Thesis-NoiseDetection-attributes/predict_noisy_image_svd.py

from sklearn.externals import joblib

import numpy as np

from ipfml import processing, utils
from PIL import Image

import sys, os, argparse

from modules.utils import config as cfg
from modules.utils import data as dt

path                  = cfg.dataset_path
min_max_ext           = cfg.min_max_filename_extension
metric_choices        = cfg.metric_choices_labels
normalization_choices = cfg.normalization_choices

custom_min_max_folder = cfg.min_max_custom_folder

def main():

    # getting all params
    parser = argparse.ArgumentParser(description="Script which detects if an image is noisy or not using specific model")

    parser.add_argument('--image', type=str, help='Image path')
    parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
    parser.add_argument('--model', type=str, help='.joblib or .json file (sklearn or keras model)')
    parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=normalization_choices)
    parser.add_argument('--metric', type=str, help='Metric data choice', choices=metric_choices)
    parser.add_argument('--custom', type=str, help='Name of custom min max file if use of renormalization of data', default=False)

    args = parser.parse_args()

    p_img_file   = args.image
    p_model_file = args.model
    p_interval   = list(map(int, args.interval.split(',')))
    p_mode       = args.mode
    p_metric     = args.metric
    p_custom     = args.custom

    if '.joblib' in p_model_file:
        kind_model = 'sklearn'

    if '.json' in p_model_file:
        kind_model = 'keras'

    if 'corr' in p_model_file:
        corr_model = True

        indices_corr_path = os.path.join(cfg.correlation_indices_folder, p_model_file.split('.')[0] + '.csv')

        with open(indices_corr_path, 'r') as f:
            data_corr_indices = [int(x) for x in f.readline().split(';') if x != '']
    else:
        corr_model = False


    if kind_model == 'sklearn':
        # load of model file
        model = joblib.load(p_model_file)

    if kind_model == 'keras':
        with open(p_model_file, 'r') as f:
            json_model = json.load(f)
            model = model_from_json(json_model)
            model.load_weights(p_model_file.replace('.json', '.h5'))

            model.compile(loss='binary_crossentropy',
                        optimizer='adam',
                        metrics=['accuracy'])

    # load image
    img = Image.open(p_img_file)

    data = dt.get_svd_data(p_metric, img)

    # get interval values
    begin, end = p_interval

    # check if custom min max file is used
    if p_custom:

        if corr_model:
            test_data = data[data_corr_indices]
        else:
            test_data = data[begin:end]

        if p_mode == 'svdne':

            # set min_max_filename if custom use
            min_max_file_path = custom_min_max_folder + '/' +  p_custom

            # need to read min_max_file
            file_path = os.path.join(os.path.dirname(__file__), min_max_file_path)
            with open(file_path, 'r') as f:
                min_val = float(f.readline().replace('\n', ''))
                max_val = float(f.readline().replace('\n', ''))

            test_data = utils.normalize_arr_with_range(test_data, min_val, max_val)

        if p_mode == 'svdn':
            test_data = utils.normalize_arr(test_data)

    else:

        # check mode to normalize data
        if p_mode == 'svdne':

            # set min_max_filename if custom use
            min_max_file_path = path + '/' + p_metric + min_max_ext

            # need to read min_max_file
            file_path = os.path.join(os.path.dirname(__file__), min_max_file_path)
            with open(file_path, 'r') as f:
                min_val = float(f.readline().replace('\n', ''))
                max_val = float(f.readline().replace('\n', ''))

            l_values = utils.normalize_arr_with_range(data, min_val, max_val)

        elif p_mode == 'svdn':
            l_values = utils.normalize_arr(data)
        else:
            l_values = data

        if corr_model:
            test_data = data[data_corr_indices]
        else:
            test_data = data[begin:end]


    # get prediction of model
    if kind_model == 'sklearn':
        prediction = model.predict([test_data])[0]

    if kind_model == 'keras':
        test_data = test_data.reshape(len(test_data), 1)
        prediction = model.predict_classes([test_data])[0]

    # output expected from others scripts
    print(prediction)

if __name__== "__main__":
    main()