Src_mask = reconstruct( src_cnts, h, w, val = 1 ) Shape of HxWx3, if differentiate target=TrueĬmd_mask is encoded in the one-hot style, if differentiate target=True.Ĭolor channel, R, G, and B stand for TARGET, SOURCE, and BACKGROUND classes Shape of HxWx1, if differentiate_target=False Image = cv2.resize(image, (128,128), interpolation = cv2.INTER_AREA) Image = cv2.imdecode( np.array(image_jpeg_buffer).astype('uint8').reshape(), 1 ) Lut = dict, raw decoded lut retrieved from LMDB Train_keys = ĭef _load_valid_keys( self, valid_file ) : Keys = ĭef _load_train_keys( self, train_file ) : Keys = list of str, each element is a valid key in LMDB Sample_file = str, path to sample key file '''Load sample keys from a given sample file Return len( self.sample_keys nb_samples( self ) :ĭef _load_sample_keys( self, sample_file ) : Print("INFO: successfully load USC-ISI CMD LMDB with test keys".format( len( self.test_keys ) ) ) Self.differentiate_target = differentiate_target Self.test_keys = self._load_test_keys(test_file) Self.valid_keys = self._load_valid_keys(valid_file) ain_keys = self._load_train_keys(train_file) #add train,valid and test files and load the keys of them Self.sample_keys = self._load_sample_keys(sample_file) Yue _init_( self, lmdb_dir, sample_file,train_file,valid_file,test_file, differentiate_target = True ) : In: European Conference on Computer Vision (ECCV). "BusterNet: Detecting Image Copy-Move ForgeryWith Source/Target Localization". detailed synthesis process can be found in paper However, the dimension of each sample may or may not the sameģ. the output of "get_samples" or "_call_", is a list of samples samples.keysĭifferentiate_target = bool, whether or not generate 3-class target mapġ. Sample_file = file path ot the sample list, e.g. # get the exact 50th sample in the dataset # retrieve 24 random samples in the dataset # retrieve the first 24 samples in the dataset Sample_file=os.path.join( lmdb_dir, 'samples.keys'), Lmdb_dir = os.path.dirname( os.path.realpath(_file_) )ĭataset = USCISI_CMD_API( lmdb_dir=lmdb_dir, “”" Simple API for reading the USCISI CMD dataset This API simply loads and parses CMD samples from LMDB I start working on this but I don’t know how I will convert lmdb datasets to pytorch tensor without error to start creating (new model and train the datasets) and also using pre-trained model that used in this linkįrom ansforms import ToTensorįrom import DataLoaderįrom import random_split
0 Comments
Leave a Reply. |