Datasets:

KCBtheone
/

splatatlas-core

import pandas as pd
import os
import json
import glob

# 1. 基础配置
repro_csv = "outputs/phase5a/task_vapre_splatatlas_repro.csv"
output_submission = "outputs/phase5a/appendix_B_scene_inventory_submission_ready.csv"
output_audit = "outputs/phase5a/appendix_B_scene_inventory_audit.csv"

if not os.path.exists(repro_csv):
    print(f"Error: 找不到基础文件 {repro_csv}")
    exit(1)

df_repro = pd.read_csv(repro_csv)
valid_scenes = sorted(df_repro['scene'].unique().tolist())

# 2. 知识库映射字典
dataset_map = {}
resolution_map = {}
type_map = {}
display_name_map = {}

# Mip-NeRF 360 Indoor
for s in ["bonsai", "counter", "kitchen", "room"]:
    dataset_map[s] = "Mip-NeRF 360"
    resolution_map[s] = "2"
    type_map[s] = "real-world indoor"
    display_name_map[s] = s.capitalize()

# Mip-NeRF 360 Outdoor
for s in ["bicycle", "flowers", "garden", "stump", "treehill"]:
    dataset_map[s] = "Mip-NeRF 360"
    resolution_map[s] = "4"
    type_map[s] = "real-world outdoor"
    display_name_map[s] = s.capitalize()

# Tanks & Temples
tnt_indoor = ["auditorium", "ballroom", "courtroom", "museum", "palace", "temple"]
tnt_outdoor = ["barn", "caterpillar", "truck", "playground", "lighthouse", "train"]
for s in tnt_indoor + tnt_outdoor:
    dataset_map[s] = "Tanks&Temples"
    resolution_map[s] = "2"
    type_map[s] = "real-world indoor" if s in tnt_indoor else "real-world outdoor"
    display_name_map[s] = s.capitalize()

# NeRF-Synthetic
for s in ["chair", "drums", "ficus", "hotdog", "lego", "materials", "mic", "ship"]:
    dataset_map[s] = "NeRF-Synthetic"
    resolution_map[s] = "1"
    type_map[s] = "synthetic object"
    display_name_map[s] = s.capitalize()

# Deep Blending
for s in ["playroom", "drjohnson"]:
    dataset_map[s] = "Deep Blending"
    resolution_map[s] = "1"
    type_map[s] = "real-world indoor"
    display_name_map[s] = "Playroom" if s == "playroom" else "DrJohnson"

# 3. 提取引擎
inventory = []
for s in valid_scenes:
    ds = dataset_map.get(s, "UNKNOWN")
    res = resolution_map.get(s, "NEEDS_MANUAL_CHECK")
    stype = type_map.get(s, "UNKNOWN")
    dname = display_name_map.get(s, s)
    
    train_views = "NEEDS_MANUAL_CHECK"
    test_views = "NEEDS_MANUAL_CHECK"
    evidence = "inferred_from_dataset_convention"
    notes = ""

    # 尝试从 NeRF-Synthetic 目录读取 view 数量
    if ds == "NeRF-Synthetic":
        # 考虑到可能的大小写目录名
        for scene_dir in [s, s.capitalize()]:
            train_json = f"/root/autodl-tmp/dataset/nerf_synthetic_off/nerf_synthetic/{scene_dir}/transforms_train.json"
            test_json = f"/root/autodl-tmp/dataset/nerf_synthetic_off/nerf_synthetic/{scene_dir}/transforms_test.json"
            
            if os.path.exists(train_json) and os.path.exists(test_json):
                try:
                    with open(train_json, 'r') as f:
                        train_data = json.load(f)
                        train_views = str(len(train_data['frames']))
                    with open(test_json, 'r') as f:
                        test_data = json.load(f)
                        test_views = str(len(test_data['frames']))
                    evidence = f"parsed_from_transforms_json"
                    break
                except Exception:
                    pass

    # 若未能解析，则统一归为需手工确认
    inventory.append({
        "SceneKey": s,
        "DisplayName": dname,
        "Dataset": ds,
        "TrainViews": train_views,
        "TestViews": test_views,
        "Resolution": res,
        "SceneType": stype,
        "SourceEvidence": evidence,
        "Notes": notes
    })

df_inv = pd.DataFrame(inventory)

# 4. 导出表格
os.makedirs("outputs/phase5a", exist_ok=True)

# 投稿版
sub_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "Resolution", "SceneType"]
df_inv[sub_cols].to_csv(output_submission, index=False)

# 审计版
audit_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "Resolution", "SceneType", "SourceEvidence", "Notes"]
df_inv[audit_cols].to_csv(output_audit, index=False)

print(f"========================================================")
print(f"Submission 级表格已保存至: {output_submission}")
print(f"Audit 级审计表已保存至: {output_audit}")
print(f"========================================================\n")

