--- /dev/null
+#!/usr/bin/env python
+
+#
+# flatland is a simple 2d physical simulator
+#
+# Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+# Written by Francois Fleuret <francois.fleuret@idiap.ch>
+#
+# This file is part of flatland
+#
+# flatland is free software: you can redistribute it and/or modify it
+# under the terms of the GNU General Public License version 3 as
+# published by the Free Software Foundation.
+#
+# flatland is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+# General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with flatland. If not, see <http://www.gnu.org/licenses/>.
+#
+
+import torch
+import torchvision
+import argparse
+
+import flatland
+
+######################################################################
+
+parser = argparse.ArgumentParser(
+ description = 'Dummy test of the flatland sequence generation.',
+ formatter_class = argparse.ArgumentDefaultsHelpFormatter
+)
+
+parser.add_argument('--seed',
+ type = int, default = 0,
+ help = 'Random seed, < 0 is no seeding')
+
+parser.add_argument('--width',
+ type = int, default = 80,
+ help = 'Image width')
+
+parser.add_argument('--height',
+ type = int, default = 80,
+ help = 'Image height')
+
+parser.add_argument('--nb_shapes',
+ type = int, default = 8,
+ help = 'Image height')
+
+parser.add_argument('--nb_sequences',
+ type = int, default = 8,
+ help = 'How many sequences to generate')
+
+parser.add_argument('--nb_images_per_sequences',
+ type = int, default = 16,
+ help = 'How many images per sequence')
+
+parser.add_argument('--randomize_colors',
+ action='store_true', default=True,
+ help = 'Should the shapes be of different colors')
+
+parser.add_argument('--randomize_shape_size',
+ action='store_true', default=False,
+ help = 'Should the shapes be of different size')
+
+args = parser.parse_args()
+
+if args.seed >= 0:
+ torch.manual_seed(args.seed)
+
+######################################################################
+
+def sequences_to_image(x, gap = 1, gap_color = (0, 128, 255)):
+ from PIL import Image
+
+ nb_sequences = x.size(0)
+ nb_images_per_sequences = x.size(1)
+ nb_channels = 3
+
+ if x.size(2) != nb_channels:
+ print('Can only handle 3 channel tensors.')
+ exit(1)
+
+ height = x.size(3)
+ width = x.size(4)
+
+ result = torch.ByteTensor(nb_channels,
+ gap + nb_sequences * (height + gap),
+ gap + nb_images_per_sequences * (width + gap))
+
+ result.copy_(torch.Tensor(gap_color).view(-1, 1, 1).expand_as(result))
+
+ for s in range(0, nb_sequences):
+ for i in range(0, nb_images_per_sequences):
+ result.narrow(1, gap + s * (height + gap), height) \
+ .narrow(2, gap + i * (width + gap), width) \
+ .copy_(x[s][i])
+
+ result_numpy = result.cpu().byte().transpose(0, 2).transpose(0, 1).numpy()
+
+ return Image.fromarray(result_numpy, 'RGB')
+
+######################################################################
+
+x = flatland.generate_sequence(False,
+ args.nb_sequences,
+ args.nb_images_per_sequences,
+ args.height, args.width,
+ args.nb_shapes,
+ args.randomize_shape_size,
+ args.randomize_colors)
+
+sequences_to_image(x, gap = 3, gap_color = (0, 150, 200)).save('sequences.png')
+
+print('Saved sequences.png.')