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filter=lfs diff=lfs merge=lfs -text +bench/matting8k_v1/scenes/v1_8k__multi_L3_105/3840/instances/alpha_0.png filter=lfs diff=lfs merge=lfs -text +bench/matting8k_v1/scenes/v1_8k__multi_L3_105/3840/instances/alpha_1.png filter=lfs diff=lfs merge=lfs -text diff --git a/bench/matting8k_v1/README.md b/bench/matting8k_v1/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0315cb543148e112e77cec81ffc34956b9326314 --- /dev/null +++ b/bench/matting8k_v1/README.md @@ -0,0 +1,108 @@ +# matting8k 벤치마크 v1 (8K, 24장면) + +Blender Cycles로 렌더한 합성 matting 벤치마크다. 입력 이미지와 GT α를 같은 장면에서 렌더 패스를 조합해 계산한다. 사람이 칠한 GT가 아니다. + +- **GT의 정확성:** 모든 장면이 렌더 검증을 통과했다 (`index.json`에는 all_pass 장면만 들어 있다). + - α와 F, B는 입력 I에 대해 I = αF + (1−α)B를 정확히 만족한다. + - 검증: 완전 전경 픽셀에서 I = Tb, 완전 배경 픽셀에서 I = B가 성립한다 (p99 오차 ≤ 1e-6 수준). +- **v1의 범위:** 24장면, 물체별 α 50개다 (L1 6장면: 식물 2, 오브젝트 2, 털 1, 그물 1 / L2·L3·L4 각 6장면). + - v0(12장면)와 Poly Haven 에셋이 하나도 겹치지 않고, v1 안에서도 같은 에셋을 두 번 쓰지 않았다. 털과 그물은 절차적으로 매번 새로 만든다. + - v0와 합치면 36장면이다. 두 폴더는 형식이 같아서 같은 `evaluate.py`로 각각 평가하면 된다. + - 기준 해상도는 8K(7680×4320)다. 에셋 텍스처가 8K를 받쳐주지 못하는 장면은 4K로 렌더했다 (24장면 중 2장면) (`index.json`의 `native_resolution`). + - 모든 장면에 1/2, 1/4 해상도 버전이 함께 있다. 정확한 premultiplied 다운샘플이라 GT도 그대로 정확하다. + - 렌더 품질은 256 spp다. 최종본은 spp를 올릴 예정이다. + +## 트랙 + +| 트랙 | 난이도 | 내용 | GT | +|---|---|---|---| +| `single` | L1 | 전경 물체 1개 (식물, 오브젝트, 방충망) | `alpha.png` | +| `multi` | L2/L3/L4 | 물체 2~3개 | 물체마다 보이는 α: `instances/alpha_.png` | + +multi의 난이도: +- **L2:** 물체들이 화면에서 떨어져 있다. +- **L3:** 화면에서 겹친다. 물체 표면을 화면에 투영한 실루엣 겹침이 15~50%가 되도록 배치했다 (실측 16~56%, v0는 bbox 기준). +- **L4:** L3에 0.8~3 m 깊이차와 얕은 DoF가 더해지고, 물체 하나에만 초점이 맞는다. + +물체별 α는 그 물체가 **실제로 보이는** coverage다. 앞 물체에 가려진 만큼은 빠지고, 모든 물체의 α를 더하면 전체 α와 정확히 같다. +- 물체를 지정하는 입력으로 `instances/guide_.png`(α_k > 0.5인 이진 마스크)와 `instances/trimap_.png`를 제공한다. +- 모델이 받는 guidance 방식에 맞게 골라 쓰면 된다. + +## 폴더 구조 + +``` +index.json 장면 목록과 메타데이터 (카테고리, 난이도, 카메라, 배경, unknown 비율 ...) +evaluate.py 평가 스크립트 (numpy, scipy, pillow만 필요) +scenes///image.png 입력 (8-bit sRGB) + image16.png 입력 (16-bit sRGB, 같은 이미지) + alpha.png GT α (16-bit) [single] + trimap_narrow.png trimap 0/128/255. 폭은 alphamatting의 22 px @ 3000 px 폭을 해상도에 비례해 조정 [single] + trimap_wide.png 같은 방식, 44 px @ 3000 px [single] + ignore.png 255 = 평가 제외 (굴절 유리, 아래 참고) + instances/alpha_.png, guide_.png, trimap_.png [multi] +``` + +``는 이미지 폭이다. 장면마다 원본, 1/2, 1/4 세 가지가 있다 (예: 7680 / 3840 / 1920). `index.json`의 `tiers[].downsample`이 1, 2, 4다. + +## 예측을 만드는 법 + +예측은 벤치마크와 같은 상대 경로에, 같은 크기의 8/16-bit grayscale PNG로 저장한다. + +```python +import json, os, numpy as np +from PIL import Image +bench, pred = "/path/to/matting8k_v1", "/path/to/preds" +index = json.load(open(f"{bench}/index.json")) +for sc in index["scenes"]: + for t in sc["tiers"]: # 원하는 폭만 고르려면 t["width"]로 거른다 + d = os.path.join(bench, t["path"]); f = t["files"] + img = np.asarray(Image.open(os.path.join(d, f["image"]))) + out = os.path.join(pred, t["path"]); os.makedirs(out, exist_ok=True) + if sc["track"] == "single": + tri = np.asarray(Image.open(os.path.join(d, f["trimap_wide"]))) + alpha = my_model(img, tri) # float [0,1], (H, W) + Image.fromarray((alpha * 65535).astype(np.uint16)).save(f"{out}/alpha.png") + else: + os.makedirs(f"{out}/instances", exist_ok=True) + for k, v in f["instances"].items(): + guide = np.asarray(Image.open(os.path.join(d, v["guide"]))) + alpha = my_instance_model(img, guide) + Image.fromarray((alpha * 65535).astype(np.uint16)).save(f"{out}/instances/alpha_{k}.png") +``` + +## 평가 + +```bash +python evaluate.py --bench . --baseline gt # 정상이면 모든 오차가 0 +python evaluate.py --bench . --baseline trimap # 비교 기준: unknown 영역을 0.5로 채움 +python evaluate.py --bench . --pred /path/to/preds --out results/ # 전체 (해상도 3가지 모두) +python evaluate.py --bench . --pred /path/to/preds --scale 1 --out results/ # 원본 해상도만 +python evaluate.py --bench . --pred /path/to/preds --scale 4 --level 3 # 1/4 해상도, L3만 +# --no-conn: 8K에서 느린 connectivity 계산을 건너뛴다 +``` + +지표는 alphamatting 규약을 따르고, α는 [0, 1] 범위다. + +| 지표 | 정의 | +|---|---| +| SAD | Σ\|p−g\|/1000 | +| MAD | mean\|p−g\|. 해상도와 무관하므로 해상도 간 비교에는 이것을 쓴다 | +| MSE | mean (p−g)² | +| Grad | σ=1.4 Gaussian 기울기 차이의 제곱합 /1000 | +| Grad_s | σ = 1.4·W/1920. 해상도가 달라도 같은 물리적 흐림 폭 | +| Conn | connectivity 오차 /1000 | + +- **영역:** `unknown`(wide trimap = 128)과 `all`(이미지 전체) 두 가지로 보고한다. +- **집계:** 트랙, 해상도 배율(1/1, 1/2, 1/4), 영역별로 내고, 카테고리와 난이도별로도 나눈다. +- **해상도 간 비교:** 저해상도로 추론한 뒤 업샘플링하는 방법은, 같은 장면의 1/1과 1/4 결과를 MAD와 Grad_s로 비교하면 된다. +- **출력:** `results/per_scene.csv`와 `results/summary.json`. + +## 알려진 한계 + +- **굴절 유리** (샹들리에 유리, 안경 렌즈 등): + - 렌더러에서 굴절된 광선에는 배경판이 보이지 않는다. 그래서 α에는 에셋의 opacity map 값만 들어가고, 굴절된 배경은 F에 포함된다. + - 이 픽셀은 `ignore.png`로 표시되고 평가에서 빠진다. +- **입력 형식:** 입력은 노출을 정규화한 뒤 sRGB로 변환했다. 밝은 하이라이트가 1을 넘는 곳은 8/16-bit에서 잘린다. α GT에는 영향이 없다. +- **노이즈:** 256 spp라서 어두운 실내 장면에는 렌더 노이즈가 보인다. 이 노이즈는 입력과 GT에 똑같이 들어가 있으므로 GT 정확성에는 영향이 없다. +- **에셋 수:** 전경 에셋은 Poly Haven 모델과 절차적 털·그물이다. v0+v1에서 Poly Haven 전경 에셋 40개를 썼고, 남은 미사용 에셋은 많지 않다. +- **해상도:** 각 장면의 해상도는 에셋 텍스처가 받쳐주는 최대 해상도 이하로 정했다. diff --git a/bench/matting8k_v1/evaluate.py b/bench/matting8k_v1/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..df41824c0956c1980873c6239b94b51e13d72c5f --- /dev/null +++ b/bench/matting8k_v1/evaluate.py @@ -0,0 +1,194 @@ +"""Evaluate alpha predictions on a matting8k benchmark folder. + + python evaluate.py --bench . --pred /path/to/preds [--scale 1|2|4] [--level 1-4] [--track single] --out results/ + python evaluate.py --bench . --baseline trimap # sanity check without a model + python evaluate.py --bench . --baseline gt # must give all-zero errors + +Predictions mirror the benchmark layout: + /scenes///alpha.png single track + /scenes///instances/alpha_.png multi track (one per instance) +8- or 16-bit grayscale PNG with the same size as image.png. + +Metrics (alphamatting.com / Rhemann et al. 2009 conventions, alpha in [0, 1]): + sad sum |p - g| / 1000 + mad mean |p - g| (resolution independent; use it across tiers) + mse mean (p - g)^2 + grad sum (|grad G_s * p| - |grad G_s * g|)^2 / 1000, s = 1.4 + grad_s same with s = 1.4 * W / 1920 (same physical blur at every tier) + conn connectivity error / 1000 (threshold step 0.1) +Regions: 'unknown' (wide trimap == 128) and 'all' (whole image); pixels in ignore.png +(refractive glass, where alpha only holds the opacity map) are excluded from both. +For the multi track each instance is scored against its own visible alpha. +""" +import argparse +import csv +import json +import os +import sys +from collections import defaultdict + +import numpy as np +from PIL import Image +from scipy import ndimage + +Image.MAX_IMAGE_PIXELS = None + + +def read_alpha(path): + a = np.asarray(Image.open(path)) # 16-bit PNGs load as uint16 or int32 ("I;16" / "I") + if a.ndim == 3: + a = a[..., 0] + return a.astype(np.float64) / (255.0 if a.dtype == np.uint8 else 65535.0) + + +def gradient_mag(a, sigma): + gx = ndimage.gaussian_filter(a, sigma, order=(0, 1)) + gy = ndimage.gaussian_filter(a, sigma, order=(1, 0)) + return np.hypot(gx, gy) + + +def connectivity(p, g, mask, step=0.1): + steps = np.arange(0, 1 + step, step) + l_map = -np.ones_like(g) + for i in range(1, len(steps)): + both = (p >= steps[i]) & (g >= steps[i]) + lab, n = ndimage.label(both) + if n == 0: + omega = np.zeros_like(both) + else: + sizes = np.bincount(lab.ravel()) + sizes[0] = 0 + omega = lab == sizes.argmax() + flag = (l_map == -1) & ~omega + l_map[flag] = steps[i - 1] + l_map[l_map == -1] = 1 + pd, gd = p - l_map, g - l_map + p_phi = 1 - pd * (pd >= 0.15) + g_phi = 1 - gd * (gd >= 0.15) + return float(np.abs(p_phi - g_phi)[mask].sum() / 1000) + + +def metrics(p, g, regions, width, conn=True): + out = {} + gp, gg = gradient_mag(p, 1.4), gradient_mag(g, 1.4) + s = 1.4 * width / 1920 + gps, ggs = (gp, gg) if abs(s - 1.4) < 1e-6 else (gradient_mag(p, s), gradient_mag(g, s)) + d = np.abs(p - g) + for name, m in regions.items(): + n = int(m.sum()) + if n == 0: + continue + out[name] = { + "pixels": n, + "sad": float(d[m].sum() / 1000), + "mad": float(d[m].mean()), + "mse": float((d[m] ** 2).mean()), + "grad": float(((gp - gg)[m] ** 2).sum() / 1000), + "grad_s": float(((gps - ggs)[m] ** 2).sum() / 1000), + "conn": connectivity(p, g, m) if conn else float("nan"), + } + return out + + +def baseline_pred(kind, gt, trimap): + if kind == "gt": + return gt + if kind == "trimap": # known regions copied, unknown = 0.5 + return np.where(trimap == 255, 1.0, np.where(trimap == 0, 0.0, 0.5)) + raise ValueError(kind) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--bench", default=os.path.dirname(os.path.abspath(__file__))) + ap.add_argument("--pred", default=None) + ap.add_argument("--baseline", choices=["gt", "trimap"], default=None) + ap.add_argument("--tier", default="all", help="all | native | ") + ap.add_argument("--scale", type=int, default=None, choices=[1, 2, 4], + help="only this downsample factor (1 = native, 2 = half, 4 = quarter)") + ap.add_argument("--level", type=int, default=None, help="only this difficulty level (1-4)") + ap.add_argument("--track", default="all", choices=["all", "single", "multi"]) + ap.add_argument("--out", default=None) + ap.add_argument("--no-conn", action="store_true", help="skip the slow connectivity metric") + a = ap.parse_args() + if not (a.pred or a.baseline): + sys.exit("give --pred or --baseline") + index = json.load(open(os.path.join(a.bench, "index.json"))) + rows, missing = [], [] + for sc in index["scenes"]: + if a.track != "all" and sc["track"] != a.track: + continue + if a.level is not None and sc.get("level") != a.level: + continue + for t in sc["tiers"]: + if a.tier == "native" and t["downsample"] != 1: + continue + if a.scale and t["downsample"] != a.scale: + continue + if a.tier not in ("all", "native") and t["width"] != int(a.tier): + continue + d = os.path.join(a.bench, t["path"]) + f = t["files"] + ignore = (np.asarray(Image.open(os.path.join(d, f["ignore"]))) > 0) if "ignore" in f else None + targets = [] # (instance id or None, gt alpha path, trimap path, pred rel path) + if sc["track"] == "single": + targets.append((None, f["alpha"], f["trimap_wide"], "alpha.png")) + else: + for k, v in f["instances"].items(): + targets.append((k, v["alpha"], v["trimap"], f"instances/alpha_{k}.png")) + for inst, ga, tri, pr in targets: + g = read_alpha(os.path.join(d, ga)) + tm = np.asarray(Image.open(os.path.join(d, tri))) + if a.baseline: + p = baseline_pred(a.baseline, g, tm) + else: + pp = os.path.join(a.pred, t["path"], pr) + if not os.path.exists(pp): + missing.append(os.path.join(t["path"], pr)) + continue + p = read_alpha(pp) + if p.shape != g.shape: + sys.exit(f"{pp}: shape {p.shape} != {g.shape}") + keep = np.ones_like(g, bool) if ignore is None else ~ignore + regions = {"unknown": (tm == 128) & keep, "all": keep} + m = metrics(np.clip(p, 0, 1), g, regions, t["width"], conn=not a.no_conn) + for region, vals in m.items(): + rows.append({"scene": sc["id"], "track": sc["track"], "category": sc["category"], + "level": sc.get("level"), "width": t["width"], + "scale": t["downsample"], "instance": inst, + "region": region, **vals}) + print(f"[eval] {sc['id']}", file=sys.stderr, flush=True) + if missing: + print(f"[eval] {len(missing)} predictions missing, e.g. {missing[:3]}", file=sys.stderr) + # summary: mean over (scene, instance) per track / tier / region (+ per category, level) + keys = ["sad", "mad", "mse", "grad", "grad_s", "conn"] + groups = defaultdict(list) + for r in rows: + groups[(r["track"], r["scale"], r["region"], "ALL")].append(r) + groups[(r["track"], r["scale"], r["region"], f"category={r['category']}")].append(r) + if r["level"]: + groups[(r["track"], r["scale"], r["region"], f"level={r['level']}")].append(r) + summary = [] + for (track, sc_, region, sub), rs in sorted(groups.items(), key=lambda x: x[0]): + summary.append({"track": track, "scale": sc_, "region": region, "subset": sub, "n": len(rs), + "widths": sorted({r["width"] for