예제 #1
0
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)
예제 #2
0
    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 )
예제 #3
0
파일: text2image.py 프로젝트: danieljf/cmrf
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)
예제 #4
0
파일: text2image.py 프로젝트: danieljf/cmrf
    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 )
예제 #5
0
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)
예제 #6
0
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)