├── .gitignore ├── LICENSE ├── Obj2SchemticConverter.py ├── README.md ├── config └── block_info.json ├── convert_obj2schematic.ipynb ├── empty_schematic ├── images ├── sample_obj_view.png └── sample_schematic_view.png └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | 132 | data/ 133 | .vscode/ 134 | 135 | *.schematic 136 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 takeshi noguchi 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Obj2SchemticConverter.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pandas as pd 3 | import numpy as np 4 | import json 5 | from tqdm import tqdm 6 | import traceback 7 | import cv2 8 | 9 | import pywavefront 10 | from nbtlib import nbt, tag 11 | 12 | 13 | class Obj2SchematicConverter(object): 14 | def __init__(self, obj_path, output_dir, height_max, width_max): 15 | ''' 16 | constructor 17 | ''' 18 | # DataFrame Column 19 | self.coordinates = ['x', 'y', 'z'] 20 | self.colors = ['r', 'g', 'b'] 21 | self.denorm_colors = ['denorm_r', 'denorm_g', 'denorm_b'] 22 | self.coor_id = 'coordinate_id' 23 | 24 | if output_dir == None: 25 | self.output_dir = os.path.join( 26 | os.path.dirname(__file__), 'output') 27 | else: 28 | self.output_dir = output_dir 29 | self.HEIGHT_MAX = height_max 30 | self.WIDTH_MAX = width_max 31 | self.typical_colors = {} 32 | self.blocks = [] 33 | self.data = [] 34 | self.out_schem = os.path.splitext(os.path.basename(obj_path))[ 35 | 0] + '.schematic' 36 | 37 | # create .obj DataFrame 38 | self.df = self._create_obj_df(obj_path) 39 | 40 | self._preprocess_data() 41 | self._load_config() 42 | 43 | def _create_obj_df(self, obj_path): 44 | scene = pywavefront.Wavefront(obj_path) 45 | if len(scene.mtllibs) != 0: 46 | vertices = self._analyze_mtl(obj_path, scene) 47 | else: 48 | vertices = scene.vertices 49 | return pd.DataFrame( 50 | vertices, columns=self.coordinates + self.colors) 51 | 52 | def _analyze_mtl(self, obj_path, scene): 53 | is_first = True 54 | for _, material in scene.materials.items(): 55 | if is_first: 56 | is_first = False 57 | else: 58 | raise Exception('material should be single') 59 | img_name = material.texture.file_name 60 | img_path = os.path.join(os.path.dirname(obj_path), img_name) 61 | if not os.path.exists(img_path): 62 | raise Exception('mapping file does not exists') 63 | img = cv2.imread(img_path) 64 | img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 65 | h, w, _ = img.shape 66 | 67 | vt_colors = [] 68 | for v in self._get_whole_lines(obj_path): 69 | contents = v.split(' ') 70 | if contents[0] == 'vt': 71 | y = float(contents[1]) * (w - 1) 72 | x = float(contents[2]) * (h - 1) 73 | vt_colors.append(img_rgb[(w-1) - int(x), int(y)]) 74 | 75 | new_vertices = [] 76 | COLOR_MAX = 255 77 | for index, v in enumerate(scene.vertices): 78 | rgb = vt_colors[index] / COLOR_MAX 79 | new_vertices.append( 80 | (v[0], v[1], v[2], rgb[0], rgb[1], rgb[2])) 81 | return new_vertices 82 | 83 | def _get_whole_lines(self, path): 84 | with open(path, 'r') as f: 85 | return f.readlines() 86 | 87 | def _preprocess_data(self): 88 | ''' 89 | preprocess .obj data 90 | ''' 91 | print('start data preprocessing...') 