Python progressbar 模块，Bar() 实例源码

def render(self, length=None, progress=False):
        """
        Render this signal into an numpy array of floats. Return the array.

        :param length:      The length to render, in seconds. Optional.
        :param progress:    Whether to show a progress bar for rendering
        """
        if progress and not progressbar:
            print('Install the progressbar module to see a progress bar for rendering')
            progress = False

        duration = self.duration if length is None else length * SAMPLE_RATE
        if duration == float('inf'):
            duration = 3*SAMPLE_RATE
        else:
            duration = int(duration)
        out = numpy.empty((duration, 1))

        pbar = progressbar.ProgressBar(widgets=['Rendering: ', progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()], maxval=duration-1).start() if progress else None

        for i in range(duration):
            out[i] = self.amplitude(i)
            if pbar: pbar.update(i)
        if pbar: pbar.finish()
        return out

def main():
    uri, outfile, dataset = get_arguments()
    fd = tempfile.NamedTemporaryFile()
    progress = ProgressBar(widgets=[Percentage(), ' ', Bar(), ' ', ETA(), ' ', FileTransferSpeed()])

    def update(count, blockSize, totalSize):
        if progress.maxval is None:
            progress.maxval = totalSize
            progress.start()
        progress.update(min(count * blockSize, totalSize))

    urllib.urlretrieve(uri, fd.name, reporthook = update)
    if dataset == 'zinc12':
        df = pandas.read_csv(fd.name, delimiter = '\t')
        df = df.rename(columns={'SMILES':'structure'})
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)
    elif dataset == 'chembl22':
        df = pandas.read_table(fd.name,compression='gzip')
        df = df.rename(columns={'canonical_smiles':'structure'})
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)
        pass
    else:
        df = pandas.read_csv(fd.name, delimiter = '\t')
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)

def knn_masked_data(trX,trY,missing_data_dir, input_shape, k):

    raw_im_data = np.loadtxt(join(script_dir,missing_data_dir,'index.txt'),delimiter=' ',dtype=str)
    raw_mask_data = np.loadtxt(join(script_dir,missing_data_dir,'index_mask.txt'),delimiter=' ',dtype=str)
    # Using 'brute' method since we only want to do one query per classifier
    # so this will be quicker as it avoids overhead of creating a search tree
    knn_m = KNeighborsClassifier(algorithm='brute',n_neighbors=k)
    prob_Y_hat = np.zeros((raw_im_data.shape[0],int(np.max(trY)+1)))
    total_images = raw_im_data.shape[0]
    pbar = progressbar.ProgressBar(widgets=[progressbar.FormatLabel('\rProcessed %(value)d of %(max)d Images '), progressbar.Bar()], maxval=total_images, term_width=50).start()
    for i in range(total_images):
        mask_im=load_image(join(script_dir,missing_data_dir,raw_mask_data[i][0]), input_shape,1).reshape(np.prod(input_shape))
        mask = np.logical_not(mask_im > eps) # since mask is 1 at missing locations
        v_im=load_image(join(script_dir,missing_data_dir,raw_im_data[i][0]), input_shape, 255).reshape(np.prod(input_shape))
        rep_mask = np.tile(mask,(trX.shape[0],1))
        # Corrupt whole training set according to the current mask
        corr_trX = np.multiply(trX, rep_mask)        
        knn_m.fit(corr_trX, trY)
        prob_Y_hat[i,:] = knn_m.predict_proba(v_im.reshape(1,-1))
        pbar.update(i)
    pbar.finish()
    return prob_Y_hat

def preprocess(self, questions: List[QASetting],
                   answers: Optional[List[List[Answer]]] = None,
                   is_eval: bool = False) -> List[XQAAnnotation]:

        if answers is None:
            answers = [None] * len(questions)
        preprocessed = []
        if len(questions) > 1000:
            bar = progressbar.ProgressBar(
                max_value=len(questions),
                widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
            for q, a in bar(zip(questions, answers)):
                preprocessed.append(self.preprocess_instance(q, a))
        else:
            for q, a in zip(questions, answers):
                preprocessed.append(self.preprocess_instance(q, a))

        return preprocessed

def preprocess(self, questions: List[QASetting],
                   answers: Optional[List[List[Answer]]] = None,
                   is_eval: bool = False) -> List[MCAnnotation]:
        if answers is None:
            answers = [None] * len(questions)
        preprocessed = []
        if len(questions) > 1000:
            bar = progressbar.ProgressBar(
                max_value=len(questions),
                widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
            for i, (q, a) in bar(enumerate(zip(questions, answers))):
                preprocessed.append(self.preprocess_instance(i, q, a))
        else:
            for i, (q, a) in enumerate(zip(questions, answers)):
                preprocessed.append(self.preprocess_instance(i, q, a))

        return preprocessed

def collect_mailids(server):
    folders = server.list_folders()

    #construct progressbar
    progressbar_widgets = [
        '[Searching for mails on server] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ']
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=len(folders)).start()