# 5. Markdown 预览
print("=== Appendix B: Scene Inventory (Preview) ===")
print(df_inv[sub_cols].to_markdown(index=False))

# 6. Sanity Checks
print("\n========================================================")
print("SANITY CHECKS")
print("========================================================")

print(f"1. Scene 总数是否正好 31: {len(df_inv) == 31} ({len(df_inv)})")

c2 = all(s in df_repro['scene'].unique().tolist() for s in df_inv['SceneKey'])
print(f"2. SceneKey 是否和 repro CSV 完全一致: {c2}")

allowed_datasets = ["Tanks&Temples", "Deep Blending", "Mip-NeRF 360", "NeRF-Synthetic"]
c3 = df_inv['Dataset'].isin(allowed_datasets).all()
print(f"3. Dataset 是否全部属于四类之一: {c3}")

print(f"4. 四个 Dataset 的 scene count 分布:")
for k, v in df_inv['Dataset'].value_counts().items():
    print(f"   - {k}: {v}")

c5 = df_inv['Resolution'].notnull().all() and not (df_inv['Resolution'] == "NEEDS_MANUAL_CHECK").any()
print(f"5. Resolution 是否全部非空且无 NEEDS_MANUAL_CHECK: {c5}")

missing_train = (df_inv['TrainViews'] == "NEEDS_MANUAL_CHECK").sum()
missing_test = (df_inv['TestViews'] == "NEEDS_MANUAL_CHECK").sum()
print(f"6. TrainViews 缺失数量 (NEEDS_MANUAL_CHECK): {missing_train}")
print(f"7. TestViews 缺失数量 (NEEDS_MANUAL_CHECK): {missing_test}")

inferred_count = (df_inv['SourceEvidence'] == 'inferred_from_dataset_convention').sum()
print(f"8. 字段为 inferred_from_dataset_convention 的数量: {inferred_count}")

# 风险检测
capitalization_risks = ["lego", "drjohnson", "playroom"]
print("\n9. 大小写风险检查 (SceneKey vs DisplayName):")
for r in capitalization_risks:
    row = df_inv[df_inv['SceneKey'] == r]
    if not row.empty:
        print(f"   - {r} -> {row.iloc[0]['DisplayName']}")

print("\n10. 需要手动补全 View 数量的场景:")
manual_views = df_inv[df_inv['TrainViews'] == 'NEEDS_MANUAL_CHECK']['SceneKey'].tolist()
print(f"   - {', '.join(manual_views)}")

	import pandas as pd
	import os
	import json
	import glob

	# 1. 基础配置
	repro_csv = "outputs/phase5a/task_vapre_splatatlas_repro.csv"
	output_submission = "outputs/phase5a/appendix_B_scene_inventory_submission_ready.csv"
	output_audit = "outputs/phase5a/appendix_B_scene_inventory_audit.csv"

	if not os.path.exists(repro_csv):
	print(f"Error: 找不到基础文件 {repro_csv}")
	exit(1)

	df_repro = pd.read_csv(repro_csv)
	valid_scenes = sorted(df_repro['scene'].unique().tolist())

	# 2. 知识库映射字典
	dataset_map = {}
	resolution_map = {}
	type_map = {}
	display_name_map = {}

	# Mip-NeRF 360 Indoor
	for s in ["bonsai", "counter", "kitchen", "room"]:
	dataset_map[s] = "Mip-NeRF 360"
	resolution_map[s] = "2"
	type_map[s] = "real-world indoor"
	display_name_map[s] = s.capitalize()

	# Mip-NeRF 360 Outdoor
	for s in ["bicycle", "flowers", "garden", "stump", "treehill"]:
	dataset_map[s] = "Mip-NeRF 360"
	resolution_map[s] = "4"
	type_map[s] = "real-world outdoor"
	display_name_map[s] = s.capitalize()

	# Tanks & Temples
	tnt_indoor = ["auditorium", "ballroom", "courtroom", "museum", "palace", "temple"]
	tnt_outdoor = ["barn", "caterpillar", "truck", "playground", "lighthouse", "train"]
	for s in tnt_indoor + tnt_outdoor:
	dataset_map[s] = "Tanks&Temples"
	resolution_map[s] = "2"
	type_map[s] = "real-world indoor" if s in tnt_indoor else "real-world outdoor"
	display_name_map[s] = s.capitalize()

	# NeRF-Synthetic
	for s in ["chair", "drums", "ficus", "hotdog", "lego", "materials", "mic", "ship"]:
	dataset_map[s] = "NeRF-Synthetic"
	resolution_map[s] = "1"
	type_map[s] = "synthetic object"
	display_name_map[s] = s.capitalize()

	# Deep Blending
	for s in ["playroom", "drjohnson"]:
	dataset_map[s] = "Deep Blending"
	resolution_map[s] = "1"
	type_map[s] = "real-world indoor"
	display_name_map[s] = "Playroom" if s == "playroom" else "DrJohnson"

