File size: 2,941 Bytes
1d1bd5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import os
import sys
import cv2
import numpy as np
import torch
from PIL import Image, ImageOps
from tqdm import tqdm
# Settings --------------------------------------------------------------------
DATA_DIR = "/home/nobus/Raid0/DataSet/Images1"
OUT_DIR = "/home/nobus/Raid0/DataSet/hed_maps_768" # saved as {stem}.jpg alongside originals
IMG_SIZE = 768
DEVICE = "cuda:0"
BATCH_SIZE = 8 # images processed in parallel through HED
HED_CKPT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "checkpoints/ControlNetHED.pth")
# -----------------------------------------------------------------------------
_D = os.path.dirname(os.path.abspath(__file__))
_ROOT = os.path.abspath(os.path.join(_D, "../.."))
sys.path.insert(0, os.path.join(_ROOT, "Sana"))
from einops import rearrange
from hed import ControlNetHED_Apache2
def load_hed(device):
net = ControlNetHED_Apache2().float().to(device).eval()
ckpt = torch.load(HED_CKPT, map_location="cpu", weights_only=False)
if "state_dict" in ckpt:
ckpt = ckpt["state_dict"]
net.load_state_dict(ckpt)
return net
@torch.no_grad()
def run_hed(net, arr_hwc, device):
H, W = arr_hwc.shape[:2]
t = torch.from_numpy(arr_hwc.copy()).float().to(device)
t = rearrange(t, "h w c -> 1 c h w")
edges = net(t)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
edges = np.stack(edges, axis=2)
edge = 1.0 / (1.0 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
return (edge * 255).clip(0, 255).astype(np.uint8)
def main():
os.makedirs(OUT_DIR, exist_ok=True)
exts = {".jpg", ".jpeg", ".png", ".webp"}
paths = sorted(
os.path.join(r, f)
for r, _, files in os.walk(DATA_DIR)
for f in files
if os.path.splitext(f)[1].lower() in exts
)
print(f"Found {len(paths)} images")
# Skip already processed
pending = []
for p in paths:
stem = os.path.splitext(os.path.basename(p))[0]
out = os.path.join(OUT_DIR, f"{stem}.jpg")
if not os.path.exists(out):
pending.append(p)
print(f"Pending: {len(pending)} (already done: {len(paths) - len(pending)})")
if not pending:
print("All done!")
return
print(f"Loading HED from {HED_CKPT}...")
net = load_hed(DEVICE)
for path in tqdm(pending, unit="img"):
stem = os.path.splitext(os.path.basename(path))[0]
out = os.path.join(OUT_DIR, f"{stem}.jpg")
img = ImageOps.fit(
Image.open(path).convert("RGB"),
(IMG_SIZE, IMG_SIZE),
method=Image.LANCZOS,
)
arr = np.asarray(img, dtype=np.uint8)
edge = run_hed(net, arr, DEVICE)
Image.fromarray(edge).save(out, quality=90)
print(f"Done -> {OUT_DIR}/")
if __name__ == "__main__":
main()
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