    #collect all mailids for all folders
    folder_contents = {}
    folder_progress = 0
    for flags, delimiter, folder in folders:
        #read all mailids for the folder
        server.select_folder(folder, readonly=True)
        folder_contents[folder] = server.search()

        #update progrssbar
        folder_progress += 1
        progressbar_instance.update(folder_progress)

    progressbar_instance.finish()
    return folder_contents

def download(download_list, total_download_size):
    progressbar_widgets = [
        '[Downloading mails            ] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ',
        progressbar.ETA(), ' ',
        bitmath.integrations.BitmathFileTransferSpeed()]
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=int(total_download_size)).start()

    downloaded_size = bitmath.Byte(0)
    for folder, mails in download_list.items():
        server.select_folder(folder, readonly=True)
        for mailid, mailfilename, mailsize in mails:
            #make parent directory
            if not os.path.isdir(os.path.dirname(mailfilename)):
                os.makedirs(os.path.dirname(mailfilename))

            #download mail
            with open(mailfilename, 'wb') as mailfile:
                mailfile.write(server.fetch([mailid], ['RFC822'])[mailid][b'RFC822'])

            #update progressbar
            downloaded_size += mailsize
            progressbar_instance.update(int(downloaded_size))
    progressbar_instance.finish()

def bruteforce():
    import progressbar
    from time import sleep
    bar = progressbar.ProgressBar(maxval=60, \
        widgets=[progressbar.Bar('==', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    for i in xrange(10):
        bar.update(i+1)
        sleep(0.05)
        wordlist = "/root/2fassassin/crack/wordlist/2fa-wordlist.txt"
        target = "/root/2fassassin/loot/*.pfx"
        sign = ""
        sign += "crackpkcs12 -v -b"
        sign += " "
        sign += target
        sign += "| tee crack.log"
        os.system(sign)
    bar.finish()
    sys.exit()

def bruteforce():
    import progressbar
    from time import sleep
    bar = progressbar.ProgressBar(maxval=60, \
        widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    for i in xrange(10):
        bar.update(i+1)
        sleep(0.05)
        wordlist = "/root/2fassassin/crack/wordlist/2fa-wordlist.txt"
        target = "/root/2fassassin/loot/*.pfx"
        sign = ""
        sign += "crackpkcs12 -v -b"
        sign += " "
        sign += target
        sign += "| tee crack.log"
        os.system(sign)
    bar.finish()
    sys.exit()

def Steg_brute(ifile, dicc):
    i = 0
    ofile = ifile.split('.')[0] + "_flag.txt"
    nlines = len(open(dicc).readlines())
    with open(dicc, 'r') as passFile:
        pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=nlines).start()
        for line in passFile.readlines():
            password = line.strip('\n')
            r = commands.getoutput("steghide extract -sf %s -p '%s' -xf %s" % (ifile, password, ofile))
            if not "no pude extraer" in r and not "could not extract" in r:
                print(color.GREEN + "\n\n " + r + color.ENDC)
                print("\n\n [+] " + color.INFO + "Information obtained with password:" + color.GREEN + " %s\n" % password + color.ENDC)
                if check_file(ofile):
                    with open(ofile, 'r') as outfile:
                        for line in outfile.readlines():
                            print(line)
                break
            pbar.update(i + 1)
            i += 1

def progress_bar(n):
    import progressbar
    return progressbar.ProgressBar(
        max_value=n,
        widgets=[
            progressxbar.Percentage(),
            ' ',
            '(',
            progressbar.SimpleProgress(),
            ')',
            ' ',
            progressbar.Bar(),
            ' ',
            progressbar.AdaptiveETA(),
        ])


# http://code.activestate.com/recipes/577058/

def __call__(self, progress, data, width):
        if progress.end_time:
            return self.finish_msg

        if progress.max_value is progressbar.UnknownLength:
            bar = progressbar.BouncingBar
        else:
            bar = progressbar.Bar
        line = bar.__call__(self, progress, data, width)

        if data["percentage"] is None:
            msg = self.msg
        else:
            msg = "%s (%d%%)" % (self.msg, data["percentage"])

        offset = width // 2 - len(msg) // 2

        return line[:offset] + msg + line[offset + len(msg):]

def __init__(self, *args, **kwargs):
        self.dld = FileDownloader()
        self.dld.stage(self.cmd_name)

        load_continents()
        load_oceans()
        load_currencies()
        load_languages()

        self.widgets = [
            MemoryUsage(),
            progressbar.ETA(),
            ' |Processed: ',
            progressbar.Counter(),
            ' |Done: ',
            progressbar.Percentage(),
            progressbar.Bar(),
        ]
        return super().__init__(*args, **kwargs)

def __init__(self, name, max_value=100, history_len=5, display=True,
            display_data={'train':['loss', 'accuracy'], 'test':['loss', 'accuracy']},
            level=logging.INFO, train_log_mode='TRAIN_PROGRESS', test_log_mode='TEST_PROGRESS'):
        super(ProgressbarLogger, self).__init__(
                name, level=level, display=display, logfile=None,
                train_log_mode=train_log_mode, test_log_mode=test_log_mode)

        self.train_log_data = {}
        self.test_log_data = {}
        self.max_value = max_value
        self.history_len = history_len
        self.display_data = display_data
        self.mode['TRAIN_PROGRESS'] = self.log_train_progress
        self.mode['TEST_PROGRESS'] = self.log_test_progress

