X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=world.py;h=43126d5d63466e948317839e727a410e2b267c62;hb=6917d3d52a4b473d31121a471ab98fa114bdb1a6;hp=bac9e761e248bb64547aada2bac7109f0099d38d;hpb=e38b98574f1966ea3a91ffb8fd9042f10a75ca88;p=culture.git

diff --git a/world.py b/world.py
index bac9e76..43126d5 100755
--- a/world.py
+++ b/world.py
@@ -1,150 +1,129 @@
 #!/usr/bin/env python
 
-import math
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, sys, tqdm
 
 import torch, torchvision
 
 from torch import nn
 from torch.nn import functional as F
-import cairo
-
-
-class Box:
-    def __init__(self, x, y, w, h, r, g, b):
-        self.x = x
-        self.y = y
-        self.w = w
-        self.h = h
-        self.r = r
-        self.g = g
-        self.b = b
-
-    def collision(self, scene):
-        for c in scene:
-            if (
-                self is not c
-                and max(self.x, c.x) <= min(self.x + self.w, c.x + c.w)
-                and max(self.y, c.y) <= min(self.y + self.h, c.y + c.h)
-            ):
-                return True
-        return False
-
-
-def scene2tensor(xh, yh, scene, size=512):
-    width, height = size, size
-    pixel_map = torch.ByteTensor(width, height, 4).fill_(255)
-    data = pixel_map.numpy()
-    surface = cairo.ImageSurface.create_for_data(
-        data, cairo.FORMAT_ARGB32, width, height
-    )
 
-    ctx = cairo.Context(surface)
-    ctx.set_fill_rule(cairo.FILL_RULE_EVEN_ODD)
-
-    for b in scene:
-        ctx.move_to(b.x * size, b.y * size)
-        ctx.rel_line_to(b.w * size, 0)
-        ctx.rel_line_to(0, b.h * size)
-        ctx.rel_line_to(-b.w * size, 0)
-        ctx.close_path()
-        ctx.set_source_rgba(b.r, b.g, b.b, 1.0)
-        ctx.fill_preserve()
-        ctx.set_source_rgba(0, 0, 0, 1.0)
-        ctx.stroke()
-
-    hs = size * 0.05
-    ctx.set_source_rgba(0.0, 0.0, 0.0, 1.0)
-    ctx.move_to(xh * size - hs / 2, yh * size - hs / 2)
-    ctx.rel_line_to(hs, 0)
-    ctx.rel_line_to(0, hs)
-    ctx.rel_line_to(-hs, 0)
-    ctx.close_path()
-    ctx.fill()
-
-    return pixel_map[None, :, :, :3].flip(-1).permute(0, 3, 1, 2).float() / 255
-
-
-def random_scene():
-    scene = []
-    colors = [
-        (1.00, 0.00, 0.00),
-        (0.00, 1.00, 0.00),
-        (0.00, 0.00, 1.00),
-        (1.00, 1.00, 0.00),
-        (0.75, 0.75, 0.75),
-    ]
+######################################################################
+
 
