SYMBOL INDEX (113 symbols across 16 files)

FILE: noiseprint/feat_spam/mapping.py
  function getIdemMapper (line 15) | def getIdemMapper(num):
  function getSignSymMapper (line 18) | def getSignSymMapper(occo, n):
  function getSignMapper (line 45) | def getSignMapper(occo, n):
  function getPos (line 66) | def getPos(P, n, occo):
  function getCombinations (line 70) | def getCombinations(occo, n):
  function mapper2filter (line 84) | def mapper2filter(mapper, dtype=np.float32):

FILE: noiseprint/feat_spam/residue.py
  function getFiltersResidue (line 16) | def getFiltersResidue(res):
  function getFilterOcco (line 65) | def getFilterOcco(occo, values):

FILE: noiseprint/feat_spam/spam_np_opt.py
  function quantizerScalarEncoder (line 18) | def quantizerScalarEncoder(x, values):
  function getParams (line 25) | def getParams(ordResid, symTranspose, q, T, ordCooc, mapper, strides):
  function computeSpamRes (line 60) | def computeSpamRes(res, params, weights = list(),  normalize = True):
  function getSpamRes (line 138) | def getSpamRes(res, params, ksize, weights = list(), paddingModality = 0):

FILE: noiseprint/network.py
  class FullConvNet (line 15) | class FullConvNet(object):
    method __init__ (line 18) | def __init__(self, images, bnorm_decay, falg_train, num_levels = 17, p...
    method _batch_norm (line 55) | def _batch_norm(self, x, name = 'bnorm'):
    method _bias (line 91) | def _bias(self, x, name = 'bias'):
    method _conv (line 103) | def _conv(self, x, filter_size, out_filters, stride, name='conv'):

FILE: noiseprint/noiseprint.py
  function genNoiseprint (line 32) | def genNoiseprint(img, QF=101, model_name='net'):

FILE: noiseprint/noiseprint_blind.py
  function noiseprint_blind_file (line 22) | def noiseprint_blind_file(filename, model_name='net'):
  function noiseprint_blind (line 38) | def noiseprint_blind(img, QF, model_name='net'):
  function noiseprint_blind_post (line 43) | def noiseprint_blind_post(res, img):
  function genMappFloat (line 54) | def genMappFloat(mapp, valid, range0, range1, imgsize):
  function genMappUint8 (line 62) | def genMappUint8(mapp, valid, range0, range1, imgsize, vmax=None, vmin=N...

FILE: noiseprint/post_em.py
  function faetReduce (line 35) | def faetReduce(feat_list, inds, whiteningFlag = False):
  function getWeights (line 45) | def getWeights(img, res):
  function getCoocValues (line 59) | def getCoocValues(res, img_gray, n_clusters=4, random_state=0):
  function getSpamFromNoiseprint (line 67) | def getSpamFromNoiseprint(res, img_gray, ksize=ksize_default, stride=str...
  function EMgu (line 87) | def EMgu(feats, seed = 0, maxIter = 100, replicates = 10, outliersNlogl ...
  function EMgu_img (line 115) | def EMgu_img(spam, valid, extFeat = range(32), seed = 0, maxIter = 100, ...

FILE: noiseprint/utility/gaussianMixture.py
  class gm (line 16) | class gm:
    method __init__ (line 33) | def __init__(self, dim, listSigmaInds, listSigmaType, outliersProb = -...
    method setRandomParams (line 54) | def setRandomParams(self, X, regularizer = 0, randomState = np.random....
    method setRandomParamsW (line 83) | def setRandomParamsW(self, X, weights, regularizer = 0, randomState = ...
    method getNlogl (line 121) | def getNlogl(self, X):
    method getLoglh (line 188) | def getLoglh(self, X):
    method getLoglhInlier (line 198) | def getLoglhInlier(self, X):
    method maximizationParam (line 210) | def maximizationParam(self, X, post, regularizer = 0):
    method expectation (line 273) | def expectation(self, X):
    method expectationWeighed (line 277) | def expectationWeighed(self, X, weighed):
    method MEstep (line 281) | def MEstep(self, X, post, regularizer = 0):
    method MEstepWeighed (line 286) | def MEstepWeighed(self, X, weights, post, regularizer = 0):
    method EM (line 291) | def EM(self, X, regularizer, maxIter, relErr = 1e-5):
    method EMweighed (line 309) | def EMweighed(self, X, weights, regularizer, maxIter, relErr=1e-5):
  function softmax (line 326) | def softmax(logit):
  function softmaxWeighed (line 334) | def softmaxWeighed(logit, weights):

FILE: noiseprint/utility/utilityRead.py
  function imread2f_pil (line 18) | def imread2f_pil(stream, channel = 1, dtype = np.float32):
  function imread2f_raw (line 37) | def imread2f_raw(stream, channel = 1, dtype = np.float32):
  function imread2f (line 60) | def imread2f(stream, channel = 1, dtype = np.float32):
  function jpeg_qtableinv (line 67) | def jpeg_qtableinv(stream, tnum=0, force_baseline=None):
  function resizeMapWithPadding (line 123) | def resizeMapWithPadding(x, range0, range1, shapeOut):
  function computeMetricsContinue (line 133) | def computeMetricsContinue(values, gt0, gt1):
  function computeMCC (line 150) | def computeMCC(values, gt0, gt1):

