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Added baseline evaluation scripts.

master
Brian Davis 8 years ago
parent
914a579cf9
commit
9fb5af4584
2 changed files with 313 additions and 0 deletions
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  1. +134
    -0
      baselines/grabCutCropEval.py
  2. +179
    -0
      baselines/noCutMeanCutCropEval.py

+ 134
- 0
baselines/grabCutCropEval.py View File

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#from grabCutCrop import cropMask
from multiprocessing.dummy import Pool as ThreadPool
import cv2
import numpy as np

import sys

def cropMask(imageDir,saveDir,imagePath, border):
interName = saveDir+'grabCut_'+imagePath.replace('/','_')+'.png'
mask=cv2.imread(interName,0)
if mask is None or mask.shape[0]==0:
img = cv2.imread(imageDir+imagePath)
if img is None:
print "Could not read: "+imageDir+imagePath
mask = np.zeros(img.shape[:2],np.uint8)
bgdModel = np.zeros((1,65),np.float64)
fgdModel = np.zeros((1,65),np.float64)
rect = (border,border,img.shape[1]-2*border,img.shape[0]-2*border) #(x,y,w,h)
cv2.grabCut(img,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_RECT)
cv2.imwrite(interName,mask)


mask2 = np.where((mask==2)|(mask==0),0,1).astype('uint8')

revMask = 1-mask2
#fix the mask so that there is only one CC
num, labels, stats, centiods = cv2.connectedComponentsWithStats(revMask, 8, cv2.CV_32S)
maxLabel=None
maxSize=0
for l in range(1,num):
if stats[l,cv2.CC_STAT_AREA] > maxSize:
maxSize = stats[l,cv2.CC_STAT_AREA]
maxLabel = l


return (labels!=maxLabel).astype('uint8')

if len(sys.argv)<5:
print 'This evalutes using grab cut'
print 'usage: '+sys.argv[0]+' gtFile.csv imageDir saveIntermDir numThreads' # [reverse]'
exit(0)

gtFile=sys.argv[1]
imageDir=sys.argv[2]
if imageDir[-1]!='/':
imageDir+='/'
saveDir=sys.argv[3]
if saveDir[-1]!='/':
saveDir+='/'
numThreads = int(sys.argv[4])
reverse=False
#if len(sys.argv)>5 and sys.argv[5][0]=='r':
# reverse=True

sumIOU=0
countIOU=0

scale=1

print 'eval on '+gtFile

outFile = gtFile+'_fullgrab.res'
#numLines=0
#try:
# with open(outFile,'r') as f:
# numLines = len(f.readlines())
#except IOError:
# numLines=0
try:
out = open(outFile,'a')
except IOError:
out = open(outFile,'w')



def worker(line):
global imageDir, saveDir, reverse
try:
p = line.split(',')
imagePath = p[0]
x1 = float(p[1])
y1 = float(p[2])
x2 = float(p[3])
y2 = float(p[4])
x3 = float(p[5])
y3 = float(p[6])
x4 = float(p[7])
y4 = float(p[8])
if reverse:
tmpX=x4
tmpY=y4
x4=x3
y4=y3
x3=tmpX
y4=tmpY
#type = p[9]
if x1<0:
return None,None

#cc+=1
#if cc<=numLines:
# return None, None

#image = cv2.imread(imageDir+imagePath)
#if image.shape[0]==0:
# print 'failed to open '+imageDir+imagePath
#image = cv2.resize(image,(0,0),None,scale,scale)
mask = cropMask(imageDir,saveDir,imagePath,5)
gtMask = np.zeros(mask.shape,np.uint8)
cv2.fillConvexPoly(gtMask, np.array([[x1,y1], [x2,y2], [x3,y3], [x4,y4]], np.int32), 1, 8)

intersection = np.sum(mask&gtMask)
union = np.sum(mask|gtMask)
if float(intersection)/union < 0.6:
cv2.imwrite(saveDir+"ERR_"+imagePath.replace('/','_')+'.png',mask*255);
return imagePath, float(intersection)/union
except:
print 'Error: '
print sys.exc_info()

with open(gtFile) as f:
pool = ThreadPool(numThreads)
results = pool.map(worker, f.readlines())
for (imagePath, iou) in results:
if imagePath is not None:
sumIOU += iou
out.write(imagePath+' '+str(iou)+'\n')
countIOU += 1

out.write('mean IOU for '+gtFile+': '+str(sumIOU/countIOU)+'\n')
out.close()
print 'mean IOU for '+gtFile+': '+str(sumIOU/countIOU)



