Data generation scripts update

generate/generate_all_data_augmentation.py

@@ -117,8 +117,8 @@ def generate_data_svd(data_type, mode, path):
 
             for val in data:
                 current_file.write(str(val) + ";")
-                
-            print(data_type + "_" + mode + "_" + scene_name + " - " + "{0:.2f}".format((index + 1) / number_of_images * 100.) + "%")
+
+            print(data_type + "_" + mode + " - " + "{0:.2f}".format((index + 1) / number_of_images * 100.) + "%")
             sys.stdout.write("\033[F")
 
             current_file.write('\n')

generate/generate_data_model_random_augmented.py

@@ -0,0 +1,237 @@
+# main imports
+import sys, os, argparse
+import numpy as np
+import pandas as pd
+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_image_features
+
+
+# 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
+
+normalization_choices   = cfg.normalization_choices
+path                    = cfg.dataset_path
+zones                   = cfg.zones_indices
+seuil_expe_filename     = cfg.seuil_expe_filename
+
+renderer_choices        = cfg.renderer_choices
+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(interval, line_data, choice, each, norm):
+    begin, end = interval
+
+    label = line_data[2]
+    features = line_data[begin+3:end+3]
+
+    # keep only if modulo result is 0 (keep only each wanted values)
+    features = [float(m) for id, m in enumerate(features) if id % each == 0]
+
+    # 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)
+
+    line = label
+
+    for val in features:
+        line += ';'
+        line += str(val)
+    line += '\n'
+
+    return line
+
+def get_min_max_value_interval(_scenes_list, _interval, _feature):
+
+    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]
+
+    data_filename = _feature + "_svd" + generic_output_file_svd
+
+    data_file_path = os.path.join(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(';')
+
+        features = line_data[begin+1:end+1]
+        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, _each=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  = []
+
+    # 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(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)
+
+    counter = 0
+    # check if user select current scene data line of training data set
+    for data in lines:
+
+        percent = counter / num_lines
+
+        data = data.split(';')
+        scene_name = data[0]
+        image_index = int(data[1])
+
+        if image_index % _step == 0:
+            line = construct_new_line(_interval, data, _choice, _each, _custom)
+
+            if scene_name 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('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
+    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('--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('--each', type=int, help='Each features to keep from interval', 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_interval = list(map(int, args.interval.split(',')))
+    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_each     = args.each
+    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])
+
+    # find min max value if necessary to renormalize data
+    if p_custom:
+        get_min_max_value_interval(scenes_list, p_interval, p_feature)
+
+        # write new file to save
+        if not os.path.exists(custom_min_max_folder):
+            os.makedirs(custom_min_max_folder)
+
+        min_max_filename_path = os.path.join(custom_min_max_folder, 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_feature, scenes_selected, p_nb_zones, p_percent, p_random, p_step, p_each, p_custom)
+
+if __name__== "__main__":
+    main()