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 plot_grid(lon, lat, dhdt, cell=None, plot_cell=False, contourf=True):
fig = plt.figure()
m = viz.make_proj_stere(BBOX_REG)
plon, plat = lon[GRID_CELL[1]], lat[GRID_CELL[0]]
lon, lat = np.meshgrid(lon, lat)
xx, yy = m(lon, lat)
x, y = m(plon, plat)
print 'point cell (lon lat x y):', plon, plat, x, y
dhdt = np.ma.masked_invalid(dhdt)
m.pcolormesh(xx, yy, dhdt, cmap=CMAP, vmin=-ABS_VAL, vmax=ABS_VAL, alpha=1)
m.plot(x, y, 'mo', markersize=8)
if plot_cell:
lon, lat = util.box(cell)
x, y = m(lon, lat)
m.plot(x, y, 'k', linewidth=2)
if contourf:
fig2 = plt.figure()
m.contourf(xx, yy, dhdt, 25, cmap=CMAP)
#plt.colorbar().set_label(LABEL)
viz.colorbar(fig, CMAP, (-ABS_VAL,ABS_VAL))
'''
w = 50.0 / (axis[1] - axis[0])
rect = 0.142 - w, 0.08, 2 * w, 0.02
cmap = CMAP
viz.colorbar(fig, cmap, colorlim, legend, rect, ticks, ticklabels,
size=7, weight='bold', color='k', edgecolor='w')
leg = fig.add_axes([0, 0, 1, 1])
leg.set_axis_off()
viz.text(leg, 0.70, 0.90, title, ha='center', va='top', size=10,
weight='bold', color='k', edgecolor='w')
'''
return fig
def generate(model_path, meta, n_predict, input_file, wordy=False):
processed = get_reader(input_file, meta)
if wordy: print("got processed with data: {}\nLength of input data: {}".format(processed.data, len(processed.data)))
config = meta.config
config.n_possibilities = meta.n_possibilities
config.hidden_size = meta.hidden_size
config.num_layers = meta.num_layers
config.init_scale = meta.init_scale
config.mode = meta.mode
lm = general_lm.Model(
config,
processed.vocab_size,
is_training=False,
is_generating=True)
if wordy: print("finished creating model")
saver = tf.train.Saver()
with tf.Session() as session:
# start threads
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
current_state = np.zeros((lm.num_layers, 2, lm.batch_size, lm.hidden_size))
# restore the trained model
saver.restore(session, model_path)
if wordy: print("restored session, predicting")
sentence = ' '.join(processed.data) + '...'
pred = None # for scope
# set seed state
for (X, Y) in processed.doc_slice(lm.batch_size, lm.seq_length):
pred, current_state = session.run(
[lm.predict, lm.state],
feed_dict={lm.init_state: current_state, lm.X: X})
# do the actual predicting
for ii in range(n_predict):
if wordy: print('making prediction {}'.format(ii))
input = np.array(pred) # TODO: implicit shape? might be confusing (formerly [pred])
pred, current_state = session.run(
[lm.predict, lm.state],
feed_dict={lm.init_state: current_state, lm.X: input})
pred_word = meta.id_to_word[pred[0][0]] # formerly just one [0]
sentence += ' ' * int(config.mode == 'word') # only add space in word mode
sentence += pred_word.replace('_', ' ').replace('<eos>', os.linesep)
if wordy: print('finished making prediction {}: {}'.format(ii, pred_word))
#print("\n\t{}\n".format(sentence))
print(box(sentence, alignment='left'))
# close threads
coord.request_stop()
coord.join(threads)
def houseBox():
w = faceFromVectors([O,x(10000),xy(10000,10000),y(10000)])
