class Text2Image:
def __init__(self,
nnquery_file,
qryClick_file,
dev_feat_path,
train_feat_path,
top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(
nnquery_file, top_n) # format: query_id query_id score ...
print(
"[%s] %d queris and these top %d nearest neighbours with score loaded from %s"
% (self.__class__.__name__, len(
self.qid2nnqidscore), top_n, nnquery_file))
self.qry2img_clk = readQueryClickture(
qryClick_file) # format: query_id \t image_id click ...
print("[%s] %d queris and these click info loaded from %s" %
(self.__class__.__name__, len(self.qry2img_clk), qryClick_file))
self.imgSimer = ImageSimer(dev_feat_path, train_feat_path)
#......................text2image.............................
def text2image(self, qid, img_list, qrythres=0.3, clickthres=1):
scorelist = []
for img in img_list:
score = self.text2image_one(qid, img, qrythres, clickthres)
scorelist.append(score)
return scorelist
def text2image_one(self, qid, image, qrythres=0.3, clickthres=1):
score_list = []
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
# score = float(score)
if score < qrythres: break
# TODO
# clickthres
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0:
score_list.append(score * self.imgSimer.calsimiImagewithClick(
image, self.qry2img_clk[int(trainqid)], clickthres))
break
score_list.append(score * self.imgSimer.calsimiImagewithClick(
image, self.qry2img_clk[int(trainqid)], clickthres))
return 0 if len(score_list) == 0 else sum(score_list) / len(score_list)
def __init__(self, nnquery_file, qryClick_file, dev_feat_path, train_feat_path, top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(nnquery_file, top_n) # format: query_id query_id score ...
print ("[%s] %d queris and these top %d nearest neighbours with score loaded from %s" %
(self.__class__.__name__, len(self.qid2nnqidscore) , top_n, nnquery_file))
self.qry2img_clk = readQueryClickture(qryClick_file) # format: query_id \t image_id click ...
print ("[%s] %d queris and these click info loaded from %s" %
(self.__class__.__name__, len(self.qry2img_clk) , qryClick_file))
self.imgSimer = ImageSimer( dev_feat_path, train_feat_path )
class Text2Image:
def __init__(self, nnquery_file, qryClick_file, dev_feat_path, train_feat_path, top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(nnquery_file, top_n) # format: query_id query_id score ...
print ("[%s] %d queris and these top %d nearest neighbours with score loaded from %s" %
(self.__class__.__name__, len(self.qid2nnqidscore) , top_n, nnquery_file))
self.qry2img_clk = readQueryClickture(qryClick_file) # format: query_id \t image_id click ...
print ("[%s] %d queris and these click info loaded from %s" %
(self.__class__.__name__, len(self.qry2img_clk) , qryClick_file))
self.imgSimer = ImageSimer( dev_feat_path, train_feat_path )
#......................text2image.............................
def text2image(self, qid, img_list, qrythres = 0.3, clickthres = 1):
scorelist = []
for img in img_list:
score = self.text2image_one(qid, img, qrythres, clickthres )
scorelist.append(score)
return scorelist
def text2image_one(self, qid, image, qrythres = 0.3, clickthres = 1):
score_list = []
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
# score = float(score)
if score < qrythres: break
# TODO
# clickthres
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0:
score_list.append(score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres))
break
score_list.append(score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres))
return 0 if len(score_list) == 0 else sum(score_list) / len(score_list)
def __init__(self, nnquery_file, qryClick_file, dev_feat_path, train_feat_path, top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(nnquery_file, top_n) # format: query_id query_id score ...
print ("[%s] %d queris and these top %d nearest neighbours with score loaded from %s" %
(self.__class__.__name__, len(self.qid2nnqidscore) , top_n, nnquery_file))
self.qry2img_clk = readQueryClickture(qryClick_file) # format: query_id \t image_id click ...
print ("[%s] %d queris and these click info loaded from %s" %
(self.__class__.__name__, len(self.qry2img_clk) , qryClick_file))
self.imgSimer = ImageSimer( dev_feat_path, train_feat_path )
class Text2Image:
def __init__(self, nnquery_file, qryClick_file, dev_feat_path, train_feat_path, top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(nnquery_file, top_n) # format: query_id query_id score ...
print ("[%s] %d queris and these top %d nearest neighbours with score loaded from %s" %
(self.__class__.__name__, len(self.qid2nnqidscore) , top_n, nnquery_file))
self.qry2img_clk = readQueryClickture(qryClick_file) # format: query_id \t image_id click ...
print ("[%s] %d queris and these click info loaded from %s" %
(self.__class__.__name__, len(self.qry2img_clk) , qryClick_file))
self.imgSimer = ImageSimer( dev_feat_path, train_feat_path )
#......................text2image.............................