92 | # coordinates 93 | self._move_to_zero_point() 94 | self._zoom_coordinates() 95 | self._round_down_coordinates() 96 | # color 97 | self._denormalize_color() 98 | 99 | self._add_coordinate_id_col() 100 | 101 | def _move_to_zero_point(self): 102 | ''' 103 | x,y,z座標を0始点に変換 104 | ''' 105 | self.df[self.coordinates] = self.df[self.coordinates] - \ 106 | self.df[self.coordinates].apply(min) 107 | 108 | def _zoom_coordinates(self): 109 | ''' 110 | 指定された最大値になるように座標値を拡大する 111 | ''' 112 | max_series = self.df[self.coordinates].apply(max) 113 | max_val = max_series.max() 114 | max_idx = max_series.idxmax() 115 | if max_idx in ['x', 'z']: 116 | zoom_ratio = self.WIDTH_MAX / max_val 117 | else: 118 | zoom_ratio = self.HEIGHT_MAX / max_val 119 | self.df[self.coordinates] = self.df[self.coordinates] * zoom_ratio 120 | 121 | def _round_down_coordinates(self): 122 | self.df[self.coordinates] = self.df[self.coordinates].apply( 123 | np.floor).astype(int) 124 | 125 | def _denormalize_color(self): 126 | ''' 127 | 正規化された色情報を復元する 128 | ''' 129 | COLOR_MAX = 255 130 | self.df[self.denorm_colors] = self.df[self.colors] * COLOR_MAX 131 | 132 | def _add_coordinate_id_col(self): 133 | ''' 134 | ボクセル位置を表す識別子用の列を追加 135 | ''' 136 | self.df[self.coor_id] = self.df[self.coordinates].apply( 137 | self._gen_coordinates_id, axis=1) 138 | 139 | def _gen_coordinates_id(self, row): 140 | ''' 141 | DataFrameの各行をコンマ区切りも文字列に変換する 142 | ''' 143 | return ','.join([str(v) for v in row.values]) 144 | 145 | def _load_config(self): 146 | with open(os.path.join(os.path.dirname(__file__), 'config/block_info.json'), 'r', encoding='utf-8') as f: 147 | self.config = json.load(f) 148 | 149 | def _calc_nearest_block(self, obj_color, config): 150 | best = float('inf') 151 | for c in config: 152 | p_color = c['COLOR'] 153 | diff = obj_color - p_color 154 | diff_dist = sum(diff ** 2) 155 | if best > diff_dist: 156 | best = diff_dist 157 | color_data = c 158 | if best == 0: 159 | break 160 | return color_data 161 | 162 | def convert(self): 163 | print('start converting...') 164 | voxels = self._calc_voxel_color() 165 | self._convert_to_block(voxels) 166 | 167 | def _calc_voxel_color(self): 168 | ''' 169 | 各ボクセルの色の代表値を決める 170 | ''' 171 | for coor_id_val in tqdm(set(self.df[self.coor_id])): 172 | target = self.df[self.df[self.coor_id] == 173 | coor_id_val][self.colors + self.denorm_colors] 174 | self.typical_colors[coor_id_val] = target.mean() 175 | 176 | # 177 | bounds_e = self.df[self.coordinates].apply(max) 178 | voxels = np.zeros( 179 | list((bounds_e + 1).apply(np.ceil).values.astype(int)) + [3]) 180 | for coor, color in tqdm(self.typical_colors.items()): 181 | x, y, z = [int(v) for v in coor.split(',')] 182 | voxels[x, y, z] = color[self.denorm_colors].values 183 | return voxels 184 | 185 | def _convert_to_block(self, voxels): 186 | self.width, self.height, self.length = voxels.shape[:3] 187 | for y in tqdm(range(self.height)): 188 | for z in range(self.length): 189 | for x in range(self.width): 190 | if (voxels[x, y, z] == 0).all(): 191 | # air 192 | self.blocks.append(0) 193 | self.data.append(0) 194 | else: 195 | color_data = self._calc_nearest_block( 196 | voxels[x, y, z], self.config) 197 | self.blocks.append(color_data['BLOCK_ID']) 198 | self.data.append(color_data['DATA']) 199 | 200 | def output(self): 201 | schem = nbt.load(os.path.join( 202 | os.path.dirname(__file__), 'empty_schematic')) 203 | out_path = os.path.join(self.output_dir, self.out_schem) 204 | os.makedirs(self.output_dir, exist_ok=True) 205 | self._packing(schem) 206 | self._save_schematic(out_path, schem) 207 | 208 | def _packing(self, schematic_obj): 209 | schematic_obj.root['Blocks'] = tag.ByteArray(self.blocks) 210 | schematic_obj.root['Data'] = tag.ByteArray(self.data) 211 | schematic_obj.root['Width'] = tag.Short(self.width) 212 | schematic_obj.root['Length'] = tag.Short(self.length) 213 | schematic_obj.root['Height'] = tag.Short(self.height) 214 | 215 | def _save_schematic(self, out_file, schematic_obj): 216 | schematic_obj.save(out_file) 217 | print('successfully finished: ', out_file) 218 | 219 | 220 | def get_args(): 221 | import argparse 222 | parser = argparse.ArgumentParser() 223 | parser.add_argument("obj_file", 224 | help="input .obj file path") 225 | parser.add_argument('--output_dir', type=str, default=None, 226 | help='Output dir of generated .schematic file.') 227 | parser.add_argument('--h_max', type=int, default=100, 228 | help='Max height of converted schematic object.') 229 | parser.add_argument('--w_max', type=int, default=100, 230 | help='Max width(length) of converted schematic object.') 231 | args = parser.parse_args() 232 | return args 233 | 234 | 235 | if __name__ == '__main__': 236 | try: 237 | print('checking arguments...') 238 | args = get_args() 239 | print('start converting...') 240 | converter = Obj2SchematicConverter( 241 | args.obj_file, args.output_dir, args.h_max, args.w_max) 242 | converter.convert() 243 | converter.output() 244 | except Exception as e: 245 | print('convert failed: ', e) 246 | print(traceback.format_exc()) 247 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # obj2schematic 2 | 3 | Convert [Wavefront `.obj` file](#Supported-`.obj`-format) to [Minecraft `.schematic` file](https://minecraft.gamepedia.com/Schematic_file_format) 4 | 5 | **input `.obj`** 6 | ![obj file](images/sample_obj_view.png) 7 | 8 | **output `.schematic`** 9 | ![schematic file](images/sample_schematic_view.png) 10 | 11 | ## Supported `.obj` format 12 | 13 | ### 1. geometric vertices with `(x, y, z, r, g, b)` 14 | 15 | From [Wavefront .obj file Wiki](https://en.wikipedia.org/wiki/Wavefront_.obj_file) 16 | 17 | > Geometric vertex 18 | A vertex can be specified in a line starting with the letter v. That is followed by (x,y,z[,w]) coordinates. W is optional and defaults to 1.0. **Some applications support vertex colors, by putting red, green and blue values after x y and z. The color values range from 0 to 1.