtbbox=[{"bbox":x,"img":imname.split("/")[-1]} for x in bbox]
else:
gtbbox=None
notsave=False
#if cfg.__dict__.has_key("test"):
# notsave=cfg.test
f=pyrHOG2.pyrHOG(imname,interv=10,savedir=cfg.auxdir+"/hog/",notsave=not(cfg.savefeat),notload=not(cfg.loadfeat),hallucinate=cfg.hallucinate,cformat=True)
res=[]
for clm,m in enumerate(models):
res.append(pyrHOG2.detect(f,m,gtbbox,hallucinate=cfg.hallucinate,initr=cfg.initr,ratio=cfg.ratio,deform=cfg.deform,bottomup=cfg.bottomup,usemrf=cfg.usemrf,numneg=cfg.numneg,thr=cfg.thr,posovr=cfg.posovr,minnegincl=cfg.minnegincl,small=cfg.small,show=cfg.show,cl=clm,mythr=cfg.mythr,mpos=cfg.mpos,usefather=cfg.usefather,useprior=cfg.useprior,emptybb=False,K=cfg.k))
if cfg.show:
pylab.show()
return res
def loadDetectionsRamanan(fn):
f = util.loadmat(fn)
ids = f['ids']
scores = []
if f.has_key('sc'):
scores = f['sc']
boxes = f['BB']
n = len(ids)
det = []
for i in range(n):
this_id = ids[i][0][0].split(".")[0]
if not scores == []:
this_score = scores[i][0]
else:
this_score = 1.0
box = boxes[:, i]
x1 = float(box[0])
y1 = float(box[1])
x2 = float(box[2])
y2 = float(box[3])
det.append([this_id, this_score, x1, y1, x2, y2])
if 0:
im = util.myimread(
"/users/visics/mpederso/databases/afw/testimages/" + this_id + ".jpg")
pylab.clf()
pylab.imshow(im)
util.box([y1, x1, y2, x2])
pylab.draw()
pylab.show()
raw_input()
dets = sorted(det, key=itemgetter(1), reverse=True)
return dets
def collec_posamples_2(minA,trPosImagesNoTrunc,cfg):
lfy=cfg.fy;lfx=cfg.fx
check = False
dratios=numpy.array(lfy[:cfg.numcl])/numpy.array(lfx[:cfg.numcl])
hogp=[[] for x in range(cfg.numcl*2)]
#hogpcl=[]
annp=[[] for x in range(cfg.numcl*2)]
#from scipy.ndimage import zoom
from extra import myzoom as zoom
for im in trPosImagesNoTrunc: # for each image
#print im["name"]
aim=util.myimread(im["name"])
for bb in im["bbox"]: # for each bbox (y1,x1,y2,x2)
imy=bb[2]-bb[0]
imx=bb[3]-bb[1]
cropratio= imy/float(imx)
#select the right model based on aspect ratio
idm=numpy.argmin(abs(dratios-cropratio))
area=imy*imx
usesmall=False
if area/16/cfg.N**2<minA[idm]:#use small model if there
idm=idm+cfg.numcl
fy=lfy[idm];fx=lfx[idm]
crop=aim[max(0,bb[0]-imy/fy/2):min(bb[2]+imy/fy/2,aim.shape[0]),max(0,bb[1]-imx/fx/2):min(bb[3]+imx/fx/2,aim.shape[1])]
#crop=extra.getfeat(aim,abb[0]-imy/(lfy[idm]*2),bb[2]+imy/(lfy[idm]*2),bb[1]-imx/(cfg.fx[idm]*2),bb[3]+imx/(cfg.fx[idm]*2))
imy=crop.shape[0]
imx=crop.shape[1]
zcim=zoom(crop,(((fy*cfg.N+2)*8/float(imy)),((fx*cfg.N+2)*8/float(imx)),1),order=1)
hogp[idm].append(numpy.ascontiguousarray(pyrHOG2.hog(zcim)))
if cfg.trunc:
hogp[idm][-1]=numpy.concatenate((hogp[idm][-1],numpy.zeros((hogp[idm][-1].shape[0],hogp[idm][-1].shape[1],1))),2)
#hogpcl.append(idm)
annp[idm].append({"file":im["name"],"bbox":bb})
if check:
print "Aspect:",idm,"Det Size",fy*cfg.N,fx*cfg.N,"Shape:",zcim.shape
pl.figure(1,figsize=(20,5))
pl.clf()
pl.subplot(1,3,1)
pl.imshow(aim,interpolation="nearest")
pl.subplot(1,3,2)
pl.imshow(zcim,interpolation="nearest")
pl.subplot(1,3,3)
import drawHOG
imh=drawHOG.drawHOG(hogp[-1])
pl.imshow(imh,interpolation="nearest")
pl.draw()
pl.show()
raw_input()
return hogp
auxcl = tr.mixture(nfuseneg)[0]
dns = buildense([aux], [auxcl], cumsize)[0]
dscr = numpy.sum(dns * w)
#print "Scr:",nfuseneg[0]["scr"],"DesneSCR:",dscr,"Diff:",abs(nfuseneg[0]["scr"]-dscr)
if abs(nfuseneg[0]["scr"] - dscr) > 0.0001:
print "Warning: the two scores must be the same!!!"
