Datasets:

KCBtheone
/

splatatlas-core

import pandas as pd
import os
import json
import glob
import re
import math

# 1. 基础配置
input_csv = "outputs/phase5a/appendix_B_scene_inventory_submission_ready.csv"
output_submission = "outputs/phase5a/appendix_B_scene_inventory_submission_ready_v2.csv"
output_audit = "outputs/phase5a/appendix_B_scene_inventory_audit_v2.csv"

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

df = pd.read_csv(input_csv)
if 'Resolution' in df.columns:
    df.rename(columns={'Resolution': 'ResolutionSetting'}, inplace=True)

# 尝试探测项目中的评估配置
eval_holdout_rule = None
eval_evidence = "unknown_from_files"

# 检查配置或脚本中是否存在 --eval 或 holdout
config_paths = glob.glob("configs/*.yaml") + glob.glob("scripts/*.py")
for cp in config_paths:
    if not os.path.exists(cp): continue
    with open(cp, 'r', encoding='utf-8') as f:
        content = f.read()
        if '--eval' in content or 'test_every' in content or 'hold' in content:
            # 3DGS 默认 COLMAP holdout 是 8
            eval_holdout_rule = 8 
            eval_evidence = f"holdout_every_8_inferred_from_project_scripts"
            break

# 2. 辅助函数：统计图片数
def count_images(dir_path):
    if not os.path.exists(dir_path): return 0
    exts = ['*.png', '*.jpg', '*.jpeg', '*.PNG', '*.JPG', '*.JPEG']
    count = 0
    for ext in exts:
        count += len(glob.glob(os.path.join(dir_path, ext)))
    return count

def get_colmap_total(base_path):
    # 尝试按 3DGS 常用规范找
    for img_dir in ['images', 'images_2', 'images_4', 'images_8', 'input', 'rgb']:
        p = os.path.join(base_path, img_dir)
        c = count_images(p)
        if c > 0: return c
    return 0

# 3. 提取引擎
records = []
for index, row in df.iterrows():
    s_key = row['SceneKey']
    d_name = row['DisplayName']
    ds = row['Dataset']
    res_set = row['ResolutionSetting']
    s_type = row['SceneType']
    
    data_path = "NOT_FOUND"
    train_views = "NEEDS_SPLIT_RULE"
    test_views = "NEEDS_SPLIT_RULE"
    total_views = "UNKNOWN"
    split_rule = "unknown_from_files"
    source_evidence = ""
    notes = ""

    # 确定物理路径
    possible_paths = []
    if ds == "NeRF-Synthetic":
        base = "/root/autodl-tmp/dataset/Synthetic_NeRF_Verified/Synthetic_NeRF"
        possible_paths = [os.path.join(base, d_name), os.path.join(base, s_key), os.path.join(base, s_key.capitalize())]
    elif ds == "Mip-NeRF 360":
        possible_paths = [f"/root/autodl-tmp/dataset/360/{s_key}"]
    elif ds == "Tanks&Temples":
        possible_paths = [f"/root/autodl-tmp/dataset/tnt/{s_key}"]
    elif ds == "Deep Blending":
        possible_paths = [f"/root/autodl-tmp/dataset/deepblending_clean/{d_name}", f"/root/autodl-tmp/dataset/deepblending_clean/{s_key}"]

    for p in possible_paths:
        if os.path.exists(p):
            data_path = p
            break

    if data_path != "NOT_FOUND":
        # 优先级 A: JSON splits (主要针对 NeRF-Synthetic)
        train_json = os.path.join(data_path, "transforms_train.json")
        test_json = os.path.join(data_path, "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 = len(train_data['frames'])
                with open(test_json, 'r') as f:
                    test_data = json.load(f)
                    test_views = len(test_data['frames'])
                
                total_views = train_views + test_views
                # 检查是否存在 val，如果有也加进 total 但不一定加进 test
                val_json = os.path.join(data_path, "transforms_val.json")
                if os.path.exists(val_json):
                    with open(val_json, 'r') as f:
                        total_views += len(json.load(f)['frames'])

                split_rule = "transforms_train/test"
                source_evidence = f"parsed_from_{train_json}"
            except Exception as e:
                notes = f"Error parsing JSON: {e}"

        else:
            # 优先级 B/C: 图像目录 / COLMAP 结构
            train_dir_count = count_images(os.path.join(data_path, "train"))
            test_dir_count = count_images(os.path.join(data_path, "test"))
            
            if train_dir_count > 0 and test_dir_count > 0:
                train_views = train_dir_count
                test_views = test_dir_count
                total_views = train_views + test_views
                split_rule = "explicit_train_test_dirs"
                source_evidence = "parsed_from_image_folders"
            else:
                # COLMAP 格式 - 统计 total 然后推断
                total = get_colmap_total(data_path)
                if total > 0:
                    total_views = total
                    
