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23e73f9 | 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 | #!/usr/bin/env python
"""Dump <20dB render_vs_render cam pairs for manual inspection."""
import os, sys, inspect, math
import numpy as np
import torch
from PIL import Image, ImageOps
from plyfile import PlyData
sys.path.insert(0, '/root/autodl-tmp/3dgsAtlas_official')
import gsplat
DATASET_ROOT = "/root/autodl-tmp/dataset/tnt"
OUTPUT_ROOT = "/root/autodl-tmp/SplatAtlas/outputs"
DUMP_ROOT = "/tmp/tnt_diagnose"
SCENE = "lighthouse"
THRESHOLD = 20.0 # render_vs_render PSNR < THRESHOLD 的 cam 会被 dump
def build_args(source_path, img_dir):
from scene.dataset_readers import readColmapSceneInfo
sig = inspect.signature(readColmapSceneInfo)
a = []
for i, (k, p) in enumerate(sig.parameters.items()):
if i == 0: a.append(source_path)
elif i == 1: a.append(img_dir)
elif k == "eval": a.append(True)
elif k == "train_test_exp": a.append(False)
else: a.append(p.default if p.default != inspect.Parameter.empty else "")
return a
def to_u8(arr):
return np.clip(arr * 255, 0, 255).astype(np.uint8) if arr.dtype != np.uint8 else arr
def hstack_panels(arrs, gap=20):
imgs = [to_u8(a) for a in arrs]
H = imgs[0].shape[0]
W = sum(i.shape[1] for i in imgs) + gap * (len(imgs) - 1)
out = np.ones((H, W, 3), dtype=np.uint8) * 240
x = 0
for img in imgs:
out[:, x:x + img.shape[1]] = img
x += img.shape[1] + gap
return out
def main():
source_path = os.path.join(DATASET_ROOT, SCENE)
cell = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{SCENE}")
dump = os.path.join(DUMP_ROOT, SCENE)
os.makedirs(dump, exist_ok=True)
# 清空旧内容
for f in os.listdir(dump):
os.remove(os.path.join(dump, f))
img_dir = "images_2"; resolution = 2
from scene.dataset_readers import readColmapSceneInfo
from utils.graphics_utils import getWorld2View2
from utils.general_utils import PILtoTorch
scene_info = readColmapSceneInfo(*build_args(source_path, img_dir))
test_cams = scene_info.test_cameras
print(f"test cams: {len(test_cams)}")
# reverse-lookup: real image_name → native_idx
native_render_dir = os.path.join(cell, "renders_test_30000")
if os.path.isdir(os.path.join(native_render_dir, "renders")):
native_render_dir = os.path.join(native_render_dir, "renders")
native_gt_dir = os.path.join(cell, "gt_test_30000")
native_renders = sorted([f for f in os.listdir(native_render_dir)
if f.lower().endswith(('.png', '.jpg'))])
native_gts = sorted([f for f in os.listdir(native_gt_dir)
if f.lower().endswith(('.png', '.jpg'))])
THUMB = 128
i2_files = sorted([f for f in os.listdir(os.path.join(source_path, img_dir))
if f.lower().endswith(('.png', '.jpg', '.jpeg'))])
i2_thumbs = np.stack([
np.asarray(Image.open(os.path.join(source_path, img_dir, f)).convert('RGB')
.resize((THUMB, THUMB), Image.LANCZOS), dtype=np.float32) / 255.0
for f in i2_files])
real_to_native_idx = {}
for nidx, ng in enumerate(native_gts):
t = np.asarray(Image.open(os.path.join(native_gt_dir, ng)).convert('RGB')
.resize((THUMB, THUMB), Image.LANCZOS), dtype=np.float32) / 255.0
mse = ((i2_thumbs - t) ** 2).mean(axis=(1, 2, 3))
real_to_native_idx[i2_files[int(np.argmin(mse))]] = nidx
# PLY
pd = PlyData.read(os.path.join(cell, "point_cloud", "iteration_30000", "point_cloud.ply"))
vv = pd['vertex']
device = torch.device("cuda")
def t32(x): return torch.tensor(x, dtype=torch.float32, device=device)
means = t32(np.stack((vv['x'], vv['y'], vv['z']), -1))
quats = t32(np.stack((vv['rot_0'], vv['rot_1'], vv['rot_2'], vv['rot_3']), -1))
scales = torch.exp(t32(np.stack((vv['scale_0'], vv['scale_1'], vv['scale_2']), -1)))
opacities = torch.sigmoid(t32(np.asarray(vv['opacity'])))
f_dc = t32(np.stack((vv['f_dc_0'], vv['f_dc_1'], vv['f_dc_2']), -1)).unsqueeze(1)
