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+ {
74
+ "uid": "1524/model",
75
+ "jid": "dfb24fa9-8ca1-4e22-bf56-286e6b4d4f1a",
76
+ "size": [
77
+ 2.7,
78
+ 2.23,
79
+ 2.58
80
+ ],
81
+ "desc": "Modern whimsical blue and white wooden bunk bed with built-in staircase, heart-shaped cutout, and shutter design details.",
82
+ "is_scaled": false,
83
+ "rot": [
84
+ 0,
85
+ 0.70711,
86
+ 0,
87
+ 0.70711
88
+ ],
89
+ "pos": [
90
+ 0.6,
91
+ 0.0,
92
+ -0.12
93
+ ],
94
+ "instance_id": "furniture/347"
95
+ },
96
+ {
97
+ "uid": "1526/model",
98
+ "jid": "9556a5c9-6d6a-4500-851f-571a5a0494aa",
99
+ "size": [
100
+ 1.33,
101
+ 1.87,
102
+ 0.57
103
+ ],
104
+ "desc": "A minimalist modern desk featuring a pastel pink tabletop and white frame with two drawers.",
105
+ "is_scaled": false,
106
+ "rot": [
107
+ 0,
108
+ 1,
109
+ 0,
110
+ 0
111
+ ],
112
+ "pos": [
113
+ -1.07,
114
+ 0.0,
115
+ 1.25
116
+ ],
117
+ "instance_id": "furniture/348"
118
+ },
119
+ {
120
+ "uid": "1527/model",
121
+ "jid": "343dad12-3fba-4f75-8fa3-19f7aa3d5871-(0.98)-(1.0)-(0.97)",
122
+ "size": [
123
+ 0.64,
124
+ 0.4,
125
+ 0.45
126
+ ],
127
+ "desc": "Mint green contemporary desk with a minimalist wooden construction, rectangular top, and round tapered legs featuring soft edges.",
128
+ "is_scaled": true,
129
+ "rot": [
130
+ 0,
131
+ 0,
132
+ 0,
133
+ 1
134
+ ],
135
+ "pos": [
136
+ -1.07,
137
+ 0.0,
138
+ 0.72
139
+ ],
140
+ "instance_id": "furniture/349"
141
+ },
142
+ {
143
+ "uid": "1531/model",
144
+ "jid": "892a7ad4-1199-43b6-b426-1fe5886567a4",
145
+ "size": [
146
+ 0.56,
147
+ 0.21,
148
+ 0.56
149
+ ],
150
+ "desc": "Art Deco-inspired silver ceiling lamp with fan-shaped, scalloped metal design and central accent.",
151
+ "is_scaled": false,
152
+ "rot": [
153
+ 0,
154
+ -0.70711,
155
+ 0,
156
+ 0.70711
157
+ ],
158
+ "pos": [
159
+ 0.04,
160
+ 2.39,
161
+ 0.03
162
+ ],
163
+ "instance_id": "furniture/353"
164
+ },
165
+ {
166
+ "uid": "1561/model",
167
+ "jid": "ca503a74-f0c5-458c-b946-d4f676263cd5-(0.99)-(1.0)-(1.03)",
168
+ "size": [
169
+ 1.1,
170
+ 2.24,
171
+ 0.66
172
+ ],
173
+ "desc": "A playful, contemporary children\u2019s cabinet featuring a traffic light design with four wooden compartments, red, yellow, and green circular accents, and a tiered structure.",
174
+ "is_scaled": true,
175
+ "rot": [
176
+ 0,
177
+ 0,
178
+ 0,
179
+ 1
180
+ ],
181
+ "pos": [
182
+ -1.09,
183
+ 0.0,
184
+ -1.22
185
+ ],
186
+ "instance_id": "furniture/365"
187
+ }
188
+ ],
189
+ "doors": [],
190
+ "windows": [],
191
+ "floor_material": {
192
+ "jid": "2ca67c08-f98d-4b2d-8f36-a6612008a74a",
193
+ "material_type": "flooring - hardwood",
194
+ "texture_path": "3D-FRONT-texture/2ca67c08-f98d-4b2d-8f36-a6612008a74a/texture.png"
195
+ }
196
+ }
eval/respace/dataset-ssr3dfront_stage1_with_doors_windows/a35c271d-20fd-4579-a702-e41478c2d24e-ccfe5f4b-2935-472f-af8d-5b019d52d86a.json ADDED
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1
+ {
2
+ "orig_scene_uid": "a35c271d-20fd-4579-a702-e41478c2d24e",
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+ "uid": "ccfe5f4b-2935-472f-af8d-5b019d52d86a",
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+ "room_id": "DiningRoom-12765",
5
+ "bounds_top": [
6
+ [
7
+ -1.45,
8
+ 2.6,
9
+ 1.9
10
+ ],
11
+ [
12
+ 1.45,
13
+ 2.6,
14
+ 1.9
15
+ ],
16
+ [
17
+ 1.45,
18
+ 2.6,
19
+ -1.9
20
+ ],
21
+ [
22
+ -1.45,
23
+ 2.6,
24
+ -1.9
25
+ ]
26
+ ],
27
+ "bounds_bottom": [
28
+ [
29
+ -1.45,
30
+ 0.0,
31
+ 1.9
32
+ ],
33
+ [
34
+ 1.45,
35
+ 0.0,
36
+ 1.9
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+ ],
38
+ [
39
+ 1.45,
40
+ 0.0,
41
+ -1.9
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+ ],
43
+ [
44
+ -1.45,
45
+ 0.0,
46
+ -1.9
47
+ ]
48
+ ],
49
+ "objects": [
50
+ {
51
+ "uid": "7980/model",
52
+ "jid": "84e42153-bfb5-4f05-8e28-84e4772351ae",
53
+ "size": [
54
+ 2.23,
55
+ 0.72,
56
+ 0.92
57
+ ],
58
+ "desc": "Modern minimalist brown wooden rectangular dining table with angled legs and simple design.",
59
+ "is_scaled": false,
60
+ "rot": [
61
+ 0,
62
+ 0.70711,
63
+ 0,
64
+ 0.70711
65
+ ],
66
+ "pos": [
67
+ 0.08,
68
+ 0.0,
69
+ -0.02
70
+ ],
71
+ "instance_id": "furniture/383"
72
+ },
73
+ {
74
+ "uid": "7981/model",
75
+ "jid": "47d40cd4-1ae6-4dbb-87f4-cf0f715eeff8",
76
+ "size": [
77
+ 0.49,
78
+ 0.87,
79
+ 0.55
80
+ ],
81
+ "desc": "A modern minimalist dining chair with clean lines, a gray upholstered seat, natural wood tapered legs, and a distinctive cut-out wood backrest.",
82
+ "is_scaled": false,
83
+ "rot": [
84
+ 0,
85
+ 0.70711,
86
+ 0,
87
+ 0.70711
88
+ ],
89
+ "pos": [
90
+ -0.48,
91
+ 0.0,
92
+ -0.26
93
+ ],
94
+ "instance_id": "furniture/384"
95
+ },
96
+ {
97
+ "uid": "7981/model",
98
+ "jid": "47d40cd4-1ae6-4dbb-87f4-cf0f715eeff8",
99
+ "size": [
100
+ 0.49,
101
+ 0.87,
102
+ 0.55
103
+ ],
104
+ "desc": "A modern minimalist dining chair with clean lines, a gray upholstered seat, natural wood tapered legs, and a distinctive cut-out wood backrest.",
105
+ "is_scaled": false,
106
+ "rot": [
107
+ 0,
108
+ -0.70711,
109
+ 0,
110
+ 0.70711
111
+ ],
112
+ "pos": [
113
+ 0.65,
114
+ 0.0,
115
+ -0.4
116
+ ],
117
+ "instance_id": "furniture/385"
118
+ },
119
+ {
120
+ "uid": "7981/model",
121
+ "jid": "47d40cd4-1ae6-4dbb-87f4-cf0f715eeff8",
122
+ "size": [
123
+ 0.49,
124
+ 0.87,
125
+ 0.55
126
+ ],
127
+ "desc": "A modern minimalist dining chair with clean lines, a gray upholstered seat, natural wood tapered legs, and a distinctive cut-out wood backrest.",
128
+ "is_scaled": false,
129
+ "rot": [
130
+ 0,
131
+ -0.70711,
132
+ 0,
133
+ 0.70711
134
+ ],
135
+ "pos": [
136
+ 0.69,
137
+ 0.0,
138
+ 0.56
139
+ ],
140
+ "instance_id": "furniture/386"
141
+ },
142
+ {
143
+ "uid": "7981/model",
144
+ "jid": "47d40cd4-1ae6-4dbb-87f4-cf0f715eeff8",
145
+ "size": [
146
+ 0.49,
147
+ 0.87,
148
+ 0.55
149
+ ],
150
+ "desc": "A modern minimalist dining chair with clean lines, a gray upholstered seat, natural wood tapered legs, and a distinctive cut-out wood backrest.",
151
+ "is_scaled": false,
152
+ "rot": [
153
+ 0,
154
+ 0.70711,
155
+ 0,
156
+ 0.70711
157
+ ],
158
+ "pos": [
159
+ -0.5,
160
+ 0.0,
161
+ 0.64
162
+ ],
163
+ "instance_id": "furniture/387"
164
+ },
165
+ {
166
+ "uid": "8049/model",
167
+ "jid": "89b7c9e6-dc40-4e23-b64b-0c9876967430",
168
+ "size": [
169
+ 1.08,
170
+ 2.08,
171
+ 0.49
172
+ ],
173
+ "desc": "Classic white wine cabinet with glass panel upper doors, fixed shelves, lower wooden storage, crown molding, and turned feet.",
174
+ "is_scaled": false,
175
+ "rot": [
176
+ 0,
177
+ 0.70711,
178
+ 0,
179
+ 0.70711
180
+ ],
181
+ "pos": [
182
+ -1.18,
183
+ 0.0,
184
+ -1.4
185
+ ],
186
+ "instance_id": "furniture/389"
187
+ },
188
+ {
189
+ "uid": "8487/model",
190
+ "jid": "d3b90781-9764-4b5f-b70d-1a82e9fb365e",
191
+ "size": [
192
+ 0.56,
193
+ 2.75,
194
+ 0.56
195
+ ],
196
+ "desc": "A contemporary minimalist pendant lamp featuring a spiral design with multiple silver and grey conical metal and glass pendants hanging from thin cables.",
197
+ "is_scaled": false,
198
+ "rot": [
199
+ 0,
200
+ 0,
201
+ 0,
202
+ 1
203
+ ],
204
+ "pos": [
205
+ 0.08,
206
+ -0.01,
207
+ 0.07
208
+ ],
209
+ "instance_id": "furniture/391"
210
+ }
211
+ ],
212
+ "doors": [],
213
+ "windows": [],
214
+ "floor_material": {
215
+ "jid": "c46b54f5-e221-4b0c-9e83-f9fcaa1bfc0e",
216
+ "material_type": "flooring - reinforced",
217
+ "texture_path": "3D-FRONT-texture/c46b54f5-e221-4b0c-9e83-f9fcaa1bfc0e/texture.png"
218
+ }
219
+ }
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tools/__pycache__/asset_description_manager.cpython-313.pyc ADDED