                    # 检查单个 output 目录下是否记录了 metrics
                    metric_file = f"outputs/vanilla_3dgs_{s_key}/metrics_test_iter30000.json"
                    metric_file_bak = f"outputs/vanilla_3dgs_{s_key}_bak/metrics_test_iter30000.json"
                    if not os.path.exists(metric_file) and os.path.exists(metric_file_bak):
                        metric_file = metric_file_bak
                        
                    if eval_holdout_rule:
                        # 3DGS 标准: llffhold=8 means test views are indices % 8 == 0
                        # 0, 8, 16...
                        tst = len([i for i in range(total) if i % eval_holdout_rule == 0])
                        trn = total - tst
                        train_views = trn
                        test_views = tst
                        split_rule = eval_evidence
                        source_evidence = "total_images_and_standard_eval_holdout"
                    else:
                        split_rule = "unknown_from_files"
                        source_evidence = "total_images_only_no_split_rule_found"

    records.append({
        "SceneKey": s_key,
        "DisplayName": d_name,
        "Dataset": ds,
        "TrainViews": train_views,
        "TestViews": test_views,
        "TotalViews": total_views,
        "ResolutionSetting": res_set,
        "SceneType": s_type,
        "SplitRule": split_rule,
        "DataPath": data_path,
        "SourceEvidence": source_evidence,
        "Notes": notes
    })

df_res = pd.DataFrame(records)

# 4. 导出
os.makedirs("outputs/phase5a", exist_ok=True)
sub_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SceneType", "SplitRule"]
audit_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SceneType", "SplitRule", "DataPath", "SourceEvidence", "Notes"]

df_res[sub_cols].to_csv(output_submission, index=False)
df_res[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 (Submission Ready v2) ===")
preview_cols = ["SceneKey", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SplitRule"]
print(df_res[preview_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_res) == 31} ({len(df_res)})")
repro_scenes = pd.read_csv("outputs/phase5a/task_vapre_splatatlas_repro.csv")['scene'].unique().tolist()
print(f"2. SceneKey 是否和 repro CSV 完全一致: {set(df_res['SceneKey']) == set(repro_scenes)}")

dist = df_res['Dataset'].value_counts().to_dict()
print(f"3. Dataset 分布 (预期 12/9/8/2):")
for k, v in dist.items(): print(f"   - {k}: {v}")

nerf_syn = df_res[df_res['Dataset'] == 'NeRF-Synthetic']
syn_check = all(t == 100 for t in nerf_syn['TrainViews']) and all(t == 200 for t in nerf_syn['TestViews'])
print(f"4. NeRF-Synthetic (8 scenes) 是否全部为 100 train / 200 test: {syn_check}")

resolved_count = len(df_res[df_res['TrainViews'] != 'NEEDS_SPLIT_RULE'])
needs_count = len(df_res[df_res['TrainViews'] == 'NEEDS_SPLIT_RULE'])
print(f"5. 得到明确 TrainViews/TestViews 的 scene 数量: {resolved_count}")
print(f"6. 仍是 NEEDS_SPLIT_RULE 的 scene 数量: {needs_count}")

if needs_count > 0:
    print("7. NEEDS_SPLIT_RULE scene 的 TotalViews:")
    for _, r in df_res[df_res['TrainViews'] == 'NEEDS_SPLIT_RULE'].iterrows():
        print(f"   - {r['SceneKey']}: TotalViews = {r['TotalViews']}, Path = {r['DataPath']}")

print(f"\n8. 大小写风险探查 (已自动处理的):")
for s in ["lego", "drjohnson", "playroom"]:
    r = df_res[df_res['SceneKey'] == s].iloc[0]
    print(f"   - Key: {s} -> Found Path: {r['DataPath']}")

res_check = df_res['ResolutionSetting'].notnull().all()
print(f"9. ResolutionSetting 是否全部非空: {res_check}")

	import pandas as pd
	import os
	import json
	import glob
	import re
	import math

	# 1. 基础配置
	input_csv = "outputs/phase5a/appendix_B_scene_inventory_submission_ready.csv"
	output_submission = "outputs/phase5a/appendix_B_scene_inventory_submission_ready_v2.csv"
	output_audit = "outputs/phase5a/appendix_B_scene_inventory_audit_v2.csv"

