--- a
+++ b/experiments/toy_exp/generate_toys.py
@@ -0,0 +1,138 @@
+#!/usr/bin/env python
+# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import os, time
+import numpy as np
+import pandas as pd
+import pickle
+import argparse
+from multiprocessing import Pool
+
+DO_MP = True
+
+
+def create_image(out_dir, six, foreground_margin, class_diameters, mode, noisy_bg):
+
+    print('\rprocessing {} {}'.format(out_dir, six), end="", flush=True)
+
+    img = np.random.rand(320, 320) if noisy_bg else np.zeros((320, 320))
+    seg = np.zeros((320, 320)).astype('uint8')
+    center_x = np.random.randint(foreground_margin, img.shape[0] - foreground_margin)
+    center_y = np.random.randint(foreground_margin, img.shape[1] - foreground_margin)
+    class_id = np.random.randint(0, 2)
+
+    for y in range(img.shape[0]):
+        for x in range(img.shape[0]):
+            if ((x - center_x) ** 2 + (y - center_y) ** 2 - class_diameters[class_id] ** 2) < 0:
+                img[y][x] += 0.2
+                seg[y][x] = 1
+
+    if 'donuts' in mode:
+        hole_diameter = 4
+        if class_id == 1:
+            for y in range(img.shape[0]):
+                for x in range(img.shape[0]):
+                    if ((x - center_x) ** 2 + (y - center_y) ** 2 - hole_diameter ** 2) < 0:
+                        img[y][x] -= 0.2
+                        if mode == 'donuts_shape':
+                            seg[y][x] = 0
+
+    out = np.concatenate((img[None], seg[None]))
+    out_path = os.path.join(out_dir, '{}.npy'.format(six))
+    np.save(out_path, out)
+
+    with open(os.path.join(out_dir, 'meta_info_{}.pickle'.format(six)), 'wb') as handle:
+        pickle.dump([out_path, class_id, str(six)], handle)
+
+
+def generate_dataset(cf, exp_name, n_train_images, n_test_images, mode, class_diameters=(20, 20), noisy_bg=False):
+
+    train_dir = os.path.join(cf.root_dir, exp_name, 'train')
+    test_dir = os.path.join(cf.root_dir, exp_name, 'test')
+    if os.path.isdir(train_dir) or os.path.isdir(test_dir):
+        raise Exception("A dataset directory already exists at {}. ".format(cf.root_dir)+
+                        "Please make sure to generate data in an empty or new directory.")
+    os.makedirs(train_dir, exist_ok=False)
+    os.makedirs(test_dir, exist_ok=False)
+
+    # enforced distance between object center and image edge.
+    foreground_margin = int(np.ceil(np.max(class_diameters) / 1.25))
+
+    info = []
+    info += [[train_dir, six, foreground_margin, class_diameters, mode, noisy_bg] for six in range(n_train_images)]
+    info += [[test_dir, six, foreground_margin, class_diameters, mode, noisy_bg] for six in range(n_test_images)]
+
+    print('starting creation of {} images'.format(len(info)))
+    if DO_MP:
+        pool = Pool(processes=os.cpu_count()-1)
+        pool.starmap(create_image, info, chunksize=1)
+        pool.close()
+        pool.join()
+    else:
+        for inputs in info:
+            create_image(*inputs)
+    print()
+    aggregate_meta_info(train_dir)
+    aggregate_meta_info(test_dir)
+
+
+def aggregate_meta_info(exp_dir):
+
+    files = [os.path.join(exp_dir, f) for f in os.listdir(exp_dir) if 'meta_info' in f]
+    df = pd.DataFrame(columns=['path', 'class_id', 'pid'])
+    for f in files:
+        with open(f, 'rb') as handle:
+            df.loc[len(df)] = pickle.load(handle)
+
+    df.to_pickle(os.path.join(exp_dir, 'info_df.pickle'))
+    print("aggregated meta info to df with length", len(df))
+
+
+if __name__ == '__main__':
+    stime = time.time()
+    import sys
+    sys.path.append("../..")
+    import utils.exp_utils as utils
+
+    parser = argparse.ArgumentParser()
+    mode_choices = ['donuts_shape', 'donuts_pattern', 'circles_scale']
+    parser.add_argument('-m', '--modes', nargs='+', type=str, default=mode_choices, choices=mode_choices)
+    parser.add_argument('--noise', action='store_true', help="if given, add noise to the sample bg.")
+    parser.add_argument('--n_train', type=int, default=2500, help="Nr. of train images to generate.")
+    parser.add_argument('--n_test', type=int, default=1000, help="Nr. of test images to generate.")
+    args = parser.parse_args()
+
+
+    cf_file = utils.import_module("cf", "configs.py")
+    cf = cf_file.configs()
+
+    class_diameters = {
+        'donuts_shape': (20, 20),
+        'donuts_pattern': (20, 20),
+        'circles_scale': (19, 20)
+    }
+
+    for mode in args.modes:
+        generate_dataset(cf, mode + ("_noise" if args.noise else ""), n_train_images=args.n_train, n_test_images=args.n_test, mode=mode,
+                            class_diameters=class_diameters[mode], noisy_bg=args.noise)
+
+
+    mins, secs = divmod((time.time() - stime), 60)
+    h, mins = divmod(mins, 60)
+    t = "{:d}h:{:02d}m:{:02d}s".format(int(h), int(mins), int(secs))
+    print("{} total runtime: {}".format(os.path.split(__file__)[1], t))
+
+