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tools/data_gen/__init__.py ADDED
@@ -0,0 +1 @@
 
 
1
+ # Data generation utilities for LM fine-tuning
tools/data_gen/build_finetune_data.py ADDED
@@ -0,0 +1,736 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ 整合 user_design_prompt + CoT + layout.json 生成 LM fine-tuning 数据
4
+
5
+ 数据来源:
6
+ - user_design_prompts_sampled_{coarse,medium,fine}/: 用户设计指令（每场景一条）
7
+ - layout_design_cot/: Chain-of-Thought 推理过程
8
+ - unified-layout/: 最终的 layout.json
9
+
10
+ 输出格式 (JSONL):
11
+ {
12
+ "scene_id": "scannet/scene0000_00",
13
+ "granularity": "coarse", # coarse/medium/fine
14
+ "input": "<user_design_prompt>",
15
+ "cot": "<chain_of_thought_reasoning>",
16
+ "output": {<layout_json>}
17
+ }
18
+ """
19
+
20
+ import json
21
+ import os
22
+ import argparse
23
+ import random
24
+ from pathlib import Path
25
+ from typing import List, Dict, Any, Optional, Tuple
26
+ from concurrent.futures import ThreadPoolExecutor, as_completed
27
+ from tqdm import tqdm
28
+
29
+
30
+ # ============================================
31
+ # 路径配置
32
+ # ============================================
33
+ BASE_DATA_DIR = os.path.expanduser("~/backup/datas")
34
+
35
+ # 输入路径
36
+ UNIFIED_LAYOUT_BASE = os.path.join(BASE_DATA_DIR, "unified-layout-aligned")
37
+ COT_OUTPUT_BASE = os.path.join(BASE_DATA_DIR, "layout_design_cot_aligned_coarse")
38
+
39
+ # User design prompts (已按粒度抽样)
40
+ USER_PROMPT_SAMPLED = {
41
+ "coarse": os.path.join(BASE_DATA_DIR, "user_design_prompts_sampled_coarse"),
42
+ "medium": os.path.join(BASE_DATA_DIR, "user_design_prompts_sampled_medium"),
43
+ "fine": os.path.join(BASE_DATA_DIR, "user_design_prompts_sampled_fine"),
44
+ }
45
+
46
+ # 输出路径
47
+ OUTPUT_BASE = os.path.join(BASE_DATA_DIR, "lm_finetune_data")
48
+
49
+ # 数据集列表
50
+ DATASETS = ["scannet", "3rscan", "arkitscenes", "3d-front", "matterport3d"]
51
+
52
+
53
+ def load_text_file(path: str) -> Optional[str]:
54
+ """读取文本文件"""
55
+ if not os.path.exists(path):
56
+ return None
57
+ with open(path, 'r', encoding='utf-8') as f:
58
+ return f.read().strip()
59
+
60
+
61
+ def load_json_file(path: str) -> Optional[Dict]:
62
+ """读取 JSON 文件"""
63
+ if not os.path.exists(path):
64
+ return None
65
+ with open(path, 'r', encoding='utf-8') as f:
66
+ return json.load(f)
67
+
68
+
69
+ def _reorder_dict(data: Dict[str, Any], key_order: List[str]) -> Dict[str, Any]:
70
+ """Return a new dict following key_order then remaining keys in original order."""
71
+ out: Dict[str, Any] = {}
72
+ for k in key_order:
73
+ if k in data:
74
+ out[k] = data[k]
75
+ for k, v in data.items():
76
+ if k not in out:
77
+ out[k] = v
78
+ return out
79
+
80
+
81
+ def _normalize_asset(asset: Dict[str, Any]) -> Dict[str, Any]:
82
+ """Flatten transform into pos/rot/size and reorder keys."""
83
+ transform = asset.pop("transform", None)
84
+ if isinstance(transform, dict):
85
+ for k in ("pos", "rot", "size"):
86
+ if k in transform and k not in asset:
87
+ asset[k] = transform[k]
88
+
89
+ order = ["id", "model_id", "category", "role", "description", "pos", "rot", "size"]
90
+ return _reorder_dict(asset, order)
91
+
92
+
93
+ def _normalize_zone(zone: Dict[str, Any]) -> Dict[str, Any]:
94
+ assets = zone.get("assets", [])
95
+ zone["assets"] = [_normalize_asset(a) for a in assets]
96
+ order = ["id", "semantic_label", "assets", "spatial_graph"]
97
+ return _reorder_dict(zone, order)
98
+
99
+
100
+ def normalize_layout(layout: Dict[str, Any]) -> Dict[str, Any]:
101
+ """Normalize layout json: drop scene_type, flatten asset.transform, reorder keys."""
102
+ layout = dict(layout) # shallow copy
103
+ layout.pop("scene_type", None)
104
+
105
+ if "functional_zones" in layout:
106
+ layout["functional_zones"] = [_normalize_zone(z) for z in layout.get("functional_zones", [])]
107
+
108
+ top_order = ["meta", "architecture", "zone_topology", "functional_zones"]
109
+ return _reorder_dict(layout, top_order)
110
+
111
+
112
+ def get_scene_paths(scene_path: str, granularity: str = "coarse") -> Tuple[str, str, str]:
113
+ """
114
+ 根据场景路径获取各数据文件的路径
115
+
116
+ Args:
117
+ scene_path: 相对路径，如 "scannet/scene0000_00" 或 "arkitscenes/Training/12345"
118
+ granularity: 粒度 (coarse/medium/fine)，用于确定 CoT 路径
119
+
120
+ Returns:
121
+ (layout_path, cot_path, scene_id)
122
+ """
123
+ parts = scene_path.split('/')
124
+ dataset = parts[0]
125
+
126
+ # CoT 基础路径根据粒度变化
127
+ cot_base = os.path.join(BASE_DATA_DIR, f"layout_design_cot_aligned_{granularity}")
128
+
129
+ if dataset == "scannet" or dataset == "3rscan":
130
+ scene_id = parts[1]
131
+ layout_path = os.path.join(UNIFIED_LAYOUT_BASE, scene_path, "layout.json")
132
+ cot_path = os.path.join(cot_base, dataset, f"{scene_id}.txt")
133
+
134
+ elif dataset == "arkitscenes":
135
+ split = parts[1] # Training or Validation
136
+ scene_id = parts[2]
137
+ layout_path = os.path.join(UNIFIED_LAYOUT_BASE, scene_path, "layout.json")
138
+ cot_path = os.path.join(cot_base, f"{dataset}/{split}", f"{scene_id}.txt")
139
+
140
+ elif dataset == "3d-front":
141
+ seq_id = parts[1]
142
+ room_id = parts[2]
143
+ scene_id = room_id
144
+ layout_path = os.path.join(UNIFIED_LAYOUT_BASE, scene_path, "layout.json")
145
+ cot_path = os.path.join(cot_base, f"3d-front/{seq_id}", f"{room_id}.txt")
146
+
147
+ elif dataset == "matterport3d":
148
+ scene_id = parts[1]
149
+ region_id = parts[2]
150
+ layout_path = os.path.join(UNIFIED_LAYOUT_BASE, scene_path, "layout.json")
151
+ cot_path = os.path.join(cot_base, f"matterport3d/{scene_id}", f"{region_id}.txt")
152
+ scene_id = region_id # 输出使用 region_id
153
+ else:
154
+ raise ValueError(f"Unknown dataset: {dataset}")
155
+
156
+ return layout_path, cot_path, scene_id
157
+
158
+
159
+ def get_user_prompt_path(scene_path: str, granularity: str) -> str:
160
+ """
161
+ 获取用户设计指令文件路径
162
+
163
+ Args:
164
+ scene_path: 相对路径
165
+ granularity: coarse/medium/fine
166
+
167
+ Returns:
168
+ 用户指令文件路径
169
+ """
170
+ parts = scene_path.split('/')
171
+ dataset = parts[0]
172
+
173
+ base = USER_PROMPT_SAMPLED[granularity]
174
+
175
+ if dataset == "scannet" or dataset == "3rscan":
176
+ scene_id = parts[1]
177
+ return os.path.join(base, dataset, f"{scene_id}.txt")
178
+
179
+ elif dataset == "arkitscenes":
180
+ split = parts[1]
181
+ scene_id = parts[2]
182
+ return os.path.join(base, f"{dataset}/{split}", f"{scene_id}.txt")
183
+
184
+ elif dataset == "3d-front":
185
+ seq_id = parts[1]
186
+ room_id = parts[2]
187
+ return os.path.join(base, f"3d-front/{seq_id}", f"{room_id}.txt")
188
+
189
+ elif dataset == "matterport3d":
190
+ scene_id = parts[1]
191
+ region_id = parts[2]
192
+ return os.path.join(base, f"matterport3d/{scene_id}", f"{region_id}.txt")
193
+
194
+ return ""
195
+
196
+
197
+ def process_single_scene(
198
+ scene_path: str,
199
+ granularity: str,
200
+ include_cot: bool = True
201
+ ) -> Optional[Dict[str, Any]]:
202
+ """
203
+ 处理单个场景，生成训练数据
204
+
205
+ Args:
206
+ scene_path: 场景相对路径
207
+ granularity: 粒度 (coarse/medium/fine)
208
+ include_cot: 是否包含 CoT
209
+
210
+ Returns:
211
+ 训练数据字典，或 None（如果数据不完整）
212
+ """
213
+ try:
214
+ layout_path, cot_path, scene_id = get_scene_paths(scene_path, granularity)
215
+ user_prompt_path = get_user_prompt_path(scene_path, granularity)
216
+
217
+ # 加载数据
218
+ layout_json = load_json_file(layout_path)
219
+ user_prompt = load_text_file(user_prompt_path)
220
+ cot_text = load_text_file(cot_path) if include_cot else None
221
+
222
+ # 检查必要数据
223
+ if layout_json is None:
224
+ return None
225
+ if user_prompt is None:
226
+ return None
227
+ if include_cot and cot_text is None:
228
+ return None
229
+
230
+ # 规范化 layout
231
+ layout_json = normalize_layout(layout_json)
232
+
233
+ # 构建训练样本
234
+ sample = {
235
+ "scene_id": scene_path,
236
+ "granularity": granularity,
237
+ "input": user_prompt,
238
+ "output": layout_json,
239
+ }
240
+
241
+ if include_cot and cot_text:
242
+ sample["cot"] = cot_text
243
+
244
+ return sample
245
+
246
+ except Exception as e:
247
+ print(f"Error processing {scene_path}: {e}")
248
+ return None
249
+
250
+
251
+ def collect_all_scenes(scenes_list_file: str) -> List[str]:
252
+ """从场景列表文件收集所有场景路径"""
253
+ with open(scenes_list_file, 'r') as f:
254
+ scenes = [line.strip() for line in f if line.strip() and not line.startswith('#')]
255
+ return scenes
256
+
257
+
258
+ def generate_finetune_data(
259
+ scenes_list_file: str,
260
+ granularities: List[str] = ["coarse", "medium", "fine"],
261
+ include_cot: bool = True,
262
+ output_dir: str = None,
263
+ num_workers: int = 16,
264
+ format: str = "jsonl" # jsonl or json
265
+ ) -> Dict[str, int]:
266
+ """
267
+ 生成 LM fine-tuning 数据
268
+
269
+ Args:
270
+ scenes_list_file: 场景列表文件
271
+ granularities: 要处理的粒度列表
272
+ include_cot: 是否包含 CoT
273
+ output_dir: 输出目录
274
+ num_workers: 并发数
275
+ format: 输出格式 (jsonl 或 json)
276
+
277
+ Returns:
278
+ 统计信息
279
+ """
280
+ if output_dir is None:
281
+ output_dir = OUTPUT_BASE
282
+
283
+ os.makedirs(output_dir, exist_ok=True)
284
+
285
+ # 收集所有场景
286
+ scenes = collect_all_scenes(scenes_list_file)
287
+ print(f"Found {len(scenes)} scenes to process")
288
+
289
+ stats = {
290
+ "total_scenes": len(scenes),
291
+ "processed": 0,
292
+ "skipped": 0,
293
+ }
294
+
295
+ all_samples = []
296
+
297
+ # 为每个粒度分别处理
298
+ for granularity in granularities:
299
+ print(f"\n{'='*60}")
300
+ print(f"Processing granularity: {granularity}")
301
+ print(f"{'='*60}")
302
+
303
+ granularity_samples = []
304
+
305
+ # 并发处理
306
+ with ThreadPoolExecutor(max_workers=num_workers) as executor:
307
+ futures = {
308
+ executor.submit(process_single_scene, scene, granularity, include_cot): scene
309
+ for scene in scenes
310
+ }
311
+
312
+ for future in tqdm(as_completed(futures), total=len(futures), desc=f"{granularity}"):
313
+ scene = futures[future]
314
+ try:
315
+ result = future.result()
316
+ if result:
317
+ granularity_samples.append(result)
318
+ stats["processed"] += 1
319
+ else:
320
+ stats["skipped"] += 1
321
+ except Exception as e:
322
+ print(f"Exception for {scene}: {e}")
323
+ stats["skipped"] += 1
324
+
325
+ print(f"Collected {len(granularity_samples)} samples for {granularity}")
326
+ all_samples.extend(granularity_samples)
327
+
328
+ # 单独保存每个粒度的数据
329
+ granularity_output = os.path.join(output_dir, f"train_{granularity}.jsonl")
330
+ with open(granularity_output, 'w', encoding='utf-8') as f:
331
+ for sample in granularity_samples:
332
+ f.write(json.dumps(sample, ensure_ascii=False) + '\n')
333
+ print(f"Saved {len(granularity_samples)} samples to {granularity_output}")
334
+
335
+ # 保存合并的数据
336
+ if format == "jsonl":
337
+ output_file = os.path.join(output_dir, "train_all.jsonl")
338
+ with open(output_file, 'w', encoding='utf-8') as f:
339
+ for sample in all_samples:
340
+ f.write(json.dumps(sample, ensure_ascii=False) + '\n')
341
+ else:
342
+ output_file = os.path.join(output_dir, "train_all.json")
343
+ with open(output_file, 'w', encoding='utf-8') as f:
344
+ json.dump(all_samples, f, ensure_ascii=False, indent=2)
345
+
346
+ print(f"\n{'='*60}")
347
+ print(f"Total samples: {len(all_samples)}")
348
+ print(f"Saved to: {output_file}")
349
+ print(f"Stats: {stats}")
350
+
351
+ return stats
352
+
353
+
354
+ def _build_legacy_entry(scene_path: str, granularity: str, include_cot: bool, split: str = "train") -> Dict[str, Any]:
355
+ """构造 legacy v2 兼容的数据条目 (training_data_with_content_v2.json 对齐)"""
356
+
357
+ layout_path, cot_path, scene_id = get_scene_paths(scene_path, granularity)
358
+ user_prompt_path = get_user_prompt_path(scene_path, granularity)
359
+
360
+ user_exists = os.path.exists(user_prompt_path)
361
+ cot_exists = os.path.exists(cot_path) if include_cot else True
362
+ answer_exists = os.path.exists(layout_path)
363
+ complete = user_exists and cot_exists and answer_exists
364
+
365
+ content: Dict[str, Any] = {}
366
+ if user_exists:
367
+ content["user_input"] = load_text_file(user_prompt_path)
368
+ if include_cot and cot_exists:
369
+ content["cot"] = load_text_file(cot_path)
370
+ if answer_exists:
371
+ # 保存为字符串以匹配旧版格式 (stringified JSON)
372
+ layout_json = load_json_file(layout_path)
373
+ layout_json = normalize_layout(layout_json)
374
+ content["answer"] = json.dumps(layout_json, ensure_ascii=False)
375
+
376
+ entry = {
377
+ "id": f"{scene_path}:{granularity}",
378
+ "scene_path": scene_path,
379
+ "granularity": granularity,
380
+ "split": split,
381
+ "files": {
382
+ "user_input_path": user_prompt_path,
383
+ "cot_path": cot_path if include_cot else None,
384
+ "answer_path": layout_path,
385
+ },
386
+ "status": {
387
+ "user_input_exists": user_exists,
388
+ "cot_exists": cot_exists,
389
+ "answer_exists": answer_exists,
390
+ "complete": complete,
391
+ "split_found": True,
392
+ },
393
+ }
394
+
395
+ if content:
396
+ entry["content"] = content
397
+
398
+ return entry
399
+
400
+
401
+ def generate_legacy_v2_format(
402
+ scenes_list_file: str,
403
+ granularities: List[str],
404
+ include_cot: bool,
405
+ output_file: str,
406
+ num_workers: int = 16,
407
+ split: str = "train",
408
+ include_content: bool = True,
409
+ test_size: int = 500,
410
+ ) -> Dict[str, Any]:
411
+ """
412
+ 生成与早期 `training_data_with_content_v2.json` 对齐的结构。
413
+
414
+ 返回:
415
+ metadata + data 列表的字典
416
+ """
417
+
418
+ scenes = collect_all_scenes(scenes_list_file)
419
+ print(f"[legacy_v2] scenes: {len(scenes)}, granularities: {granularities}, include_cot={include_cot}")
420
+
421
+ entries: List[Dict[str, Any]] = []
422
+
423
+ # 并发处理
424
+ with ThreadPoolExecutor(max_workers=num_workers) as executor:
425
+ futures = {}
426
+ for scene in scenes:
427
+ for granularity in granularities:
428
+ # arkitscenes Validation -> split=val, others default train (later some sampled to test)
429
+ parts = scene.split('/')
430
+ scene_split = "train"
431
+ if len(parts) >= 3 and parts[0] == "arkitscenes" and parts[1].lower() == "validation":
432
+ scene_split = "val"
433
+ futures[executor.submit(_build_legacy_entry, scene, granularity, include_cot, scene_split)] = (scene, granularity)
434
+
435
+ for future in tqdm(as_completed(futures), total=len(futures), desc="legacy_v2"):
436
+ scene, granularity = futures[future]
437
+ try:
438
+ entry = future.result()
439
+ if not include_content:
440
+ entry.pop("content", None)
441
+ entries.append(entry)
442
+ except Exception as e:
443
+ print(f"[legacy_v2] error for {scene}:{granularity} -> {e}")
444
+
445
+ # 追加随机 test 划分（不覆盖val）
446
+ train_indices = [i for i, e in enumerate(entries) if e.get("split") == "train"]
447
+ random.seed(42)
448
+ chosen = set(random.sample(train_indices, k=min(test_size, len(train_indices))))
449
+ for idx in chosen:
450
+ entries[idx]["split"] = "test"
451
+
452
+ total_entries = len(entries)
453
+ complete_matches = sum(1 for e in entries if e["status"]["complete"])
454
+
455
+ metadata = {
456
+ "total_entries": total_entries,
457
+ "complete_matches": complete_matches,
458
+ "partial_matches": total_entries - complete_matches,
459
+ "split_mapping_loaded": len(scenes), # 这里没有 split 文件，直接视为全部找到
460
+ "directories": {
461
+ "user_designs": USER_PROMPT_SAMPLED.get(granularities[0], ""),
462
+ "cot": COT_OUTPUT_BASE,
463
