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cb8518a | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import cv2
import numpy as np
from PIL import Image
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Evaluate rendered GT/model images")
parser.add_argument("--manifest", required=True)
parser.add_argument("--clip_model_path", required=True)
parser.add_argument("--out_json", required=True)
parser.add_argument("--models", default=None)
parser.add_argument("--batch_size", type=int, default=32)
return parser.parse_args()
def read_image_rgb(path: Path) -> np.ndarray:
bgr = cv2.imread(str(path), cv2.IMREAD_COLOR)
if bgr is None:
raise ValueError(f"Unable to decode image: {path}")
return cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
def photometric_loss(gt_frame: np.ndarray, pred_frame: np.ndarray) -> float:
if pred_frame.shape[:2] != gt_frame.shape[:2]:
h, w = gt_frame.shape[:2]
pred_frame = cv2.resize(pred_frame, (w, h), interpolation=cv2.INTER_AREA)
gt = gt_frame[..., :3].astype(np.float32) / 255.0
pred = pred_frame[..., :3].astype(np.float32) / 255.0
return float(np.mean(np.square(gt - pred)))
def score_lower_is_better(raw: float, bad_value: float = 1.0) -> float:
if bad_value <= 0:
return 0.0
return float(100.0 * max(0.0, min(1.0, 1.0 - raw / bad_value)))
def encode_image_features(
paths: list[Path],
model: Any,
processor: Any,
device: str,
batch_size: int,
) -> Any:
import torch
feats: list[torch.Tensor] = []
with torch.no_grad():
for start in range(0, len(paths), batch_size):
chunk_paths = paths[start : start + batch_size]
images = [Image.fromarray(read_image_rgb(p)) for p in chunk_paths]
inputs = processor(images=images, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
image_features = model.get_image_features(**inputs)
image_features = image_features / image_features.norm(dim=-1, keepdim=True)
feats.append(image_features)
return torch.cat(feats, dim=0)
def main() -> None:
args = parse_args()
manifest_path = Path(args.manifest).resolve()
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
requested = [x.strip() for x in args.models.split(",") if x.strip()] if args.models else None
models = requested or [x for x in manifest["renders"] if x != "gt"]
from transformers import CLIPModel, CLIPProcessor
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model = CLIPModel.from_pretrained(args.clip_model_path, local_files_only=True).to(device)
clip_processor = CLIPProcessor.from_pretrained(args.clip_model_path, local_files_only=True)
clip_model.eval()
gt_by_frame = {int(item["gt_frame"]): Path(item["path"]) for item in manifest["renders"]["gt"]}
results: dict[str, Any] = {}
summary: list[dict[str, Any]] = []
for key in models:
pairs: list[tuple[Path, Path, int, int]] = []
for item in manifest["renders"].get(key, []):
gt_frame = int(item["gt_frame"])
pred_frame = int(item["pred_frame"])
gt_path = gt_by_frame.get(gt_frame)
if gt_path is None:
raise KeyError(f"Missing rendered GT image for gt_frame={gt_frame}")
pairs.append((gt_path, Path(item["path"]), gt_frame, pred_frame))
losses = [photometric_loss(read_image_rgb(g), read_image_rgb(p)) for g, p, _, _ in pairs]
gt_feats = encode_image_features([g for g, _, _, _ in pairs], clip_model, clip_processor, device, args.batch_size)
pred_feats = encode_image_features([p for _, p, _, _ in pairs], clip_model, clip_processor, device, args.batch_size)
sims = torch.nn.functional.cosine_similarity(pred_feats, gt_feats, dim=-1)
sim_values = sims.detach().cpu().numpy().astype(float).tolist()
nclip_values = [float(1.0 - x) for x in sim_values]
pl_raw = float(np.mean(losses)) if losses else 0.0
nclip_raw = float(np.mean(nclip_values)) if nclip_values else 0.0
result = {
"frame_count": len(pairs),
"pairs": [
{"gt": str(g), "pred": str(p), "gt_frame": gt_frame, "pred_frame": pred_frame}
for g, p, gt_frame, pred_frame in pairs
],
"pl": {
"metric": "PhotometricLoss_RenderedAlignedGTCamera",
"pl_raw": pl_raw,
"score_100": score_lower_is_better(pl_raw, bad_value=1.0),
"frame_losses": losses,
"direction": "lower_is_better",
},
"n_clip": {
"metric": "VIGA_N_CLIP_RenderedAlignedGTCamera",
"n_clip_raw": nclip_raw,
"clip_similarity_mean": float(np.mean(sim_values)) if sim_values else 0.0,
"score_100": float(100.0 * max(0.0, min(1.0, 1.0 - nclip_raw))),
"frame_clip_similarity": sim_values,
"frame_n_clip": nclip_values,
"formula": "mean_i(1 - CLIPSim(pred_render_i, gt_render_i))",
"direction": "lower_is_better",
},
}
results[key] = result
summary.append(
{
"model": key,
"pl_raw": pl_raw,
"pl_score_100": result["pl"]["score_100"],
"n_clip_raw": nclip_raw,
"n_clip_score_100": result["n_clip"]["score_100"],
"clip_similarity_mean": result["n_clip"]["clip_similarity_mean"],
"frame_count": len(pairs),
}
)
payload = {
"manifest": str(manifest_path),
"clip_model_path": args.clip_model_path,
"clip_device": device,
"definition": {
"pl": "MSE between GT-camera renders after alignment",
"n_clip": "VIGA-style mean(1 - CLIPSim(pred_render, gt_render)) after alignment",
},
"results": results,
"summary": summary,
"rankings": {
"pl_score_desc": sorted(summary, key=lambda x: x["pl_score_100"], reverse=True),
"nclip_score_desc": sorted(summary, key=lambda x: x["n_clip_score_100"], reverse=True),
},
}
out_path = Path(args.out_json).resolve()
out_path.parent.mkdir(parents=True, exist_ok=True)
out_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
print(json.dumps(payload["summary"], indent=2))
print(f"SAVED {out_path}")
if __name__ == "__main__":
main()
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