r in rs}), + **{k: float(np.mean([r[k] for r in rs])) for k in keys}}) + if not rows: + sys.exit("no scenes evaluated") + hdr = f"{'track':6s} {'scale':>5s} {'region':8s} {'subset':24s} {'n':>4s} " + " ".join(f"{k:>9s}" for k in keys) + print(hdr) + for s in summary: + print(f"{s['track']:6s} {'1/' + str(s['scale']):>5s} {s['region']:8s} {s['subset'][:24]:24s} {s['n']:4d} " + + " ".join(f"{s[k]:9.4g}" for k in keys)) + if a.out: + os.makedirs(a.out, exist_ok=True) + with open(os.path.join(a.out, "per_scene.csv"), "w", newline="") as fh: + wr = csv.DictWriter(fh, fieldnames=list(rows[0])) + wr.writeheader() + wr.writerows(rows) + json.dump({"summary": summary, "missing": missing}, open(os.path.join(a.out, "summary.json"), "w"), + indent=1) + + +if __name__ == "__main__": + main() diff --git a/bench/matting8k_v1/index.json b/bench/matting8k_v1/index.json new file mode 100644 index 0000000000000000000000000000000000000000..76839635ac3401b10bd4c7a8ff1cde48943af992 --- /dev/null +++ b/bench/matting8k_v1/index.json @@ -0,0 +1,2850 @@ +{ + "name": "matting8k_v1", + "version": 0, + "n_scenes": 24, + "skipped_not_pass": [], + "scenes": [ + { + "id": "v1_8k__fur_101", + "source_run": "v1_8k/fur_101", + "track": "single", + "category": "fur", + "level": 1, + "native_resolution": [ + 7680, + 4320 + ], + "samples": 256, + "tiers": [ + { + "width": 7680, + "height": 4320, + "downsample": 1, + "path": "scenes/v1_8k__fur_101/7680", + "files": { + "image": "image.png", + "image16": "image16.png", + "alpha": "alpha.png", + "trimap_narrow": "trimap_narrow.png", + "trimap_wide": "trimap_wide.png" + }, + "trimap_radius_px": { + "narrow": 56, + "wide": 113 + } + }, + { + "width": 3840, + "height": 2160, + "downsample": 2, + "path": "scenes/v1_8k__fur_101/3840", + "files": { + "image": "image.png", + "image16": "image16.png", + "alpha": "alpha.png", + "trimap_narrow": "trimap_narrow.png", + "trimap_wide": "trimap_wide.png" + }, + "trimap_radius_px": { + "narrow": 28, + "wide": 56 + } + }, + { + "width": 1920, + "height": 1080, + "downsample": 4, + "path": "scenes/v1_8k__fur_101/1920", + "files": { + "image": "image.png", + "image16": "image16.png", + "alpha": "alpha.png", + "trimap_narrow": "trimap_narrow.png", + "trimap_wide": "trimap_wide.png" + }, + "trimap_radius_px": { + "narrow": 14, + "wide": 28 + } + } + ], + "fg": "torus", + "instances": [], + "unknown_fraction": 0.01982084297839506, + "instance_overlap_fraction": null, + "refractive_fraction_of_alpha": null, + "camera": { + "lens_mm": 35.0, + "fstop": 11.0, + "camera_distance_m": 0.831, + "elevation_deg": 13.9 + }, + "background": { + "bg_layout": "nature", + "bg_ground": "brick_floor", + "bg_wall": null, + "hdri": "ballawley_park" + }, + "structure_width_px": 1.865 + }, + { + "id": "v1_8k__multi_L2_101", + "source_run": "v1_8k/multi_L2_101", + "track": "multi", + "category": "multi", + "level": 2, + "native_resolution": [ + 7680, + 4320 + ], + "samples": 256, + "tiers": [ + { + "width": 7680, + "height": 4320, + "downsample": 1, + "path": "scenes/v1_8k__multi_L2_101/7680", + "files": { + "image": "image.png", + "image16": "image16.png", + "instances": { + "0": { + "alpha": "instances/alpha_0.png", + "guide": "instances/guide_0.png", + "trimap": "instances/trimap_0.png" + }, + "1": { + "alpha": "instances/alpha_1.png", + "guide": "instances/guide_1.png", + "trimap": "instances/trimap_1.png" + } + } + }, + "trimap_radius_px": { + "narrow": 56, + "wide": 113 + } + }, + { + "width": 3840, + "height": 2160, + "downsample": 2, + "path": "scenes/v1_8k__multi_L2_101/3840", + "files": { + "image": "image.png", + "image16": "image16.png", + "instances": { + "0": { + "alpha": "instances/alpha_0.png", + "guide": "instances/guide_0.png", + "trimap": "instances/trimap_0.png" + }, + "1": { + "alpha": "instances/alpha_1.png", + "guide": "instances/guide_1.png", + "trimap": "instances/trimap_1.png" + } + } + }, + "trimap_radius_px": { + "narrow": 28, + "wide": 56 + } + }, + { + "width": 1920, + "height": 1080, + "downsample": 4, + "path": "scenes/v1_8k__multi_L2_101/1920", + "files": { + "image": "image.png", + "image16": "image16.png", + "instances": { + "0": { + "alpha": "instances/alpha_0.png", + "guide": "instances/guide_0.png", + "trimap": "instances/trimap_0.png" + }, + "1": { + "alpha": "instances/alpha_1.png", + "guide": "instances/guide_1.png", + "trimap": "instances/trimap_1.png" + } + } + }, + "trimap_radius_px": { + "narrow": 14, + "wide": 28 + } + } + ], + "fg": null, + "instances": [ + { + "id": 0, + "category": "plant", + "fg": "weed_plant_02" + }, + { + "id": 1, + "category": "fur", + "fg": "ellipsoid" + } + ], + "unknown_fraction": 0.04751989293981482, + "instance_overlap_fraction": 0.0, + "refractive_fraction_of_alpha": null, + "camera": { + "lens_mm": 70.0, + "fstop": 2.8, + "camera_distance_m": 2.474, + "elevation_deg": 6.8 + }, + "background": { + "bg_layout": "nature", + "bg_ground": "brown_mud_leaves_01", + "bg_wall": null, + "hdri": "abandoned_pathway" + }, + "structure_width_px": 0.766 + }, + { + "id": "v1_8k__multi_L2_102", + "source_run": "v1_8k/multi_L2_102", + "track": "multi", + "category": "multi", + "level": 2, + "native_resolution": [ + 3840, + 2160 + ], + "samples": 256, + "tiers": [ + { + "width": 3840, + "height": 2160, + "downsample": 1, + "path": "scenes/v1_8k__multi_L2_102/3840", + "files": { + "image": "image.png", + "image16": "image16.png", + "instances": { + "0": { + "alpha": "instances/alpha_0.png", + "guide": "instances/guide_0.png", + "trimap": "instances/trimap_0.png" + }, + "1": { + "alpha": "instances/alpha_1.png", + "guide": "instances/guide_1.png", + "trimap": "instances/trimap_1.png" + }, + "2": { + "alpha": "instances/alpha_2.png", + "guide": "instances/guide_2.png", + "trimap": "instances/trimap_2.png" + } + } + }, + "trimap_radius_px": { + "narrow": 28, + "wide": 56 + } + }, + 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