def text2image(self, qid, img_list, qrythres = 0.3, clickthres = 1):
scorelist = []
for img in img_list:
score = self.text2image_one(qid, img, qrythres, clickthres )
scorelist.append(score)
return scorelist
def text2image_one(self, qid, image, qrythres = 0.3, clickthres = 1):
score_list = []
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
# score = float(score)
if score < qrythres: break
# TODO
# clickthres
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0:
score_list.append(score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres))
break
score_list.append(score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres))
return 0 if len(score_list) == 0 else sum(score_list) / len(score_list)
# .......................textAnnotion...........................
def textAnnotation(self, qid, img_list, topImages = 50, qrythres = 0.3, clickthres = 1):
scorelist = []
iid_list, weight_list = self.getWeightedImages(qid, topImages, qrythres, clickthres)
if len(iid_list) == 0 or sum(weight_list) == 0: # cannot find similiar images with similiar query
return scorelist
else:
# for img in img_list:
# score = self.imgSimer.clasimiImgwithWeightImgs(img, iid_list, weight_list)
# scorelist.append(score)
scorelist = self.imgSimer.simiImgs_WeightImgs(img_list, iid_list, weight_list)
return scorelist
def getWeightedImages(self, qid, topImages = 50, qrythres = 0.3, clickthres = 1):
im2click = {}
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
score = float(score)
if score < qrythres: continue
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0 and len(im2click) >= 5:
break
for iid, click in self.qry2img_clk[int(trainqid)]:
if int(click) < clickthres: continue
im2click[iid] = im2click.get(iid,0) + math.log(int(click)+1)*score
weightedImages = sorted(im2click.iteritems(), key=lambda v:v[1], reverse=True)
img_list = [x[0] for x in weightedImages[:topImages] ]
weight_list = [x[1] for x in weightedImages[:topImages] ]
return (img_list, weight_list)
class Text2Image:
def __init__(self, nnquery_file, qryClick_file, dev_feat_path, train_feat_path, top_n=50):
# load nn info with score of query
self.qid2nnqidscore = readNnData(nnquery_file, top_n) # format: query_id query_id score ...
print(
"[%s] %d queris and these top %d nearest neighbours with score loaded from %s"
% (self.__class__.__name__, len(self.qid2nnqidscore), top_n, nnquery_file)
)
self.qry2img_clk = readQueryClickture(qryClick_file) # format: query_id \t image_id click ...
print(
"[%s] %d queris and these click info loaded from %s"
% (self.__class__.__name__, len(self.qry2img_clk), qryClick_file)
)
self.imgSimer = ImageSimer(dev_feat_path, train_feat_path)
# ......................text2image.............................
def text2image(self, qid, img_list, qrythres=0.3, clickthres=1):
scorelist = []
for img in img_list:
score = self.text2image_one(qid, img, qrythres, clickthres)
scorelist.append(score)
return scorelist
def text2image_one(self, qid, image, qrythres=0.3, clickthres=1):
score_list = []
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
# score = float(score)
if score < qrythres:
break
# TODO
# clickthres
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0:
score_list.append(
score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres)
)
break
score_list.append(
score * self.imgSimer.calsimiImagewithClick(image, self.qry2img_clk[int(trainqid)], clickthres)
)
return 0 if len(score_list) == 0 else sum(score_list) / len(score_list)
# .......................textAnnotion...........................
def textAnnotation(self, qid, img_list, topImages=50, qrythres=0.3, clickthres=1):
scorelist = []
iid_list, weight_list = self.getWeightedImages(qid, topImages, qrythres, clickthres)
if len(iid_list) == 0 or sum(weight_list) == 0: # cannot find similiar images with similiar query
return scorelist
else:
# for img in img_list:
# score = self.imgSimer.clasimiImgwithWeightImgs(img, iid_list, weight_list)
# scorelist.append(score)
scorelist = self.imgSimer.simiImgs_WeightImgs(img_list, iid_list, weight_list)
return scorelist
def getWeightedImages(self, qid, topImages=50, qrythres=0.3, clickthres=1):
im2click = {}
flag = 0
for trainqid, score in self.qid2nnqidscore[qid]:
score = float(score)
if score < qrythres:
continue
if score == 1.0:
flag = 1
if score < 1.0 and flag > 0 and len(im2click) >= 5:
break
for iid, click in self.qry2img_clk[int(trainqid)]:
if int(click) < clickthres:
continue
im2click[iid] = im2click.get(iid, 0) + math.log(int(click) + 1) * score
weightedImages = sorted(im2click.iteritems(), key=lambda v: v[1], reverse=True)
img_list = [x[0] for x in weightedImages[:topImages]]
weight_list = [x[1] for x in weightedImages[:topImages]]
return (img_list, weight_list)