FILE: training/code/FCnet.py
  class FullConvNet (line 19) | class FullConvNet(object):
    method __init__ (line 22) | def __init__(self, bnorm_decay=0.9, num_levels=17, outchannels=1):
    method __call__ (line 43) | def __call__(self, x, flag_train, padding='VALID'):
    method _batch_norm (line 59) | def _batch_norm(self, x, flag_train, name='bnorm'):
    method _bias (line 104) | def _bias(self, x, name='bias'):
    method _conv (line 118) | def _conv(self, x, filter_size, out_filters, stride, name='conv', padd...

FILE: training/code/Producer2.py
  class setWorkers_mock (line 2) | class setWorkers_mock():
    method __init__ (line 3) | def __init__(self, indexer, pre_fun, app_fun, buffer_size=100, workers...
    method info (line 20) | def info(self):
    method __enter__ (line 23) | def __enter__(self):
    method __exit__ (line 28) | def __exit__(self, exc_type, exc_value, traceback):
    method __len__ (line 31) | def __len__(self):
    method __iter__ (line 34) | def __iter__(self):
    method __next__ (line 37) | def __next__(self):
  class setWorkers_process (line 55) | class setWorkers_process():
    method __init__ (line 56) | def __init__(self, indexer, pre_fun, app_fun, buffer_size=100, workers...
    method _run_in (line 75) | def _run_in(self, indexer, queue, pre_fun, flag_reset):
    method _run_fun (line 88) | def _run_fun(self, index_process):
    method info (line 97) | def info(self):
    method __enter__ (line 102) | def __enter__(self):
    method __exit__ (line 110) | def __exit__(self, exc_type, exc_value, traceback):
    method __len__ (line 124) | def __len__(self):
    method __iter__ (line 127) | def __iter__(self):
    method __next__ (line 130) | def __next__(self):
  class setWorkers_thread (line 154) | class setWorkers_thread():
    method __init__ (line 155) | def __init__(self, indexer, pre_fun, app_fun, buffer_size=100, workers...
    method _run_in (line 173) | def _run_in(self, indexer, queue, pre_fun, flag_reset):
    method _run_fun (line 186) | def _run_fun(self, index_process):
    method info (line 195) | def info(self):
    method __enter__ (line 200) | def __enter__(self):
    method __exit__ (line 208) | def __exit__(self, exc_type, exc_value, traceback):
    method __len__ (line 220) | def __len__(self):
    method __iter__ (line 223) | def __iter__(self):
    method __next__ (line 226) | def __next__(self):

FILE: training/code/db_utility.py
  function get_list_valid (line 23) | def get_list_valid():
  function get_list_train (line 39) | def get_list_train():
  function jpeg_compression (line 56) | def jpeg_compression(img, quality):
  function jpeg_compression_numpy (line 63) | def jpeg_compression_numpy(x, quality):

FILE: training/code/train_denoiser.py
  function onlyOpenImage (line 72) | def onlyOpenImage(img, *other):
  function clipImage (line 75) | def clipImage(img, wSize, crop0, crop1, indRot):

FILE: training/code/train_noiseprint.py
  function onlyOpenImage (line 77) | def onlyOpenImage(img, *other):
  function clipImage (line 80) | def clipImage(img, wSize, crop0, crop1, indRot):

FILE: training/code/train_utility.py
  function distmxt (line 18) | def distmxt(res):
  function my_loss_paper (line 28) | def my_loss_paper(corr, cm):
  function defLabelClass (line 35) | def defLabelClass(batch_size, nearImg, repMat):
  function make_whitening (line 45) | def make_whitening(x, block_size, regularize_type):
  function getAUC (line 66) | def getAUC(scores, labels):
  function getAUC_dict (line 83) | def getAUC_dict(scores, labels):
  function genListWithCliped (line 114) | def genListWithCliped(list_input, element_num, cliped_num, wSize):
  function genList (line 127) | def genList(imgs, numMaxImg, nearImg, rip):
  class genBatchList (line 148) | class genBatchList():
    method __init__ (line 149) | def __init__(self, batch_size, iterator, flag_reset=True):
    method getNumSamples (line 157) | def getNumSamples(self):
    method __len__ (line 160) | def __len__(self):
    method __iter__ (line 163) | def __iter__(self):
  class genBatch (line 179) | class genBatch():
    method __init__ (line 180) | def __init__(self, batch_size, iterator, flag_reset=True):
    method getNumSamples (line 188) | def getNumSamples(self):
    method __len__ (line 191) | def __len__(self):
    method __iter__ (line 194) | def __iter__(self):

FILE: training/dataset/download_images.py
  function download (line 16) | def download(url, output_dir ):