#!/usr/bin/env python """Round-5 — exposure compensation hypothesis (decisive).""" import os, sys, json, math, inspect 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" SCENES = [("truck", "PASS"), ("lighthouse", "FAIL")] def sec(t): print("\n" + "=" * 70); print(f" {t}"); print("=" * 70) 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 # ---------- Probe 17: exposure.json 结构 ---------- def probe_exposure_structure(scene): sec(f"PROBE 17 — exposure.json structure [{scene}]") p = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{scene}", "exposure.json") if not os.path.exists(p): print(f"[!] not found") return None data = json.load(open(p)) if not isinstance(data, dict): print(f"Not a dict: {type(data)}") return None keys = list(data.keys()) print(f"Entries : {len(keys)}") print(f"Sample keys : {keys[:5]}") sample = np.asarray(data[keys[0]]) print(f"Sample shape: {sample.shape}") print(f"Sample[0]:\n{sample}") all_m = np.stack([np.asarray(data[k]) for k in keys]) print(f"\nAll exposures shape: {all_m.shape}") if all_m.ndim == 3 and all_m.shape[1] == 3 and all_m.shape[2] >= 3: linear = all_m[:, :3, :3] bias = all_m[:, :3, 3] if all_m.shape[2] == 4 else np.zeros((len(keys), 3)) eye = np.eye(3)[None] lin_dev = np.abs(linear - eye).reshape(len(keys), -1).max(axis=1) bias_mag = np.linalg.norm(bias, axis=1) print(f"\nLinear max |dev from I|:") print(f" min={lin_dev.min():.5f} p50={np.percentile(lin_dev,50):.5f} " f"p95={np.percentile(lin_dev,95):.5f} max={lin_dev.max():.5f}") print(f"Bias magnitude ||b||:") print(f" min={bias_mag.min():.5f} p50={np.percentile(bias_mag,50):.5f} " f"p95={np.percentile(bias_mag,95):.5f} max={bias_mag.max():.5f}") trivial = ((lin_dev < 0.01) & (bias_mag < 0.01)).sum() large = ((lin_dev >= 0.05) | (bias_mag >= 0.05)).sum() print(f"\nNear-identity (lin_dev<0.01 & bias<0.01): {trivial} / {len(keys)}") print(f"Large deviation (>=0.05) : {large} / {len(keys)}") if trivial > len(keys) * 0.95: print("[VERDICT] 曝光几乎都是单位阵 → 曝光补偿不是根因") elif large > 0: print("[VERDICT] 存在显著曝光偏移 → 曝光补偿极可能是根因") return data # ---------- Probe 18: 应用 exposure 后重算 PSNR ---------- def probe_exposure_corrected(scene, exposures): sec(f"PROBE 18 — Apply exposure to render & recompute PSNR [{scene}]") if exposures is None: print("[!] no exposure data, skip") return from scene.dataset_readers import readColmapSceneInfo from utils.graphics_utils import getWorld2View2 from utils.general_utils import PILtoTorch source_path = os.path.join(DATASET_ROOT, scene) cell = os.path.join(OUTPUT_ROOT, f"vanilla_3dgs_{scene}") img_dir = "images_2"; resolution = 2 scene_info = readColmapSceneInfo(*build_args(source_path, img_dir)) test_cams = scene_info.test_cameras pd = PlyData.read(os.path.join(cell, "point_cloud", "iteration_30000", "point_cloud.ply")) v = pd['vertex'] device = torch.device("cuda") def t32(x): return torch.tensor(x, dtype=torch.float32, device=device) means = t32(np.stack((v['x'], v['y'], v['z']), -1)) quats = t32(np.stack((v['rot_0'], v['rot_1'], v['rot_2'], v['rot_3']), -1)) scales = torch.exp(t32(np.stack((v['scale_0'], v['scale_1'], v['scale_2']), -1))) opacities = torch.sigmoid(t32(np.asarray(v['opacity']))) f_dc = t32(np.stack((v['f_dc_0'], v['f_dc_1'], v['f_dc_2']), -1)).unsqueeze(1) f_rest = t32(np.stack([v[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) # exposure name 映射策略：尝试多种 key 形式 def get_exp(image_name): candidates = [image_name, os.path.splitext(image_name)[0], image_name.lower(), os.path.splitext(image_name)[0].lower()] for c in candidates: if c in exposures: return torch.tensor(np.asarray(exposures[c]), dtype=torch.float32, device=device) return None # sanity: check key匹配率 matched = sum(1 for c in test_cams if get_exp(c.image_name) is not None) print(f"Exposure key-match rate: {matched} / {len(test_cams)} test cams") if matched == 0: print(f" test cam names: {[c.image_name for c in test_cams[:3]]}") print(f" exposure keys : {list(exposures.keys())[:3]}") print("[!] 无法匹配，请检查 key 命名") return raw_psnrs, corr_psnrs = [], [] print(f"\n {'i':>3} {'cam':>14} {'raw':>8} {'corrected':>10} {'gain':>8}") print(" " + "-" * 50) 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) # (H, W, 3) pil = Image.open(os.path.join(source_path, img_dir, c.image_name)) pil = ImageOps.exif_transpose(pil) gt_chw = PILtoTorch(pil, (w, h)).to(device) if gt_chw.shape[0] == 4: gt_chw = gt_chw[:3] gt = gt_chw.permute(1, 2, 0) # (H, W, 3) mse_raw = ((render - gt) ** 2).mean().item() raw_p = 10 * math.log10(1.0 / max(mse_raw, 1e-10)) raw_psnrs.append(raw_p) exp_mat = get_exp(c.image_name) if exp_mat is not None: A = exp_mat[:3, :3]; b = exp_mat[:3, 3] if exp_mat.shape[1] >= 4 else torch.zeros(3, device=device) # render (H,W,3) @ A.T + b (3DGS 官方公式) corrected = render @ A.T + b corrected = corrected.clamp(0, 1) mse_c = ((corrected - gt) ** 2).mean().item() corr_p = 10 * math.log10(1.0 / max(mse_c, 1e-10)) else: corr_p = raw_p corr_psnrs.append(corr_p) if i < 15 or (corr_p - raw_p) > 2: print(f" {i:>3} {c.image_name:>14} {raw_p:>8.2f} {corr_p:>10.2f} {corr_p - raw_p:>+8.2f}") raw_arr, corr_arr = np.array(raw_psnrs), np.array(corr_psnrs) print(f"\n Ours RAW mean PSNR: {raw_arr.mean():.4f} dB") print(f" Ours CORRECTED mean PSNR: {corr_arr.mean():.4f} dB") native_psnr = None bp = os.path.join(cell, "metrics_test_iter30000.json") if os.path.exists(bp): bd = json.load(open(bp)) native_psnr = bd.get("photometric", {}).get("PSNR", bd.get("PSNR")) print(f" Native baseline PSNR : {native_psnr:.4f} dB") if native_psnr: print(f" Δ raw : {raw_arr.mean() - native_psnr:+.4f} dB") print(f" Δ corrected : {corr_arr.mean() - native_psnr:+.4f} dB") if abs(corr_arr.mean() - native_psnr) < 0.1: print(f"\n [VERDICT CONFIRMED] 曝光补偿就是根因。") elif abs(corr_arr.mean() - native_psnr) < abs(raw_arr.mean() - native_psnr) - 0.3: print(f"\n [PARTIAL] 曝光补偿解释了大部分差异，但残差>0.3dB，可能还有次级因素") else: print(f"\n [REJECTED] 曝光补偿影响有限，需另寻根因") def main(): for scene, label in SCENES: print(f"\n\n{'#'*70}\n# SCENE: {scene} [{label}]\n{'#'*70}") exp = probe_exposure_structure(scene) probe_exposure_corrected(scene, exp) if __name__ == "__main__": main()