print "Scr:", nfuseneg[0][
"scr"], "DesneSCR:", dscr, "Diff:", abs(
nfuseneg[0]["scr"] - dscr)
raw_input()
if cfg.show:
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img = util.myimread(trPosImages[ii]["name"])
pylab.imshow(img, interpolation="nearest", animated=True)
tr.show(nfuse, parts=cfg.show)
pylab.show()
#raw_input()
del itr
#fulltrpos=trpos
#fulltrposcl=trposcl
#trpos=remove_empty(fulltrpos)
#trposcl=remove_empty(fulltrposcl)
numoldtrpos = len(trpos)
if it > 0:
moldloss = loss_pos(trpos, trposcl, cumsize)
else:
moldloss = 1
def detectWrap(a):
i = a[0]
imname = a[1]
bbox = a[2]
models = a[3]
cfg = a[4]
if len(a) <= 5:
imageflip = False
else:
imageflip = a[5]
img = util.myimread(imname, resize=cfg.resize)
if imageflip:
img = util.myimread(imname, True, resize=cfg.resize)
if bbox != None:
bbox = util.flipBBox(img, bbox)
if bbox != None:
gtbbox = [{"bbox": x, "img": imname.split("/")[-1]} for x in bbox]
else:
gtbbox = None
if cfg.show:
img = util.myimread(imname, imageflip, resize=cfg.resize)
pylab.figure(10)
pylab.ioff()
pylab.clf()
pylab.axis("off")
pylab.imshow(img, interpolation="nearest", animated=True)
notsave = False
#if cfg.__dict__.has_key("test"):
# notsave=cfg.test
#f=pyrHOG2.pyrHOG(imname,interv=10,savedir=cfg.auxdir+"/hog/",notsave=not(cfg.savefeat),notload=not(cfg.loadfeat),hallucinate=cfg.hallucinate,cformat=True,flip=imageflip,resize=cfg.resize)
f = pyrHOG2.pyrHOG(img,
interv=10,
savedir=cfg.auxdir + "/hog/",
notsave=not (cfg.savefeat),
notload=not (cfg.loadfeat),
hallucinate=cfg.hallucinate,
cformat=True) #,flip=imageflip,resize=cfg.resize)
res = []
for clm, m in enumerate(models):
if cfg.useRL:
res.append(
pyrHOG2RL.detectflip(f,
m,
gtbbox,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
numneg=cfg.numneg,
thr=cfg.thr,
posovr=cfg.posovr,
minnegincl=cfg.minnegincl,
small=cfg.small,
show=cfg.show,
cl=clm,
mythr=cfg.mythr,
mpos=cfg.mpos,
usefather=cfg.usefather,
useprior=cfg.useprior,
K=cfg.k))
else:
res.append(
pyrHOG2.detect(f,
m,
gtbbox,
hallucinate=cfg.hallucinate,
initr=cfg.initr,
ratio=cfg.ratio,
deform=cfg.deform,
bottomup=cfg.bottomup,
usemrf=cfg.usemrf,
numneg=cfg.numneg,
thr=cfg.thr,
posovr=cfg.posovr,
minnegincl=cfg.minnegincl,
small=cfg.small,
show=cfg.show,
cl=clm,
mythr=cfg.mythr,
mpos=cfg.mpos,
usefather=cfg.usefather,
useprior=cfg.useprior,
emptybb=False,
K=cfg.k))
if cfg.show:
pylab.draw()
pylab.show()
return res
auxcl = tr.mixture(nfuseneg)[0]
dns = buildense([aux], [auxcl], cumsize)[0]
dscr = numpy.sum(dns * w)
#print "Scr:",nfuseneg[0]["scr"],"DesneSCR:",dscr,"Diff:",abs(nfuseneg[0]["scr"]-dscr)
if abs(nfuseneg[0]["scr"] - dscr) > 0.0001:
print "Warning: the two scores must be the same!!!"
print "Scr:", nfuseneg[0][
"scr"], "DesneSCR:", dscr, "Diff:", abs(
nfuseneg[0]["scr"] - dscr)
raw_input()
if cfg.show:
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img = util.myimread(trPosImages[ii / 2]["name"], flip=ii % 2)
pylab.imshow(img, interpolation="nearest", animated=True)
tr.show(nfuse, parts=cfg.show)
pylab.show()
#raw_input()
del itr
#fulltrpos=trpos
#fulltrposcl=trposcl
#trpos=remove_empty(fulltrpos)
#trposcl=remove_empty(fulltrposcl)
numoldtrpos = len(trpos)
if it > 0:
moldloss, oldscr = loss_pos(trpos, trposcl, cumsize)
else:
moldloss = 1
auxcl = tr.mixture(nfuseneg)[0]
dns = buildense([aux], [auxcl], cumsize)[0]
dscr = numpy.sum(dns * w)
#print "Scr:",nfuseneg[0]["scr"],"DesneSCR:",dscr,"Diff:",abs(nfuseneg[0]["scr"]-dscr)
if abs(nfuseneg[0]["scr"] - dscr) > 0.0001:
print "Warning: the two scores must be the same!!!"
print "Scr:", nfuseneg[0][
"scr"], "DesneSCR:", dscr, "Diff:", abs(
nfuseneg[0]["scr"] - dscr)
raw_input()
if cfg.show:
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img = util.myimread(trPosImages[ii]["name"])
pylab.imshow(img, interpolation="nearest", animated=True)
tr.show(nfuse, parts=cfg.show)
pylab.show()
#raw_input()
del itr
#fulltrpos=trpos
#fulltrposcl=trposcl
#trpos=remove_empty(fulltrpos)
#trposcl=remove_empty(fulltrposcl)
numoldtrpos = len(trpos)
if it > 0:
moldloss = loss_pos(trpos, trposcl, cumsize)
else:
moldloss = 1
def test(thr, cfg, it=9):
#cfg.test=True
import util
#it=it
models = util.load("%s%d.model" % (cfg.testname, it))
#skip occlusion
#for l in models:
# del l["occl"]
w = []
rho = []
cfg.mythr = thr
#for l in range(cfg.numcl):
# w.append(util.ModeltoW(models[l],cfg.usemrf,cfg.usefather,cfg.k,lastlev=1))
# rho.append(models[l]["rho"])
#cfg.mythr=cfg.mythr*numpy.mean([numpy.sum(x**2) for x in w])#-numpy.mean(rho)
