From: François Fleuret Date: Sat, 8 Jul 2023 20:18:56 +0000 (+0200) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=62e273047aee0a1d606fbe0312abc16a74d23906;p=culture.git Update. --- diff --git a/world.py b/world.py index bac9e76..5ba0f36 100755 --- a/world.py +++ b/world.py @@ -30,7 +30,7 @@ class Box: return False -def scene2tensor(xh, yh, scene, size=512): +def scene2tensor(xh, yh, scene, size=64): width, height = size, size pixel_map = torch.ByteTensor(width, height, 4).fill_(255) data = pixel_map.numpy() @@ -48,11 +48,9 @@ def scene2tensor(xh, yh, scene, size=512): 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() + ctx.fill() - hs = size * 0.05 + hs = size * 0.1 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) @@ -87,7 +85,7 @@ def random_scene(): return scene -def sequence(length=10): +def sequence(nb_steps=10, all_frames=False): delta = 0.1 effects = [ (False, 0, 0), @@ -102,12 +100,15 @@ def sequence(length=10): ] while True: + + frames =[] + scene = random_scene() xh, yh = tuple(x.item() for x in torch.rand(2)) - frame_start = scene2tensor(xh, yh, scene) + frames.append(scene2tensor(xh, yh, scene)) - actions = torch.randint(len(effects), (length,)) + actions = torch.randint(len(effects), (nb_steps,)) change = False for a in actions: @@ -137,14 +138,20 @@ def sequence(length=10): if xh < 0 or xh > 1 or yh < 0 or yh > 1: xh, yh = x, y - frame_end = scene2tensor(xh, yh, scene) + if all_frames: + frames.append(scene2tensor(xh, yh, scene)) + + if not all_frames: + frames.append(scene2tensor(xh, yh, scene)) + if change: break - return frame_start, frame_end, actions + return frames, 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") + frames, actions = sequence(nb_steps=31,all_frames=True) + frames = torch.cat(frames,0) + print(f"{frames.size()=}") + torchvision.utils.save_image(frames, "seq.png", nrow=8)