+ "grouped_layouts": UNIFIED_LAYOUT_BASE,
464
+ },
465
+ "include_content": include_content,
466
+ "split_statistics": {
467
+ "split_found": len(scenes),
468
+ "split_not_found": 0,
469
+ },
470
+ }
471
+
472
+ payload = {
473
+ "metadata": metadata,
474
+ "data": entries,
475
+ }
476
+
477
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
478
+ with open(output_file, "w", encoding="utf-8") as f:
479
+ json.dump(payload, f, ensure_ascii=False, indent=2)
480
+
481
+ print(f"[legacy_v2] saved {total_entries} entries to {output_file}")
482
+ return payload
483
+
484
+
485
+ def generate_legacy_v2_format_random_granularity(
486
+ scenes_list_file: str,
487
+ include_cot: bool,
488
+ output_file: str,
489
+ num_workers: int = 16,
490
+ split: str = "train",
491
+ include_content: bool = True,
492
+ test_size: int = 500,
493
+ seed: int = 42,
494
+ ) -> Dict[str, Any]:
495
+ """
496
+ 生成与早期 `training_data_with_content_v2.json` 对齐的结构。
497
+ 每条数据随机选择一个粒度 (coarse/medium/fine)。
498
+
499
+ 返回:
500
+ metadata + data 列表的字典
501
+ """
502
+ all_granularities = ["coarse", "medium", "fine"]
503
+
504
+ scenes = collect_all_scenes(scenes_list_file)
505
+ print(f"[legacy_v2_random] scenes: {len(scenes)}, random granularity from {all_granularities}, include_cot={include_cot}")
506
+
507
+ # 为每个场景随机分配粒度
508
+ random.seed(seed)
509
+ scene_granularities = [(scene, random.choice(all_granularities)) for scene in scenes]
510
+
511
+ entries: List[Dict[str, Any]] = []
512
+
513
+ # 并发处理
514
+ with ThreadPoolExecutor(max_workers=num_workers) as executor:
515
+ futures = {}
516
+ for scene, granularity in scene_granularities:
517
+ # arkitscenes Validation -> split=val, others default train (later some sampled to test)
518
+ parts = scene.split('/')
519
+ scene_split = "train"
520
+ if len(parts) >= 3 and parts[0] == "arkitscenes" and parts[1].lower() == "validation":
521
+ scene_split = "val"
522
+ futures[executor.submit(_build_legacy_entry, scene, granularity, include_cot, scene_split)] = (scene, granularity)
523
+
524
+ for future in tqdm(as_completed(futures), total=len(futures), desc="legacy_v2_random"):
525
+ scene, granularity = futures[future]
526
+ try:
527
+ entry = future.result()
528
+ if not include_content:
529
+ entry.pop("content", None)
530
+ entries.append(entry)
531
+ except Exception as e:
532
+ print(f"[legacy_v2_random] error for {scene}:{granularity} -> {e}")
533
+
534
+ # 追加随机 test 划分（不覆盖val）
535
+ train_indices = [i for i, e in enumerate(entries) if e.get("split") == "train"]
536
+ random.seed(seed + 1) # 使用不同的 seed 来选择 test
537
+ chosen = set(random.sample(train_indices, k=min(test_size, len(train_indices))))
538
+ for idx in chosen:
539
+ entries[idx]["split"] = "test"
540
+
541
+ total_entries = len(entries)
542
+ complete_matches = sum(1 for e in entries if e["status"]["complete"])
543
+
544
+ # 统计各粒度分布
545
+ granularity_counts = {}
546
+ for e in entries:
547
+ g = e.get("granularity", "unknown")
548
+ granularity_counts[g] = granularity_counts.get(g, 0) + 1
549
+
550
+ metadata = {
551
+ "total_entries": total_entries,
552
+ "complete_matches": complete_matches,
553
+ "partial_matches": total_entries - complete_matches,
554
+ "split_mapping_loaded": len(scenes),
555
+ "granularity_distribution": granularity_counts,
556
+ "directories": {
557
+ "user_designs_coarse": USER_PROMPT_SAMPLED.get("coarse", ""),
558
+ "user_designs_medium": USER_PROMPT_SAMPLED.get("medium", ""),
559
+ "user_designs_fine": USER_PROMPT_SAMPLED.get("fine", ""),
560
+ "cot_base": BASE_DATA_DIR,
561
+ "grouped_layouts": UNIFIED_LAYOUT_BASE,
562
+ },
563
+ "include_content": include_content,
564
+ "split_statistics": {
565
+ "split_found": len(scenes),
566
+ "split_not_found": 0,
567
+ },
568
+ }
569
+
570
+ payload = {
571
+ "metadata": metadata,
572
+ "data": entries,
573
+ }
574
+
575
+ os.makedirs(os.path.dirname(output_file) if os.path.dirname(output_file) else ".", exist_ok=True)
576
+ with open(output_file, "w", encoding="utf-8") as f:
577
+ json.dump(payload, f, ensure_ascii=False, indent=2)
578
+
579
+ print(f"[legacy_v2_random] saved {total_entries} entries to {output_file}")
580
+ print(f"[legacy_v2_random] granularity distribution: {granularity_counts}")
581
+ return payload
582
+
583
+
584
+ def generate_conversation_format(
585
+ input_jsonl: str,
586
+ output_file: str,
587
+ system_prompt: str = None
588
+ ):
589
+ """
590
+ 将 JSONL 数据转换为对话格式（适用于 chat 模型微调）
591
+
592
+ 输出格式:
593
+ {
594
+ "conversations": [
595
+ {"role": "system", "content": "..."},
596
+ {"role": "user", "content": "<design_prompt>"},
597
+ {"role": "assistant", "content": "<cot>\n\n```json\n<layout>\n```"}
598
+ ]
599
+ }
600
+ """
601
+ if system_prompt is None:
602
+ system_prompt = (
603
+ "You are an expert interior designer AI. Given a design brief, "
604
+ "you will think step-by-step about how to arrange furniture and objects "
605
+ "in the room, then output the final layout as a structured JSON."
606
+ )
607
+
608
+ conversations = []
609
+
610
+ with open(input_jsonl, 'r', encoding='utf-8') as f:
611
+ for line in f:
612
+ sample = json.loads(line.strip())
613
+
614
+ # 构建 assistant 回复
615
+ if "cot" in sample:
616
+ assistant_content = f"{sample['cot']}\n\n```json\n{json.dumps(sample['output'], ensure_ascii=False, indent=2)}\n```"
617
+ else:
618
+ assistant_content = f"```json\n{json.dumps(sample['output'], ensure_ascii=False, indent=2)}\n```"
619
+
620
+ conv = {
621
+ "id": sample["scene_id"],
622
+ "conversations": [
623
+ {"role": "system", "content": system_prompt},
624
+ {"role": "user", "content": sample["input"]},
625
+ {"role": "assistant", "content": assistant_content}
626
+ ]
627
+ }
628
+ conversations.append(conv)
629
+
630
+ with open(output_file, 'w', encoding='utf-8') as f:
631
+ for conv in conversations:
632
+ f.write(json.dumps(conv, ensure_ascii=False) + '\n')
633
+
634
+ print(f"Generated {len(conversations)} conversation samples to {output_file}")
635
+
636
+
637
+ def main():
638
+ parser = argparse.ArgumentParser(description="Generate LM fine-tuning data")
639
+
640
+ parser.add_argument("--scenes-list", type=str,
641
+ default="unified_layout_scenes.txt",
642
+ help="Path to scenes list file")
643
+ parser.add_argument("--granularity", type=str, nargs="+",
644
+ default=["medium"],
645
+ choices=["coarse", "medium", "fine"],
646
+ help="Granularity levels to process")
647
+ parser.add_argument("--random-granularity", action="store_true",
648
+ help="Randomly select granularity (coarse/medium/fine) for each scene")
649
+ parser.add_argument("--no-cot", action="store_true",
650
+ help="Exclude Chain-of-Thought from output")
651
+ parser.add_argument("--output-dir", type=str, default=None,
652
+ help="Output directory")
653
+ parser.add_argument("--workers", "-w", type=int, default=16,
654
+ help="Number of parallel workers")
655
+ parser.add_argument("--format", type=str, default="jsonl",
656
+ choices=["jsonl", "json"],
657
+ help="Output format")
658
+ parser.add_argument("--to-conversation", type=str, default=None,
659
+ help="Convert existing JSONL to conversation format")
660
+ parser.add_argument("--legacy-v2", action="store_true",
661
+ help="Output legacy training_data_with_content_v2.json compatible format")
662
+ parser.add_argument("--legacy-output", type=str, default="/home/v-meiszhang/amlt-project/InternScenes/data/zones_data_mixed.json",
663
+ help="Output path for legacy v2 json (default: data/zones_data_mixed.json)")
664
+ parser.add_argument("--legacy-test-size", type=int, default=500,
665
+ help="Number of train samples to mark as test (random).")
