Thesis-NoiseDetection-attributes/generate_data_model.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
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
output_data_folder      = cfg.output_data_folder
custom_min_max_folder   = cfg.min_max_custom_folder
min_max_ext             = cfg.min_max_filename_extension
zones_indices           = cfg.zones_indices

generic_output_file_svd = '_random.csv'

min_value_interval = sys.maxsize
max_value_interval = 0

def construct_new_line(path_seuil, interval, line, choice, each, norm):
    begin, end = interval

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

    metrics = [float(m) for id, m in enumerate(metrics) if id % each == 0 ]

    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, _interval, _metric):

    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)

            for id_zone, zone_folder in enumerate(zones_folder):
                zone_path = os.path.join(scene_path, zone_folder)
                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:

                    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


def generate_data_model(_filename, _interval, _choice, _metric, _scenes = scenes_list, _zones = zones_indices, _percent = 1, _step=1, _each=1, _norm=False, _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 = open(output_train_filename, 'w')
    test_file = open(output_test_filename, 'w')

    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
                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)

                lines_indexes = np.arange(num_lines)
                random.shuffle(lines_indexes)

                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 index in lines_indexes:

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

                    if image_index % _step == 0:
                        line = construct_new_line(path_seuil, _interval, lines[index], _choice, _each, _norm)

                        if id_zone in _zones and folder_scene in _scenes and percent <= _percent:
                            train_file.write(line)
                        else:
                            test_file.write(line)

                    counter += 1

                f.close()

    train_file.close()
    test_file.close()


def main():

    p_custom = False
    p_step      = 1
    p_renderer  = 'all'
    p_each      = 1

    if len(sys.argv) <= 1:
        print('python generate_data_model.py --output xxxx --interval 0,20  --kind svdne --metric lab --scenes "A, B, D" --zones "1, 2, 3, 4" --percent 0.7 --renderer all --step 10 --each 1 --custom min_max_filename')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "ho:i:k:s:z:p:r:c", ["help=", "output=", "interval=", "kind=", "metric=","scenes=", "zones=", "percent=", "renderer=", "step=", "each=", "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" --zones "1, 2, 3, 4" --percent 0.7 --renderer all --step 10 --each 1 --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" --zones "1, 2, 3, 4" --percent 0.7 --renderer all --step 10 --each 1 --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
        elif o in ("-m", "--metric"):
            p_metric = a
        elif o in ("-s", "--scenes"):
            p_scenes = a.split(',')
        elif o in ("-z", "--zones"):
            if ',' in a:
                p_zones = list(map(int, a.split(',')))
            else:
                p_zones = [a.strip()]
        elif o in ("-p", "--percent"):
            p_percent = float(a)
        elif o in ("-s", "--step"):
            p_step = int(a)
        elif o in ("-e", "--each"):
            p_each = 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_indexes.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_interval, 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(p_filename, p_interval, p_kind, p_metric, scenes_selected, p_zones, p_percent, p_step, p_each, p_custom)

if __name__== "__main__":
    main()