File size: 849 Bytes
87d0bc2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | import numpy as np, json, imageio
from pathlib import Path
root=Path("data/bucket/ogbench-cube-success-1000-v1")
row=json.loads((root/"subsets"/"subset-0010.jsonl").read_text().splitlines()[0])
with np.load(root/"data"/row["archive_file"], allow_pickle=False) as z:
front=z["pixels.front"]; side=z["pixels.side"]
print("front", front.shape, "side", side.shape)
imageio.imwrite("ds_front_first.png", front[0])
imageio.imwrite("ds_front_last.png", front[-1])
imageio.imwrite("ds_side_first.png", side[0])
# downsample front to 64x64 to compare with env obs
import numpy as np
def ds64(img):
# simple 224->64 area-ish downsample via strided mean on 224/64 blocks is non-integer; use PIL
from PIL import Image
return np.array(Image.fromarray(img).resize((64,64), Image.BILINEAR))
imageio.imwrite("ds_front_first_64.png", ds64(front[0]))
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