Thesis-NoiseDetection-attributes/generate_data_model_corr_random.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 pandas as pd
import random
import time
import json
import subprocess

from PIL import Image
from ipfml import processing, metrics, utils

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

# getting configuration information
config_filename         = cfg.config_filename
learned_folder          = cfg.learned_zones_folder
min_max_filename        = cfg.min_max_filename_extension

# define all scenes values
all_scenes_list         = cfg.scenes_names
all_scenes_indices      = cfg.scenes_indices

renderer_choices        = cfg.renderer_choices
normalization_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
output_data_folder      = cfg.output_data_folder
custom_min_max_folder   = cfg.min_max_custom_folder
min_max_ext             = cfg.min_max_filename_extension

generic_output_file_svd = '_random.csv'

min_value_interval      = sys.maxsize
max_value_interval      = 0


def construct_new_line(path_seuil, indices, line, choice, norm):

    # increase indices values by one to avoid label
    f = lambda x : x + 1
    indices = f(indices)

    line_data = np.array(line.split(';'))
    seuil = line_data[0]
    metrics = line_data[indices]

    # TODO : check if it's always necessary to do that (loss of information for svd)
    if norm:
        if choice == 'svdne':
            metrics = utils.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
        if choice == 'svdn':
            metrics = utils.normalize_arr(metrics)

    with open(path_seuil, "r") as seuil_file:
        seuil_learned = int(seuil_file.readline().strip())

    if seuil_learned > int(seuil):
        line = '1'
    else:
        line = '0'

    for idx, val in enumerate(metrics):
        line += ';'
        line += str(val)
    line += '\n'

    return line

def get_min_max_value_interval(_scenes_list, _indices, _metric):

    global min_value_interval, max_value_interval

    # increase indices values by one to avoid label
    f = lambda x : x + 1
    indices = f(_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]

    for id_scene, folder_scene in enumerate(scenes):

        # only take care of maxwell scenes
        if folder_scene in _scenes_list:

            scene_path = os.path.join(path, folder_scene)

            zones_folder = []
            # create zones list
            for index in zones:
                index_str = str(index)
                if len(index_str) < 2:
                    index_str = "0" + index_str
                zones_folder.append("zone"+index_str)

            for id_zone, zone_folder in enumerate(zones_folder):

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

                # if custom normalization choices then we use svd values not already normalized
                data_filename = _metric + "_svd"+ generic_output_file_svd

                data_file_path = os.path.join(zone_path, data_filename)

                # getting number of line and read randomly lines
                f = open(data_file_path)
                lines = f.readlines()

                # check if user select current scene and zone to be part of training data set
                for line in lines:

                    line_data = np.array(line.split(';'))

                    metrics = line_data[[_indices]]
                    metrics = [float(m) for m in metrics]

                    min_value = min(metrics)
                    max_value = max(metrics)

                    if min_value < min_value_interval:
                        min_value_interval = min_value

                    if max_value > max_value_interval:
                        max_value_interval = max_value


def generate_data_model(_scenes_list, _filename, _interval, _choice, _metric, _scenes, _nb_zones = 4, _percent = 1, _random=0, _step=1, _custom = False):

    output_train_filename = _filename + ".train"
    output_test_filename = _filename + ".test"

    if not '/' in output_train_filename:
        raise Exception("Please select filename with directory path to save data. Example : data/dataset")

    # create path if not exists
    if not os.path.exists(output_data_folder):
        os.makedirs(output_data_folder)

    train_file_data = []
    test_file_data  = []

    for id_scene, folder_scene in enumerate(_scenes_list):

        scene_path = os.path.join(path, folder_scene)

        zones_indices = zones

        # shuffle list of zones (=> randomly choose zones)
        # only in random mode
        if _random:
            random.shuffle(zones_indices)

        # store zones learned
        learned_zones_indices = zones_indices[:_nb_zones]

        # write into file
        folder_learned_path = os.path.join(learned_folder, _filename.split('/')[1])

        if not os.path.exists(folder_learned_path):
            os.makedirs(folder_learned_path)

        file_learned_path = os.path.join(folder_learned_path, folder_scene + '.csv')

        with open(file_learned_path, 'w') as f:
            for i in learned_zones_indices:
                f.write(str(i) + ';')

        for id_zone, index_folder in enumerate(zones_indices):

            index_str = str(index_folder)
            if len(index_str) < 2:
                index_str = "0" + index_str
            current_zone_folder = "zone" + index_str

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

            # if custom normalization choices then we use svd values not already normalized
            if _custom:
                data_filename = _metric + "_svd"+ generic_output_file_svd
            else:
                data_filename = _metric + "_" + _choice + generic_output_file_svd

            data_file_path = os.path.join(zone_path, data_filename)

            # getting number of line and read randomly lines
            f = open(data_file_path)
            lines = f.readlines()

            num_lines = len(lines)