        # create logging format
        self.widgets = [progressbar.FormatLabel('(%(value)d of %(max)s)'),
                ' ', progressbar.Percentage(),
                ' ', progressbar.Bar()]
        self.dynamic_data = {k+'_'+kk: 0.0 for k in display_data.keys() for kk in display_data[k]}
        diff_data = {'diff_'+k+'_'+kk: 0.0 for k in display_data.keys() for kk in display_data[k]}
        self.dynamic_data.update(diff_data)
        for t in display_data.keys():
            ddstr = ' [' + t + ']'
            for s in display_data[t]:
                value_name = t + '_' + s
                ddstr = ddstr + ' ' + s + ':' + '%(' + value_name + ').3f (%(diff_' + value_name + ').3f)'
            self.widgets.append(progressbar.FormatLabel(ddstr))
        self.widgets.extend(['|', progressbar.FormatLabel('Time: %(elapsed)s'), '|', progressbar.AdaptiveETA()])

def train(self, epochs, batch_size, learning_rate, save_to=None):

        self.train_step = pt.apply_optimizer(tf.train.AdamOptimizer(learning_rate, epsilon=1), losses = [self.error_function])
        init = tf.initialize_all_variables()
        self.sess.run(init)
        pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=epochs).start()
        while self.get_epoch() < epochs:
            input_data = self.hdf5reader.next()
            _, loss_value = self.sess.run(
                [self.train_step, self.error_function],
                {
                    self.encoder.input_data: input_data
                }
            )
            pbar.update(self.get_epoch())
        pbar.finish()

def images_to_hdf5(dir_path, output_hdf5, size = (112,112), channels = 3, resize_to = None):
    files = sorted(os.listdir(dir_path))
    nr_of_images = len(files)
    if resize_to:
        size = resize_to
    i = 0
    pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=nr_of_images).start()
    data = np.empty(shape=(nr_of_images, size[0], size[1], channels), dtype=np.uint8)
    for f in files:
        datum = imread(dir_path + '/' + f)
        if resize_to:
            datum = np.asarray(Image.fromarray((datum), 'RGB').resize((size[0],size[1]), PIL.Image.ANTIALIAS))
        data[i,:,:,:] = datum
        i = i + 1
        pbar.update(i)
    pbar.finish()
    with h5py.File(output_hdf5, 'w') as hf:
        hf.create_dataset('data', data=data)

def load_corpus(self, corenlpserver, process=True):
        """
        Use the PubMed web services to retrieve the title and abstract of each PMID
        :param corenlpserver:
        :param process:
        :return:
        """
        time_per_abs = []
        widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.AdaptiveETA(), ' ', pb.Timer()]
        pbar = pb.ProgressBar(widgets=widgets, maxval=len(self.pmids), redirect_stdout=True).start()
        for i, pmid in enumerate(self.pmids):
            t = time.time()
            newdoc = PubmedDocument(pmid)
            if newdoc.abstract == "":
                logging.info("ignored {} due to the fact that no abstract was found".format(pmid))
                continue
            newdoc.process_document(corenlpserver, "biomedical")
            self.documents["PMID" + pmid] = newdoc
            abs_time = time.time() - t
            time_per_abs.append(abs_time)
            pbar.update(i+1)
        pbar.finish()
        abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
        logging.info("average time per abstract: %ss" % abs_avg)

def getProgress(self, url, fileSize):
        status = json.loads(urllib.urlopen(url).read())
        if len(status["data"]) ==0 :
            logger.info(url + " upload done ")
            return  True
        widgets = ['Progress: ', Percentage(), ' ', Bar(
            marker=RotatingMarker('>-=')), ' ', ETA(), ' ', FileTransferSpeed()]
        pbar = ProgressBar(widgets=widgets, maxval=fileSize).start()
        upload_size = 0
        while upload_size < fileSize:
            _response = self.doGet(url)
            _data = json.loads(_response)
            upload_size = long(_data["data"]["upload_size"])
            total_size = long(_data["data"]["total_size"])
            if upload_size == 0 and total_size == 0:
                break
            pbar.update(upload_size)
            time.sleep(1)
        pbar.finish()
        logger.info(url + " upload done")
        return True