** 19 | 20 | 21 | *`.obj` sample* 22 | 23 | ```txt 24 | # v (x) (y) (z) (red) (green) (blue) 25 | v -0.3438 -0.1250 0.0312 0.4563 0.3357 0.2624 26 | v -0.3438 -0.1250 0.0296 0.4552 0.3348 0.2617 27 | v -0.3438 -0.1256 0.0312 0.4548 0.3343 0.2613 28 | v -0.3438 -0.1250 0.0369 0.4579 0.3367 0.2631 29 | v -0.3444 -0.1172 0.0234 0.4736 0.3508 0.2737 30 | v -0.3438 -0.1172 0.0186 0.4751 0.3515 0.2738 31 | v -0.3438 -0.1223 0.0234 0.4610 0.3400 0.2655 32 | v -0.3448 -0.1172 0.0312 0.4752 0.3522 0.2750 33 | v -0.3438 -0.1247 0.0391 0.4596 0.3380 0.2640 34 | v -0.3446 -0.1172 0.0391 0.4747 0.3515 0.2741 35 | : 36 | ``` 37 | 38 | ### 2. `.obj` with `.mtl` 39 | 40 | *`.obj` sample* 41 | 42 | ```txt 43 | # OBJ File: 44 | 45 | mtllib ./result.mtl 46 | 47 | # vertices: 48 | v -9.1863 9.1863 -5.0000 49 | v -8.8947 9.1863 -5.0000 50 | v -8.6904 9.2796 -6.0667 51 | : 52 | 53 | # vertice textures: 54 | vt 0.0000 1.0000 55 | vt 0.0159 1.0000 56 | vt 0.0317 1.0000 57 | : 58 | 59 | # vertice normals: 60 | vn -0.0000 0.0000 1.0000 61 | vn -0.0000 0.0000 1.0000 62 | vn -0.0000 0.0000 1.0000 63 | : 64 | 65 | # faces: 66 | 67 | usemtl tex 68 | f 1/1/1 65/65/65 4097/4097/4097 69 | f 2/2/2 66/66/66 4098/4098/4098 70 | f 3/3/3 67/67/67 4099/4099/4099 71 | : 72 | ``` 73 | 74 | *`.mtl` sample* 75 | 76 | ```txt 77 | newmtl tex 78 | Ka 1.0000 1.0000 1.0000 79 | Kd 1.0000 1.0000 1.0000 80 | Ks 0.0000 0.0000 0.0000 81 | d 1.0 82 | illum 0 83 | map_Kd ./img.png 84 | ``` 85 | 86 | ## How to Use 87 | 88 | ### 1. Clone this repo 89 | 90 | ```bash 91 | $ git clone https://github.com/takecx/obj2schematic.git 92 | $ cd obj2schematic 93 | ``` 94 | 95 | ### 2. Create Environment 96 | 97 | You should create python environment using `requirements.txt`. 98 | 99 | ```bash 100 | $ conda create -n obj2schematic python=3.7 101 | $ conda activate obj2schematic 102 | $ pip install -r requirements.txt 103 | ``` 104 | 105 | ### 3. Prepare input `.obj` file 106 | 107 | Put input `.obj` file to `./data/` dir for example. 108 | 109 | ### 4. Run script 110 | 111 | Run `Obj2SchematicConverter.py` with some arguments. 112 | 113 | ```bash 114 | $ python Obj2SchemticConverter.py (.obj_file) --output_dir (output dir) --h_max (height max value) --w_max (width max value) 115 | ``` 116 | 117 | #### Arguments 118 | 119 | 1. input `.obj` file (**required**) 120 | 2. `--output_dir` : Output dir of generated .schematic file (optional) 121 | 3. `--h_max` : max height of converted schematic (optional) 122 | 4. `--w_max` : max width(length) of converted schematic (optional) 123 | 124 | For example, 125 | 126 | - input `.obj` file : `./data/sample_data.obj` 127 | - `--output_dir` : `./output/` 128 | - `--h_max` : `100` 129 | - `--w_max` : `150` 130 | 131 | then, you run 132 | 133 | ```bash 134 | $ python Obj2SchemticConverter.py ./data/sample_data.obj --output_dir ./output/ --h_max 100 --w_max 150 135 | ``` 136 | 137 | ### 5. Import `.schematic` to your Minecraft World!! 138 | 139 | Import generated `.schematic` file to your own Minecraft World using [WorldEdit mod](https://www.curseforge.com/minecraft/mc-mods/worldedit). 