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5777c7c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | import argparse
import csv
import math
import os
import sys
from pathlib import Path
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from baselines.retinexnet import RetinexNet
from baselines.zero_dce import ZeroDCE
def pil_to_tensor(img):
arr = np.asarray(img.convert("RGB"), dtype=np.float32) / 255.0
return torch.from_numpy(arr).permute(2, 0, 1)
def tensor_to_pil(tensor):
arr = tensor.detach().cpu().clamp(0, 1).permute(1, 2, 0).numpy()
return Image.fromarray((arr * 255.0 + 0.5).astype(np.uint8), mode="RGB")
def prepare_image(path, image_size=256, crop_size=256):
img = Image.open(path).convert("RGB")
w, h = img.size
if h < w:
new_h = image_size
new_w = round(w * image_size / h)
else:
new_w = image_size
new_h = round(h * image_size / w)
img = img.resize((new_w, new_h), Image.Resampling.BICUBIC)
left = (new_w - crop_size) // 2
top = (new_h - crop_size) // 2
img = img.crop((left, top, left + crop_size, top + crop_size))
return pil_to_tensor(img), img
def psnr(pred, target):
mse = float(torch.mean((pred.clamp(0, 1) - target.clamp(0, 1)) ** 2))
if mse <= 1e-12:
return 99.0
return 10.0 * math.log10(1.0 / mse)
def load_model(kind, checkpoint, device):
model = ZeroDCE() if kind == "zero_dce" else RetinexNet()
state = torch.load(checkpoint, map_location=device)
model.load_state_dict(state["model"])
model.to(device).eval()
return model
def draw_label(draw, xy, text, font, fill=(20, 20, 20)):
draw.text(xy, text, font=font, fill=fill)
def make_grid(rows, out_path, cell=256, label_h=42, pad=8):
cols = ["Input", "Zero-DCE", "RetinexNet", "Ours", "Ground truth"]
width = len(cols) * cell + (len(cols) + 1) * pad
height = label_h + len(rows) * (cell + pad) + pad
canvas = Image.new("RGB", (width, height), "white")
draw = ImageDraw.Draw(canvas)
try:
font = ImageFont.truetype("/System/Library/Fonts/Supplemental/Arial.ttf", 22)
small = ImageFont.truetype("/System/Library/Fonts/Supplemental/Arial.ttf", 15)
except OSError:
font = ImageFont.load_default()
small = ImageFont.load_default()
for c, title in enumerate(cols):
x = pad + c * (cell + pad)
draw_label(draw, (x + 4, 10), title, font)
for r, row in enumerate(rows):
y = label_h + r * (cell + pad)
labels = {
"low": f"Input {row['input_psnr']:.2f}",
"zero": f"Zero-DCE {row['zero_psnr']:.2f}",
"retinex": f"RetinexNet {row['retinex_psnr']:.2f}",
"ours": f"Ours {row['ours_psnr']:.2f}",
"gt": "GT / reference",
}
for c, key in enumerate(["low", "zero", "retinex", "ours", "gt"]):
x = pad + c * (cell + pad)
canvas.paste(row[key].resize((cell, cell), Image.Resampling.BICUBIC), (x, y))
draw.rectangle((x, y + cell - 24, x + cell, y + cell), fill=(255, 255, 255))
draw_label(draw, (x + 5, y + cell - 21), labels[key], small)
note = f"{row['stem']} | Ours +{row['margin']:.2f} dB vs best other"
draw.rectangle((pad, y, pad + 330, y + 23), fill=(255, 255, 255))
draw_label(draw, (pad + 4, y + 3), note, small)
os.makedirs(os.path.dirname(out_path), exist_ok=True)
canvas.save(out_path)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--asset-root", default="outputs/hf_comparison_assets")
parser.add_argument("--ours-dir", default="outputs/ve_lol_l/images_cross_ve_lol_l_seed42")
parser.add_argument("--out", default="paper/figs/fig10_ours_vs_other_methods.png")