666
+ parser.add_argument("--seed", type=int, default=42,
667
+ help="Random seed for granularity selection")
668
+
669
+ args = parser.parse_args()
670
+
671
+ # 如果是转换模式
672
+ if args.to_conversation:
673
+ input_file = args.to_conversation
674
+ output_file = input_file.replace('.jsonl', '_conv.jsonl')
675
+ generate_conversation_format(input_file, output_file)
676
+ return
677
+
678
+ # 场景列表文件路径
679
+ scenes_list = args.scenes_list
680
+ if not os.path.isabs(scenes_list):
681
+ # 相对于项目根目录
682
+ project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
683
+ scenes_list = os.path.join(project_root, scenes_list)
684
+
685
+ if args.legacy_v2:
686
+ legacy_output = args.legacy_output
687
+ if legacy_output is None:
688
+ legacy_output = os.path.join(OUTPUT_BASE, "training_data_v3.json")
689
+
690
+ print(f"Using scenes list: {scenes_list}")
691
+ print(f"Include CoT: {not args.no_cot}")
692
+ print(f"Legacy v2 output: {legacy_output}")
693
+
694
+ if args.random_granularity:
695
+ print(f"Random granularity mode (seed={args.seed})")
696
+ generate_legacy_v2_format_random_granularity(
697
+ scenes_list_file=scenes_list,
698
+ include_cot=not args.no_cot,
699
+ output_file=legacy_output,
700
+ num_workers=args.workers,
701
+ split="train",
702
+ include_content=True,
703
+ test_size=args.legacy_test_size,
704
+ seed=args.seed,
705
+ )
706
+ else:
707
+ print(f"Granularities: {args.granularity}")
708
+ generate_legacy_v2_format(
709
+ scenes_list_file=scenes_list,
710
+ granularities=args.granularity,
711
+ include_cot=not args.no_cot,
712
+ output_file=legacy_output,
713
+ num_workers=args.workers,
714
+ split="train",
715
+ include_content=True,
716
+ test_size=args.legacy_test_size,
717
+ )
718
+ return
719
+
720
+ print(f"Using scenes list: {scenes_list}")
721
+ print(f"Granularities: {args.granularity}")
722
+ print(f"Include CoT: {not args.no_cot}")
723
+ print(f"Output format: {args.format}")
724
+
725
+ generate_finetune_data(
726
+ scenes_list_file=scenes_list,
727
+ granularities=args.granularity,
728
+ include_cot=not args.no_cot,
729
+ output_dir=args.output_dir,
730
+ num_workers=args.workers,
731
+ format=args.format
732
+ )
733
+
734
+
735
+ if __name__ == "__main__":
736
+ main()
tools/data_gen/canonicalize_2d_obb.py ADDED
@@ -0,0 +1,605 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ 2D OBB 规范化工具
4
+
5
+ 核心思路：
6
+ 1. 先应用原始变换（init_rotation @ scale @ layout_rot），此时物体在世界空间是正立的
7
+ 2. 只在 XY 平面计算 2D OBB，提取 Yaw 角度
8
+ 3. 将 mesh 反向旋转到轴对齐状态
9
+ 4. 输出：canonical mesh + layout 数据 (rot 只有 yaw)
10
+
11
+ 这样可以保证：
12
+ - Z 轴方向不变（床不会竖起来）
13
+ - 物体变成轴对齐（边线平行于 X/Y 轴）
14
+ - Layout 中只有 Yaw 旋转
15
+
16
+ 新增功能：
17
+ - 当 model_uid 为空时，使用 description 通过 CLIP 检索 3D 资产
18
+ """
19
+
20
+ import os
21
+ import sys
22
+ import json
23
+ import copy
24
+ import argparse
25
+ import numpy as np
26
+ import trimesh
27
+ from pathlib import Path
28
+ from typing import Dict, List, Tuple, Optional
29
+ from tqdm import tqdm
30
+
31
+ # 添加路径
32
+ BASE_DIR = Path(__file__).resolve().parents[2]
33
+ sys.path.insert(0, str(BASE_DIR / "InternScenes" / "InternScenes_Real2Sim"))
34
+ sys.path.insert(0, str(BASE_DIR / "tools" / "asset_description"))
35
+
36
+ from compose_scenes import AssetMeshLoader
37
+
38
+ # 尝试导入 CLIP 检索模块
39
+ try:
40
+ from asset_embedding_manager import AssetEmbeddingManager
41
+ HAS_EMBEDDING = True
42
+ except ImportError:
43
+ HAS_EMBEDDING = False
44
+ print("Warning: asset_embedding_manager not available, CLIP retrieval disabled")
45
+
46
+
47
+ class Canonicalizer2D:
48
+ """使用 2D OBB 进行规范化"""
49
+
50
+ def __init__(self, output_dir: str = None, enable_retrieval: bool = True):
51
+ self.asset_loader = AssetMeshLoader()
52
+ self.output_dir = output_dir
53
+
54
+ # 初始化 CLIP 检索器
55
+ self.embedding_manager = None
56
+ if enable_retrieval and HAS_EMBEDDING:
57
+ try:
58
+ self.embedding_manager = AssetEmbeddingManager()
59
+ print(f"✓ CLIP retrieval enabled ({self.embedding_manager.get_stats()['total_embeddings']} embeddings)")
60
+ except Exception as e:
61
+ print(f"Warning: Failed to init embedding manager: {e}")
62
+
63
+ def retrieve_model_uid(self, description: str, category: str = None, top_k: int = 1) -> Optional[str]:
64
+ """
65
+ 使用 CLIP 根据描述检索最匹配的 model_uid
66
+
67
+ Args:
68
+ description: 资产描述
69
+ category: 资产类别（可选，用于优化检索）
70
+ top_k: 返回前 k 个结果
71
+
72
+ Returns:
73
+ 最匹配的 model_uid，如果检索失败返回 None
74
+ """
75
+ if not self.embedding_manager:
76
+ return None
77
+
78
+ # 构建查询：结合 category 和 description
79
+ query = description
80
+ if category:
81
+ query = f"{category}: {description}"
82
+
83
+ try:
84
+ results = self.embedding_manager.search_similar(query, top_k=top_k)
85
+ if results:
86
+ best_uid, score = results[0]
87
+ print(f" Retrieved: {best_uid} (score: {score:.4f})")
88
+ return best_uid
89
+ except Exception as e:
90
+ print(f" Retrieval error: {e}")
91
+
92
+ return None
93
+
94
+ def get_scale_transform_from_rules(self, mesh_size: np.ndarray, target_size: np.ndarray, category: str) -> np.ndarray:
95
+ """
96
+ 根据资产类型计算 scale 变换矩阵（从 compose_scenes.py 移植）
97
+
98
+ 特殊处理:
99
+ - carpet: 检测错误朝向导致的过度拉伸，做修正旋转，Z 方向缩放 /100
100
+ - clothes: 使用均匀缩放（min_scale）
101
+ """
102
+ if category not in ["carpet", "clothes"]:
103
+ # 普通资产：直接缩放
104
+ scale = target_size / mesh_size
105
+ scale_matrix = np.diag([scale[0], scale[1], scale[2], 1])
106
+ return scale_matrix
107
+
108
+ elif category == "carpet":
109
+ # 地毯：检测错误朝向导致的过度拉伸
110
+ scale_factors = target_size / mesh_size
111
+
112
+ if target_size[2]/target_size[0] > 150 or target_size[2]/target_size[1] > 150:
113
+ # 地毯错误朝向导致过度拉伸
114
+ if target_size[2]/target_size[0] > target_size[2]/target_size[1]:
115
+ rotation_matrix = trimesh.transformations.rotation_matrix(0.5 * np.pi, [0, 1, 0]) # 绕 Y 轴转90度
116
+ new_target = np.array([target_size[2], target_size[0], target_size[1]])
117
+ scale_factors = new_target / mesh_size
118
+ scale_matrix = np.diag([scale_factors[0], scale_factors[1], scale_factors[2]/100.0, 1])
119
+ else:
120
+ rotation_matrix = trimesh.transformations.rotation_matrix(0.5 * np.pi, [1, 0, 0]) # 绕 X 轴转90度
121
+ new_target = np.array([target_size[0], target_size[2], target_size[1]])
122
+ scale_factors = new_target / mesh_size
123
+ scale_matrix = np.diag([scale_factors[0], scale_factors[1], scale_factors[2]/100.0, 1])
124
+ return scale_matrix @ rotation_matrix
125
+ else:
126
+ # 地毯正常朝向
127
+ scale_matrix = np.diag([scale_factors[0], scale_factors[1], scale_factors[2]/100.0, 1])
128
+ return scale_matrix
129
+
130
+ elif category == "clothes":
131
+ # 衣服：使用均匀缩放（min_scale）
132
+ scale = target_size / mesh_size
133
+ min_scale = min(scale)
134
+ scale_matrix = np.diag([min_scale, min_scale, min_scale, 1])
135
+ return scale_matrix
136
+
137
+ # 默认
138
+ scale = target_size / mesh_size
139
+ return np.diag([scale[0], scale[1], scale[2], 1])
140
+
141
+ def load_mesh_as_trimesh(self, uid: str, use_texture: bool = False) -> trimesh.Trimesh:
142
+ """加载 mesh 并确保返回单个 Trimesh"""
143
+ mesh_or_scene = self.asset_loader.load_canonical_mesh(uid, use_texture=use_texture)
144
+
145
+ if isinstance(mesh_or_scene, trimesh.Scene):
146
+ meshes = []
147
+ for name, geom in mesh_or_scene.geometry.items():
148
+ if isinstance(geom, trimesh.Trimesh):
149
+ # 获取该 geometry 的 transform
150
+ transform = mesh_or_scene.graph.get(name)[0] if name in mesh_or_scene.graph else np.eye(4)
151
+ geom_copy = geom.copy()
152
+ geom_copy.apply_transform(transform)
153
+ meshes.append(geom_copy)
154
+ if meshes:
155