f = faceFromVectors([xy(5000-1,5000-1),xy(5000+1,5000-1),xy(5000+1,5001),xy(5000-1,5001)])
f.translate(z(wallMaxHeight-wallMinHeight))
b=util.box(10000,10000,wallMinHeight)
roof=rich(Part.makeLoft([
w.Wires[0],
f.Wires[0],
],True)).transO(z(wallMinHeight))
return rich(util.fuse([b,roof])
.transO(xy(-10000+width,-10000+length)))
def viewSortDet(gtImages,detlist,numim=numpy.inf,opt="all",usetr=True,usedf=False,ovr=0.5):
dimg={}
tot=0
for idx in range(min(gtImages.getTotal(),numim)):
rect=gtImages.getBBox(idx)
if rect!=[]:
#print gtImages.getImageName(idx).split("/")[-1].split(".")[0]
dimg[gtImages.getImageName(idx).split("/")[-1].split(".")[0]]=rect
tot=tot+len(rect)
imname=[]
cnt=0
tp=numpy.zeros(len(detlist))
fp=numpy.zeros(len(detlist))
thr=numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx,detbb in enumerate(detlist):
#print detbb[1]
found=False
if dimg.has_key(detbb[0]):
rect=dimg[detbb[0]]
found=False
for r in rect:
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
#print "GT:",r
#print "DET:",rb
if overlap(rb,r)>=ovr:
dimg[detbb[0]].remove(r)
found=True
break
if found:
tp[idx]=1
else:
fp[idx]=1
thr[idx]=detbb[1]
if show:
pylab.ioff()
prec=numpy.sum(tp)/float(numpy.sum(tp)+numpy.sum(fp))
rec=numpy.sum(tp)/tot
print "Scr:",detbb[1],"Prec:",prec,"Rec:",rec
img=gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0],r[1],r[2],r[3],'b',lw=1.5)
if found:
box(rb[0],rb[1],rb[2],rb[3],'g',lw=1.5)
else:
box(rb[0],rb[1],rb[2],rb[3],'r',lw=1.5)
pylab.draw()
pylab.show()
rect=[]
raw_input()
return tp,fp,thr,tot
def test():
util.clear()
reload(util);
s = sBuild()
bb = s.BoundBox
box = util.box(max(1,bb.XLength),max(1,bb.YLength),max(1,bb.ZLength))\
.transO(v(bb.XMin,bb.YMin, bb.ZMin))
util.show(box)
util.show(s)
e = s.Edges[3]
util.show(e)
splits = util.splitFaceAlong(s,util.dir(e),[100,500,800]);
util.addGroup("splits",map(lambda f: util.trimFace(f,10),splits))
def plot_grid(lon, lat, dhdt, cell=None, plot_cell=False, contourf=True, savefig=None):
fig = plt.figure()
m = viz.make_proj_stere(BBOX_REG)
plon, plat = lon[GRID_CELL[1]], lat[GRID_CELL[0]]
# shift grid half degree
lon -= (lon[1] - lon[0])/2.
lat -= (lat[1] - lat[0])/2.
lon, lat = np.meshgrid(lon, lat)
xx, yy = m(lon, lat)
x, y = m(plon, plat)
print 'point cell (lon lat x y):', plon, plat, x, y
dhdt = np.ma.masked_invalid(dhdt)
m.pcolormesh(xx, yy, dhdt, cmap=CMAP, vmin=-ABS_VAL, vmax=ABS_VAL, alpha=1)
p_ = m.drawparallels(np.arange(-90.,-60, 2), labels=[1,0,0,0], color='0.3')
m_ = m.drawmeridians(np.arange(-180,180., 15), labels=[0,0,1,0], color='0.3')
#m.plot(x, y, 'mo', markersize=8)
if plot_cell:
lon, lat = util.box(cell)
x, y = m(lon, lat)
m.plot(x, y, 'k', linewidth=2)
if contourf:
fig2 = plt.figure()
m.contourf(xx, yy, dhdt, 25, cmap=CMAP)
###
'''
x2, y2 = np.loadtxt('/Users/fpaolo/data/masks/scripps/scripps_iceshelves_v1_geod.txt',
usecols=(0,1), unpack=True, comments='%')
x2, y2 = m(x2, y2)
m.plot(x2, y2, 'o', markersize=1.2)
'''
###
#plt.colorbar().set_label(LABEL)
rect = 0.25, 0.11, 0.5, 0.02
