X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=flatland.git;a=blobdiff_plain;f=flatland.c;h=d92d2256d519781c84096386fc14c1a923c9c9f9;hp=032336a0b4e6dd120d9caaa8b9b92e11c3d3c206;hb=c99cbb5f30e1e2052bfc36880a1425db600454b5;hpb=e0f96aaef35ffaf34b912c7fa1473cb67b7a3dae

diff --git a/flatland.c b/flatland.c
index 032336a..d92d225 100644
--- a/flatland.c
+++ b/flatland.c
@@ -1,31 +1,45 @@
+
 /*
 
-  Example of FFI extension I started from:
+   flatland is a simple 2d physical simulator
 
-    https://github.com/pytorch/extension-ffi.git
+   Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+   Written by Francois Fleuret <francois.fleuret@idiap.ch>
 
-  There is this tutorial
+   This file is part of flatland
 
-    https://github.com/pytorch/tutorials/blob/master/Creating%20Extensions%20using%20FFI.md
+   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.
 
-  And TH's Tensor definition are here in my install:
+   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.
 
-    anaconda3/lib/python3.5/site-packages/torch/lib/include/TH/generic/THTensor.h
+   You should have received a copy of the GNU General Public License
+   along with flatland.  If not, see <http://www.gnu.org/licenses/>.
 
- */
+*/
 
 #include <TH/TH.h>
 
 #include "sequence_generator.h"
 
-THByteTensor *generate_sequence(long nb_sequences, long nb_images_per_sequence, long image_width, long image_height) {
+THByteTensor *generate_sequence(int pulling,
+                                long nb_sequences,
+                                long nb_images,
+                                long image_height, long image_width,
+                                long nb_shapes,
+                                int random_shape_size, int random_colors) {
+
   long nb_channels = 3;
   unsigned char *a, *b;
   long s, c, k, i, j, st0, st1, st2, st3, st4;
 
   THLongStorage *size = THLongStorage_newWithSize(5);
   size->data[0] = nb_sequences;
-  size->data[1] = nb_images_per_sequence;
+  size->data[1] = nb_images;
   size->data[2] = nb_channels;
   size->data[3] = image_height;
   size->data[4] = image_width;
@@ -38,13 +52,16 @@ THByteTensor *generate_sequence(long nb_sequences, long nb_images_per_sequence,
   st3 = THByteTensor_stride(result, 3);
   st4 = THByteTensor_stride(result, 4);
 
-  unsigned char tmp_buffer[nb_images_per_sequence * nb_channels * image_width * image_height];
+  unsigned char tmp_buffer[nb_images * nb_channels * image_width * image_height];
 
   for(s = 0; s < nb_sequences; s++) {
     a = THByteTensor_storage(result)->data + THByteTensor_storageOffset(result) + s * st0;
-    fl_generate_sequences(1, nb_images_per_sequence, image_width, image_height, tmp_buffer);
+    fl_generate_sequence(nb_images, image_width, image_height, nb_shapes,
+                         random_shape_size, random_colors,
+                         pulling,
+                         tmp_buffer);
     unsigned char *r = tmp_buffer;
-    for(k = 0; k < nb_images_per_sequence; k++) {
+    for(k = 0; k < nb_images; k++) {
       for(c = 0; c < nb_channels; c++) {
         for(i = 0; i < image_height; i++) {
           b = a + k * st1 + c * st2 + i * st3;