+ return trimesh.util.concatenate(meshes)
156
+ else:
157
+ raise ValueError(f"No valid meshes in scene for {uid}")
158
+ return mesh_or_scene
159
+
160
+ def canonicalize_asset(
161
+ self,
162
+ uid: str,
163
+ bbox: List[float],
164
+ category: str = None,
165
+ save_mesh: bool = False
166
+ ) -> Tuple[Optional[dict], Optional[trimesh.Trimesh]]:
167
+ """
168
+ 使用 2D OBB 规范化单个资产
169
+
170
+ Args:
171
+ uid: model_uid
172
+ bbox: [cx, cy, cz, sx, sy, sz, rz, rx, ry] (ZXY euler order)
173
+ category: 资产类别（用于特殊处理 carpet/clothes）
174
+ save_mesh: 是否保存 mesh
175
+
176
+ Returns:
177
+ (new_layout_info, canonical_mesh)
178
+ """
179
+ try:
180
+ # 1. 加载 canonical mesh（已应用 init_rotation）
181
+ mesh = self.load_mesh_as_trimesh(uid, use_texture=True)
182
+ original_mesh_size = mesh.bounding_box.extents
183
+
184
+ # 2. 计算并应用 scale（考虑 carpet/clothes 特殊处理）
185
+ target_size = np.array(bbox[3:6])
186
+ scale_matrix = self.get_scale_transform_from_rules(
187
+ original_mesh_size, target_size, category or ''
188
+ )
189
+
190
+ # 3. 检测物体是否"正立"
191
+ euler_angles = np.array(bbox[6:9]) # [rz, rx, ry]
192
+ rotation_matrix = trimesh.transformations.euler_matrix(
193
+ euler_angles[0], euler_angles[1], euler_angles[2], axes='rzxy'
194
+ )
195
+
196
+ # 提取 Z 轴方向（物体的"up"方向）
197
+ R = rotation_matrix[:3, :3]
198
+ z_axis = R[:, 2] # 第三列
199
+ tilt_from_vertical = np.arccos(np.clip(abs(z_axis[2]), 0, 1)) # abs 处理倒立情况
200
+ tilt_deg = np.degrees(tilt_from_vertical)
201
+
202
+ # 判断是否正立（tilt < 15°）或倒立（tilt > 165°）
203
+ is_upright = tilt_deg < 15 or tilt_deg > 165
204
+
205
+ if not is_upright:
206
+ # 非正立物体：只应用 scale，保留原始 rot
207
+ # 这种情况物体是"侧躺"的，2D OBB 不适用
208
+ mesh.apply_transform(scale_matrix)
209
+
210
+ # 居中 mesh
211
+ bbox_min, bbox_max = mesh.bounds
212
+ center = (bbox_min + bbox_max) / 2.0
213
+ offset = -center
214
+ translation_matrix = np.eye(4)
215
+ translation_matrix[:3, 3] = offset
216
+ mesh.apply_transform(translation_matrix)
217
+
218
+ result = {
219
+ 'pos': bbox[0:3],
220
+ 'size': mesh.extents.tolist(),
221
+ 'rot': list(euler_angles), # 保留原始 rot！
222
+ 'is_tilted': True,
223
+ 'tilt_deg': tilt_deg
224
+ }
225
+
226
+ # 保存 mesh
227
+ if save_mesh and self.output_dir:
228
+ safe_uid = uid.replace('/', '_').replace('@', '_')
229
+ mesh_filename = f"{safe_uid}.glb"
230
+ mesh_path = os.path.join(self.output_dir, 'meshes', mesh_filename)
231
+ os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
232
+ mesh.export(mesh_path)
233
+ result['mesh_path'] = mesh_path
234
+
235
+ return result, mesh
236
+
237
+ # 4. 正立物体：应用完整变换并用 2D OBB 规范化
238
+ transform = rotation_matrix @ scale_matrix
239
+ mesh.apply_transform(transform)
240
+
241
+ # 5. 【核心】只计算 XY 平面上的 2D OBB
242
+ points_2d = mesh.vertices[:, :2]
243
+
244
+ # 使用 trimesh 的 2D 凸包算法计算最小外接矩形
245
+ from trimesh.bounds import oriented_bounds_2D
246
+ obb_2d_transform, rectangle_extents = oriented_bounds_2D(points_2d)
247
+
248
+ # 提取 2D 旋转角度 (Yaw)
249
+ cos_theta = obb_2d_transform[0, 0]
250
+ sin_theta = obb_2d_transform[1, 0]
251
+ theta = np.arctan2(sin_theta, cos_theta)
252
+
253
+ # 6. 生成规范化 Mesh（只绕 Z 轴旋转，使其轴对齐）
254
+ correction_matrix = np.eye(4)
255
+ correction_matrix[:2, :2] = obb_2d_transform[:2, :2] # 只取旋转部分
256
+
257
+ canonical_mesh = mesh.copy()
258
+ canonical_mesh.apply_transform(correction_matrix)
259
+
260
+ # 7. 归一化位置：XYZ 居中
261
+ bbox_min, bbox_max = canonical_mesh.bounds
262
+ center = (bbox_min + bbox_max) / 2.0
263
+ offset = -center
264
+ translation_matrix = np.eye(4)
265
+ translation_matrix[:3, 3] = offset
266
+ canonical_mesh.apply_transform(translation_matrix)
267
+
268
+ # 8. 计算最终的 Layout 数据
269
+ new_size = canonical_mesh.extents.tolist()
270
+ final_yaw = -theta
271
+ final_pos = [bbox[0], bbox[1], bbox[2]]
272
+
273
+ result = {
274
+ 'pos': final_pos,
275
+ 'size': new_size,
276
+ 'rot': [0.0, 0.0, float(final_yaw)], # 只有 Z 轴旋转！
277
+ 'is_tilted': False,
278
+ 'tilt_deg': tilt_deg
279
+ }
280
+
281
+ # 9. 保存 mesh
282
+ if save_mesh and self.output_dir:
283
+ safe_uid = uid.replace('/', '_').replace('@', '_')
284
+ mesh_filename = f"{safe_uid}.glb"
285
+ mesh_path = os.path.join(self.output_dir, 'meshes', mesh_filename)
286
+ os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
287
+ canonical_mesh.export(mesh_path)
288
+ result['mesh_path'] = mesh_path
289
+
290
+ return result, canonical_mesh
291
+
292
+ except Exception as e:
293
+ print(f"Error canonicalizing {uid}: {e}")
294
+ import traceback
295
+ traceback.print_exc()
296
+ return None, None
297
+
298
+ def analyze_asset(self, uid: str, bbox: List[float], category: str = None) -> dict:
299
+ """分析单个资产"""
300
+ result = {
301
+ 'uid': uid,
302
+ 'original_pos': bbox[0:3],
303
+ 'original_size': bbox[3:6],
304
+ 'original_rot_deg': [np.degrees(r) for r in bbox[6:9]],
305
+ }
306
+
307
+ new_info, _ = self.canonicalize_asset(uid, bbox, category=category, save_mesh=False)
308
+ if new_info:
309
+ result['canonical'] = new_info
310
+ result['canonical_yaw_deg'] = np.degrees(new_info['rot'][2])
311
+ else:
312
+ result['error'] = 'Failed'
313
+
314
+ return result
315
+
316
+
317
+ def load_layout(layout_path: str) -> Tuple[dict, List[dict], str]:
318
+ """加载 layout 并提取资产列表"""
319
+ with open(layout_path, 'r') as f:
320
+ layout = json.load(f)
321
+
322
+ assets = []
323
+
324
+ if isinstance(layout, list):
325
+ # 旧格式
326
+ for i, item in enumerate(layout):
327
+ assets.append({
328
+ 'index': i,
329
+ 'uid': item.get('model_uid', ''),
330
+ 'category': item.get('category', 'unknown'),
331
+ 'description': item.get('description', ''),
332
+ 'bbox': item.get('bbox', [0]*9),
333
+ 'original': item
334
+ })
335
+ return layout, assets, 'list'
336
+ else:
337
+ # 新格式 (functional_zones)
338
+ idx = 0
339
+ for zone_idx, zone in enumerate(layout.get('functional_zones', [])):
340
+ for asset_idx, asset in enumerate(zone.get('assets', [])):
341
+ transform = asset.get('transform', {})
342
+ bbox = (
343
+ transform.get('pos', [0, 0, 0]) +
344
+ transform.get('size', [1, 1, 1]) +
345
+ transform.get('rot', [0, 0, 0])
346
+ )
347
+ assets.append({
348
+ 'index': idx,
349
+ 'zone_idx': zone_idx,
350
+ 'asset_idx': asset_idx,
351
+ 'uid': asset.get('model_id', asset.get('model_uid', '')),
352
+ 'category': asset.get('category', 'unknown'),
353
+ 'description': asset.get('description', ''),
354
+ 'bbox': bbox,
355
+ 'original': asset
356
+ })
357
+ idx += 1
358
+ return layout, assets, 'zones'
359
+
360
+
361
+ def update_layout(layout: dict, assets: List[dict], results: List[dict], format_type: str) -> dict:
362
+ """更新 layout 数据"""
363
+ new_layout = copy.deepcopy(layout)
364
+
365
+ if format_type == 'list':
366
+ for asset, result in zip(assets, results):
367
+ if result:
368
+ idx = asset['index']
369
+ new_layout[idx]['bbox'] = (
370
+ result['pos'] + result['size'] + result['rot']
371
+ )
372
+ if 'mesh_path' in result:
373
+ new_layout[idx]['canonical_mesh'] = result['mesh_path']
374
+ # 如果是通过检索获取的 model_uid，更新它
375
+ if 'retrieved_uid' in result:
376
+ new_layout[idx]['model_uid'] = result['retrieved_uid']
377
+ else:
378
+ for asset, result in zip(assets, results):
379
+ if result:
380
+ zone_idx = asset['zone_idx']
381
+ asset_idx = asset['asset_idx']
382
+ zone = new_layout['functional_zones'][zone_idx]
383
+ zone['assets'][asset_idx]['transform'] = {
384
+ 'pos': result['pos'],
385
+ 'size': result['size'],
386
+ 'rot': result['rot'],
387
+ 'is_tilted': result.get('is_tilted', False),
388
+ 'tilt_deg': result.get('tilt_deg', 0.0)
389
+ }
390
