def load_lfw_dataset(): fin=open('data/Foreign_Dataset/LFW_model_5_feature.txt','r') lines=fin.read().splitlines() data_num=len(lines)/2 dataset=Dataset() index=3 gt=dict() num=0 gt_dict=dict() for i in xrange(data_num): dataset.imageNameList.append(['data/Foreign_Dataset/LFW_align/'+lines[i*2]]) dataset.rect.append([0,0,178,218]) name=lines[i*2].split('/')[0] if gt_dict.has_key(name): gt_dict[name].append(i) else: gt_dict[name]=list() if gt.has_key(name): dataset.imgID.append(gt[name]) else: gt[name]=index index+=1 dataset.imgID.append(gt[name]) dataset.feature.append(map(float,lines[i*2+1].split())) dataset.size+=1 #dataset.computeAffinity() dataset.Quality=[0.99 for i in xrange(dataset.size)] return dataset,gt_dict
def load_test_data_set(filename): fin = open(filename, 'r') lines = fin.read().splitlines() #data_num = len(lines) / 3 data_num = len(lines) / 4 dataset = Dataset() dataset.Quality=[] for i in xrange(data_num): ''' dataset.imageNameList.append([lines[i * 3]]) dataset.imgID.append(int(lines[i * 3 + 1])) dataset.feature.append(map(float, lines[i * 3 + 2].split())) ''' dataset.imageNameList.append([lines[i * 4]]) dataset.rect.append(map(int,lines[i*4+1].split())) dataset.imgID.append(int(lines[i * 4 + 2])) dataset.feature.append(map(float, lines[i * 4 + 3].split())) #dataset.feature[-1]=dataset.feature[-1][:128] #dataset.Quality.append(1.0) dataset.size += 1 dataset.computeAffinity() dataset.computeQuality() ''' #finetune Quality quality_path='data/Quality/'+filename.split('/')[-1] fin_q=open(quality_path,'r') data_q=fin_q.read().splitlines() fin_q.close() dataset.Quality=map(float,data_q) if len(dataset.Quality)!=dataset.size: return None ''' return dataset
def load_LFW_dataset(filepath): fin=open(filepath,'r') lines=fin.read().splitlines() data_num=len(lines)/3 dataset=Dataset() for i in xrange(data_num): dataset.imageNameList.append([lines[i*3]]) dataset.imgID.append(int(lines[i*3+1])) dataset.rect.append([0,0,178,218]) dataset.feature.append(map(float,lines[i*3+2].split())) dataset.size+=1 dataset.computeAffinity() #dataset.computeQuality() dataset.Quality=[0.9 for i in xrange(dataset.size)] return dataset
def load_nongt_nonquality(filename): fin = open(filename, 'r') lines = fin.read().splitlines() data_num = len(lines) / 3 dataset = Dataset() for i in xrange(data_num): dataset.imageNameList.append([lines[i * 3]]) dataset.rect.append(map(int,lines[i*3+1].split())) dataset.feature.append(map(float, lines[i * 3 + 2].split())) dataset.imgID.append(0) dataset.size += 1 dataset.computeAffinity() dataset.Quality=[0.99 for i in xrange(dataset.size)] #dataset.computeQuality() return dataset
def load_HP_dataset(noise=False): fin=open('/media/heyue/8d1c3fac-68d3-4428-af91-bc478fbdd541/ClusterResearch/clusterQNet/data/HP_model_5_feature.txt','r') lines=fin.read().splitlines() data_num=len(lines)/5 dataset=Dataset() for i in xrange(data_num): dataset.imageNameList.append(['data/HP/'+lines[i*5]]) dataset.rect.append([0,0,100,100]) dataset.imgID.append(int(lines[i*5+4])) dataset.feature.append(map(float,lines[i*5+2].split())) dataset.size+=1 if dataset.imgID[-1]==1 and noise==False: dataset.imageNameList.pop() dataset.rect.pop() dataset.imgID.pop() dataset.feature.pop() dataset.size-=1 dataset.computeAffinity() #dataset.computeQuality() dataset.Quality=[1.0 for i in xrange(dataset.size)] return dataset