X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=flatland.git;a=blobdiff_plain;f=flatland-test.py;fp=flatland-test.py;h=0000000000000000000000000000000000000000;hp=2c269ba0eb9dcc35dcb15ef7ca6df2bc9e0fddc3;hb=fcb630408ea274f4cbed2fdfd7b07b759b80914d;hpb=924ef5308c54784d2c0a9bbd282e55a16efbf318

diff --git a/flatland-test.py b/flatland-test.py
deleted file mode 100755
index 2c269ba..0000000
--- a/flatland-test.py
+++ /dev/null
@@ -1,118 +0,0 @@
-#!/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.')