+ if 'mesh_path' in result:
391
+ zone['assets'][asset_idx]['canonical_mesh'] = result['mesh_path']
392
+ # 如果是通过检索获取的 model_uid，更新它
393
+ if 'retrieved_uid' in result:
394
+ zone['assets'][asset_idx]['model_uid'] = result['retrieved_uid']
395
+
396
+ return new_layout
397
+
398
+
399
+ def cmd_test(args):
400
+ """测试单个资产"""
401
+ canonicalizer = Canonicalizer2D()
402
+ layout, assets, _ = load_layout(args.layout_path)
403
+
404
+ if args.asset_index >= len(assets):
405
+ print(f"Error: index {args.asset_index} out of range (0-{len(assets)-1})")
406
+ return
407
+
408
+ asset = assets[args.asset_index]
409
+ print(f"\n{'='*60}")
410
+ print(f"测试资产 #{args.asset_index}: {asset['category']}")
411
+ print(f"{'='*60}")
412
+ print(f"UID: {asset['uid']}")
413
+
414
+ analysis = canonicalizer.analyze_asset(asset['uid'], asset['bbox'], category=asset.get('category'))
415
+
416
+ print(f"\n原始参数:")
417
+ print(f" pos: {analysis['original_pos']}")
418
+ print(f" size: {analysis['original_size']}")
419
+ print(f" rot (deg): {analysis['original_rot_deg']}")
420
+
421
+ if 'canonical' in analysis:
422
+ print(f"\n规范化后:")
423
+ print(f" pos: {analysis['canonical']['pos']}")
424
+ print(f" size: {analysis['canonical']['size']}")
425
+ print(f" rot: {analysis['canonical']['rot']}")
426
+ print(f" yaw (deg): {analysis['canonical_yaw_deg']:.2f}")
427
+ else:
428
+ print(f"\n错误: {analysis.get('error')}")
429
+
430
+
431
+ def cmd_process(args):
432
+ """处理单个场景"""
433
+ os.makedirs(args.output_dir, exist_ok=True)
434
+
435
+ canonicalizer = Canonicalizer2D(output_dir=args.output_dir, enable_retrieval=args.enable_retrieval)
436
+ layout, assets, format_type = load_layout(args.layout_path)
437
+
438
+ print(f"\n{'='*60}")
439
+ print(f"处理场景: {args.layout_path}")
440
+ print(f"{'='*60}")
441
+ print(f"资产数量: {len(assets)}")
442
+
443
+ results = []
444
+ success = fail = retrieved = 0
445
+
446
+ for asset in tqdm(assets, desc="规范化"):
447
+ uid = asset['uid']
448
+ retrieved_uid = None
449
+
450
+ # 如果 uid 为空，尝试使用 description 进行 CLIP 检索
451
+ if not uid and asset.get('description'):
452
+ print(f"\n [{asset['category']}] model_uid 为空，尝试 CLIP 检索...")
453
+ uid = canonicalizer.retrieve_model_uid(
454
+ asset['description'],
455
+ category=asset.get('category')
456
+ )
457
+ if uid:
458
+ retrieved_uid = uid
459
+ retrieved += 1
460
+ else:
461
+ print(f" 检索失败: {asset['description'][:50]}...")
462
+
463
+ if not uid:
464
+ results.append(None)
465
+ fail += 1
466
+ continue
467
+
468
+ result, _ = canonicalizer.canonicalize_asset(
469
+ uid,
470
+ asset['bbox'],
471
+ category=asset.get('category'),
472
+ save_mesh=args.save_meshes
473
+ )
474
+
475
+ # 如果是检索得到的 uid，记录下来
476
+ if result and retrieved_uid:
477
+ result['retrieved_uid'] = retrieved_uid
478
+
479
+ results.append(result)
480
+ if result:
481
+ success += 1
482
+ else:
483
+ fail += 1
484
+
485
+ # 更新并保存 layout
486
+ new_layout = update_layout(layout, assets, results, format_type)
487
+ output_path = os.path.join(args.output_dir, 'layout.json')
488
+ with open(output_path, 'w') as f:
489
+ json.dump(new_layout, f, indent=2)
490
+
491
+ print(f"\n完成! 成功: {success}, 失败: {fail}, CLIP检索: {retrieved}")
492
+ print(f"输出: {output_path}")
493
+
494
+
495
+ def cmd_batch(args):
496
+ """批量处理"""
497
+ input_dir = Path(args.input_dir)
498
+ output_dir = Path(args.output_dir)
499
+
500
+ layout_files = list(input_dir.rglob('layout.json'))
501
+ print(f"找到 {len(layout_files)} 个场景")
502
+
503
+ total_success = total_fail = total_retrieved = 0
504
+
505
+ for layout_path in tqdm(layout_files, desc="处理场景"):
506
+ rel_path = layout_path.parent.relative_to(input_dir)
507
+ scene_output_dir = output_dir / rel_path
508
+
509
+ canonicalizer = Canonicalizer2D(
510
+ output_dir=str(scene_output_dir),
511
+ enable_retrieval=args.enable_retrieval
512
+ )
513
+
514
+ try:
515
+ layout, assets, format_type = load_layout(str(layout_path))
516
+
517
+ results = []
518
+ for asset in assets:
519
+ uid = asset['uid']
520
+ retrieved_uid = None
521
+
522
+ # 如果 uid 为空，尝试使用 description 进行 CLIP 检索
523
+ if not uid and asset.get('description') and args.enable_retrieval:
524
+ uid = canonicalizer.retrieve_model_uid(
525
+ asset['description'],
526
+ category=asset.get('category')
527
+ )
528
+ if uid:
529
+ retrieved_uid = uid
530
+ total_retrieved += 1
531
+
532
+ if not uid:
533
+ results.append(None)
534
+ total_fail += 1
535
+ continue
536
+
537
+ result, _ = canonicalizer.canonicalize_asset(
538
+ uid,
539
+ asset['bbox'],
540
+ category=asset.get('category'),
541
+ save_mesh=args.save_meshes
542
+ )
543
+
544
+ if result and retrieved_uid:
545
+ result['retrieved_uid'] = retrieved_uid
546
+
547
+ results.append(result)
548
+ if result:
549
+ total_success += 1
550
+ else:
551
+ total_fail += 1
552
+
553
+ new_layout = update_layout(layout, assets, results, format_type)
554
+ os.makedirs(scene_output_dir, exist_ok=True)
555
+ with open(scene_output_dir / 'layout.json', 'w') as f:
556
+ json.dump(new_layout, f, indent=2)
557
+
558
+ except Exception as e:
559
+ print(f"\nError: {layout_path}: {e}")
560
+
561
+ print(f"\n{'='*60}")
562
+ print(f"批量处理完成")
563
+ print(f"成功: {total_success}, 失败: {total_fail}, CLIP检索: {total_retrieved}")
564
+ print(f"输出: {output_dir}")
565
+
566
+
567
+ def main():
568
+ parser = argparse.ArgumentParser(description='2D OBB 规范化工具')
569
+ subparsers = parser.add_subparsers(dest='command')
570
+
571
+ # test
572
+ p = subparsers.add_parser('test', help='测试单个资产')
573
+ p.add_argument('layout_path')
574
+ p.add_argument('--asset-index', '-i', type=int, default=0)
575
+
576
+ # process
577
+ p = subparsers.add_parser('process', help='处理单个场景')
578
+ p.add_argument('layout_path')
579
+ p.add_argument('--output-dir', '-o', required=True)
580
+ p.add_argument('--save-meshes', action='store_true')
581
+ p.add_argument('--enable-retrieval', action='store_true',
582
+ help='启用 CLIP 检索（当 model_uid 为空时根据 description 检索）')
583
+
584
+ # batch
585
+ p = subparsers.add_parser('batch', help='批量处理')
586
+ p.add_argument('input_dir')
587
+ p.add_argument('--output-dir', '-o', required=True)
588
+ p.add_argument('--save-meshes', action='store_true')
589
+ p.add_argument('--enable-retrieval', action='store_true',
590
+ help='启用 CLIP 检索（当 model_uid 为空时根据 description 检索）')
591
+
592
+ args = parser.parse_args()
593
+
594
+ if args.command == 'test':
595
+ cmd_test(args)
596
+ elif args.command == 'process':
597
+ cmd_process(args)
598
+ elif args.command == 'batch':
599
+ cmd_batch(args)
600
+ else:
601
+ parser.print_help()
602
+
603
+
604
+ if __name__ == '__main__':
605
+ main()
tools/data_gen/compose_baked_scene.py ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ 从烘焙后的 mesh 组合场景
4
+
5
+ 直接加载 baked_mesh 而不是通过 AssetMeshLoader
6
+ """
7
+
8
+ import os
9
+ import sys
10
+ import json
11
+ import argparse
12
+ import numpy as np
13
+ import trimesh
14
+ from pathlib import Path
15
+
16
+ # 添加路径
17
+ BASE_DIR = Path(__file__).resolve().parents[2]
18
+ sys.path.insert(0, str(BASE_DIR / "InternScenes" / "InternScenes_Real2Sim"))
19
+
20
+
21
+ def compose_baked_scene(layout_path: str, output_path: str):
22
+ """
23
+ 从烘焙后的 layout 组合场景
24
+
25
+ 烘焙后的 layout 每个 asset 有 baked_mesh 字段指向 GLB 文件
26
+ transform 中的 rot=[0,0,0]，pos 和 size 是最终值
27
+ """
28
+ with open(layout_path, 'r') as f:
29
+ layout = json.load(f)
30
+
31
+ scene = trimesh.Scene()
32
+
33
+ # 提取所有资产
34
+ if isinstance(layout, list):
35
+ assets = layout
36
+ else:
37
+ assets = []
38
+ for zone in layout.get('functional_zones', []):