        """
        ??????
        """

def scrape_mlb_odds_range(min_date=None, max_date=None):
  min_date = min_date or datetime.datetime.today() - datetime.timedelta(days=1)
  max_date = max_date or datetime.datetime.today()

  if isinstance(min_date, basestring):
    min_date = parser.parse(min_date)
  if isinstance(max_date, basestring):
    max_date = parser.parse(max_date)

  date = min_date
  pbar = progressbar.ProgressBar(widgets=[progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()],
                                 maxval=int((max_date-min_date).total_seconds() / (60*60*24)) + 1)
  pbar.start()
  saved = 0
  hit = 0
  while date <= max_date:
    day_odds = load_odds_for_day(date)
    if day_odds is not None and len(day_odds) > 0:
      save_sbr_odds_info('mlb', date, day_odds)
      saved += 1
    hit += 1
    date += datetime.timedelta(days=1)
    pbar.update(value=hit)
  pbar.finish()
  return saved

def scrape_nba_odds_range(min_date=None, max_date=None):
  min_date = min_date or datetime.datetime.today() - datetime.timedelta(days=1)
  max_date = max_date or datetime.datetime.today()

  if isinstance(min_date, basestring):
    min_date = parser.parse(min_date)
  if isinstance(max_date, basestring):
    max_date = parser.parse(max_date)

  date = min_date
  pbar = progressbar.ProgressBar(widgets=[progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()],
                                 maxval=int((max_date-min_date).total_seconds() / (60*60*24)) + 1)
  pbar.start()
  saved = 0
  hit = 0
  while date <= max_date:
    day_odds = load_odds_for_day(date)
    if day_odds is not None and len(day_odds) > 0:
      save_sbr_odds_info('nba', date, day_odds)
      saved += 1
    hit += 1
    date += datetime.timedelta(days=1)
    pbar.update(value=hit)
  pbar.finish()
  return saved

def __iter__(self):
        if self.count != 0:
            widgets = [
                '%s: ' % (self.caption,),
                progressbar.Percentage(),
                ' ',
                progressbar.Bar(),
                ' ',
                progressbar.ETA(),
            ]
            pbar = progressbar.ProgressBar(widgets=widgets, maxval=self.count)
            pbar.start()
            for idx, item in enumerate(self.iterator):
                yield item
                pbar.update(idx)
            pbar.finish()

def _setup_progress(self, options):
        if options.progress:
            if self.beanstalk:
                # With Beanstalk C&C we don't know how many...
                self.progress = progressbar.ProgressBar(
                    redirect_stdout=True,
                    redirect_stderr=True,
                    widgets=[
                        'Total: ',
                        progressbar.Counter(),
                        ', ',
                        progressbar.Timer()
                    ])
            else:
                self.progress = progressbar.ProgressBar(
                    redirect_stdout=True,
                    redirect_stderr=True,
                    widgets=[
                        progressbar.Percentage(),
                        progressbar.Bar(),
                        ' (', progressbar.ETA(), ') ',
                    ])
        else:
            self.progress = None

def __init__(self, options):
        self.wildcards = []
        self.options = options
        self.domains = []
        if options.domains:
            self.domains += filter(None, options.domains.read().split("\n"))
        self.domains += options.domain
        self.domains = list(set(self.domains))
        random.shuffle(self.domains)
        self.resolvers = map(str.strip, filter(None, options.resolvers.read().split("\n")))
        random.shuffle(self.resolvers)
        self.names = [X for X in self._load_names(options.names)]
        if options.progress:
            self.progress = progressbar.ProgressBar(
                redirect_stdout=True,
                redirect_stderr=True,
                widgets=[
                    progressbar.Percentage(),
                    progressbar.Bar(),
                    ' (', progressbar.ETA(), ') ',
                ])
        else:
            self.progress = None
        self.finished = 0
        LOG.info("%d names, %d resolvers, %d domains",
                 len(self.names), len(self.resolvers), len(self.domains))

def compute_embeddings(images):
  """Runs inference on an image.

  Args:
    image: Image file names.

  Returns:
    Dict mapping image file name to embedding.
  """

  # Creates graph from saved GraphDef.
  create_graph()
  filename_to_emb = {}
  config = tf.ConfigProto(device_count = {'GPU': 0})
  bar = progressbar.ProgressBar(widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
  with tf.Session(config=config) as sess:
    i = 0
    for image in bar(images):
      if not tf.gfile.Exists(image):
        tf.logging.fatal('File does not exist %s', image) 
      image_data = tf.gfile.FastGFile(image, 'rb').read()
      # Some useful tensors:
      # 'softmax:0': A tensor containing the normalized prediction across
      #   1000 labels.
      # 'pool_3:0': A tensor containing the next-to-last layer containing 2048
      #   float description of the image.
      # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG
      #   encoding of the image.
      # Runs the softmax tensor by feeding the image_data as input to the graph.
      softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
      embedding_tensor = sess.graph.get_tensor_by_name('pool_3:0')
      embedding = sess.run(embedding_tensor,
                             {'DecodeJpeg/contents:0': image_data})
      filename_to_emb[image] = embedding.reshape(2048)
      i += 1
      # print(image, i, len(images))
  return filename_to_emb