viz.colorbar(fig, CMAP, (-ABS_VAL,ABS_VAL), title='Elevation change rate (m/yr)',
rect=rect)
'''
w = 50.0 / (axis[1] - axis[0])
rect = 0.142 - w, 0.08, 2 * w, 0.02
cmap = CMAP
viz.colorbar(fig, cmap, colorlim, legend, rect, ticks, ticklabels,
size=7, weight='bold', color='k', edgecolor='w')
leg = fig.add_axes([0, 0, 1, 1])
leg.set_axis_off()
viz.text(leg, 0.70, 0.90, title, ha='center', va='top', size=10,
weight='bold', color='k', edgecolor='w')
'''
if savefig is not None:
plt.savefig(savefig+'_map.png')
return fig
def VOCprlistfast(gtImages,detlist,show=False,usetr=True,usedf=False,ovr=0.5):
"""
calculate the precision recall curve
"""
dimg={}
tot=0
for idx in range(gtImages.getTotal()):
rect=gtImages.getBBox(idx)
if rect!=[]:
dimg[gtImages.getImageName(idx).split("/")[-1].split(".")[0]]=rect
tot=tot+len(rect)
#print tot
imname=[]
cnt=0
tp=numpy.zeros(len(detlist))
fp=numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx,detbb in enumerate(detlist):#detlist[sortlist]):#gtImages.getTotal()):
found=False
if dimg.has_key(detbb[0]):
rect=dimg[detbb[0]]#gtImages.getBBox(idx,usetr=usetr,usedf=usedf)
#print rect
found=False
for r in rect:
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
if overlap(rb,r)>=ovr:
dimg[detbb[0]].remove(r)
found=True
break
if found:
tp[idx]=1#.append(float(detbb[1]))
else:
fp[idx]=1#.append(float(detbb[1]))
if show:
pylab.ioff()
img=gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0],r[1],r[2],r[3],'b',lw=1.5)
if found:
box(rb[0],rb[1],rb[2],rb[3],'g',lw=1.5)
else:
box(rb[0],rb[1],rb[2],rb[3],'r',lw=1.5)
pylab.draw()
pylab.show()
rect=[]
raw_input()
return tp,fp,tot
def plot_grid(lon, lat, dhdt, cell=None, plot_cell=False, contourf=True):
fig = plt.figure()
m = viz.make_proj_stere(BBOX_REG)
lon, lat = np.meshgrid(lon, lat)
xx, yy = m(lon, lat)
dhdt = np.ma.masked_invalid(dhdt)
m.pcolormesh(xx, yy, dhdt, cmap=CMAP, vmin=-ABS_VAL, vmax=ABS_VAL, alpha=1)
p_ = m.drawparallels(np.arange(-90.,-60, 5), labels=[0,0,0,1], color='0.8')
m_ = m.drawmeridians(np.arange(-180,180., 10), labels=[1,0,0,0], color='0.8')
###
'''
xx, yy = np.loadtxt('/Users/fpaolo/data/masks/scripps/scripps_iceshelves_v1_geod.txt', usecols=(0,1), unpack=True, comments='%')
xx, yy = m(xx, yy)
m.plot(xx, yy, 'o', markersize=0.1)
'''
###
#m.plot(x, y, 'mo', markersize=8)
if plot_cell:
lon, lat = util.box(cell)
x, y = m(lon, lat)
m.plot(x, y, 'k', linewidth=2)
if contourf:
fig2 = plt.figure()
m.contourf(xx, yy, dhdt, 25, cmap=CMAP)
#plt.colorbar().set_label(LABEL)
viz.colorbar(fig, CMAP, (-ABS_VAL,ABS_VAL))
'''
w = 50.0 / (axis[1] - axis[0])
rect = 0.142 - w, 0.08, 2 * w, 0.02
cmap = CMAP
viz.colorbar(fig, cmap, colorlim, legend, rect, ticks, ticklabels,
size=7, weight='bold', color='k', edgecolor='w')
leg = fig.add_axes([0, 0, 1, 1])
leg.set_axis_off()
viz.text(leg, 0.70, 0.90, title, ha='center', va='top', size=10,
weight='bold', color='k', edgecolor='w')
'''
return fig
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
def VOCprRecord_wrong(gtImages,detlist,show=False,usetr=True,usedf=False,ovr=0.5):