+ 179
- 0
baselines/noCutMeanCutCropEval.py View File

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#from grabCutCrop import cropMask
import cv2
import numpy as np

import sys

def cropMaskNo(imgH,imgW):
mask = np.ones((imgH,imgW),np.uint8)
return mask
def cropMaskPortion(imgH,imgW,xp1,yp1,xp2,yp2,xp3,yp3,xp4,yp4):
mask = np.zeros((imgH,imgW),np.uint8)
y1 = yp1*imgH
x1 = xp1*imgW
y2 = yp2*imgH
x2 = xp2*imgW
y3 = yp3*imgH
x3 = xp3*imgW
y4 = yp4*imgH
x4 = xp4*imgW
cv2.fillConvexPoly(mask, np.array([[x1,y1], [x2,y2], [x3,y3], [x4,y4]], np.int32), 1, 8)
return mask

if len(sys.argv)<3:
print 'Computes no-crop and mean-crop baselines, generates two files {gtFile}_fullno.res and {gtFile}_fullmean.res'
print 'usage: '+sys.argv[0]+' gtFile.csv imageDir [mean_x1 mean_y1 mean_x2 mean_y2 mean_x3 mean_y3 mean_x4 mean_y4]'
print ' (optional mean box)'
exit()

gtFile=sys.argv[1]
imageDir=sys.argv[2]
if imageDir[-1]!='/':
imageDir+='/'
reverse=False

sumIOU_no=0
sumIOU_mean=0
countIOU=0

scale=1

print 'eval on '+gtFile

outFile_no = gtFile+'_fullno.res'
outFile_mean = gtFile+'_fullmean.res'
numLines=0
try:
with open(outFile_no,'r') as f:
numLines = len(f.readlines())
except IOError:
numLines=0
try:
out_no = open(outFile_no,'a')
except IOError:
out_no = open(outFile_no,'w')

try:
out_mean = open(outFile_mean,'a')
except IOError:
out_mean = open(outFile_mean,'w')

with open(gtFile) as f:
cc=0
lines = f.readlines()
xp1=0
yp1=0
xp2=0
yp2=0
xp3=0
yp3=0
xp4=0
yp4=0
imgHW={}
for line in lines:
p = line.split(',')
imagePath = p[0]
x1 = float(p[1])*scale
y1 = float(p[2])*scale
x2 = float(p[3])*scale
y2 = float(p[4])*scale
x3 = float(p[5])*scale
y3 = float(p[6])*scale
x4 = float(p[7])*scale
y4 = float(p[8])*scale
if reverse:
tmpX=x4
tmpY=y4
x4=x3
y4=y3
x3=tmpX
y4=tmpY
#type = p[9]
if x1<0:
continue
cc+=1
image = cv2.imread(imageDir+imagePath)
imgHW[imagePath]=(int(image.shape[0]*scale),int(image.shape[1]*scale))
xp1 += x1/(image.shape[1]*scale)
yp1 += y1/(image.shape[0]*scale)
xp2 += x2/(image.shape[1]*scale)
yp2 += y2/(image.shape[0]*scale)
xp3 += x3/(image.shape[1]*scale)
yp3 += y3/(image.shape[0]*scale)
xp4 += x4/(image.shape[1]*scale)
yp4 += y4/(image.shape[0]*scale)
xp1/=cc
yp1/=cc
xp2/=cc
yp2/=cc
xp3/=cc
yp3/=cc
xp4/=cc
yp4/=cc
if len(sys.argv)>9:
xp1=float(sys.argv[3])
yp1=float(sys.argv[4])
xp2=float(sys.argv[5])
yp2=float(sys.argv[6])
xp3=float(sys.argv[7])
yp3=float(sys.argv[8])
xp4=float(sys.argv[9])
yp4=float(sys.argv[10])
else:
print str(xp1)+' '+str(yp1)+' '+str(xp2)+' '+str(yp2)+' '+str(xp3)+' '+str(yp3)+' '+str(xp4)+' '+str(yp4)

cc=0
for line in lines:
p = line.split(',')
imagePath = p[0]
x1 = float(p[1])*scale
y1 = float(p[2])*scale
x2 = float(p[3])*scale
y2 = float(p[4])*scale
x3 = float(p[5])*scale
y3 = float(p[6])*scale
x4 = float(p[7])*scale
y4 = float(p[8])*scale
if reverse:
tmpX=x4
tmpY=y4
x4=x3
y4=y3
x3=tmpX
y4=tmpY
#type = p[9]
if x1<0:
continue

cc+=1
if cc<=numLines:
continue

#image = cv2.imread(imageDir+imagePath)
hw = imgHW[imagePath]
#print hw
mask_no = cropMaskNo(hw[0],hw[1])
mask_mean = cropMaskPortion(hw[0],hw[1],xp1,yp1,xp2,yp2,xp3,yp3,xp4,yp4)
gtMask = np.zeros(mask_no.shape,np.uint8)
cv2.fillConvexPoly(gtMask, np.array([[x1,y1], [x2,y2], [x3,y3], [x4,y4]], np.int32), 1, 8)

intersection_no = np.sum(mask_no&gtMask)
union_no = np.sum(mask_no|gtMask)
sumIOU_no += float(intersection_no)/union_no
out_no.write(imagePath+' '+str(float(intersection_no)/union_no)+'\n')
out_no.flush()

intersection_mean = np.sum(mask_mean&gtMask)
union_mean = np.sum(mask_mean|gtMask)
sumIOU_mean += float(intersection_mean)/union_mean
out_mean.write(imagePath+' '+str(float(intersection_mean)/union_mean)+'\n')
out_mean.flush()

countIOU += 1

out_no.write('mean IOU for '+gtFile+': '+str(sumIOU_no/countIOU)+'\n')
out_no.close()

out_mean.write('mean IOU for '+gtFile+': '+str(sumIOU_mean/countIOU)+'\n')
out_mean.close()


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