X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=world.py;h=2e13ec38c0c9c073fef95dc55f3bb5925d75cdcd;hb=276696d1e7407a40d55c2a50b12839b52c0fed1e;hp=a5e960104897e370efdad54dea171ed7dbf6ddd6;hpb=ef2311240c7a683e3e0a750827e3e350c3a96c4f;p=culture.git

diff --git a/world.py b/world.py
index a5e9601..2e13ec3 100755
--- a/world.py
+++ b/world.py
@@ -103,8 +103,9 @@ def generate(
 
 
 def sample2img(seq, height, width, upscale=15):
-    f_start = seq[:, : height * width].reshape(-1, height, width)
-    f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
+    f_first = seq[:, : height * width].reshape(-1, height, width)
+    f_second = seq[:, height * width + 1 :].reshape(-1, height, width)
+    direction = seq[:, height * width]
 
     def mosaic(x, upscale):
         x = x.reshape(-1, height, width)
@@ -124,7 +125,46 @@ def sample2img(seq, height, width, upscale=15):
 
         return x
 
-    return torch.cat([mosaic(f_start, upscale), mosaic(f_end, upscale)], dim=3)
+    direction_symbol = torch.full((direction.size(0), height * upscale, upscale), 0)
+    direction_symbol = colors[direction_symbol].permute(0, 3, 1, 2)
+    separator = torch.full((direction.size(0), 3, height * upscale, 1), 0)
+
+    for n in range(direction_symbol.size(0)):
+        if direction[n] == token_forward:
+            for k in range(upscale):
+                direction_symbol[
+                    n,
+                    :,
+                    (height * upscale) // 2 - upscale // 2 + k,
+                    3 + abs(k - upscale // 2),
+                ] = 0
+        elif direction[n] == token_backward:
+            for k in range(upscale):
+                direction_symbol[
+                    n,
+                    :,
+                    (height * upscale) // 2 - upscale // 2 + k,
+                    3 + upscale // 2 - abs(k - upscale // 2),
+                ] = 0
+        else:
+            for k in range(2, upscale - 2):
+                direction_symbol[
+                    n, :, (height * upscale) // 2 - upscale // 2 + k, k
+                ] = 0
+                direction_symbol[
+                    n, :, (height * upscale) // 2 - upscale // 2 + k, upscale - 1 - k
+                ] = 0
+
+    return torch.cat(
+        [
+            mosaic(f_first, upscale),
+            separator,
+            direction_symbol,
+            separator,
+            mosaic(f_second, upscale),
+        ],
+        dim=3,
+    )
 
 
 def seq2str(seq):
@@ -141,18 +181,18 @@ if __name__ == "__main__":
 
     height, width = 6, 8
     start_time = time.perf_counter()
-    seq = generate(nb=64, height=height, width=width, max_nb_obj=3)
+    seq = generate(nb=90, height=height, width=width, max_nb_obj=3)
     delay = time.perf_counter() - start_time
     print(f"{seq.size(0)/delay:02f} samples/s")
 
     print(seq2str(seq[:4]))
 
-    # m = (torch.rand(seq.size()) < 0.05).long()
-    # seq = (1 - m) * seq + m * 23
+    m = (torch.rand(seq.size()) < 0.05).long()
+    seq = (1 - m) * seq + m * 23
 
     img = sample2img(seq, height, width)
     print(img.size())
 
     torchvision.utils.save_image(
-        img.float() / 255.0, "/tmp/world.png", nrow=8, padding=2
+        img.float() / 255.0, "/tmp/world.png", nrow=6, padding=4
     )