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

	df = pd.read_csv(input_csv)
	if 'Resolution' in df.columns:
	df.rename(columns={'Resolution': 'ResolutionSetting'}, inplace=True)

	# 尝试探测项目中的评估配置
	eval_holdout_rule = None
	eval_evidence = "unknown_from_files"

	# 检查配置或脚本中是否存在 --eval 或 holdout
	config_paths = glob.glob("configs/.yaml") + glob.glob("scripts/.py")
	for cp in config_paths:
	if not os.path.exists(cp): continue
	with open(cp, 'r', encoding='utf-8') as f:
	content = f.read()
	if '--eval' in content or 'test_every' in content or 'hold' in content:
	# 3DGS 默认 COLMAP holdout 是 8
	eval_holdout_rule = 8
	eval_evidence = f"holdout_every_8_inferred_from_project_scripts"
	break

	# 2. 辅助函数：统计图片数
	def count_images(dir_path):
	if not os.path.exists(dir_path): return 0
	exts = ['.png', '.jpg', '.jpeg', '.PNG', '.JPG', '.JPEG']
	count = 0
	for ext in exts:
	count += len(glob.glob(os.path.join(dir_path, ext)))
	return count

	def get_colmap_total(base_path):
	# 尝试按 3DGS 常用规范找
	for img_dir in ['images', 'images_2', 'images_4', 'images_8', 'input', 'rgb']:
	p = os.path.join(base_path, img_dir)
	c = count_images(p)
	if c > 0: return c
	return 0

	# 3. 提取引擎
	records = []
	for index, row in df.iterrows():
	s_key = row['SceneKey']
	d_name = row['DisplayName']
	ds = row['Dataset']
	res_set = row['ResolutionSetting']
	s_type = row['SceneType']

	data_path = "NOT_FOUND"
	train_views = "NEEDS_SPLIT_RULE"
	test_views = "NEEDS_SPLIT_RULE"
	total_views = "UNKNOWN"
	split_rule = "unknown_from_files"
	source_evidence = ""
	notes = ""

	# 确定物理路径
	possible_paths = []
	if ds == "NeRF-Synthetic":
	base = "/root/autodl-tmp/dataset/Synthetic_NeRF_Verified/Synthetic_NeRF"
	possible_paths = [os.path.join(base, d_name), os.path.join(base, s_key), os.path.join(base, s_key.capitalize())]
	elif ds == "Mip-NeRF 360":
	possible_paths = [f"/root/autodl-tmp/dataset/360/{s_key}"]
	elif ds == "Tanks&Temples":
	possible_paths = [f"/root/autodl-tmp/dataset/tnt/{s_key}"]
	elif ds == "Deep Blending":
	possible_paths = [f"/root/autodl-tmp/dataset/deepblending_clean/{d_name}", f"/root/autodl-tmp/dataset/deepblending_clean/{s_key}"]

	for p in possible_paths:
	if os.path.exists(p):
	data_path = p
	break

	if data_path != "NOT_FOUND":
	# 优先级 A: JSON splits (主要针对 NeRF-Synthetic)
	train_json = os.path.join(data_path, "transforms_train.json")
	test_json = os.path.join(data_path, "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 = len(train_data['frames'])
	with open(test_json, 'r') as f:
	test_data = json.load(f)
	test_views = len(test_data['frames'])

	total_views = train_views + test_views
	# 检查是否存在 val，如果有也加进 total 但不一定加进 test
	val_json = os.path.join(data_path, "transforms_val.json")
	if os.path.exists(val_json):
	with open(val_json, 'r') as f:
	total_views += len(json.load(f)['frames'])

	split_rule = "transforms_train/test"
	source_evidence = f"parsed_from_{train_json}"
	except Exception as e:
	notes = f"Error parsing JSON: {e}"

	else:
	# 优先级 B/C: 图像目录 / COLMAP 结构
	train_dir_count = count_images(os.path.join(data_path, "train"))
	test_dir_count = count_images(os.path.join(data_path, "test"))

	if train_dir_count > 0 and test_dir_count > 0:
	train_views = train_dir_count
	test_views = test_dir_count
	total_views = train_views + test_views
	split_rule = "explicit_train_test_dirs"
	source_evidence = "parsed_from_image_folders"
	else:
	# COLMAP 格式 - 统计 total 然后推断
	total = get_colmap_total(data_path)
	if total > 0:
	total_views = total