#raw_input()
#cfg.mythr=cfg.mythr#-numpy.mean(rho)
if cfg.multipr == 1:
numcore = None
else:
numcore = cfg.multipr
mypool = Pool(numcore)
if cfg.cls == "inria":
if cfg.select == "pos":
tsImages = getRecord(InriaTestData(basepath=cfg.dbpath),
cfg.maxtest)
else:
tsImages = getRecord(InriaTestFullData(basepath=cfg.dbpath),
cfg.maxtest)
if select == "dir": #"." or select[0]=="/"
import glob
#lst=glob.glob("/home/marcopede/ivan/zebra/CVC_Zebra1/*.jpeg")[150:]
lst = glob.glob(
"/media/ca0567b8-ee6d-4590-8462-0d093addb4cf/video/*.png")
lst.sort()
lst = lst[3900:]
total = len(lst)
tsImages = numpy.zeros(total,
dtype=[("id", numpy.int32),
("name", object), ("bbox", list)])
for idl, l in enumerate(lst):
tsImages[idl]["id"] = idl
tsImages[idl]["name"] = l #l.split("/")[-1]
tsImages[idl]["bbox"] = None
else:
tsImages = getRecord(
VOC07Data(
select=cfg.select,
cl="%s_test.txt" % cfg.cls,
basepath=cfg.dbpath, #"/home/databases/",
usetr=True,
usedf=False),
cfg.maxtest)
elif cfg.cls == "cvc02":
#tsImages=getRecord(CVC02test(),cfg.maxtest)
tsImages = getRecord(
CVC02test(
basepath="/media/OS/data/DATASET-CVC-02/CVC-02-System/",
images="/sequence-15/color/",
annotations="/sequence-15/annotations/",
), cfg.maxtest)
tsImagesFull = tsImages
elif cfg.cls == "ivan":
stest = 10000
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101/CLEAR06_PVTRA101a01_502_Bbox.txt",imgpath="/media/OS/data/PVTRA101/images/"),stest+cfg.maxtest)[stest:]
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/GrTr_CLEAR06_PVTRA101a19.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=1000),cfg.maxtest)[:600]#[:1950]#the other frames do not have GT
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/GrTr_CLEAR06_PVTRA101a19_only12.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)[:(1950/12)]#the other frames do not have GT
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_people_Celik_allfr.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)#pedestrian
tsImages = getRecord(
ImgFile(
"/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_vehicles_Celik_allfr.txt",
imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",
sort=True,
amin=100), cfg.maxtest) #vechicles
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_PV_Celik_allfr.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)#pedestrian+vechicles
tsImagesFull = tsImages
#mypool = Pool(numcore)
print "Test"
print "Pruning Threshold:", cfg.mythr
numhog = 0
initime = time.time()
detlist = []
mycfg = copy.copy(cfg)
mycfg.numneg = 0
mycfg.show = False
arg = [[i, tsImages[i]["name"], None, models, mycfg]
for i in range(len(tsImages))]
t = time.time()
if not (cfg.multipr):
itr = itertools.imap(detectWrap, arg)
else:
itr = mypool.imap(detectWrap, arg)
pylab.figure(20)
pylab.clf()
ax = pylab.subplot(111)
img = util.myimread(tsImages[0]["name"], resize=cfg.resize)
pylab.ioff()
axes = pylab.Axes(pylab.gcf(), [
.0, .0, 1.0, 1.0
]) # [left, bottom, width, height] where each value is between 0 and 1
pylab.gcf().add_axes(axes)
pylab.axis("off")
aim = pylab.imshow(img, interpolation="nearest", animated=True)
pylab.axis((0, img.shape[1], img.shape[0], 0))
pylab.draw()
pylab.show()
for ii, res in enumerate(itr):
totneg = 0
fuse = []
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
#for h in fuse:
# h["scr"]+=models[h["cl"]]["ra"]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=cfg.ovrasp, inclusion=False)
#print "----Test Image %d----"%ii
print "----Test Image %s----" % tsImages[ii]["name"].split("/")[-1]
for idl, l in enumerate(nfuse):
#print "DET:",l["bbox"]
#raw_input()
#if l["bbox"][0]/cfg.resize<125:
# nfuse.pop(idl)
# continue
detlist.append([
tsImages[ii]["name"].split("/")[-1].split(".")[0], l["scr"],
l["bbox"][1] / cfg.resize, l["bbox"][0] / cfg.resize,
l["bbox"][3] / cfg.resize, l["bbox"][2] / cfg.resize
])
print "Detections:", len(nfuse)
if cfg.show:
if cfg.show == True:
showlabel = False #"Parts"#False#"Parts"
else:
showlabel = False
pylab.figure(20)
xx = pylab.gca()
del xx.texts[:]
del xx.lines[:]
#print pylab.gca().images
#pylab.ioff()
# axes=pylab.Axes(pylab.gcf(), [.0,.0,1.0,1.0]) # [left, bottom, width, height] where each value is between 0 and 1
# pylab.gcf().add_axes(axes)
# pylab.axis("off")
#img=util.myimread(tsImages[ii]["name"],resize=cfg.resize)
img = pylab.imread(
tsImages[ii]["name"]) #myimread has memory problems!!!!!!!