39
+ assets.extend(zone.get('assets', []))
40
+
41
+ success_count = 0
42
+ fail_count = 0
43
+
44
+ for i, asset in enumerate(assets):
45
+ mesh_path = asset.get('baked_mesh') or asset.get('canonical_mesh')
46
+ if not mesh_path or not os.path.exists(mesh_path):
47
+ print(f"Warning: No baked mesh for asset {i}: {asset.get('category')}")
48
+ fail_count += 1
49
+ continue
50
+
51
+ try:
52
+ # 加载烘焙后的 mesh
53
+ mesh = trimesh.load(mesh_path)
54
+
55
+ # 获取 transform
56
+ transform = asset.get('transform', {})
57
+ pos = np.array(transform.get('pos', [0, 0, 0]))
58
+
59
+ # 创建平移矩阵（rot 和 scale 已经烘焙进 mesh）
60
+ translation = np.eye(4)
61
+ translation[:3, 3] = pos
62
+
63
+ # 添加到场景
64
+ category = asset.get('category', 'unknown')
65
+ node_name = f"{i}_{category}"
66
+
67
+ if isinstance(mesh, trimesh.Scene):
68
+ for name, geom in mesh.geometry.items():
69
+ geom_transform = mesh.graph.get(name)[0] if name in mesh.graph else np.eye(4)
70
+ combined = translation @ geom_transform
71
+ scene.add_geometry(geom, transform=combined, node_name=f"{node_name}/{name}")
72
+ else:
73
+ scene.add_geometry(mesh, transform=translation, node_name=node_name)
74
+
75
+ success_count += 1
76
+
77
+ except Exception as e:
78
+ print(f"Error loading mesh for asset {i}: {e}")
79
+ fail_count += 1
80
+
81
+ print(f"加载了 {success_count} 个资产，失败 {fail_count} 个")
82
+
83
+ # 导出
84
+ scene.export(output_path)
85
+ print(f"导出到: {output_path}")
86
+
87
+ return scene
88
+
89
+
90
+ def main():
91
+ parser = argparse.ArgumentParser(description='从烘焙后的 mesh 组合场景')
92
+ parser.add_argument('layout_path', help='烘焙后的 layout.json 路径')
93
+ parser.add_argument('--output', '-o', default='baked_scene.glb', help='输出 GLB 路径')
94
+
95
+ args = parser.parse_args()
96
+ compose_baked_scene(args.layout_path, args.output)
97
+
98
+
99
+ if __name__ == '__main__':
100
+ main()
tools/data_gen/test_render_compare.py ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """
3
+ 场景渲染对比测试
4
+
5
+ 使用 compose_scenes.py 导出 GLB，然后用 blender_renderer.py 渲染
6
+ 对比原始 layout 和重分配后的 layout 的渲染效果
7
+ """
8
+
9
+ import os
10
+ import sys
11
+ import json
12
+ import argparse
13
+ import subprocess
14
+ from pathlib import Path
15
+
16
+ # 添加路径
17
+ BASE_DIR = Path(__file__).resolve().parents[2]
18
+ sys.path.insert(0, str(BASE_DIR / "InternScenes" / "InternScenes_Real2Sim"))
19
+
20
+ from compose_scenes import SceneComposer, AssetMeshLoader
21
+
22
+
23
+ def compose_and_render(
24
+ layout_path: str,
25
+ output_dir: str,
26
+ scene_name: str = "test_scene",
27
+ use_texture: bool = True,
28
+ render: bool = True
29
+ ):
30
+ """
31
+ 从 layout.json 导出 GLB 并渲染
32
+
33
+ Args:
34
+ layout_path: layout.json 路径
35
+ output_dir: 输出目录
36
+ scene_name: 场景名称
37
+ use_texture: 是否使用纹理
38
+ render: 是否渲染
39
+ """
40
+ os.makedirs(output_dir, exist_ok=True)
41
+
42
+ # 读取 layout
43
+ with open(layout_path, 'r') as f:
44
+ layout = json.load(f)
45
+
46
+ # 转换为 instance_infos 格式
47
+ if isinstance(layout, list):
48
+ instance_infos = layout
49
+ else:
50
+ # 新格式：从 functional_zones 提取
51
+ instance_infos = []
52
+ for zone in layout.get('functional_zones', []):
53
+ for asset in zone.get('assets', []):
54
+ # 转换格式
55
+ transform = asset.get('transform', {})
56
+ pos = transform.get('pos', [0, 0, 0])
57
+ size = transform.get('size', [1, 1, 1])
58
+ rot = transform.get('rot', [0, 0, 0])
59
+
60
+ # 构建 bbox: [cx, cy, cz, sx, sy, sz, rx, ry, rz]
61
+ bbox = pos + size + rot
62
+
63
+ instance_info = {
64
+ 'category': asset.get('category', 'unknown'),
65
+ 'model_uid': asset.get('model_id', asset.get('model_uid', '')),
66
+ 'bbox': bbox
67
+ }
68
+ instance_infos.append(instance_info)
69
+
70
+ print(f"加载了 {len(instance_infos)} 个资产")
71
+
72
+ # 导出 GLB
73
+ glb_path = os.path.join(output_dir, f"{scene_name}.glb")
74
+ print(f"导出 GLB 到: {glb_path}")
75
+
76
+ composer = SceneComposer()
77
+ try:
78
+ scene = composer.compose_scene_from_instance_infos(
79
+ instance_infos,
80
+ glb_path,
81
+ use_texture=use_texture,
82
+ bbox_data_key="bbox"
83
+ )
84
+ print(f"✓ GLB 导出成功: {glb_path}")
85
+ except Exception as e:
86
+ print(f"✗ GLB 导出失败: {e}")
87
+ return None
88
+
89
+ # 渲染
90
+ if render:
91
+ render_path = os.path.join(output_dir, f"{scene_name}.png")
92
+ print(f"渲染到: {render_path}")
93
+
94
+ blender_script = str(BASE_DIR / "InternScenes" / "InternScenes_Real2Sim" / "blender_renderer.py")
95
+
96
+ cmd = [
97
+ "python", blender_script,
98
+ "--input", glb_path,
99
+ "--output", render_path,
100
+ "--width", "1600",
101
+ "--height", "900",
102
+ "--view-mode", "topdown",
103
+ "--samples", "64",
104
+ "--engine", "CYCLES"
105
+ ]
106
+
107
+ try:
108
+ result = subprocess.run(cmd, capture_output=True, text=True, timeout=300)
109
+ if result.returncode == 0:
110
+ print(f"✓ 渲染成功: {render_path}")
111
+ else:
112
+ print(f"✗ 渲染失败: {result.stderr}")
113
+ return glb_path
114
+ except subprocess.TimeoutExpired:
115
+ print("✗ 渲染超时")
116
+ return glb_path
117
+ except Exception as e:
118
+ print(f"✗ 渲染错误: {e}")
119
+ return glb_path
120
+
121
+ return glb_path
122
+
123
+
124
+ def compare_layouts(
125
+ original_layout: str,
126
+ reassigned_layout: str,
127
+ output_dir: str,
128
+ use_texture: bool = True
129
+ ):
130
+ """
131
+ 对比原始和重分配后的 layout 渲染效果
132
+ """
133
+ os.makedirs(output_dir, exist_ok=True)
134
+
135
+ print("="*60)
136
+ print("原始 Layout")
137
+ print("="*60)
138
+ compose_and_render(
139
+ original_layout,
140
+ output_dir,
141
+ scene_name="original",
142
+ use_texture=use_texture
143
+ )
144
+
145
+ print("\n" + "="*60)
146
+ print("重分配后 Layout")
147
+ print("="*60)
148
+ compose_and_render(
149
+ reassigned_layout,
150
+ output_dir,
151
+ scene_name="reassigned",
152
+ use_texture=use_texture
153
+ )
154
+
155
+ print("\n" + "="*60)
156
+ print(f"对比结果已保存到: {output_dir}")
157
+ print(" - original.glb / original.png")
158
+ print(" - reassigned.glb / reassigned.png")
159
+ print("="*60)
160
+
161
+
162
+ def main():
163
+ parser = argparse.ArgumentParser(description='场景渲染对比测试')
164
+
165
+ subparsers = parser.add_subparsers(dest='command')
166
+
167
+ # compose 命令
168
+ p_compose = subparsers.add_parser('compose', help='从 layout 导出 GLB')
169
+ p_compose.add_argument('layout_path', help='layout.json 路径')
170
+ p_compose.add_argument('--output-dir', default='/tmp/compose_test', help='输出目录')
171
+ p_compose.add_argument('--name', default='scene', help='场景名称')
172
+ p_compose.add_argument('--no-texture', action='store_true', help='不使用纹理')
173
+ p_compose.add_argument('--no-render', action='store_true', help='不渲染')
174
+
175
+ # compare 命令
176
+ p_compare = subparsers.add_parser('compare', help='对比两个 layout 的渲染效果')
177
+ p_compare.add_argument('original', help='原始 layout.json')
178
+ p_compare.add_argument('reassigned', help='重分配后的 layout.json')
179
+ p_compare.add_argument('--output-dir', default='/tmp/compare_test', help='输出目录')
180
+ p_compare.add_argument('--no-texture', action='store_true', help='不使用纹理')
181
+
182
+ args = parser.parse_args()
183
+
184
+ if args.command == 'compose':
185
+ compose_and_render(
186
+ args.layout_path,
187
+ args.output_dir,
188
+ scene_name=args.name,
189
+ use_texture=not args.no_texture,
190
+ render=not args.no_render
191
+ )
192
+ elif args.command == 'compare':
193
+ compare_layouts(
194
+ args.original,
195
+ args.reassigned,
196
+ args.output_dir,
197
+ use_texture=not args.no_texture
198
+ )
199
+ else:
200
+ parser.print_help()
201
+
202
+
203
+ if __name__ == '__main__':
204
+ main()