# temp_dir is a subdir of temp

def main(project_id, video_basename, sampling_rate=3):
    # os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'  # or any {'0', '1', '2'}
    video_name = video_basename[:video_basename.index('.')]
    # extract video frames
    extracted_frame_dir = os.path.join('temp', project_id, video_name, 'frames')
    mkdir_p(extracted_frame_dir)
    if not os.path.isdir(extracted_frame_dir):
        os.mkdir(extracted_frame_dir)
    video_path = os.path.join('videos', project_id, video_basename)
    vidcap = cv2.VideoCapture(video_path)
    print('Extracting video frames...')
    bar = progressbar.ProgressBar(maxval=101, widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    fps = vidcap.get(CV_CAP_PROP_FPS)# TODO
    fps = fps if fps != float('nan') else 25
    print 'actual fps', fps, 'sampling rate', sampling_rate
    success, image = vidcap.read()
    frames_to_extract = range(0, int(vidcap.get(CV_CAP_PROP_FRAME_COUNT)), int(round(fps / sampling_rate)))
    frame_count = len(frames_to_extract)
    for frame_pos in bar(frames_to_extract):
        vidcap.set(CV_CAP_PROP_POS_FRAMES, frame_pos)
        success, image = vidcap.read()
        # print('Read a new frame: %f ms'% vidcap.get(CV_CAP_PROP_POS_MSEC), success)
        cv2.imwrite(os.path.join(extracted_frame_dir, "%09d.jpg" % vidcap.get(CV_CAP_PROP_POS_MSEC)), image) # TODO (might still work)

    bar.finish()

def download(number, save_dir='./'):
    """Download pre-trained word vector
    :param number: integer, default ``None``
    :param save_dir: str, default './'
    :return: file path for downloaded file
    """
    df = load_datasets()

    row = df.iloc[[number]]
    url = ''.join(row.URL)
    if not url:
        print('The word vector you specified was not found. Please specify correct name.')

    widgets = ['Test: ', Percentage(), ' ', Bar(marker=RotatingMarker()), ' ', ETA(), ' ', FileTransferSpeed()]
    pbar = ProgressBar(widgets=widgets)

    def dlProgress(count, blockSize, totalSize):
        if pbar.max_value is None:
            pbar.max_value = totalSize
            pbar.start()

        pbar.update(min(count * blockSize, totalSize))

    file_name = url.split('/')[-1]
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
    save_path = os.path.join(save_dir, file_name)
    path, _ = urlretrieve(url, save_path, reporthook=dlProgress)
    pbar.finish()
    return path

def __enter__(self):
        self.bar = progressbar.ProgressBar(
            widgets=[
                progressbar.Percentage(),
                ' ',
                progressbar.Bar(),
                progressbar.FileTransferSpeed(),
                ' ',
                progressbar.ETA(),
            ],
            max_value=self.max_value,
        )
        self.fd = open(self.output_path, 'wb')
        return self

def train(self):

        data = Data(self.train_dat, self.train_lab)
        batch_num = self.length/self.batch_size if self.length%self.batch_size == 0 else self.length/self.batch_size + 1

        model = self.add_model()

        with self.sess as sess:

            tf.initialize_all_variables().run()

            for ite in range(self.iterations):
                print "Iteration {}".format(ite)
                cost = 0.
                pbar = pb.ProgressBar(widgets=[pb.Percentage(), pb.Bar(), pb.ETA()], maxval=batch_num).start()
                for i in range(batch_num):
                    batch_x, batch_y = data.next_batch(self.batch_size)

                    c, _ = self.sess.run([model['loss'], model['optimizer']], feed_dict={model['train_x']:batch_x, model['train_y']:batch_y, model['p_keep_dens']:0.75})

                    cost += c / batch_num
                    pbar.update(i+1)
                pbar.finish()

                print ">>cost: {}".format(cost)

                t_acc, d_acc = self.eval(model, 3000)
                # early stop
                if t_acc >= 0.995 and d_acc >= 0.995:
                    break

            self.predict(model)

def bar_update(self, epoch, logs):
        ologs = {}
        for k in self.custom_log_functions:
            ologs[k] = self.custom_log_functions[k]()
        for k in logs:
            if len(k) > 5:
                ologs[k[-5:]] = logs[k]
            else:
                ologs[k] = logs[k]

        if not hasattr(self,'bar'):
            import progressbar
            widgets = [
                progressbar.Timer(format='%(elapsed)s'),
                ' ', progressbar.Counter(), 
                progressbar.Bar(),
                progressbar.AbsoluteETA(format='%(eta)s'), ' ',
            ]
            keys = []
            for k in ologs:
                keys.append(k)
            keys.sort()
            for k in keys:
                widgets.append(progressbar.DynamicMessage(k))
                widgets.append(' ')
            self.bar = progressbar.ProgressBar(max_value=self.max_epoch, widgets=widgets)
        self.bar.update(epoch+1, **ologs)

def __call__(self, epoch):
        if self._batches is None:
            logger.info("Preparing evaluation data...")
            self._batches = self.reader.input_module.batch_generator(self._dataset, self._batch_size, is_eval=True)

