The main function is
```
-torch.ByteTensor generate_sequence(bool pulling,
- int nb_sequences,
- int nb_images,
- int image_height, int image_width,
- int nb_shapes,
- bool random_shape_size, bool random_colors)
+torch.ByteTensor flatland.generate_sequence(bool pulling,
+ int nb_sequences,
+ int nb_images,
+ int image_height, int image_width,
+ int nb_shapes,
+ bool random_shape_size, bool random_colors)
```
-with
- * `pulling` indicating if one of the shape should be pulled upward,
- * `nb_sequences` the number of sequences to generate
- * `nb_images` the number of frames per sequence
- * `image_height` an `image_width` the individual image size
- * `nb_shapes` how many rectangular shapes to put in the simulation
- * `random_shape_size` should they be of different size
- * `random_colors` should they be of different colors
+where
+
+ * `pulling` indicates if one of the shape should be pulled upward,
+ * `nb_sequences` is the number of sequences to generate,
+ * `nb_images` is the number of frames per sequence,
+ * `image_height` and `image_width` is the individual image size,
+ * `nb_shapes` is the number of rectangular shapes in the simulation,
+ * `random_shape_size` states if the shapes should be of different sizes,
+ * `random_colors` states if they should be of different colors.
The returned ByteTensor has five dimensions:
* Sequence index
* Image index
- * Channel (3, as for RGB)
+ * Channel (3 since it is RGB)
* Pixel row
* Pixel col
```
make -j -k
-./built.py
-./flatland-test.py
+./test-flatland.py
```