"""
calculate the precision recall curve
"""
dimg={}
tot=0
for idx in range(len(gtImages)):
rect=gtImages[idx]["bbox"][:]
#if idx>288:
# print idx,rect
if rect!=[]:
#print gtImages.getImageName(idx).split("/")[-1].split(".")[0]
dimg[gtImages[idx]["name"].split("/")[-1].split(".")[0]]={"bbox":rect,"det":[False]*len(rect)}
tot=tot+len(rect)
imname=[]
cnt=0
tp=numpy.zeros(len(detlist))
fp=numpy.zeros(len(detlist))
thr=numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx,detbb in enumerate(detlist):
#print detbb[1]
found=False
maxovr=0
#gtdet=[False]
gt=0
if dimg.has_key(detbb[0]):
rect=dimg[detbb[0]]["bbox"]
found=False
for ir,r in enumerate(rect):
#gtdet.append(False)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
#print "GT:",r
#print "DET:",rb
covr=overlap(rb,r)
if covr>=maxovr:
maxovr=covr
gt=ir
#dimg[detbb[0]].remove(r)
#found=True
#break
if maxovr>ovr:
#if not(dimg[detbb[0]]["det"][gt]):
tp[idx]=1
#dimg[detbb[0]]["det"][gt]=True
#else:
# fp[idx]=1
else:
fp[idx]=1
thr[idx]=detbb[1]
if show:
prec=numpy.sum(tp)/float(numpy.sum(tp)+numpy.sum(fp))
rec=numpy.sum(tp)/tot
print "Scr:",detbb[1],"Prec:%.3f"%prec,"Rec:%.3f"%rec
ss=raw_input()
if ss=="s" or not(found):
pylab.ioff()
img=gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0],r[1],r[2],r[3],'b',lw=1.5)
if found:
box(rb[0],rb[1],rb[2],rb[3],'g',lw=1.5)
else:
box(rb[0],rb[1],rb[2],rb[3],'r',lw=1.5)
pylab.draw()
pylab.show()
rect=[]
return tp,fp,thr,tot
def VOCprRecordthr(gtImages,detlist,show=False,ovr=0.5,pixels=None):
"""
calculate the precision recall curve
"""
dimg={}
tot=0
posd=[]
for idx in range(len(gtImages)):
rect=gtImages[idx]["bbox"][:]
#if idx>288:
# print idx,rect
if rect!=[]:
#print gtImages.getImageName(idx).split("/")[-1].split(".")[0]
dimg[gtImages[idx]["name"].split("/")[-1].split(".")[0]]={"bbox":rect,"det":[False]*len(rect)}
for i, recti in enumerate(rect):
if recti[5] == 0:
tot=tot+1
imname=[]
cnt=0
tp=numpy.zeros(len(detlist))
fp=numpy.zeros(len(detlist))
thr=numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx,detbb in enumerate(detlist):
#print detbb[1]
found=False
maxovr=0
#gtdet=[False]
gt=0
if dimg.has_key(detbb[0]):
rect=dimg[detbb[0]]["bbox"]
found=False
for ir,r in enumerate(rect):
#gtdet.append(False)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
#print "GT:",r
#print "DET:",rb
if pixels==None:
covr=overlap(rb,r)
else:
covr=overlapx(rb,r,pixels)
if covr>=maxovr:
maxovr=covr
gt=ir
#dimg[detbb[0]].remove(r)
#found=True
#break
if maxovr>ovr:
if dimg[detbb[0]]["bbox"][gt][5] == 0:
if not(dimg[detbb[0]]["det"][gt]):
tp[idx]=1
dimg[detbb[0]]["det"][gt]=True
posd.append(detbb[1])
else:
fp[idx]=1
else:
fp[idx]=1
########### PASCAL 2010
# if ovmax>=VOCopts.minoverlap
# if ~gt(i).diff(jmax)
# if ~gt(i).det(jmax)
# tp(d)=1; % true positive
# gt(i).det(jmax)=true;
# else
# fp(d)=1; % false positive (multiple detection)
# end
# end
# else
# fp(d)=1; % false positive
# end
########################
thr[idx]=detbb[1]
if show:
prec=numpy.sum(tp)/float(numpy.sum(tp)+numpy.sum(fp))
rec=numpy.sum(tp)/tot
print "Scr:",detbb[1],"Prec:%.3f"%prec,"Rec:%.3f"%rec
ss=raw_input()
if ss=="s" or not(found):
pylab.ioff()
img=gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb=(float(detbb[3]),float(detbb[2]),float(detbb[5]),float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0],r[1],r[2],r[3],'b',lw=1.5)
if found:
box(rb[0],rb[1],rb[2],rb[3],'g',lw=1.5)
else:
box(rb[0],rb[1],rb[2],rb[3],'r',lw=1.5)
pylab.draw()
pylab.show()
rect=[]
return tp,fp,thr,tot,posd
def VOCprlistfast(gtImages,
detlist,
show=False,
usetr=True,
usedf=False,
ovr=0.5):
"""
calculate the precision recall curve
"""
dimg = {}
tot = 0
for idx in range(gtImages.getTotal()):
rect = gtImages.getBBox(idx)
if rect != []:
dimg[gtImages.getImageName(idx).split("/")[-1].split(".")
[0]] = rect
tot = tot + len(rect)
#print tot
imname = []
cnt = 0
tp = numpy.zeros(len(detlist))
fp = numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx, detbb in enumerate(
detlist): #detlist[sortlist]):#gtImages.getTotal()):
found = False
if dimg.has_key(detbb[0]):
rect = dimg[
detbb[0]] #gtImages.getBBox(idx,usetr=usetr,usedf=usedf)
#print rect
found = False
for r in rect:
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
if overlap(rb, r) >= ovr:
dimg[detbb[0]].remove(r)
found = True
break
if found:
tp[idx] = 1 #.append(float(detbb[1]))
else:
fp[idx] = 1 #.append(float(detbb[1]))
if show:
pylab.ioff()
img = gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0], r[1], r[2], r[3], 'b', lw=1.5)
if found:
box(rb[0], rb[1], rb[2], rb[3], 'g', lw=1.5)
else:
box(rb[0], rb[1], rb[2], rb[3], 'r', lw=1.5)
pylab.draw()
pylab.show()
rect = []
raw_input()
return tp, fp, tot
def VOCprRecordOptim(gtImages, detlist, show=False, ovr=0.5, pixels=None):
"""
calculate the precision recall curve
"""
tx = []
ty = []
sx = []
sy = []
dimg = {}
tot = 0
for idx in range(len(gtImages)):
rect = gtImages[idx]["bbox"][:]
if rect != []:
dimg[gtImages[idx]["name"].split(
"/")[-1].split(".")[0]] = {"bbox": rect, "det": [False] * len(rect)}
for i, recti in enumerate(rect):
if recti[5] == 0:
tot = tot + 1
imname = []
cnt = 0
tp = numpy.zeros(len(detlist))
fp = numpy.zeros(len(detlist))
thr = numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx, detbb in enumerate(detlist):
found = False
maxovr = 0
gt = 0
if dimg.has_key(detbb[0]):
rect = dimg[detbb[0]]["bbox"]
found = False
for ir, r in enumerate(rect):
rb = (float(detbb[3]), float(detbb[2]),
float(detbb[5]), float(detbb[4]))
if pixels == None:
covr = overlap(rb, r)
else:
covr = overlapx(rb, r, pixels)
if covr >= maxovr:
maxovr = covr
gt = ir
if maxovr > ovr:
if dimg[detbb[0]]["bbox"][gt][5] == 0:
if not(dimg[detbb[0]]["det"][gt]):
tp[idx] = 1
dimg[detbb[0]]["det"][gt] = True
gtx = dimg[detbb[0]]["bbox"][gt][
3] - dimg[detbb[0]]["bbox"][gt][1]
dtx = detbb[4] - detbb[2]
gty = dimg[detbb[0]]["bbox"][gt][
2] - dimg[detbb[0]]["bbox"][gt][0]
dty = detbb[5] - detbb[3]
gtcx = (
dimg[detbb[0]]["bbox"][gt][3] + dimg[detbb[0]]["bbox"][gt][1]) / 2.