	# 3. 提取引擎
	inventory = []
	for s in valid_scenes:
	ds = dataset_map.get(s, "UNKNOWN")
	res = resolution_map.get(s, "NEEDS_MANUAL_CHECK")
	stype = type_map.get(s, "UNKNOWN")
	dname = display_name_map.get(s, s)

	train_views = "NEEDS_MANUAL_CHECK"
	test_views = "NEEDS_MANUAL_CHECK"
	evidence = "inferred_from_dataset_convention"
	notes = ""

	# 尝试从 NeRF-Synthetic 目录读取 view 数量
	if ds == "NeRF-Synthetic":
	# 考虑到可能的大小写目录名
	for scene_dir in [s, s.capitalize()]:
	train_json = f"/root/autodl-tmp/dataset/nerf_synthetic_off/nerf_synthetic/{scene_dir}/transforms_train.json"
	test_json = f"/root/autodl-tmp/dataset/nerf_synthetic_off/nerf_synthetic/{scene_dir}/transforms_test.json"

	if os.path.exists(train_json) and os.path.exists(test_json):
	try:
	with open(train_json, 'r') as f:
	train_data = json.load(f)
	train_views = str(len(train_data['frames']))
	with open(test_json, 'r') as f:
	test_data = json.load(f)
	test_views = str(len(test_data['frames']))
	evidence = f"parsed_from_transforms_json"
	break
	except Exception:
	pass

	# 若未能解析，则统一归为需手工确认
	inventory.append({
	"SceneKey": s,
	"DisplayName": dname,
	"Dataset": ds,
	"TrainViews": train_views,
	"TestViews": test_views,
	"Resolution": res,
	"SceneType": stype,
	"SourceEvidence": evidence,
	"Notes": notes
	})

	df_inv = pd.DataFrame(inventory)

	# 4. 导出表格
	os.makedirs("outputs/phase5a", exist_ok=True)

	# 投稿版
	sub_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "Resolution", "SceneType"]
	df_inv[sub_cols].to_csv(output_submission, index=False)

	# 审计版
	audit_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "Resolution", "SceneType", "SourceEvidence", "Notes"]
	df_inv[audit_cols].to_csv(output_audit, index=False)

	print(f"========================================================")
	print(f"Submission 级表格已保存至: {output_submission}")
	print(f"Audit 级审计表已保存至: {output_audit}")
	print(f"========================================================\n")

	# 5. Markdown 预览
	print("=== Appendix B: Scene Inventory (Preview) ===")
	print(df_inv[sub_cols].to_markdown(index=False))

	# 6. Sanity Checks
	print("\n========================================================")
	print("SANITY CHECKS")
	print("========================================================")

	print(f"1. Scene 总数是否正好 31: {len(df_inv) == 31} ({len(df_inv)})")

	c2 = all(s in df_repro['scene'].unique().tolist() for s in df_inv['SceneKey'])
	print(f"2. SceneKey 是否和 repro CSV 完全一致: {c2}")

	allowed_datasets = ["Tanks&Temples", "Deep Blending", "Mip-NeRF 360", "NeRF-Synthetic"]
	c3 = df_inv['Dataset'].isin(allowed_datasets).all()
	print(f"3. Dataset 是否全部属于四类之一: {c3}")

	print(f"4. 四个 Dataset 的 scene count 分布:")
	for k, v in df_inv['Dataset'].value_counts().items():
	print(f" - {k}: {v}")

	c5 = df_inv['Resolution'].notnull().all() and not (df_inv['Resolution'] == "NEEDS_MANUAL_CHECK").any()
	print(f"5. Resolution 是否全部非空且无 NEEDS_MANUAL_CHECK: {c5}")

	missing_train = (df_inv['TrainViews'] == "NEEDS_MANUAL_CHECK").sum()
	missing_test = (df_inv['TestViews'] == "NEEDS_MANUAL_CHECK").sum()
	print(f"6. TrainViews 缺失数量 (NEEDS_MANUAL_CHECK): {missing_train}")
	print(f"7. TestViews 缺失数量 (NEEDS_MANUAL_CHECK): {missing_test}")

	inferred_count = (df_inv['SourceEvidence'] == 'inferred_from_dataset_convention').sum()
	print(f"8. 字段为 inferred_from_dataset_convention 的数量: {inferred_count}")

	# 风险检测
	capitalization_risks = ["lego", "drjohnson", "playroom"]
	print("\n9. 大小写风险检查 (SceneKey vs DisplayName):")
	for r in capitalization_risks:
	row = df_inv[df_inv['SceneKey'] == r]
	if not row.empty:
	print(f" - {r} -> {row.iloc[0]['DisplayName']}")

	print("\n10. 需要手动补全 View 数量的场景:")
	manual_views = df_inv[df_inv['TrainViews'] == 'NEEDS_MANUAL_CHECK']['SceneKey'].tolist()
	print(f" - {', '.join(manual_views)}")