        logger.info("Started evaluation %s" % self._info)
        metrics = defaultdict(lambda: list())
        bar = progressbar.ProgressBar(
            max_value=len(self._dataset) // self._batch_size + 1,
            widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
        for i, batch in bar(enumerate(self._batches)):
            inputs = self._dataset[i * self._batch_size:(i + 1) * self._batch_size]
            predictions = self.reader.model_module(batch, self._ports)
            m = self.apply_metrics(inputs, predictions)
            for k in self._metrics:
                metrics[k].append(m[k])

        metrics = self.combine_metrics(metrics)
        super().add_to_history(metrics, self._iter, epoch)

        printmetrics = sorted(metrics.keys())
        res = "Epoch %d\tIter %d\ttotal %d" % (epoch, self._iter, self._total)
        for m in printmetrics:
            res += '\t%s: %.3f' % (m, metrics[m])
            self.update_summary(self._iter, self._info + '_' + m, metrics[m])
            if self._write_metrics_to is not None:
                with open(self._write_metrics_to, 'a') as f:
                    f.write("{0} {1} {2:.5}\n".format(datetime.now(), self._info + '_' + m,
                                                      np.round(metrics[m], 5)))
        res += '\t' + self._info
        logger.info(res)

        if self._side_effect is not None:
            self._side_effect_state = self._side_effect(metrics, self._side_effect_state)

def get_pbar(num, prefix=""):
    assert isinstance(prefix, str)
    pbar = pb.ProgressBar(widgets=[prefix, pb.Percentage(), pb.Bar(), pb.ETA()], maxval=num)
    return pbar

def __init__(self, nbytes, nfiles):
    self._total_bytes = nbytes
    self._pending_files = nfiles
    self._transferring_files = 0
    self._complete_files = 0
    self._lock = Lock()
    self._data = {}

    widgets = ['Progress: ', Percentage(), ' ', Bar(left='[',right=']'),
               ' ', Timer(format='Time: %s'), ' ', FileTransferSpeed()]

    if self._total_bytes > 0:
      self.pbar = ProgressBar(widgets=widgets, maxval=self._total_bytes).start()
    else:
      self.pbar = ProgressBar(widgets=widgets, maxval=nfiles).start()

def collect_mailinfos(server, folder_contents, outpath_format):
    #construct progressbar
    progressbar_widgets = [
        '[Choosing mails for download  ] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ', progressbar.ETA()]
    total_count = 0
    for folder, mailids in folder_contents.items():
        total_count += len(mailids)
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=total_count).start()

    #collect all mailinfos
    mailinfos = {}
    mailinfo_count = 0
    for folder, mailids in folder_contents.items():
        mailinfos[folder] = []

        #get mailinfo bit by bit
        server.select_folder(folder, readonly=True)
        for mailid in mailids:
            #fetch mail information
            mailinfo = server.fetch([mailid], ['ENVELOPE', 'INTERNALDATE', 'RFC822.SIZE'])[mailid]
            mailsize = bitmath.Byte(mailinfo[b'RFC822.SIZE'])
            mailfilename = construct_mailfilename(outpath_format, mailinfo, args.outdir, folder, mailid)                

            #only add if mailfilename can be constructed
            if mailfilename:
                mailinfos[folder].append((mailid, mailfilename, mailsize))

            mailinfo_count += 1
            progressbar_instance.update(mailinfo_count)

    progressbar_instance.finish()
    return mailinfos

def cleanup(stored_files, stored_dirs, download_list, outdir):
    #create list of files to keep
    keep_list = []
    for folder, mails in download_list.items():
        for mailid, mailfilename, mailsize in mails:
            keep_list.append(mailfilename)

    progressbar_widgets = [
        '[Cleaning up outdir           ] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ']
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=len(stored_files)).start()
    file_count = 0

    #delete all files we don't need to keep
    for file in stored_files:
        #delete if not on server
        if not file in keep_list:
            os.remove(file)

        #progressbar
        file_count += 1
        progressbar_instance.update(file_count)
    progressbar_instance.finish()

    #remove empty folders
    possible_empty_folders = True
    while possible_empty_folders:
        #find all subfolders
        stored_dirs = []
        for root, dirs, files in os.walk(outdir):
            for name in dirs:
                stored_dirs.append(os.path.join(root, name))

        #delete empty folders indicate next run if one folder was deleted
        possible_empty_folders = False
        for folder in stored_dirs:
            if not os.listdir(folder):
                shutil.rmtree(folder)
                possible_empty_folders = True

def create_app(load_db=True, populate_qr_cache=True, progressbar=False):
    # Set up logging
    log_level = os.environ.get('AF_LOGGING_LEVEL', None)
    if log_level is not None:
        log_levels = ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL')
        if log_level.upper() in log_levels:
            log_level = getattr(log, log_level)

            log.basicConfig(level=log_level)
        else:
            log.warning('Invalid log level: {}'.format(log_level.upper()))
    else:
        log.warning('No log level set, using default level.')

    log.info('Creating Flask application')
    app = Flask(__name__)
    app.register_blueprint(root)