#pylab.clf()
aim.set_array(img)
#ims=pylab.imshow(img,interpolation="nearest",animated=True)
tr.show(nfuse, parts=showlabel, thr=0, maxnum=100)
pylab.axis((0, img.shape[1], img.shape[0], 0))
pylab.draw()
pylab.show()
#print ax.images
#raw_input()
showGT = True
if showGT:
bb = tsImages[ii]["bbox"]
for mbb in bb:
util.box(mbb[0], mbb[1], mbb[2], mbb[3], col='r', lw=2)
pylab.axis((0, img.shape[1], img.shape[0], 0))
pylab.draw()
#raw_input()
if select == "dir":
#RSZ=0.5
f = pylab.figure(11, figsize=(9, 5))
pylab.ioff()
pylab.clf()
axes = pylab.Axes(
f, [.0, .0, 1.0, 1.0]
) # [left, bottom, width, height] where each value is between 0 and 1
f.add_axes(axes)
img = util.myimread(tsImages[ii]["name"], resize=cfg.resize)
pylab.imshow(img, interpolation="nearest", animated=True)
#raw_input()
pylab.axis("off")
tr.show(nfuse, parts=True, thr=-0.99, scr=True)
pylab.axis((0, img.shape[1], img.shape[0], 0))
pylab.ion()
pylab.draw()
pylab.show()
#raw_input()
pylab.savefig(
"/media/ca0567b8-ee6d-4590-8462-0d093addb4cf/video/det/" +
tsImages[ii]["name"].split("/")[-1].split(".")[0] + ".png")
del itr
#tp,fp,scr,tot=VOCpr.VOCprlistfastscore(tsImages,detlist,numim=cfg.maxpostest,show=False,ovr=0.5)
#tp,fp,scr,tot=VOCpr.VOCprRecord_wrong(tsImages,detlist,show=False,ovr=0.5)
tp, fp, scr, tot = VOCpr.VOCprRecord(tsImages,
detlist,
show=False,
ovr=0.5)
pylab.figure(15)
pylab.clf()
rc, pr, ap = VOCpr.drawPrfast(tp, fp, tot)
pylab.figure(16)
pylab.clf()
fppi, miss, ap = VOCpr.drawMissRatePerImage(tp, fp, tot, len(tsImages))
pylab.draw()
pylab.show()
sfdsd
#pylab.savefig("%s_ap%d_test%s%.1f.png"%(testname,it,select,cfg.mythr))
tottime = ((time.time() - initime))
print "Threshold used:", cfg.mythr
print "Total number of HOG:", numhog
print "AP(it=", it, ")=", ap
print "Testing Time: %.3f s" % tottime #/3600.0)
#results={"det":detlist,"ap":ap,"tp":tp,"fp":fp,"pr":pr,"rc":rc,"numhog":numhog,"mythr":cfg.mythr,"time":tottime}
#util.savemat("%s_ap%d_test_thr_%.3f.mat"%(cfg.testname,it,cfg.mythr),results)
#util.save("%s_ap%d_test_thr_%.3f.dat"%(testname,it,cfg.mythr),results)
#util.savedetVOC(detlist,"%s_ap%d_test_thr_%.3f.txt"%(testname,it,cfg.mythr))
#fd=open("%s_ap%d_test%s.txt"%(cfg.testname,it,select),"a")
#fd.write("Threshold used:%f\n"%cfg.mythr)
#fd.write("Total number of HOG:%d\n"%numhog)
#fd.write("Average precision:%f\n"%ap)
#fd.write("Testing Time: %.3f s\n\n"%tottime)
#fd.close()
return ap, numhog
cpit=0
cnit=0
print "Starting from scratch"
lg.info("Starting from scratch")
############################ initialize positive using cropped bounidng boxes
check = False
dratios=numpy.array(cfg.fy)/numpy.array(cfg.fx)
hogp=[[] for x in range(cfg.numcl)]
hogpcl=[]
annp=[[] for x in range(cfg.numcl)]
#from scipy.ndimage import zoom
from extra import myzoom as zoom
for im in trPosImagesNoTrunc: # for each image
aim=util.myimread(im["name"])
for bb in im["bbox"]: # for each bbox (y1,x1,y2,x2)
imy=bb[2]-bb[0]
imx=bb[3]-bb[1]
cropratio= imy/float(imx)
#select the right model based on aspect ratio
idm=numpy.argmin(abs(dratios-cropratio))
crop=aim[max(0,bb[0]-imy/cfg.fy[idm]/2):min(bb[2]+imy/cfg.fy[idm]/2,aim.shape[0]),max(0,bb[1]-imx/cfg.fx[idm]/2):min(bb[3]+imx/cfg.fx[idm]/2,aim.shape[1])]
#crop=extra.getfeat(aim,abb[0]-imy/(cfg.fy[idm]*2),bb[2]+imy/(cfg.fy[idm]*2),bb[1]-imx/(cfg.fx[idm]*2),bb[3]+imx/(cfg.fx[idm]*2))
imy=crop.shape[0]
imx=crop.shape[1]
zcim=zoom(crop,(((cfg.fy[idm]*cfg.N+2)*8/float(imy)),((cfg.fx[idm]*cfg.N+2)*8/float(imx)),1),order=1)
hogp[idm].append(numpy.ascontiguousarray(pyrHOG2.hog(zcim)))
if cfg.trunc:
hogp[idm][-1]=numpy.concatenate((hogp[idm][-1],numpy.zeros((hogp[idm][-1].shape[0],hogp[idm][-1].shape[1],1))),2)
#hogpcl.append(idm)
def test(thr, cfg):
#cfg.test=True
import util
it = 7
models = util.load("%s%d.model" % (cfg.testname, it))
w = []
rho = []
cfg.mythr = thr
#for l in range(cfg.numcl):
# w.append(util.ModeltoW(models[l],cfg.usemrf,cfg.usefather,cfg.k,lastlev=1))
# rho.append(models[l]["rho"])
#cfg.mythr=cfg.mythr*numpy.mean([numpy.sum(x**2) for x in w])#-numpy.mean(rho)
#raw_input()
#cfg.mythr=cfg.mythr#-numpy.mean(rho)
if cfg.multipr == 1:
numcore = None
else:
numcore = cfg.multipr
mypool = Pool(numcore)
if cfg.cls == "inria":
if cfg.select == "pos":
tsImages = getRecord(InriaTestData(basepath=cfg.dbpath),
cfg.maxtest)
else:
tsImages = getRecord(InriaTestFullData(basepath=cfg.dbpath),
cfg.maxtest)
else:
tsImages = getRecord(
VOC07Data(
select=cfg.select,
cl="%s_test.txt" % cfg.cls,
basepath=cfg.dbpath, #"/home/databases/",
usetr=True,