	# 检查单个 output 目录下是否记录了 metrics
	metric_file = f"outputs/vanilla_3dgs_{s_key}/metrics_test_iter30000.json"
	metric_file_bak = f"outputs/vanilla_3dgs_{s_key}_bak/metrics_test_iter30000.json"
	if not os.path.exists(metric_file) and os.path.exists(metric_file_bak):
	metric_file = metric_file_bak

	if eval_holdout_rule:
	# 3DGS 标准: llffhold=8 means test views are indices % 8 == 0
	# 0, 8, 16...
	tst = len([i for i in range(total) if i % eval_holdout_rule == 0])
	trn = total - tst
	train_views = trn
	test_views = tst
	split_rule = eval_evidence
	source_evidence = "total_images_and_standard_eval_holdout"
	else:
	split_rule = "unknown_from_files"
	source_evidence = "total_images_only_no_split_rule_found"

	records.append({
	"SceneKey": s_key,
	"DisplayName": d_name,
	"Dataset": ds,
	"TrainViews": train_views,
	"TestViews": test_views,
	"TotalViews": total_views,
	"ResolutionSetting": res_set,
	"SceneType": s_type,
	"SplitRule": split_rule,
	"DataPath": data_path,
	"SourceEvidence": source_evidence,
	"Notes": notes
	})

	df_res = pd.DataFrame(records)

	# 4. 导出
	os.makedirs("outputs/phase5a", exist_ok=True)
	sub_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SceneType", "SplitRule"]
	audit_cols = ["SceneKey", "DisplayName", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SceneType", "SplitRule", "DataPath", "SourceEvidence", "Notes"]

	df_res[sub_cols].to_csv(output_submission, index=False)
	df_res[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 (Submission Ready v2) ===")
	preview_cols = ["SceneKey", "Dataset", "TrainViews", "TestViews", "TotalViews", "ResolutionSetting", "SplitRule"]
	print(df_res[preview_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_res) == 31} ({len(df_res)})")
	repro_scenes = pd.read_csv("outputs/phase5a/task_vapre_splatatlas_repro.csv")['scene'].unique().tolist()
	print(f"2. SceneKey 是否和 repro CSV 完全一致: {set(df_res['SceneKey']) == set(repro_scenes)}")

	dist = df_res['Dataset'].value_counts().to_dict()
	print(f"3. Dataset 分布 (预期 12/9/8/2):")
	for k, v in dist.items(): print(f" - {k}: {v}")

	nerf_syn = df_res[df_res['Dataset'] == 'NeRF-Synthetic']
	syn_check = all(t == 100 for t in nerf_syn['TrainViews']) and all(t == 200 for t in nerf_syn['TestViews'])
	print(f"4. NeRF-Synthetic (8 scenes) 是否全部为 100 train / 200 test: {syn_check}")

	resolved_count = len(df_res[df_res['TrainViews'] != 'NEEDS_SPLIT_RULE'])
	needs_count = len(df_res[df_res['TrainViews'] == 'NEEDS_SPLIT_RULE'])
	print(f"5. 得到明确 TrainViews/TestViews 的 scene 数量: {resolved_count}")
	print(f"6. 仍是 NEEDS_SPLIT_RULE 的 scene 数量: {needs_count}")

	if needs_count > 0:
	print("7. NEEDS_SPLIT_RULE scene 的 TotalViews:")
	for _, r in df_res[df_res['TrainViews'] == 'NEEDS_SPLIT_RULE'].iterrows():
	print(f" - {r['SceneKey']}: TotalViews = {r['TotalViews']}, Path = {r['DataPath']}")

	print(f"\n8. 大小写风险探查 (已自动处理的):")
	for s in ["lego", "drjohnson", "playroom"]:
	r = df_res[df_res['SceneKey'] == s].iloc[0]
	print(f" - Key: {s} -> Found Path: {r['DataPath']}")

	res_check = df_res['ResolutionSetting'].notnull().all()
	print(f"9. ResolutionSetting 是否全部非空: {res_check}")