    # Now load the database if requested
    if load_db:
        from . import database_handler as dh
        log.info('Loading database.')
        dh.get_database()       # This loads the database into memory.
        log.info('Database loaded.')

    if populate_qr_cache:
        if progressbar:
            from progressbar import ProgressBar, Bar, Timer, ETA
            pbar = ProgressBar(widgets=['Populating QR cache: ', Bar(),
                                        ' ', Timer(), ' ', ETA()])
            kwargs = {'pbar': pbar}
        else:
            log.info('Populating QR cache.')
            kwargs = {}

        from .cache_utils import populate_qr_cache
        populate_qr_cache(**kwargs)

    return app

def __init__(self, msg, maxval, widgets=None, extrapos=-1):
        self.msg = msg
        self.extrapos = extrapos
        if not widgets:
            widgets = [progressbar.Percentage(), ' ', progressbar.Bar(), ' ',
            progressbar.ETA()]
            self.extrapos = 4

        try:
            self._resize_default = signal.getsignal(signal.SIGWINCH)
        except:
            self._resize_default = None
        progressbar.ProgressBar.__init__(self, maxval, [self.msg + ": "] + widgets, fd=sys.stdout)

def getCurrentStatus(self, filename_detections):
        pbar = pb.ProgressBar(maxval=len(self._seq.data), widgets=['Loading last status', pb.Percentage(), pb.Bar()])
        pbar.start()
        cache_str = ''
        with open(filename_detections, "r") as inputfile:
            cache_str = inputfile.readlines()

        for i in xrange(len(self._seq.data)):
            pbar.update(i)
            if len(self.subset_idxs) > 0:
                if i not in self.subset_idxs:
                    break

            hd = HandDetector(numpy.zeros((1, 1)), 0., 0.)  # dummy object
            com = numpy.asarray(hd.detectFromCache(filename_detections, self._seq.data[i].fileName, cache_str))
            if numpy.allclose(com[2], 0.):
                self.curFrame = i
                break
            else:
                self._seq.data[i] = self._seq.data[i]._replace(com=self.importer.jointImgTo3D(com.reshape((3,))))

        # redo last pose, it might be set to default and saved
        if self.curFrame > 0:
            if len(self.subset_idxs) > 0:
                if self.subset_idxs.index(self.curFrame) - 1 >= 0:
                    self.curFrame = self.subset_idxs[self.subset_idxs.index(self.curFrame) - 1]
            else:
                self.curFrame -= 1

def saveVideo3D(self, filename, sequence, showPC=True, showGT=False, niceColors=True, plotFrameNumbers=False,
                    height=400, width=400):
        """
        Create a video with 3D annotations
        :param filename: name of file to save
        :param sequence: sequence data
        :return: None
        """

        txt = 'Saving {}'.format(filename)
        pbar = pb.ProgressBar(maxval=self.joints.shape[0], widgets=[txt, pb.Percentage(), pb.Bar()])
        pbar.start()

        # Define the codec and create VideoWriter object
        fourcc = cv2.cv.CV_FOURCC(*'DIVX')
        video = cv2.VideoWriter('{}/depth_{}.avi'.format(self.subfolder, filename), fourcc, self.fps, (height, width))
        if not video:
            raise EnvironmentError("Error in creating video writer")

        for i in range(self.joints.shape[0]):
            jt = self.joints[i]
            img = self.plotResult3D_OS(sequence.data[i].dpt, sequence.data[i].T, sequence.data[i].gt3Dorig, jt,
                                       showPC=showPC, showGT=showGT, niceColors=niceColors, width=width, height=height)
            img = numpy.flipud(img)
            img = img[:, :, [2, 1, 0]]  # change color channels for OpenCV
            img = cv2.resize(img, (height, width))
            if plotFrameNumbers:
                if sequence.data[i].subSeqName == 'ref':
                    cv2.putText(img, "Reference Frame {}".format(i), (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255))
                # plot frame number
                cv2.putText(img, "{}".format(i), (height-50, width-10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255))
            # write frame
            video.write(img)
            pbar.update(i)

        video.release()
        del video
        cv2.destroyAllWindows()
        pbar.finish()

def saveVideoFrames(self, filename, images):
        """
        Create a video with synthesized images
        :param filename: name of file to save
        :param images: video data
        :return: None
        """

        txt = 'Saving {}'.format(filename)
        pbar = pb.ProgressBar(maxval=images.shape[0], widgets=[txt, pb.Percentage(), pb.Bar()])
        pbar.start()

        height = width = 128

        # Define the codec and create VideoWriter object
        fourcc = cv2.cv.CV_FOURCC(*'DIVX')
        video = cv2.VideoWriter('{}/synth_{}.avi'.format(self.subfolder, filename), fourcc, self.fps, (height, width))
        if not video:
            raise EnvironmentError("Error in creating video writer")

        for i in range(images.shape[0]):
            img = images[i]
            img = cv2.normalize(img, alpha=0, beta=255, norm_type=cv2.cv.CV_MINMAX, dtype=cv2.cv.CV_8UC1)
            img = cv2.cvtColor(img, cv2.cv.CV_GRAY2BGR)
            img = cv2.resize(img, (height, width))
            # write frame
            video.write(img)
            pbar.update(i)