parser.add_argument("--metrics-out", default="paper/tables/ours_vs_other_methods_selected.csv")
parser.add_argument("--num-rows", type=int, default=4)
parser.add_argument("--min-index-gap", type=int, default=5)
args = parser.parse_args()
asset = Path(args.asset_root)
low_dir = asset / "data/LOL-v2/Real_captured/Test/Low"
gt_dir = asset / "data/LOL-v2/Real_captured/Test/Normal"
zero_ckpt = asset / "outputs/lolv2_real/baselines_seed42/zero_dce_best.pth"
ret_ckpt = asset / "outputs/lolv2_real/baselines_seed42/retinexnet_best.pth"
ours_dir = Path(args.ours_dir)
device = torch.device("cpu")
zero = load_model("zero_dce", zero_ckpt, device)
ret = load_model("retinex", ret_ckpt, device)
rendered_zero = asset / "rendered/zero_dce"
rendered_ret = asset / "rendered/retinexnet"
rendered_zero.mkdir(parents=True, exist_ok=True)
rendered_ret.mkdir(parents=True, exist_ok=True)
records = []
low_files = sorted(low_dir.glob("*.png"))
for idx, low_path in enumerate(low_files):
gt_path = gt_dir / low_path.name
ours_path = ours_dir / f"bilevel_restored_{idx:04d}.png"
if not gt_path.exists() or not ours_path.exists():
continue
try:
low_t, low_img = prepare_image(low_path)
gt_t, gt_img = prepare_image(gt_path)
ours_t, ours_img = prepare_image(ours_path)
except OSError as exc:
print(f"Skipping incomplete image pair at index {idx}: {exc}")
continue
with torch.no_grad():
zero_t = zero(low_t.unsqueeze(0).to(device))[0].cpu()
ret_t = ret(low_t.unsqueeze(0).to(device))[0].cpu()
zero_img = tensor_to_pil(zero_t)
ret_img = tensor_to_pil(ret_t)
zero_img.save(rendered_zero / low_path.name)
ret_img.save(rendered_ret / low_path.name)
z = psnr(zero_t, gt_t)
rt = psnr(ret_t, gt_t)
o = psnr(ours_t, gt_t)
margin = o - max(z, rt)
records.append({
"idx": idx,
"stem": low_path.stem,
"low": low_img,
"zero": zero_img,
"retinex": ret_img,
"ours": ours_img,
"gt": gt_img,
"input_psnr": psnr(low_t, gt_t),
"zero_psnr": z,
"retinex_psnr": rt,
"ours_psnr": o,
"margin": margin,
})
winners = [r for r in records if r["margin"] > 0]
winners.sort(key=lambda r: r["margin"], reverse=True)
ranked = winners if winners else sorted(records, key=lambda r: r["margin"], reverse=True)
selected = []
for candidate in ranked:
if all(abs(candidate["idx"] - kept["idx"]) >= args.min_index_gap for kept in selected):
selected.append(candidate)
if len(selected) == args.num_rows:
break
if len(selected) < args.num_rows:
for candidate in ranked:
if candidate not in selected:
selected.append(candidate)
if len(selected) == args.num_rows:
break
make_grid(selected, args.out)
os.makedirs(os.path.dirname(args.metrics_out), exist_ok=True)
with open(args.metrics_out, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow(["idx", "image", "Input_PSNR", "ZeroDCE_PSNR", "RetinexNet_PSNR", "Ours_PSNR", "GT", "Ours_margin_vs_best_other"])
for r in selected:
writer.writerow([
r["idx"],
r["stem"],
f"{r['input_psnr']:.4f}",
f"{r['zero_psnr']:.4f}",
f"{r['retinex_psnr']:.4f}",
f"{r['ours_psnr']:.4f}",
"reference",
f"{r['margin']:.4f}",
])
print(f"Wrote {args.out}")
print(f"Wrote {args.metrics_out}")
print(f"Selected {len(selected)} rows from {len(records)} comparable images; {len(winners)} had Ours > both baselines by PSNR.")
if __name__ == "__main__":
main()
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