Thesis-NoiseDetection-CNN/modules/utils/data.py

from ipfml import processing, metrics, utils
from modules.utils.config import *
from preprocessing_functions import svd_reconstruction

from PIL import Image
from skimage import color
from sklearn.decomposition import FastICA
from sklearn.decomposition import IncrementalPCA
from sklearn.decomposition import TruncatedSVD
from numpy.linalg import svd as lin_svd

from scipy.signal import medfilt2d, wiener, cwt
import pywt

import numpy as np


_scenes_names_prefix   = '_scenes_names'
_scenes_indices_prefix = '_scenes_indices'

# store all variables from current module context
context_vars = vars()


def get_data(data_type, block, interval=(100, 200)):
    """
    Method which returns the data type expected
    """

    if data_type == 'svd_reconstruct':
        begin, end = interval
        data = svd_reconstruction(block, [begin, end])

    return data

def get_renderer_scenes_indices(renderer_name):

    if renderer_name not in renderer_choices:
        raise ValueError("Unknown renderer name")

    if renderer_name == 'all':
        return scenes_indices
    else:
        return context_vars[renderer_name + _scenes_indices_prefix]

def get_renderer_scenes_names(renderer_name):

    if renderer_name not in renderer_choices:
        raise ValueError("Unknown renderer name")

    if renderer_name == 'all':
        return scenes_names
    else:
        return context_vars[renderer_name + _scenes_names_prefix]