usedf=False),
cfg.maxtest)
mypool = Pool(numcore)
print "Test"
print "Pruning Threshold:", cfg.mythr
numhog = 0
initime = time.time()
detlist = []
mycfg = copy.copy(cfg)
mycfg.numneg = 0
arg = [[i, tsImages[i]["name"], None, models, mycfg]
for i in range(len(tsImages))]
t = time.time()
if not (cfg.multipr):
itr = itertools.imap(detectWrap, arg)
else:
itr = mypool.imap(detectWrap, arg)
for ii, res in enumerate(itr):
totneg = 0
fuse = []
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
#for h in fuse:
# h["scr"]+=models[h["cl"]]["ra"]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
print "----Test Image %d----" % ii
for l in nfuse:
detlist.append([
tsImages[ii]["name"].split("/")[-1].split(".")[0], l["scr"],
l["bbox"][1], l["bbox"][0], l["bbox"][3], l["bbox"][2]
])
print "Detections:", len(nfuse)
if cfg.show:
if cfg.show == True:
showlabel = "Parts"
else:
showlabel = False
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img = util.myimread(tsImages[ii]["name"])
pylab.imshow(img, interpolation="nearest", animated=True)
pylab.gca().set_ylim(0, img.shape[0])
pylab.gca().set_xlim(0, img.shape[1])
pylab.gca().set_ylim(pylab.gca().get_ylim()[::-1])
tr.show(nfuse, parts=showlabel, thr=-0.8, maxnum=10)
pylab.show()
#raw_input()
del itr
#tp,fp,scr,tot=VOCpr.VOCprlistfastscore(tsImages,detlist,numim=cfg.maxpostest,show=False,ovr=0.5)
#tp,fp,scr,tot=VOCpr.VOCprRecord_wrong(tsImages,detlist,show=False,ovr=0.5)
tp, fp, scr, tot = VOCpr.VOCprRecord(tsImages,
detlist,
show=False,
ovr=0.5)
pylab.figure(15)
pylab.clf()
rc, pr, ap = VOCpr.drawPrfast(tp, fp, tot)
pylab.draw()
pylab.show()
#pylab.savefig("%s_ap%d_test%s%.1f.png"%(testname,it,select,cfg.mythr))
tottime = ((time.time() - initime))
print "Threshold used:", cfg.mythr
print "Total number of HOG:", numhog
print "AP(it=", it, ")=", ap
print "Testing Time: %.3f s" % tottime #/3600.0)
#results={"det":detlist,"ap":ap,"tp":tp,"fp":fp,"pr":pr,"rc":rc,"numhog":numhog,"mythr":cfg.mythr,"time":tottime}
#util.savemat("%s_ap%d_test_thr_%.3f.mat"%(cfg.testname,it,cfg.mythr),results)
#util.save("%s_ap%d_test_thr_%.3f.dat"%(testname,it,cfg.mythr),results)
#util.savedetVOC(detlist,"%s_ap%d_test_thr_%.3f.txt"%(testname,it,cfg.mythr))
#fd=open("%s_ap%d_test%s.txt"%(cfg.testname,it,select),"a")
#fd.write("Threshold used:%f\n"%cfg.mythr)
#fd.write("Total number of HOG:%d\n"%numhog)
#fd.write("Average precision:%f\n"%ap)
#fd.write("Testing Time: %.3f s\n\n"%tottime)
#fd.close()
return ap, numhog
def getImage(self, i):
item = self.ann[i][0][0]
return myimread((self.imagepath + item))
def getImageByName(self, name):
return myimread(name)
def getImage(self, i):
item = self.selines[i]
return myimread((self.imagepath + item.split(" ")[0]) + ".jpg")
#det=util.load("/users/visics/mpederso/code/git/condor-run/N2C2_posthr105/CRFdet/data/CRF/12_10_21/%s2_N2C2k01_final.det"%(cfg.cls))["det"]
#det=util.load("/users/visics/mpederso/code/git/condor-run/N2C2_posthr105/CRFdet/data/CRF/12_10_20/%s2_N2C2_final.det"%(cfg.cls))["det"]
#det=util.load("/users/visics/mpederso/code/git/condor-run/N2C2_posthr105/CRFdet/data/CRF/12_10_20/%s2_N2C26.det"%(cfg.cls))["det"]
#cfg.N=4
#det=util.load("/users/visics/mpederso/code/git/condor-run/N3C2_highres/CRFdet/data/CRF/12_11_10/%s2_N2C2highres2_final.det"%cfg.cls)["det"]
#det=util.load("/users/visics/mpederso/code/git/condor-run/N3C2_highres/CRFdet/data/CRF/12_11_10/%s2_N2C2highres26.det"%cfg.cls)["det"]
#det=util.load("/users/visics/mpederso/code/git/condor-run/N2C2_highres/CRFdet/data/CRF/12_11_01/bicycle2_N1C2highres2.det")["det"];cfg.N=1
#det=util.load("/users/visics/mpederso/code/git/CRFdet/data/afterCVPR/12_01_10/%s2_force-bb3.det"%cfg.cls)["det"]
#det=util.load("./data/condor/%s2_condor10.det"%cfg.cls)["det"]sftp://[email protected]/users/visics/mpederso/code/git/fastDP/CRFdet/data/debug2/car2_FULLsmall4.det
det=util.load("./data/debug2/%s2_higherlimit2.det"%cfg.cls)["det"]
imgpath=cfg.dbpath+"VOC2007/VOCdevkit/VOC2007/JPEGImages/"
#imgpath=cfg.dbpath+"/buffy/images/"
line=True
cfg.N=3
for idl,l in enumerate(det):
img=util.myimread(imgpath+l["idim"])
#just for buffy
# try:
# img=util.myimread(imgpath+"buffy_s5e2/"+l["idim"])
# except:
# try:
# img=util.myimread(imgpath+"buffy_s5e3/"+l["idim"])
# except:
# try:
# img=util.myimread(imgpath+"buffy_s5e4/"+l["idim"])
# except:
# try:
# img=util.myimread(imgpath+"buffy_s5e5/"+l["idim"])
# except:
# try:
# img=util.myimread(imgpath+"buffy_s5e6/"+l["idim"])
def test(thr,cfg,it=9):
#cfg.test=True
import util
#it=it
