Thesis-NoiseDetection-attributes/display_svd_data_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
import math

from PIL import Image
from ipfml import processing, metrics, utils
import ipfml.iqa.fr as fr_iqa

from skimage import color

import matplotlib as mpl
import matplotlib.pyplot as plt

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

# getting configuration information
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
display_error       = False


def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_ylim):
    """
    @brief Method which gives information about svd curves from zone of picture
    @param p_scene, scene expected to show svd values
    @param p_interval, interval [begin, end] of svd data to display
    @param p_interval, interval [begin, end] of samples or minutes from render generation engine
    @param p_metric, metric computed to show
    @param p_mode, normalization's mode
    @param p_norm, normalization or not of selected svd data
    @param p_ylim, ylim choice to better display of data
    @return nothing
    """

    max_value_svd = 0
    min_value_svd = sys.maxsize

    image_indices = []

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

    begin_data, end_data = p_interval
    begin_index, end_index = p_indices

    data_min_max_filename = os.path.join(path, p_metric + min_max_filename)

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

        if p_scene == 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)

            images_data = []
            images_indices = []

            threshold_learned_zones = []

            for id, zone_folder in enumerate(zones_folder):

                # get threshold information

                zone_path = os.path.join(scene_path, zone_folder)
                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:
                    threshold_learned = int(seuil_file.readline().strip())
                    threshold_learned_zones.append(threshold_learned)

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

            threshold_mean = np.mean(np.asarray(threshold_learned_zones))
            threshold_image_found = False

            file_path = os.path.join(scene_path, prefix_image_name + "{}.png")

            svd_data = []

            while(current_counter_index <= end_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

                image_path = file_path.format(str(current_counter_index_str))
                img = Image.open(image_path)

                svd_values = get_svd_data(p_metric, img)

                if p_norm:
                    svd_values = svd_values[begin_data:end_data]

                #svd_values = np.asarray([math.log(x) for x in svd_values])

                # update min max values
                min_value = svd_values.min()
                max_value = svd_values.max()

                if min_value < min_value_svd:
                    min_value_svd = min_value

                if max_value > min_value_svd:
                    max_value_svd = max_value

                # keep in memory used data
                if current_counter_index % p_step == 0:
                    if current_counter_index >= begin_index and current_counter_index <= end_index:
                        images_indices.append(current_counter_index_str)
                        svd_data.append(svd_values)

                    if threshold_mean < int(current_counter_index) and not threshold_image_found:

                        threshold_image_found = True
                        threshold_image_zone = current_counter_index_str

                current_counter_index += step_counter
                print('%.2f%%' % (current_counter_index / end_counter_index * 100))
                sys.stdout.write("\033[F")


            # all indices of picture to plot
            print(images_indices)

            for id, data in enumerate(svd_data):

                current_data = data

                if not p_norm:
                    current_data = current_data[begin_data:end_data]

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

                if p_mode == 'svdne':
                    current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)

                images_data.append(current_data)


            # display all data using matplotlib (configure plt)
            #fig = plt.figure(figsize=(30, 22))
            fig, ax = plt.subplots(figsize=(30, 22))
            ax.set_facecolor('#F9F9F9')
            #fig.patch.set_facecolor('#F9F9F9')

            ax.tick_params(labelsize=22)
            #plt.rc('xtick', labelsize=22)
            #plt.rc('ytick', labelsize=22)

            #plt.title(p_scene + ' scene interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + '], ' + p_metric + ' metric, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=24)
            ax.set_ylabel('Component values', fontsize=30)
            ax.set_xlabel('Vector features', fontsize=30)

            for id, data in enumerate(images_data):

                p_label = p_scene + '_' + str(images_indices[id])

                if images_indices[id] == threshold_image_zone:
                    ax.plot(data, label=p_label + " (threshold mean)", lw=4, color='red')
                else:
                    ax.plot(data, label=p_label)

            plt.legend(bbox_to_anchor=(0.65, 0.98), loc=2, borderaxespad=0.2, fontsize=24)

            start_ylim, end_ylim = p_ylim
            #ax.set_ylim(start_ylim, end_ylim)

            plot_name = p_scene + '_' + p_metric + '_' + str(p_step) + '_' + p_mode + '_' + str(p_norm) + '.png'
            plt.savefig(plot_name, facecolor=ax.get_facecolor())

def main():

    parser = argparse.ArgumentParser(description="Display SVD data of scene")

    parser.add_argument('--scene', type=str, help='scene index to use', choices=cfg.scenes_indices)
    parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
    parser.add_argument('--indices', type=str, help='Samples interval to display', default='"0, 900"')
    parser.add_argument('--metric', type=str, help='Metric data choice', choices=metric_choices)
    parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=cfg.normalization_choices)
    parser.add_argument('--step', type=int, help='Each step samples to display', default=10)
    parser.add_argument('--norm', type=int, help='If values will be normalized or not', choices=[0, 1])
    parser.add_argument('--ylim', type=str, help='ylim interval to use', default='"0, 1"')

    args = parser.parse_args()

    p_scene    = scenes_list[scenes_indices.index(args.scene)]
    p_indices  = list(map(int, args.indices.split(',')))
    p_interval = list(map(int, args.interval.split(',')))
    p_metric   = args.metric
    p_mode     = args.mode
    p_step     = args.step
    p_norm     = args.norm
    p_ylim     = list(map(int, args.ylim.split(',')))

    display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_ylim)

if __name__== "__main__":
    main()