        video.release()
        del video
        cv2.destroyAllWindows()

        pbar.finish()

def print_status_stream(title, stream):
    widgets = [title, FormatLabel(''), ' ', Percentage(), ' ', Bar(), ' ', RotatingMarker()]
    bar = None
    if sys.stderr.isatty():
        bar = progressbar.ProgressBar(widgets=widgets, max_value=255)

    def print_error(status):
        print(status['error'])

    def print_status(status):
        progress = status.get('progressDetail')
        if progress:
            widgets[1] = FormatLabel("%12s" % (status['status']))
            prog = int(round(255 * ((progress['current'] / progress['total']))))
            if bar is not None:
                bar.update(prog)

    def print_unknown(status):
        print(status)

    for line in stream:
        status = json.loads(line.decode('utf8'))
        if 'error' in status:
            print_error(status)
        elif 'status' in status:
            print_status(status)
        else:
            print_unknown(status)

def prepare_h5py(train_image, train_label, test_image,
                 test_label, data_dir, shape=None):

    image = np.concatenate((train_image, test_image), axis=0).astype(np.uint8)
    label = np.concatenate((train_label, test_label), axis=0).astype(np.uint8)

    print('Preprocessing data...')

    import progressbar
    bar = progressbar.ProgressBar(
        maxval=100, widgets=[progressbar.Bar('=', '[', ']'),
                             ' ', progressbar.Percentage()]
    )
    bar.start()

    f = h5py.File(os.path.join(data_dir, 'data.hy'), 'w')
    with open(os.path.join(data_dir, 'id.txt'), 'w') as data_id:
        for i in range(image.shape[0]):

            if i % (image.shape[0] / 100) == 0:
                bar.update(i / (image.shape[0] / 100))

            grp = f.create_group(str(i))
            data_id.write('{}\n'.format(i))
            if shape:
                grp['image'] = np.reshape(image[i], shape, order='F')
            else:
                grp['image'] = image[i]
            label_vec = np.zeros(10)
            label_vec[label[i] % 10] = 1
            grp['label'] = label_vec.astype(np.bool)
        bar.finish()
    f.close()
    return

def __init__(self):
        super().__init__()
        self.label = Label()

        # Got messes with sys.stdout and sys.stderr in ways that confuse progressbar and cause it to output on the wrong one
        # This can be worked around by passing in a new stream, but that stream can't be the same instance as sys.stdout or sys.stderr, so I make a new one here that forwards everything
        class StreamWrapper:
            def __getattr__(self, k):
                return getattr(sys.stdout, k)

        self.bar = progressbar.ProgressBar(fd = StreamWrapper(), widgets = [self.label, ' ', progressbar.Bar(), ' ', progressbar.Percentage(), ' '])

def _create_pbar(self, max_iter):
        """
            Creates a progress bar.
        """

        self.grad_iter = 0
        self.pbar = pb.ProgressBar()
        self.pbar.widgets = ["Optimizing: ", pb.Percentage(),
                             " ", pb.Bar(marker=pb.AnimatedMarker()),
                             " ", pb.ETA()]
        self.pbar.maxval = max_iter

def convert_dataset(indices, name):
    # Open a TFRRecordWriter
    filename = os.path.join(FLAGS.out, name + '.tfrecords')
    writeOpts = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.ZLIB)
    writer = tf.python_io.TFRecordWriter(filename, options=writeOpts)

    # Load each data sample (image_a, image_b, flow) and write it to the TFRecord
    count = 0
    pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=len(indices)).start()
    for i in indices:
        image_a_path = os.path.join(FLAGS.data_dir, '%05d_img1.ppm' % (i + 1))
        image_b_path = os.path.join(FLAGS.data_dir, '%05d_img2.ppm' % (i + 1))
        flow_path = os.path.join(FLAGS.data_dir, '%05d_flow.flo' % (i + 1))

        image_a = imread(image_a_path)
        image_b = imread(image_b_path)

        # Convert from RGB -> BGR
        image_a = image_a[..., [2, 1, 0]]
        image_b = image_b[..., [2, 1, 0]]

        # Scale from [0, 255] -> [0.0, 1.0]
        image_a = image_a / 255.0
        image_b = image_b / 255.0

        image_a_raw = image_a.tostring()
        image_b_raw = image_b.tostring()
        flow_raw = open_flo_file(flow_path).tostring()

        example = tf.train.Example(features=tf.train.Features(feature={
            'image_a': _bytes_feature(image_a_raw),
            'image_b': _bytes_feature(image_b_raw),
            'flow': _bytes_feature(flow_raw)}))
        writer.write(example.SerializeToString())
        pbar.update(count + 1)
        count += 1
    writer.close()

def setup_progressbar(self):
        from progressbar import ProgressBar, Bar, Percentage
        return ProgressBar(widgets=[Bar(), ' ', Percentage()])