            # randomly shuffle image
            if _random:
                random.shuffle(lines)

            path_seuil = os.path.join(zone_path, seuil_expe_filename)

            counter = 0
            # check if user select current scene and zone to be part of training data set
            for data in lines:

                percent = counter / num_lines
                image_index = int(data.split(';')[0])

                if image_index % _step == 0:
                    line = construct_new_line(path_seuil, _interval, data, _choice, _custom)

                    if id_zone < _nb_zones and folder_scene in _scenes and percent <= _percent:
                        train_file_data.append(line)
                    else:
                        test_file_data.append(line)

                counter += 1

            f.close()

    train_file = open(output_train_filename, 'w')
    test_file = open(output_test_filename, 'w')

    for line in train_file_data:
        train_file.write(line)

    for line in test_file_data:
        test_file.write(line)

    train_file.close()
    test_file.close()


def main():

    # getting all params
    parser = argparse.ArgumentParser(description="Generate data for model using correlation matrix information from data")

    parser.add_argument('--output', type=str, help='output file name desired (.train and .test)')
    parser.add_argument('--n', type=int, help='Number of features wanted')
    parser.add_argument('--highest', type=int, help='Specify if highest or lowest values are wishes', choices=[0, 1])
    parser.add_argument('--label', type=int, help='Specify if label correlation is used or not', choices=[0, 1])
    parser.add_argument('--kind', 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('--scenes', type=str, help='List of scenes to use for training data')
    parser.add_argument('--nb_zones', type=int, help='Number of zones to use for training data set')
    parser.add_argument('--random', type=int, help='Data will be randomly filled or not', choices=[0, 1])
    parser.add_argument('--percent', type=float, help='Percent of data use for train and test dataset (by default 1)')
    parser.add_argument('--step', type=int, help='Photo step to keep for build datasets', default=1)
    parser.add_argument('--renderer', type=str, help='Renderer choice in order to limit scenes used', choices=renderer_choices, default='all')
    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_filename = args.output
    p_n        = args.n
    p_highest  = args.highest
    p_label    = args.label
    p_kind     = args.kind
    p_metric   = args.metric
    p_scenes   = args.scenes.split(',')
    p_nb_zones = args.nb_zones
    p_random   = args.random
    p_percent  = args.percent
    p_step     = args.step
    p_renderer = args.renderer
    p_custom   = args.custom

    # list all possibles choices of renderer
    scenes_list = dt.get_renderer_scenes_names(p_renderer)
    scenes_indices = dt.get_renderer_scenes_indices(p_renderer)

    # getting scenes from indexes user selection
    scenes_selected = []

    for scene_id in p_scenes:
        index = scenes_indices.index(scene_id.strip())
        scenes_selected.append(scenes_list[index])

    # Get indices to keep from correlation information
    # compute temp data file to get correlation information
    temp_filename = 'temp'
    temp_filename_path = os.path.join(cfg.output_data_folder, temp_filename)

    cmd = ['python', 'generate_data_model_random.py',
            '--output', temp_filename_path,
            '--interval', '0, 200',
            '--kind', p_kind,
            '--metric', p_metric,
            '--scenes', args.scenes,
            '--nb_zones', str(16),
            '--random', str(int(p_random)),
            '--percent', str(p_percent),
            '--step', str(p_step),
            '--each', str(1),
            '--renderer', p_renderer,
            '--custom', temp_filename + min_max_ext]

    subprocess.Popen(cmd).wait()

    temp_data_file_path = temp_filename_path + '.train'
    df = pd.read_csv(temp_data_file_path, sep=';', header=None)

    indices = []

    # compute correlation matrix from whole data scenes of renderer (using or not label column)
    if p_label:

        # compute pearson correlation between features and label
        corr = df.corr()

        features_corr = []

        for id_row, row in enumerate(corr):
            for id_col, val in enumerate(corr[row]):
                if id_col == 0 and id_row != 0:
                    features_corr.append(abs(val))

    else:
        df = df.drop(df.columns[[0]], axis=1)

        # compute pearson correlation between features using only features
        corr = df[1:200].corr()

        features_corr = []

        for id_row, row in enumerate(corr):
            correlation_score = 0
            for id_col, val in enumerate(corr[row]):
                if id_col != id_row:
                    correlation_score += abs(val)

            features_corr.append(correlation_score)

    # find `n` min or max indices to keep
    if p_highest:
        indices = utils.get_indices_of_highest_values(features_corr, p_n)
    else:
        indices = utils.get_indices_of_lowest_values(features_corr, p_n)

    indices = np.sort(indices)

    # save indices found
    if not os.path.exists(cfg.correlation_indices_folder):
        os.makedirs(cfg.correlation_indices_folder)

    indices_file_path = os.path.join(cfg.correlation_indices_folder, p_filename.replace(cfg.output_data_folder + '/', '') + '.csv')

    with open(indices_file_path, 'w') as f:
        for i in indices:
            f.write(str(i) + ';')

    # find min max value if necessary to renormalize data from `n` indices found
    if p_custom:
        get_min_max_value_interval(scenes_list, indices, p_metric)

        # write new file to save
        if not os.path.exists(custom_min_max_folder):
            os.makedirs(custom_min_max_folder)

        min_max_folder_path = os.path.join(os.path.dirname(__file__), custom_min_max_folder)
        min_max_filename_path = os.path.join(min_max_folder_path, p_custom)

        with open(min_max_filename_path, 'w') as f:
            f.write(str(min_value_interval) + '\n')
            f.write(str(max_value_interval) + '\n')

    # create database using img folder (generate first time only)
    generate_data_model(scenes_list, p_filename, indices, p_kind, p_metric, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_custom)

if __name__== "__main__":
    main()