models=util.load("%s%d.model"%(cfg.testname,it))
#skip occlusion
#for l in models:
# del l["occl"]
w=[]
rho=[]
cfg.mythr=thr
#for l in range(cfg.numcl):
# w.append(util.ModeltoW(models[l],cfg.usemrf,cfg.usefather,cfg.k,lastlev=1))
# rho.append(models[l]["rho"])
#cfg.mythr=cfg.mythr*numpy.mean([numpy.sum(x**2) for x in w])#-numpy.mean(rho)
#raw_input()
#cfg.mythr=cfg.mythr#-numpy.mean(rho)
if cfg.multipr==1:
numcore=None
else:
numcore=cfg.multipr
mypool = Pool(numcore)
if cfg.db=="inria":
if cfg.select=="pos":
tsImages=getRecord(InriaTestData(basepath=cfg.dbpath),cfg.maxtest)
else:
tsImages=getRecord(InriaTestFullData(basepath=cfg.dbpath),cfg.maxtest)
if select=="dir":#"." or select[0]=="/"
import glob
#lst=glob.glob("/home/marcopede/ivan/zebra/CVC_Zebra1/*.jpeg")[150:]
lst=glob.glob("/media/ca0567b8-ee6d-4590-8462-0d093addb4cf/video/*.png")
lst.sort()
lst=lst[3900:]
total=len(lst)
tsImages=numpy.zeros(total,dtype=[("id",numpy.int32),("name",object),("bbox",list)])
for idl,l in enumerate(lst):
tsImages[idl]["id"]=idl
tsImages[idl]["name"]=l#l.split("/")[-1]
tsImages[idl]["bbox"]=None
else:
tsImages=getRecord(VOC07Data(select=cfg.select,cl="%s_test.txt"%cfg.cls,
basepath=cfg.dbpath,#"/home/databases/",
usetr=True,usedf=False),cfg.maxtest)
elif cfg.db=="cvc02":
#tsImages=getRecord(CVC02test(),cfg.maxtest)
tsImages=getRecord(CVC02test(basepath="/media/OS/data/DATASET-CVC-02/CVC-02-System/",
images="/sequence-15/color/",
annotations="/sequence-15/annotations/",),cfg.maxtest)
tsImagesFull=tsImages
elif cfg.db=="ivan":
stest=10000
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101/CLEAR06_PVTRA101a01_502_Bbox.txt",imgpath="/media/OS/data/PVTRA101/images/"),stest+cfg.maxtest)[stest:]
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/GrTr_CLEAR06_PVTRA101a19.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=1000),cfg.maxtest)[:600]#[:1950]#the other frames do not have GT
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/GrTr_CLEAR06_PVTRA101a19_only12.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)[:(1950/12)]#the other frames do not have GT
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_people_Celik_allfr.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)#pedestrian
#tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_vehicles_Celik_allfr.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)#vechicles
tsImages=getRecord(ImgFile("/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19_PV_Celik_allfr.txt",imgpath="/media/OS/data/PVTRA101a19/CLEAR06_PVTRA101a19/",sort=True,amin=100),cfg.maxtest)#pedestrian+vechicles
tsImagesFull=tsImages
#mypool = Pool(numcore)
print "Test"
print "Pruning Threshold:",cfg.mythr
numhog=0
initime=time.time()
detlist=[]
mycfg=copy.copy(cfg)
mycfg.numneg=0
mycfg.show=False
arg=[[i,tsImages[i]["name"],None,models,mycfg] for i in range(len(tsImages))]
t=time.time()
if not(cfg.multipr):
itr=itertools.imap(detectWrap,arg)
else:
itr=mypool.imap(detectWrap,arg)
pylab.figure(20)
pylab.clf()
ax = pylab.subplot(111)
img=util.myimread(tsImages[0]["name"],resize=cfg.resize)
pylab.ioff()
axes=pylab.Axes(pylab.gcf(), [.0,.0,1.0,1.0]) # [left, bottom, width, height] where each value is between 0 and 1
pylab.gcf().add_axes(axes)
pylab.axis("off")
aim=pylab.imshow(img,interpolation="nearest",animated=True)
pylab.axis((0,img.shape[1],img.shape[0],0))
pylab.draw()
pylab.show()
for ii,res in enumerate(itr):
totneg=0
fuse=[]
for mix in res:
tr=mix[0]
fuse+=mix[1]
numhog+=mix[3]
#for h in fuse:
# h["scr"]+=models[h["cl"]]["ra"]
rfuse=tr.rank(fuse,maxnum=300)
nfuse=tr.cluster(rfuse,ovr=cfg.ovrasp,inclusion=False)
#print "----Test Image %d----"%ii
print "----Test Image %s----"%tsImages[ii]["name"].split("/")[-1]
for idl,l in enumerate(nfuse):
#print "DET:",l["bbox"]
#raw_input()
#if l["bbox"][0]/cfg.resize<125:
# nfuse.pop(idl)
# continue
detlist.append([tsImages[ii]["name"].split("/")[-1].split(".")[0],l["scr"],l["bbox"][1]/cfg.resize,l["bbox"][0]/cfg.resize,l["bbox"][3]/cfg.resize,l["bbox"][2]/cfg.resize])
print "Detections:",len(nfuse)
if cfg.show:
if cfg.show==True:
showlabel=False#"Parts"#False#"Parts"
else:
showlabel=False
pylab.figure(20)
xx=pylab.gca()
del xx.texts[:]
del xx.lines[:]
#print pylab.gca().images
#pylab.ioff()
# axes=pylab.Axes(pylab.gcf(), [.0,.0,1.0,1.0]) # [left, bottom, width, height] where each value is between 0 and 1
# pylab.gcf().add_axes(axes)
# pylab.axis("off")
#img=util.myimread(tsImages[ii]["name"],resize=cfg.resize)
img=pylab.imread(tsImages[ii]["name"])#myimread has memory problems!!!!!!!
