Thesis-NoiseDetection-attributes/display_bits_shifted_scene.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 14 21:02:42 2018

@author: jbuisine
"""

from __future__ import print_function
import sys, os, argparse
import numpy as np
import random
import time
import json

from PIL import Image
from ipfml import processing
from ipfml import metrics
from skimage import color
import matplotlib.pyplot as plt

from modules.utils import config as cfg


config_filename     = cfg.config_filename
zone_folder         = cfg.zone_folder
min_max_filename    = cfg.min_max_filename_extension

# define all scenes values
scenes_list         = cfg.scenes_names
scenes_indices      = cfg.scenes_indices
choices             = cfg.normalization_choices
path                = cfg.dataset_path
zones               = cfg.zones_indices
seuil_expe_filename = cfg.seuil_expe_filename

metric_choices      = cfg.metric_choices_labels
max_nb_bits = 8

def display_data_scenes(nb_bits, p_scene):
    """
    @brief Method display shifted values for specific scene
    @param nb_bits, number of bits expected
    @param p_scene, scene we want to show values
    @return nothing
    """

    scenes = os.listdir(path)
    # remove min max file from scenes folder
    scenes = [s for s in scenes if min_max_filename not in s]

    # go ahead each scenes
    for id_scene, folder_scene in enumerate(scenes):

        if p_scene == folder_scene:
            print(folder_scene)
            scene_path = os.path.join(path, folder_scene)

            config_file_path = os.path.join(scene_path, config_filename)

            with open(config_file_path, "r") as config_file:
                last_image_name = config_file.readline().strip()
                prefix_image_name = config_file.readline().strip()
                start_index_image = config_file.readline().strip()
                end_index_image = config_file.readline().strip()
                step_counter = int(config_file.readline().strip())

            # construct each zones folder name
            zones_folder = []

            # get zones list info
            for index in zones:
                index_str = str(index)
                if len(index_str) < 2:
                    index_str = "0" + index_str

                current_zone = "zone"+index_str
                zones_folder.append(current_zone)

            zones_images_data = []
            threshold_info = []

            for id_zone, zone_folder in enumerate(zones_folder):

                zone_path = os.path.join(scene_path, zone_folder)

                current_counter_index = int(start_index_image)
                end_counter_index = int(end_index_image)

                # get threshold information
                path_seuil = os.path.join(zone_path, seuil_expe_filename)

                # open treshold path and get this information
                with open(path_seuil, "r") as seuil_file:
                    seuil_learned = int(seuil_file.readline().strip())
                    threshold_info.append(seuil_learned)

            # compute mean threshold values
            mean_threshold = sum(threshold_info) / float(len(threshold_info))

            print(mean_threshold, "mean threshold found")
            threshold_image_found = False

            # find appropriate mean threshold picture
            while(current_counter_index <= end_counter_index and not threshold_image_found):

                if mean_threshold < int(current_counter_index):
                    current_counter_index_str = str(current_counter_index)

                    while len(start_index_image) > len(current_counter_index_str):
                        current_counter_index_str = "0" + current_counter_index_str

                    threshold_image_found = True
                    threshold_image_zone = current_counter_index_str

                current_counter_index += step_counter

            # all indexes of picture to plot
            images_indexes = [start_index_image, threshold_image_zone, end_index_image]
            images_data = []

            print(images_indexes)

            low_bits_svd_values = []

            for i in range(0, max_nb_bits - nb_bits + 1):

                low_bits_svd_values.append([])

                for index in images_indexes:

                    img_path = os.path.join(scene_path, prefix_image_name + index + ".png")

                    current_img = Image.open(img_path)

                    block_used = np.array(current_img)

                    low_bits_block = processing.rgb_to_LAB_L_bits(block_used, (i + 1, i + nb_bits + 1))
                    low_bits_svd = metrics.get_SVD_s(low_bits_block)
                    low_bits_svd = [b / low_bits_svd[0] for b in low_bits_svd]
                    low_bits_svd_values[i].append(low_bits_svd)


            fig=plt.figure(figsize=(8, 8))
            fig.suptitle("Lab SVD " + str(nb_bits) +  " bits values shifted for " + p_scene + " scene", fontsize=20)

            for id, data in enumerate(low_bits_svd_values):
                fig.add_subplot(3, 3, (id + 1))
                plt.plot(data[0], label='Noisy_' + start_index_image)
                plt.plot(data[1], label='Threshold_' + threshold_image_zone)
                plt.plot(data[2], label='Reference_' + end_index_image)
                plt.ylabel('Lab SVD ' + str(nb_bits) + ' bits values shifted by ' + str(id), fontsize=14)
                plt.xlabel('Vector features', fontsize=16)
                plt.legend(bbox_to_anchor=(0.5, 1), loc=2, borderaxespad=0.2, fontsize=14)
                plt.ylim(0, 0.1)
            plt.show()

def main():

    parser = argparse.ArgumentParser(description="Display curves of shifted bits influence of L canal on specific scene")

    parser.add_argument('--bits', type=str, help='Number of bits to display')
    parser.add_argument('--scene', type=str, help="scene index to use", choices=scenes_indices)

    args = parser.parse_args()

    p_bits  = args.bits
    p_scene = scenes_list[scenes_indices.index(args.scene)]

    display_data_scenes(p_bits, p_scene)

if __name__== "__main__":
    main()