f_rest = t32(np.stack([vv[f'f_rest_{i}'] for i in range(45)], -1)).view(-1, 3, 15).transpose(1, 2)
shs = torch.cat([f_dc, f_rest], dim=1)
bg_color = torch.tensor([0., 0., 0.], device=device)
summary = []
for i, c in enumerate(test_cams):
w = int(round(c.width / resolution)); h = int(round(c.height / resolution))
viewmat = t32(getWorld2View2(np.array(c.R), np.array(c.T))).unsqueeze(0)
fx = w / (2 * math.tan(c.FovX / 2)); fy = h / (2 * math.tan(c.FovY / 2))
K = t32(np.array([[fx, 0, w/2], [0, fy, h/2], [0, 0, 1]])).unsqueeze(0)
with torch.no_grad():
colors, _, _ = gsplat.rasterization(
means=means, quats=quats, scales=scales, opacities=opacities,
colors=shs, viewmats=viewmat, Ks=K, width=w, height=h,
sh_degree=3, packed=True, render_mode='RGB',
backgrounds=bg_color.unsqueeze(0))
render = colors[0].clamp(0, 1).cpu().numpy() # (H, W, 3) float
if c.image_name not in real_to_native_idx:
continue
nidx = real_to_native_idx[c.image_name]
nr = np.asarray(Image.open(os.path.join(native_render_dir, native_renders[nidx]))
.convert('RGB'), dtype=np.float32) / 255.0
ng = np.asarray(Image.open(os.path.join(native_gt_dir, native_gts[nidx]))
.convert('RGB'), dtype=np.float32) / 255.0
# 统一尺寸到 render
if nr.shape != render.shape:
th, tw = render.shape[:2]
nr = np.asarray(Image.fromarray(to_u8(nr)).resize((tw, th), Image.LANCZOS), dtype=np.float32) / 255.0
ng = np.asarray(Image.fromarray(to_u8(ng)).resize((tw, th), Image.LANCZOS), dtype=np.float32) / 255.0
vs_mse = ((render - nr) ** 2).mean()
vs_psnr = 10 * math.log10(1.0 / max(vs_mse, 1e-10))
ours_p = 10 * math.log10(1.0 / max(((render - ng) ** 2).mean(), 1e-10))
nat_p = 10 * math.log10(1.0 / max(((nr - ng) ** 2).mean(), 1e-10))
mark = "DUMP" if vs_psnr < THRESHOLD else "skip"
print(f" {i:>3} {c.image_name:>12} vs={vs_psnr:6.2f} ours={ours_p:6.2f} nat={nat_p:6.2f} {mark}")
if vs_psnr < THRESHOLD:
base = os.path.splitext(c.image_name)[0]
Image.fromarray(to_u8(ng)).save(os.path.join(dump, f"{base}_1_gt.png"))
Image.fromarray(to_u8(render)).save(os.path.join(dump, f"{base}_2_ours.png"))
Image.fromarray(to_u8(nr)).save(os.path.join(dump, f"{base}_3_native.png"))
diff5 = np.clip(np.abs(render - nr) * 5, 0, 1)
diff20 = np.clip(np.abs(render - nr) * 20, 0, 1)
Image.fromarray(to_u8(diff5)).save(os.path.join(dump, f"{base}_4_diff5x.png"))
Image.fromarray(to_u8(diff20)).save(os.path.join(dump, f"{base}_5_diff20x.png"))
# 4-panel: GT | OURS | NATIVE | DIFF*5
panel = hstack_panels([ng, render, nr, diff5])
Image.fromarray(panel).save(os.path.join(dump, f"{base}_panel.png"))
summary.append((i, c.image_name, vs_psnr, ours_p, nat_p))
# summary
sp = os.path.join(dump, "summary.txt")
with open(sp, "w") as f:
f.write(f"Scene: {SCENE}\n")
f.write(f"Threshold: render_vs_render < {THRESHOLD} dB\n")
f.write(f"Dumped : {len(summary)} cams\n\n")
f.write(f"{'idx':>3} {'image_name':>14} {'vs_native':>10} {'ours_psnr':>10} {'nat_psnr':>9}\n")
f.write("-" * 60 + "\n")
for s in sorted(summary, key=lambda x: x[2]):
f.write(f"{s[0]:>3} {s[1]:>14} {s[2]:>10.2f} {s[3]:>10.2f} {s[4]:>9.2f}\n")
f.write("\nPer-cam files:\n")
f.write(" <name>_1_gt.png GT (Native 存盘)\n")
f.write(" <name>_2_ours.png gsplat render\n")
f.write(" <name>_3_native.png diff-gaussian-rasterization render\n")
f.write(" <name>_4_diff5x.png |ours-native| * 5 (彩色)\n")
f.write(" <name>_5_diff20x.png |ours-native| * 20 (极端放大)\n")
f.write(" <name>_panel.png GT | OURS | NATIVE | DIFF*5 横排\n")
print(f"\nDumped {len(summary)} cams to:")
print(f" {dump}/")
print(f"Summary: {sp}")
print(f"\n拉回本地查看：")
print(f" scp -P <port> -r root@<host>:{dump} ./tnt_{SCENE}_diag")
if __name__ == "__main__":
main()
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