def METHOD_NAME(argv):
    pycaffe_dir = os.path.dirname(__file__)
    parser = argparse.ArgumentParser()
    # Required arguments: input and output.
    parser.add_argument(
        "input_file",
        help="Input txt/csv filename. If .txt, must be list of filenames.\
        If .csv, must be comma-separated file with header\
        'filename, xmin, ymin, xmax, ymax'"
    )
    parser.add_argument(
        "output_file",
        help="Output h5/csv filename. Format depends on extension."
    )
    # Optional arguments.
    parser.add_argument(
        "--model_def",
        default=os.path.join(pycaffe_dir,
                "../models/bvlc_reference_caffenet/deploy.prototxt"),
        help="Model definition file."
    )
    parser.add_argument(
        "--pretrained_model",
        default=os.path.join(pycaffe_dir,
                "../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel"),
        help="Trained model weights file."
    )
    parser.add_argument(
        "--crop_mode",
        default="selective_search",
        choices=CROP_MODES,
        help="How to generate windows for detection."
    )
    parser.add_argument(
        "--gpu",
        action='store_true',
        help="Switch for gpu computation."
    )
    parser.add_argument(
        "--mean_file",
        default=os.path.join(pycaffe_dir,
                             'caffe/imagenet/ilsvrc_2012_mean.npy'),
        help="Data set image mean of H x W x K dimensions (numpy array). " +
             "Set to '' for no mean subtraction."
    )
    parser.add_argument(
        "--input_scale",
        type=float,
        help="Multiply input features by this scale to finish preprocessing."
    )
    parser.add_argument(
        "--raw_scale",
        type=float,
        default=255.0,
        help="Multiply raw input by this scale before preprocessing."
    )
    parser.add_argument(
        "--channel_swap",
        default='2,1,0',
        help="Order to permute input channels. The default converts " +
             "RGB -> BGR since BGR is the Caffe default by way of OpenCV."
    )
    parser.add_argument(
        "--context_pad",
        type=int,
        default='16',
        help="Amount of surrounding context to collect in input window."
    )
    args = parser.parse_args()
    mean, channel_swap = None, None
    if args.mean_file:
        mean = np.load(args.mean_file)
        if mean.shape[1:] != (1, 1):
            mean = mean.mean(1).mean(1)
    if args.channel_swap:
        channel_swap = [int(s) for s in args.channel_swap.split(',')]
    if args.gpu:
        caffe.set_mode_gpu()
        print("GPU mode")
    else:
        caffe.set_mode_cpu()
        print("CPU mode")
    # Make detector.
    detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,
            input_scale=args.input_scale, raw_scale=args.raw_scale,
            channel_swap=channel_swap,
            context_pad=args.context_pad)
    # Load input.
    t = time.time()
    print("Loading input...")
    if args.input_file.lower().endswith('txt'):
        with open(args.input_file) as f:
            inputs = [_.strip() for _ in f.readlines()]
    elif args.input_file.lower().endswith('csv'):
        inputs = pd.read_csv(args.input_file, sep=',', dtype={'filename': str})
        inputs.set_index('filename', inplace=True)
    else:
        raise Exception("Unknown input file type: not in txt or csv.")
    # Detect.
    if args.crop_mode == 'list':
        # Unpack sequence of (image filename, windows).
        images_windows = [
            (ix, inputs.iloc[np.where(inputs.index == ix)][COORD_COLS].values)
            for ix in inputs.index.unique()
        ]
        detections = detector.detect_windows(images_windows)
    else:
        detections = detector.detect_selective_search(inputs)
    print("Processed {} windows in {:.3f} s.".format(len(detections),
                                                     time.time() - t))
    # Collect into dataframe with labeled fields.
    df = pd.DataFrame(detections)
    df.set_index('filename', inplace=True)
    df[COORD_COLS] = pd.DataFrame(
        data=np.vstack(df['window']), index=df.index, columns=COORD_COLS)
    del(df['window'])
    # Save results.
    t = time.time()
    if args.output_file.lower().endswith('csv'):
        # csv
        # Enumerate the class probabilities.
        class_cols = ['class{}'.format(x) for x in range(NUM_OUTPUT)]
        df[class_cols] = pd.DataFrame(
            data=np.vstack(df['feat']), index=df.index, columns=class_cols)
        df.to_csv(args.output_file, cols=COORD_COLS + class_cols)
    else:
        # h5
        df.to_hdf(args.output_file, 'df', mode='w')
    print("Saved to {} in {:.3f} s.".format(args.output_file,
                                            time.time() - t))