dtcx = (detbb[4] + detbb[2]) / 2.
gtcy = (
dimg[detbb[0]]["bbox"][gt][2] + dimg[detbb[0]]["bbox"][gt][0]) / 2.
dtcy = (detbb[5] + detbb[3]) / 2.
tx.append((gtcx - dtcx) / float(dtx))
ty.append((gtcy - dtcy) / float(dty))
sx.append(gtx / float(dtx))
sy.append(gty / float(dty))
else:
fp[idx] = 1
else:
fp[idx] = 1
thr[idx] = detbb[1]
if show:
prec = numpy.sum(tp) / float(numpy.sum(tp) + numpy.sum(fp))
rec = numpy.sum(tp) / tot
print("Scr:", detbb[1], "Prec:%.3f" % prec, "Rec:%.3f" % rec)
ss = raw_input()
if ss == "s" or not(found):
pylab.ioff()
img = gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb = (float(detbb[3]), float(detbb[2]),
float(detbb[5]), float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0], r[1], r[2], r[3], 'b', lw=1.5)
if found:
box(rb[0], rb[1], rb[2], rb[3], 'g', lw=1.5)
else:
box(rb[0], rb[1], rb[2], rb[3], 'r', lw=1.5)
pylab.draw()
pylab.show()
rect = []
return tp, fp, thr, tot, tx, ty, sx, sy
#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 bar_gen(xpos):
topPos = random.randint(round(figure_size/10), round(figure_size*2/5))
topbar = box(xpos, 0, h = topPos, w = 25)
bottombar = box(xpos, topPos + gapDistance , h = figure_size - topPos - gapDistance, w= 25)
return [topbar, bottombar]
def viewDet(gtImages,detfile,opt="all",usetr=True,usedf=False,stop=True,t=0.5):
detf=open(detfile,"r")
detect=detf.readlines()
detlst=numpy.zeros((len(detect),5))
namelst=[]
pylab.ioff()
for id,el in enumerate(detect):
aux=el.split()
namelst.append(aux[0])
detlst[id,:]=aux[1:]
srt=numpy.argsort(-detlst[:,0])
imname=[]
cnt=0
ovr=0.49
#print trPosImages.getTotal()
tp=[]
fp=[]
tot=0
pylab.figure()
bb=numpy.zeros((4))
for id in range(detlst.shape[0]):
pylab.ioff()
abb=detlst[srt[id]]
conf=abb[0]
bb[0]=abb[2];bb[1]=abb[1];bb[2]=abb[4];bb[3]=abb[3]
pylab.clf()
img=gtImages.getImageByName2(namelst[srt[id]])
gtbb=gtImages.getBBoxByName(namelst[srt[id]],usetr=usetr,usedf=usedf)
found=False
for l in range(len(gtbb)):
pylab.imshow(img)
pylab.title("%s Confidence: %f"%(namelst[srt[id]],float(conf)))
#box(gtbb[l][0],gtbb[l][1],gtbb[l][2],gtbb[l][3],col='b',lw="2")
print overlap(bb[:],gtbb[l][:4])
if overlap(bb[:],gtbb[l][:4])>0:
if overlap(bb[:],gtbb[l][:4])>ovr:
box(gtbb[l][0],gtbb[l][1],gtbb[l][2],gtbb[l][3],col='y',lw="2")
box(bb[0],bb[1],bb[2],bb[3],col='g',lw="2")
pylab.show()
pylab.draw()
if stop:
raw_input()
else:
time.sleep(t)
found=True
else:
box(gtbb[l][0],gtbb[l][1],gtbb[l][2],gtbb[l][3],col='y',lw="1")
#box(bb[0],bb[1],bb[2],bb[3],col='g',lw="2")
#raw_input()
else:
pass
#pylab.imshow(img)
#box(bb[0],bb[1],bb[2],bb[3],col='r',lw="2")
if not(found):
pylab.imshow(img)
box(bb[0],bb[1],bb[2],bb[3],col='r',lw="2")
pylab.show()
pylab.draw()
if stop:
raw_input()
else:
time.sleep(t)
def VOCprRecord_wrong(gtImages,
detlist,
show=False,
usetr=True,
usedf=False,
ovr=0.5):
"""
calculate the precision recall curve
"""
dimg = {}
tot = 0
for idx in range(len(gtImages)):
rect = gtImages[idx]["bbox"][:]
#if idx>288:
# print idx,rect
if rect != []:
#print gtImages.getImageName(idx).split("/")[-1].split(".")[0]
dimg[gtImages[idx]["name"].split("/")[-1].split(".")[0]] = {
"bbox": rect,
"det": [False] * len(rect)
}
tot = tot + len(rect)
imname = []
cnt = 0
tp = numpy.zeros(len(detlist))
fp = numpy.zeros(len(detlist))
thr = numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx, detbb in enumerate(detlist):
#print detbb[1]
found = False
maxovr = 0
#gtdet=[False]
gt = 0
if dimg.has_key(detbb[0]):
rect = dimg[detbb[0]]["bbox"]
found = False
for ir, r in enumerate(rect):
#gtdet.append(False)
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
#print "GT:",r
#print "DET:",rb
covr = overlap(rb, r)
if covr >= maxovr:
maxovr = covr
gt = ir
#dimg[detbb[0]].remove(r)
#found=True
#break
if maxovr > ovr:
#if not(dimg[detbb[0]]["det"][gt]):
tp[idx] = 1
#dimg[detbb[0]]["det"][gt]=True
#else:
# fp[idx]=1
else:
fp[idx] = 1
thr[idx] = detbb[1]
if show:
prec = numpy.sum(tp) / float(numpy.sum(tp) + numpy.sum(fp))
rec = numpy.sum(tp) / tot
print "Scr:", detbb[1], "Prec:%.3f" % prec, "Rec:%.3f" % rec
ss = raw_input()
if ss == "s" or not (found):
pylab.ioff()
img = gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0], r[1], r[2], r[3], 'b', lw=1.5)
if found:
box(rb[0], rb[1], rb[2], rb[3], 'g', lw=1.5)
else:
box(rb[0], rb[1], rb[2], rb[3], 'r', lw=1.5)
pylab.draw()
pylab.show()
rect = []
return tp, fp, thr, tot
def viewSortDet(gtImages,
detlist,
numim=numpy.inf,
opt="all",
usetr=True,
usedf=False,
ovr=0.5):
dimg = {}
tot = 0
for idx in range(min(gtImages.getTotal(), numim)):
rect = gtImages.getBBox(idx)
if rect != []:
#print gtImages.getImageName(idx).split("/")[-1].split(".")[0]
dimg[gtImages.getImageName(idx).split("/")[-1].split(".")
