Thesis-NoiseDetection-attributes/generate_data_model_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, getopt
import numpy as np
import random
import time
import json

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
zone_folder             = cfg.zone_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

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, interval, line, choice, norm):
    begin, end = interval

    line_data = line.split(';')
    seuil = line_data[0]
    metrics = line_data[begin+1:end+1]

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

    # 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, _filename, _interval, _choice, _metric, _custom):

    global min_value_interval, max_value_interval

    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)

            # shuffle list of zones (=> randomly choose zones)
            random.shuffle(zones_folder)

            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
                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()

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

                    begin, end = _interval

                    line_data = line.split(';')

                    metrics = line_data[begin+1:end+1]
                    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

                    counter += 1


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)

    scenes = os.listdir(path)

    # remove min max file from scenes folder
    scenes = [s for s in scenes if min_max_filename not in s]

    train_file_data = []
    test_file_data  = []

    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)

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

            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
                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():

    p_custom    = False
    p_step      = 1
    p_renderer  = 'all'

    if len(sys.argv) <= 1:
        print('Run with default parameters...')
        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 renderer all --custom min_max_filename')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "ho:i:k:s:n:r:p:s:r:c", ["help=", "output=", "interval=", "kind=", "metric=","scenes=", "nb_zones=", "random=", "percent=", "step=", "renderer=", "custom="])
    except getopt.GetoptError:
        # print help information and exit:
        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
        sys.exit(2)
    for o, a in opts:
        if o == "-h":
            print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --nb_zones 5 --random 1 --percent 0.7 --step 10 --renderer all --custom min_max_filename')
            sys.exit()
        elif o in ("-o", "--output"):
            p_filename = a
        elif o in ("-i", "--interval"):
            p_interval = list(map(int, a.split(',')))
        elif o in ("-k", "--kind"):
            p_kind = a

            if p_kind not in normalization_choices:
                assert False, "Invalid normalization choice, %s" % normalization_choices

        elif o in ("-m", "--metric"):
            p_metric = a
        elif o in ("-s", "--scenes"):
            p_scenes = a.split(',')
        elif o in ("-n", "--nb_zones"):
            p_nb_zones = int(a)
        elif o in ("-r", "--random"):
            p_random = int(a)
        elif o in ("-p", "--percent"):
            p_percent = float(a)
        elif o in ("-s", "--sep"):
            p_sep = a
        elif o in ("-s", "--step"):
            p_step = int(a)
        elif o in ("-r", "--renderer"):
            p_renderer = a

            if p_renderer not in cfg.renderer_choices:
                assert False, "Unknown renderer choice, %s" % cfg.renderer_choices
        elif o in ("-c", "--custom"):
            p_custom = a
        else:
            assert False, "unhandled option"

    # 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])

    # find min max value if necessary to renormalize data
    if p_custom:
        get_min_max_value_interval(scenes_list, p_filename, p_interval, p_kind, p_metric, p_custom)

        # 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, p_interval, p_kind, p_metric, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_custom)

if __name__== "__main__":
    main()