-    for k in range(10):
-        wh = torch.rand(2) * 0.2 + 0.2
-        xy = torch.rand(2) * (1 - wh)
-        c = colors[torch.randint(len(colors), (1,))]
-        b = Box(
-            xy[0].item(), xy[1].item(), wh[0].item(), wh[1].item(), c[0], c[1], c[2]
-        )
-        if not b.collision(scene):
-            scene.append(b)
-
-    return scene
-
-
-def sequence(length=10):
-    delta = 0.1
-    effects = [
-        (False, 0, 0),
-        (False, delta, 0),
-        (False, 0, delta),
-        (False, -delta, 0),
-        (False, 0, -delta),
-        (True, delta, 0),
-        (True, 0, delta),
-        (True, -delta, 0),
-        (True, 0, -delta),
+colors = torch.tensor(
+    [
+        [255, 255, 255],
+        [0, 0, 0],
+        [255, 0, 0],
+        [0, 128, 0],
+        [0, 0, 255],
+        [255, 255, 0],
+        [192, 192, 192],
     ]
+)
+
+token2char = "_X" + "".join([str(n) for n in range(len(colors) - 2)]) + ">"
+
+
+def generate(
+    nb,
+    height,
+    width,
+    max_nb_obj=2,
+    nb_iterations=2,
+):
+    f_start = torch.zeros(nb, height, width, dtype=torch.int64)
+    f_end = torch.zeros(nb, height, width, dtype=torch.int64)
+    n = torch.arange(f_start.size(0))
+
+    for n in range(nb):
+        nb_fish = torch.randint(max_nb_obj, (1,)).item() + 1
+        for c in torch.randperm(colors.size(0) - 2)[:nb_fish].sort().values:
+            i, j = (
+                torch.randint(height - 2, (1,))[0] + 1,
+                torch.randint(width - 2, (1,))[0] + 1,
+            )
+            vm = torch.randint(4, (1,))[0]
+            vi, vj = (vm // 2) * (2 * (vm % 2) - 1), (1 - vm // 2) * (2 * (vm % 2) - 1)
+
+            f_start[n, i, j] = c + 2
+            f_start[n, i - vi, j - vj] = c + 2
+            f_start[n, i + vj, j - vi] = c + 2
+            f_start[n, i - vj, j + vi] = c + 2
+
+            for l in range(nb_iterations):
+                i += vi
+                j += vj
+                if i < 0 or i >= height or j < 0 or j >= width:
+                    i -= vi
+                    j -= vj
+                    vi, vj = -vi, -vj
+                    i += vi
+                    j += vj
+
+            f_end[n, i, j] = c + 2
+            f_end[n, i - vi, j - vj] = c + 2
+            f_end[n, i + vj, j - vi] = c + 2
+            f_end[n, i - vj, j + vi] = c + 2
+
+    return torch.cat(
+        [
+            f_end.flatten(1),
+            torch.full((f_end.size(0), 1), len(colors)),
+            f_start.flatten(1),
+        ],
+        dim=1,
+    )
+
+
+def sample2img(seq, height, width):
+    f_start = seq[:, : height * width].reshape(-1, height, width)
+    f_start = (f_start >= len(colors)).long() + (f_start < len(colors)).long() * f_start
+    f_end = seq[:, height * width + 1 :].reshape(-1, height, width)
+    f_end = (f_end >= len(colors)).long() + (f_end < len(colors)).long() * f_end
+
+    img_f_start, img_f_end = colors[f_start], colors[f_end]
+
+    img = torch.cat(
+        [
+            img_f_start,
+            torch.full(
+                (img_f_start.size(0), img_f_start.size(1), 1, img_f_start.size(3)), 1
+            ),
+            img_f_end,
+        ],
+        dim=2,
+    )
+
+    return img.permute(0, 3, 1, 2)
+
+
+def seq2str(seq):
+    result = []
+    for s in seq:
+        result.append("".join([token2char[v] for v in s]))
+    return result
 
-    while True:
-        scene = random_scene()
-        xh, yh = tuple(x.item() for x in torch.rand(2))
-
-        frame_start = scene2tensor(xh, yh, scene)
-
-        actions = torch.randint(len(effects), (length,))
-        change = False
-
-        for a in actions:
-            g, dx, dy = effects[a]
-            if g:
-                for b in scene:
-                    if b.x <= xh and b.x + b.w >= xh and b.y <= yh and b.y + b.h >= yh:
-                        x, y = b.x, b.y
-                        b.x += dx
-                        b.y += dy
-                        if (
-                            b.x < 0
-                            or b.y < 0
-                            or b.x + b.w > 1
-                            or b.y + b.h > 1
-                            or b.collision(scene)
-                        ):
-                            b.x, b.y = x, y
-                        else:
-                            xh += dx
-                            yh += dy
-                            change = True
-            else:
-                x, y = xh, yh
-                xh += dx
-                yh += dy
-                if xh < 0 or xh > 1 or yh < 0 or yh > 1:
-                    xh, yh = x, y
-
-        frame_end = scene2tensor(xh, yh, scene)
-        if change:
-            break
-
-    return frame_start, frame_end, actions
 
+######################################################################
 
 if __name__ == "__main__":
-    frame_start, frame_end, actions = sequence()
-    torchvision.utils.save_image(frame_start, "world_start.png")
-    torchvision.utils.save_image(frame_end, "world_end.png")
+    import time
+
+    height, width = 6, 8
+    start_time = time.perf_counter()
+    seq = generate(nb=64, 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]))
+
+    img = sample2img(seq, height, width)
+    print(img.size())
+
+    torchvision.utils.save_image(img.float() / 255.0, "world.png", nrow=8, padding=2)