[0]] = rect
tot = tot + len(rect)
imname = []
cnt = 0
tp = numpy.zeros(len(detlist))
fp = numpy.zeros(len(detlist))
thr = numpy.zeros(len(detlist))
detlist.sort(cmpscore)
for idx, detbb in enumerate(detlist):
#print detbb[1]
found = False
if dimg.has_key(detbb[0]):
rect = dimg[detbb[0]]
found = False
for r in rect:
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
#print "GT:",r
#print "DET:",rb
if overlap(rb, r) >= ovr:
dimg[detbb[0]].remove(r)
found = True
break
if found:
tp[idx] = 1
else:
fp[idx] = 1
thr[idx] = detbb[1]
if show:
pylab.ioff()
prec = numpy.sum(tp) / float(numpy.sum(tp) + numpy.sum(fp))
rec = numpy.sum(tp) / tot
print "Scr:", detbb[1], "Prec:", prec, "Rec:", rec
img = gtImages.getImageByName2(detbb[0])
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
rb = (float(detbb[3]), float(detbb[2]), float(detbb[5]),
float(detbb[4]))
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0], r[1], r[2], r[3], 'b', lw=1.5)
if found:
box(rb[0], rb[1], rb[2], rb[3], 'g', lw=1.5)
else:
box(rb[0], rb[1], rb[2], rb[3], 'r', lw=1.5)
pylab.draw()
pylab.show()
rect = []
raw_input()
return tp, fp, thr, tot
def VOCprlist(gtImages,detlist,show=False,usetr=True,usedf=False,ovr=0.5):
"""
calculate the precision recall curve
"""
#detf=open(detfile,"r")
#detect=detf.readlines()
imname=[]
cnt=0
#ovr=0.49
#print trPosImages.getTotal()
tp=[]
fp=[]
tot=0
for idx in range(gtImages.getTotal()):
print gtImages.getImageName(idx)
if show:
img=gtImages.getImage(idx)
pylab.figure(1)
pylab.clf()
pylab.imshow(img)
#pyr=HOGcompute.HOGcrop(img,interv=interv)
#pyr.pad()
#pyr.pad()
#pyr.contrast()
rect=gtImages.getBBox(idx,usetr=usetr,usedf=usedf)
print rect
if show:
for r in rect:
pylab.figure(1)
pylab.ioff()
box(r[0],r[1],r[2],r[3],'b',lw=1.5)
#raw_input()
tot=tot+len(rect)
#print len(rect),rect
#print rect
for l in detlist:
data=l#.split(" ")
if data[0]==gtImages.getImageName(idx).split("/")[-1].split(".")[0]:
notfound=True
rb=[float(data[3]),float(data[2]),float(data[5]),float(data[4])]
if show:
pylab.ioff()
pylab.text(rb[1],rb[0],data[1])
for id,r in enumerate(rect):
#pylab.figure(1)
#box(r[0],r[1],r[2],r[3],'b',lw=1.5)
#print "entered",data
#rb=[float(data[3]),float(data[2]),float(data[5]),float(data[4])]
#print rb,r,overlap(rb,r)
#pylab.text(rb[1],rb[0],data[1])
if overlap(rb,r)>=ovr:
if show:
pylab.ioff()
box(rb[0],rb[1],rb[2],rb[3],'g',lw=1.5)
del rect[id]
tp.append(float(data[1]))
notfound=False
break
if notfound==True:
if show:
pylab.ioff()
box(rb[0],rb[1],rb[2],rb[3],'r',lw=1)
fp.append(float(data[1]))
#print len(tp),len(fp),tot
#break
if show:
pylab.figure(1)
pylab.show()
pylab.draw()
#raw_input()
return tp,fp,tot
comments are sub par
"""
pygame.init()
figure_size = 500
window = pygame.display.set_mode((figure_size, figure_size))
def draw_text(surf, text, size, x, y):
font = pygame.font.Font(pygame.font.match_font('arial'), size)
text_surface = font.render(text, True, (255, 255, 255))
text_rect = text_surface.get_rect()
text_rect.midtop = (x, y)
surf.blit(text_surface, text_rect)
floorTop = 50 # distance from the bottom
player = box(100, floorTop)
floor = [box(-15, 0, h = figure_size)]
dis = 12.5 # distance between each block
for i in range(1, math.ceil(figure_size/dis)):
colo = sequencial_color(floor[i-1].color, step = 20)
floor.append(box(i*dis, 0, color = colo, h = figure_size))
floorVelo = 5
floorLen = len(floor)
player = box(50, figure_size-floorTop-100)
keys = pygame.key.get_pressed()
playerVelo = 0