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- # main imports
- import sys, os, argparse
- import numpy as np
- import pandas as pd
- import subprocess
- import random
- # image processing imports
- from PIL import Image
- from ipfml import utils
- # modules imports
- sys.path.insert(0, '') # trick to enable import of main folder module
- import custom_config as cfg
- from modules.utils import data as dt
- from data_attributes import get_svd_data
- # getting configuration information
- learned_folder = cfg.learned_zones_folder
- min_max_filename = cfg.min_max_filename_extension
- # define all scenes variables
- 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
- features_choices = cfg.features_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]
- features = line_data[indices]
- features = features.astype('float32')
- # TODO : check if it's always necessary to do that (loss of information for svd)
- if norm:
- if choice == 'svdne':
- features = utils.normalize_arr_with_range(features, min_value_interval, max_value_interval)
- if choice == 'svdn':
- features = utils.normalize_arr(features)
- 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 val in features:
- line += ';'
- line += str(val)
- line += '\n'
- return line
- def get_min_max_value_interval(_scenes_list, _indices, _feature):
- 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 folder_scene in 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 zone_folder in 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 = _feature + "_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(';'))
- features = line_data[[_indices]]
- features = [float(m) for m in features]
- min_value = min(features)
- max_value = max(features)
- 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, _feature, _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 folder_scene in _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 = _feature + "_svd"+ generic_output_file_svd
- else:
- data_filename = _feature + "_" + _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('--feature', type=str, help='feature data choice', choices=features_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_feature = args.feature
- 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,
- '--feature', p_feature,
- '--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_feature)
- # 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_current_filename = p_filename.replace(cfg.output_data_folder + '/', '').replace('deep_keras_', '') + min_max_filename
- min_max_filename_path = os.path.join(min_max_folder_path, min_max_current_filename)
- print(min_max_filename_path)
- 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_feature, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_custom)
- if __name__== "__main__":
- main()
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