#pylab.clf()
aim.set_array(img)
#ims=pylab.imshow(img,interpolation="nearest",animated=True)
tr.show(nfuse,parts=showlabel,thr=0,maxnum=100)
pylab.axis((0,img.shape[1],img.shape[0],0))
pylab.draw()
pylab.show()
#print ax.images
#raw_input()
showGT=True
if showGT:
bb=tsImages[ii]["bbox"]
for mbb in bb:
util.box(mbb[0], mbb[1], mbb[2], mbb[3], col='r', lw=2)
pylab.axis((0,img.shape[1],img.shape[0],0))
pylab.draw()
#raw_input()
if select=="dir":
#RSZ=0.5
f=pylab.figure(11,figsize=(9,5))
pylab.ioff()
pylab.clf()
axes=pylab.Axes(f, [.0,.0,1.0,1.0]) # [left, bottom, width, height] where each value is between 0 and 1
f.add_axes(axes)
img=util.myimread(tsImages[ii]["name"],resize=cfg.resize)
pylab.imshow(img,interpolation="nearest",animated=True)
#raw_input()
pylab.axis("off")
tr.show(nfuse,parts=True,thr=-0.99,scr=True)
pylab.axis((0,img.shape[1],img.shape[0],0))
pylab.ion()
pylab.draw()
pylab.show()
#raw_input()
pylab.savefig("/media/ca0567b8-ee6d-4590-8462-0d093addb4cf/video/det/"+tsImages[ii]["name"].split("/")[-1].split(".")[0]+".png")
del itr
#tp,fp,scr,tot=VOCpr.VOCprlistfastscore(tsImages,detlist,numim=cfg.maxpostest,show=False,ovr=0.5)
#tp,fp,scr,tot=VOCpr.VOCprRecord_wrong(tsImages,detlist,show=False,ovr=0.5)
tp,fp,scr,tot=VOCpr.VOCprRecord(tsImages,detlist,show=False,ovr=0.5)
pylab.figure(15)
pylab.clf()
rc,pr,ap=VOCpr.drawPrfast(tp,fp,tot)
pylab.figure(16)
pylab.clf()
fppi,miss,ap=VOCpr.drawMissRatePerImage(tp,fp,tot,len(tsImages))
pylab.draw()
pylab.show()
sfdsd
#pylab.savefig("%s_ap%d_test%s%.1f.png"%(testname,it,select,cfg.mythr))
tottime=((time.time()-initime))
print "Threshold used:",cfg.mythr
print "Total number of HOG:",numhog
print "AP(it=",it,")=",ap
print "Testing Time: %.3f s"%tottime#/3600.0)
#results={"det":detlist,"ap":ap,"tp":tp,"fp":fp,"pr":pr,"rc":rc,"numhog":numhog,"mythr":cfg.mythr,"time":tottime}
#util.savemat("%s_ap%d_test_thr_%.3f.mat"%(cfg.testname,it,cfg.mythr),results)
#util.save("%s_ap%d_test_thr_%.3f.dat"%(testname,it,cfg.mythr),results)
#util.savedetVOC(detlist,"%s_ap%d_test_thr_%.3f.txt"%(testname,it,cfg.mythr))
#fd=open("%s_ap%d_test%s.txt"%(cfg.testname,it,select),"a")
#fd.write("Threshold used:%f\n"%cfg.mythr)
#fd.write("Total number of HOG:%d\n"%numhog)
#fd.write("Average precision:%f\n"%ap)
#fd.write("Testing Time: %.3f s\n\n"%tottime)
#fd.close()
return ap,numhog
else:
aux=tr.descr(nfuseneg)[0]
auxcl=tr.mixture(nfuseneg)[0]
dns=buildense([aux],[auxcl],cumsize)[0]
dscr=numpy.sum(dns*w)
#print "Scr:",nfuseneg[0]["scr"],"DesneSCR:",dscr,"Diff:",abs(nfuseneg[0]["scr"]-dscr)
if abs(nfuseneg[0]["scr"]-dscr)>0.0001:
print "Warning: the two scores must be the same!!!"
print "Scr:",nfuseneg[0]["scr"],"DesneSCR:",dscr,"Diff:",abs(nfuseneg[0]["scr"]-dscr)
raw_input()
if cfg.show:
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img=util.myimread(trPosImages[ii/2]["name"],flip=ii%2)
pylab.imshow(img,interpolation="nearest",animated=True)
tr.show(nfuse,parts=cfg.show)
pylab.show()
#raw_input()
del itr
#fulltrpos=trpos
#fulltrposcl=trposcl
#trpos=remove_empty(fulltrpos)
#trposcl=remove_empty(fulltrposcl)
numoldtrpos=len(trpos)
if it>0:
moldloss,oldscr=loss_pos(trpos,trposcl,cumsize)
else:
moldloss=1
else:
itr = mypool.imap(detectWrap, arg)
for i, im in enumerate(itr):
print "---- Image %d----" % i
print "Detections:", len(im[1])
dettime.append(im[2])
numhog += im[3]
tr = im[0]
f = pylab.figure(11, figsize=(9, 5))
pylab.ioff()
pylab.clf()
axes = pylab.Axes(f, [
.0, .0, 1.0, 1.0
]) # [left, bottom, width, height] where each value is between 0 and 1
f.add_axes(axes)
img = util.myimread(tsImages[i]["name"], resize=RSZ)
pylab.imshow(img, interpolation="nearest", animated=True)
pylab.axis("off")
tr.show(im[1], parts=True, thr=-0.5, scr=True, maxnum=3)
pylab.axis((0, img.shape[1], img.shape[0], 0))
pylab.ion()
pylab.draw()
pylab.show()
print[x["scr"] for x in im[1]]
raw_input()
if False:
pylab.savefig("./video/detSCRBU/" +
tsImages[i]["name"].split("/")[-1].split(".")[0] +
".png")
#raw_input()
for l in im[1]:
def getImage(self, i):
i = i + int(self.total / self.totalfold) * self.fold
item = self.selines[i]
return myimread((self.imagepath + item.split(" ")[0]))
import pylab as pl
import util
import detectCRF
#det=util.load("./data/CRF/12_10_02_parts_full/bicycle2_testN1_final.det")["det"]
#det=util.load("./data/CRF/12_10_02_parts_full/bicycle2_testN2_final.txt")
#fl=open("./data/CRF/12_10_02_parts_full/bicycle2_testN2_final.txt")
fl=open("./data/inria1_inria3.txt")
det=fl.readlines()
imgpath=cfg.dbpath+"VOC2007/VOCdevkit/VOC2007/JPEGImages/"#VOC
imgpath=cfg.dbpath+"INRIAPerson/Test/pos/"#inria
for idl,l in enumerate(det):
imname,scr,b0,b1,b2,b3=l.split()
try:
img=util.myimread(imgpath+imname+".png")
except:
img=util.myimread(imgpath+imname+".jpg")
pl.figure(100)
pl.clf()
pl.imshow(img)
