Thesis-NoiseDetection-attributes/display_svd_zone_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, getopt

import numpy as np
import random
import time
import json

from PIL import Image
from ipfml import image_processing
from ipfml import metrics
from skimage import color

import matplotlib.pyplot as plt
from modules.utils.data_type 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_indexes      = 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_svd_values(p_scene, p_interval, p_zone, p_metric, p_mode, p_step):
    """
    @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 samples or minutes from render generation engine
    @param p_zone, zone's identifier of picture
    @param p_metric, metric computed to show
    @param p_mode, normalization's mode
    @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]

    begin, end = p_interval
    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:
            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 = []
            images_indexes = []

            zone_folder = zones_folder[p_zone]

            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_image_found = False

            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


                if current_counter_index % p_step == 0:
                    if current_counter_index >= begin and current_counter_index <= end:
                        images_indexes.append(current_counter_index_str)

                    if seuil_learned < 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

            # all indexes of picture to plot
            print(images_indexes)

            for index in images_indexes:

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

                current_img = Image.open(img_path)
                img_blocks = image_processing.divide_in_blocks(current_img, (200, 200))

                # getting expected block id
                block = img_blocks[p_zone]

                # get data from mode
                # Here you can add the way you compute data
                data = get_svd_data(p_metric, block)

                ##################
                # Data mode part #
                ##################

                if p_mode == 'svdne':

                    # getting max and min information from min_max_filename
                    with open(data_min_max_filename, 'r') as f:
                        min_val = float(f.readline())
                        max_val = float(f.readline())

                    data = image_processing.normalize_arr_with_range(data, min_val, max_val)

                if p_mode == 'svdn':
                    data = image_processing.normalize_arr(data)

                zones_images_data.append(data)

            plt.title(p_scene + ' scene interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, ' + p_mode, fontsize=20)
            plt.ylabel('Image samples or time (minutes) generation', fontsize=14)
            plt.xlabel('Vector features', fontsize=16)

            for id, data in enumerate(zones_images_data):

                p_label = p_scene + "_" + images_indexes[id]

                if images_indexes[id] == threshold_image_zone:
                    plt.plot(data, label=p_label, lw=4, color='red')
                else:
                    plt.plot(data, label=p_label)

            plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14)
            plt.ylim(0, 0.1)

            plt.show()

def main():


    # by default p_step value is 10 to enable all photos
    p_step = 10

    if len(sys.argv) <= 1:
        print('Run with default parameters...')
        print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "hs:i:z:l:m:s", ["help=", "scene=", "interval=", "zone=", "metric=", "mode=", "step="])
    except getopt.GetoptError:
        # print help information and exit:
        print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50')
        sys.exit(2)
    for o, a in opts:
        if o == "-h":
            print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50')
            sys.exit()
        elif o in ("-s", "--scene"):
            p_scene = a

            if p_scene not in scenes_indexes:
                assert False, "Invalid scene choice"
            else:
                p_scene = scenes_list[scenes_indexes.index(p_scene)]
        elif o in ("-i", "--interval"):
            p_interval = list(map(int, a.split(',')))

        elif o in ("-z", "--zone"):
            p_zone = int(a)

        elif o in ("-m", "--metric"):
            p_metric = a

            if p_metric not in metric_choices:
                assert False, "Invalid metric choice"

        elif o in ("-m", "--mode"):
            p_mode = a

            if p_mode not in choices:
                assert False, "Invalid normalization choice, expected ['svd', 'svdn', 'svdne']"

        elif o in ("-s", "--step"):
            p_step = int(a)

        else:
            assert False, "unhandled option"

    display_svd_values(p_scene, p_interval, p_zone, p_metric, p_mode, p_step)

if __name__== "__main__":
    main()