def loadCOCOAndOverSeg(im_set="test", detector="sf", N_SPIX=1000, fold=0):
from pickle import dumps, loads
try:
import lz4, pickle
decompress = lambda s: pickle.loads(lz4.decompress(s))
compress = lambda o: lz4.compressHC(pickle.dumps(o))
except:
compress = lambda x: x
decompress = lambda x: x
from gop import contour, dataset, segmentation
FILE_NAME = '/tmp/coco_%s_%s_%d_%d.dat' % (im_set, detector, N_SPIX, fold)
try:
with open(FILE_NAME, 'rb') as f:
over_segs, segmentations = loads(f.read())
f.close()
over_seg = segmentation.ImageOverSegmentationVec()
for i in over_segs:
over_seg.append(decompress(i))
return over_seg, [decompress(i) for i in segmentations], []
#return over_segs,segmentations,[]
except FileNotFoundError:
pass
# Load the dataset
data = dataset.loadCOCO2014(im_set == "train", im_set == "valid", fold)
# COCO has some pretty gray scale images (WTF!!!)
images = [
e['image'] if e['image'].C == 3 else e['image'].tileC(3) for e in data
]
try:
segmentations = [e['segmentation'] for e in data]
except:
segmentations = []
# Do the over-segmentation
if detector == 'sf':
detector = contour.StructuredForest()
detector.load('../data/sf.dat')
elif detector == "mssf":
detector = contour.MultiScaleStructuredForest()
detector.load("../data/sf.dat")
elif detector == 'st':
detector = contour.SketchTokens()
detector.load('../data/st_full_c.dat')
else:
detector = contour.DirectedSobel()
if detector != None:
over_segs = segmentation.generateGeodesicKMeans(
detector, images, N_SPIX)
with open(FILE_NAME, 'wb') as f:
#f.write( dumps( (over_segs,segmentations) ) )
f.write(
dumps(
([compress(i)
for i in over_segs], [compress(i) for i in segmentations])))
f.close()
return over_segs, segmentations, []
def loadVOCAndOverSeg( im_set="test", detector="sf", N_SPIX=1000, EVAL_DIFFICULT=False, year="2012" ):
from pickle import dumps,loads
try:
import lz4, pickle
decompress = lambda s: pickle.loads( lz4.decompress( s ) )
compress = lambda o: lz4.compressHC( pickle.dumps( o ) )
except:
compress = lambda x: x
decompress = lambda x: x
from gop import contour,dataset,segmentation
FILE_NAME = '/tmp/%s_%s_%d_%d_%s.dat'%(im_set,detector,N_SPIX,EVAL_DIFFICULT,year)
try:
with open(FILE_NAME,'rb') as f:
over_segs,segmentations,boxes = loads( f.read() )
f.close()
over_seg = segmentation.ImageOverSegmentationVec()
for i in over_segs:
over_seg.append( decompress(i) )
return over_seg,[decompress(i) for i in segmentations],[decompress(i) for i in boxes]
except IOError:
pass
# Load the dataset
#data = eval("dataset.loadVOC2012_small")(im_set=="train",im_set=="valid",im_set=="test")
data = eval("dataset.loadVOC%s"%year)(im_set=="train",im_set=="valid",im_set=="test")
images = [e['image'] for e in data]
try:
segmentations = [e['segmentation'] for e in data]
except:
segmentations = []
boxes = [[a['bbox'] for a in e['annotation'] if not a['difficult'] or EVAL_DIFFICULT] for e in data]
# Do the over-segmentation
if detector=='sf':
detector = contour.StructuredForest()
detector.load( '../data/sf.dat' )
elif detector == "mssf":
detector = contour.MultiScaleStructuredForest()
detector.load( "../data/sf.dat" )
elif detector=='st':
detector = contour.SketchTokens()
detector.load( '../data/st_full_c.dat' )
else:
detector = contour.DirectedSobel()
if detector != None:
over_segs = segmentation.generateGeodesicKMeans( detector, images, N_SPIX )
#try:
with open(FILE_NAME,'wb') as f:
f.write( dumps( ([compress(i) for i in over_segs],[compress(i) for i in segmentations],[compress(i) for i in boxes]) ) )
f.close()
#except FileNotFoundError:
#pass
return over_segs,segmentations,boxes
def loadCOCOAndOverSeg( im_set="test", detector="sf", N_SPIX=1000, fold=0 ): from pickle import dumps,loads try: import lz4, pickle decompress = lambda s: pickle.loads( lz4.decompress( s ) ) compress = lambda o: lz4.compressHC( pickle.dumps( o ) ) except: compress = lambda x: x decompress = lambda x: x from gop import contour,dataset,segmentation FILE_NAME = '/tmp/coco_%s_%s_%d_%d.dat'%(im_set,detector,N_SPIX,fold) try: with open(FILE_NAME,'rb') as f: over_segs,segmentations = loads( f.read() ) f.close() over_seg = segmentation.ImageOverSegmentationVec() for i in over_segs: over_seg.append( decompress(i) ) return over_seg,[decompress(i) for i in segmentations],[] #return over_segs,segmentations,[] except FileNotFoundError: pass # Load the dataset data = dataset.loadCOCO2014( im_set=="train",im_set=="valid", fold) # COCO has some pretty gray scale images (WTF!!!) images = [e['image'] if e['image'].C==3 else e['image'].tileC(3) for e in data] try: segmentations = [e['segmentation'] for e in data] except: segmentations = [] # Do the over-segmentation if detector=='sf': detector = contour.StructuredForest() detector.load( '../data/sf.dat' ) elif detector == "mssf": detector = contour.MultiScaleStructuredForest() detector.load( "../data/sf.dat" ) elif detector=='st': detector = contour.SketchTokens() detector.load( '../data/st_full_c.dat' ) else: detector = contour.DirectedSobel() if detector != None: over_segs = segmentation.generateGeodesicKMeans( detector, images, N_SPIX ) with open(FILE_NAME,'wb') as f: #f.write( dumps( (over_segs,segmentations) ) ) f.write( dumps( ([compress(i) for i in over_segs],[compress(i) for i in segmentations]) ) ) f.close() return over_segs,segmentations,[]