util.box(int(b1),int(b0),int(b3),int(b2),"w",lw=2)
pl.title("Rank:%d Scr:%.3f"%(idl,float(scr)))
pl.axis([0,img.shape[1],img.shape[0],0])
#detectCRF.visualize2([l],2,img,text="rank:%d"%(idl))
pl.draw()
pl.show()
raw_input()
def getImage(self, i):
self.cur.execute(
"SELECT filepath FROM FaceImages WHERE file_id = '%s'" %
self.items[i][0])
impath = self.cur.fetchall()
return myimread((impath))
if not(cfg.multipr):
itr=itertools.imap(detectWrap,arg)
else:
itr=mypool.imap(detectWrap,arg)
for i,im in enumerate(itr):
print "---- Image %d----"%i
print "Detections:", len(im[1])
dettime.append(im[2])
numhog+=im[3]
tr=im[0]
f=pylab.figure(11,figsize=(9,5))
pylab.ioff()
pylab.clf()
axes=pylab.Axes(f, [.0,.0,1.0,1.0]) # [left, bottom, width, height] where each value is between 0 and 1
f.add_axes(axes)
img=util.myimread(tsImages[i]["name"],resize=RSZ)
pylab.imshow(img,interpolation="nearest",animated=True)
pylab.axis("off")
tr.show(im[1],parts=True,thr=-0.5,scr=True,maxnum=3)
pylab.axis((0,img.shape[1],img.shape[0],0))
pylab.ion()
pylab.draw()
pylab.show()
print [x["scr"] for x in im[1]]
raw_input()
if False:
pylab.savefig("./video/detSCRBU/"+tsImages[i]["name"].split("/")[-1].split(".")[0]+".png")
#raw_input()
for l in im[1]:
detlist.append([tsImages[i]["name"].split("/")[-1].split(".")[0],l["scr"],l["bbox"][1],l["bbox"][0],l["bbox"][3],l["bbox"][2]])
del itr
def getImageByName2(self, name):
return myimread(self.imagepath + name + ".jpg")
def test(thr, cfg):
# cfg.test=True
import util
it = 7
models = util.load("%s%d.model" % (cfg.testname, it))
w = []
rho = []
cfg.mythr = thr
# for l in range(cfg.numcl):
# w.append(util.ModeltoW(models[l],cfg.usemrf,cfg.usefather,cfg.k,lastlev=1))
# rho.append(models[l]["rho"])
# cfg.mythr=cfg.mythr*numpy.mean([numpy.sum(x**2) for x in w])#-numpy.mean(rho)
# raw_input()
# cfg.mythr=cfg.mythr#-numpy.mean(rho)
if cfg.multipr == 1:
numcore = None
else:
numcore = cfg.multipr
mypool = Pool(numcore)
if cfg.cls == "inria":
if cfg.select == "pos":
tsImages = getRecord(InriaTestData(basepath=cfg.dbpath), cfg.maxtest)
else:
tsImages = getRecord(InriaTestFullData(basepath=cfg.dbpath), cfg.maxtest)
else:
tsImages = getRecord(
VOC07Data(
select=cfg.select,
cl="%s_test.txt" % cfg.cls,
basepath=cfg.dbpath, # "/home/databases/",
usetr=True,
usedf=False,
),
cfg.maxtest,
)
mypool = Pool(numcore)
print "Test"
print "Pruning Threshold:", cfg.mythr
numhog = 0
initime = time.time()
detlist = []
mycfg = copy.copy(cfg)
mycfg.numneg = 0
arg = [[i, tsImages[i]["name"], None, models, mycfg] for i in range(len(tsImages))]
t = time.time()
if not (cfg.multipr):
itr = itertools.imap(detectWrap, arg)
else:
itr = mypool.imap(detectWrap, arg)
for ii, res in enumerate(itr):
totneg = 0
fuse = []
for mix in res:
tr = mix[0]
fuse += mix[1]
numhog += mix[3]
# for h in fuse:
# h["scr"]+=models[h["cl"]]["ra"]
rfuse = tr.rank(fuse, maxnum=300)
nfuse = tr.cluster(rfuse, ovr=0.3, inclusion=False)
print "----Test Image %d----" % ii
for l in nfuse:
detlist.append(
[
tsImages[ii]["name"].split("/")[-1].split(".")[0],
l["scr"],
l["bbox"][1],
l["bbox"][0],
l["bbox"][3],
l["bbox"][2],
]
)
print "Detections:", len(nfuse)
if cfg.show:
if cfg.show == True:
showlabel = "Parts"
else:
showlabel = False
pylab.figure(20)
pylab.ioff()
pylab.clf()
pylab.axis("off")
img = util.myimread(tsImages[ii]["name"])
pylab.imshow(img, interpolation="nearest", animated=True)
pylab.gca().set_ylim(0, img.shape[0])
pylab.gca().set_xlim(0, img.shape[1])
pylab.gca().set_ylim(pylab.gca().get_ylim()[::-1])
tr.show(nfuse, parts=showlabel, thr=-0.8, maxnum=10)
pylab.show()
# raw_input()
del itr
# tp,fp,scr,tot=VOCpr.VOCprlistfastscore(tsImages,detlist,numim=cfg.maxpostest,show=False,ovr=0.5)
# tp,fp,scr,tot=VOCpr.VOCprRecord_wrong(tsImages,detlist,show=False,ovr=0.5)
tp, fp, scr, tot = VOCpr.VOCprRecord(tsImages, detlist, show=False, ovr=0.5)
pylab.figure(15)
pylab.clf()
rc, pr, ap = VOCpr.drawPrfast(tp, fp, tot)
pylab.draw()
pylab.show()
# pylab.savefig("%s_ap%d_test%s%.1f.png"%(testname,it,select,cfg.mythr))
tottime = time.time() - initime
print "Threshold used:", cfg.mythr
print "Total number of HOG:", numhog
print "AP(it=", it, ")=", ap
print "Testing Time: %.3f s" % tottime # /3600.0)
# results={"det":detlist,"ap":ap,"tp":tp,"fp":fp,"pr":pr,"rc":rc,"numhog":numhog,"mythr":cfg.mythr,"time":tottime}
# util.savemat("%s_ap%d_test_thr_%.3f.mat"%(cfg.testname,it,cfg.mythr),results)
# util.save("%s_ap%d_test_thr_%.3f.dat"%(testname,it,cfg.mythr),results)
# util.savedetVOC(detlist,"%s_ap%d_test_thr_%.3f.txt"%(testname,it,cfg.mythr))
# fd=open("%s_ap%d_test%s.txt"%(cfg.testname,it,select),"a")
# fd.write("Threshold used:%f\n"%cfg.mythr)
# fd.write("Total number of HOG:%d\n"%numhog)
# fd.write("Average precision:%f\n"%ap)
# fd.write("Testing Time: %.3f s\n\n"%tottime)
# fd.close()
return ap, numhog