140 | -------------------------------------------------------------------------------- /config/block_info.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "COLOR_NAME": "GRASS", 4 | "COLOR": [ 5 | 127, 6 | 178, 7 | 56 8 | ], 9 | "BLOCK_NAME": "草ブロック", 10 | "BLOCK_ID": 2, 11 | "DATA": 0, 12 | "USE": true 13 | }, 14 | { 15 | "COLOR_NAME": "SAND", 16 | "COLOR": [ 17 | 247, 18 | 233, 19 | 163 20 | ], 21 | "BLOCK_NAME": "シラカバの木材", 22 | "BLOCK_ID": 5, 23 | "DATA": 2, 24 | "USE": true 25 | }, 26 | { 27 | "COLOR_NAME": "CLOTH", 28 | "COLOR": [ 29 | 199, 30 | 199, 31 | 199 32 | ], 33 | "BLOCK_NAME": "クモの巣", 34 | "BLOCK_ID": 30, 35 | "DATA": 0, 36 | "USE": true 37 | }, 38 | { 39 | "COLOR_NAME": "TNT", 40 | "COLOR": [ 41 | 255, 42 | 0, 43 | 0 44 | ], 45 | "BLOCK_NAME": "レッドストーンブロック", 46 | "BLOCK_ID": 152, 47 | "DATA": 0, 48 | "USE": true 49 | }, 50 | { 51 | "COLOR_NAME": "ICE", 52 | "COLOR": [ 53 | 160, 54 | 160, 55 | 255 56 | ], 57 | "BLOCK_NAME": "氷塊", 58 | "BLOCK_ID": 174, 59 | "DATA": 0, 60 | "USE": true 61 | }, 62 | { 63 | "COLOR_NAME": "IRON", 64 | "COLOR": [ 65 | 167, 66 | 167, 67 | 167 68 | ], 69 | "BLOCK_NAME": "鉄ブロック", 70 | "BLOCK_ID": 42, 71 | "DATA": 0, 72 | "USE": true 73 | }, 74 | { 75 | "COLOR_NAME": "FOLIAGE", 76 | "COLOR": [ 77 | 0, 78 | 124, 79 | 0 80 | ], 81 | "BLOCK_NAME": "オークの葉", 82 | "BLOCK_ID": 18, 83 | "DATA": 0, 84 | "USE": true 85 | }, 86 | { 87 | "COLOR_NAME": "SNOW", 88 | "COLOR": [ 89 | 255, 90 | 255, 91 | 255 92 | ], 93 | "BLOCK_NAME": "雪", 94 | "BLOCK_ID": 80, 95 | "DATA": 0, 96 | "USE": true 97 | }, 98 | { 99 | "COLOR_NAME": "CLAY", 100 | "COLOR": [ 101 | 164, 102 | 168, 103 | 184 104 | ], 105 | "BLOCK_NAME": "粘土", 106 | "BLOCK_ID": 82, 107 | "DATA": 0, 108 | "USE": true 109 | }, 110 | { 111 | "COLOR_NAME": "DIRT", 112 | "COLOR": [ 113 | 151, 114 | 109, 115 | 77 116 | ], 117 | "BLOCK_NAME": "ジャングルの木材", 118 | "BLOCK_ID": 5, 119 | "DATA": 3, 120 | "USE": true 121 | }, 122 | { 123 | "COLOR_NAME": "STONE", 124 | "COLOR": [ 125 | 112, 126 | 112, 127 | 112 128 | ], 129 | "BLOCK_NAME": "石", 130 | "BLOCK_ID": 1, 131 | "DATA": 0, 132 | "USE": true 133 | }, 134 | { 135 | "COLOR_NAME": "WATER", 136 | "COLOR": [ 137 | 64, 138 | 64, 139 | 255 140 | ], 141 | "BLOCK_NAME": "水", 142 | "BLOCK_ID": 9, 143 | "DATA": 0, 144 | "USE": true 145 | }, 146 | { 147 | "COLOR_NAME": "WOOD", 148 | "COLOR": [ 149 | 143, 150 | 119, 151 | 72 152 | ], 153 | "BLOCK_NAME": "オークの木材", 154 | "BLOCK_ID": 5, 155 | "DATA": 0, 156 | "USE": true 157 | }, 158 | { 159 | "COLOR_NAME": "QUARTZ", 160 | "COLOR": [ 161 | 255, 162 | 252, 163 | 245 164 | ], 165 | "BLOCK_NAME": "シラカバの原木", 166 | "BLOCK_ID": 17, 167 | "DATA": 6, 168 | "USE": true 169 | }, 170 | { 171 | "COLOR_NAME": "ADOBE", 172 | "COLOR": [ 173 | 216, 174 | 127, 175 | 51 176 | ], 177 | "BLOCK_NAME": "橙色の羊毛", 178 | "BLOCK_ID": 35, 179 | "DATA": 1, 180 | "USE": true 181 | }, 182 | { 183 | "COLOR_NAME": "MAGENTA", 184 | "COLOR": [ 185 | 178, 186 | 76, 187 | 216 188 | ], 189 | "BLOCK_NAME": "赤紫色の羊毛", 190 | "BLOCK_ID": 35, 191 | "DATA": 2, 192 | "USE": true 193 | }, 194 | { 195 | "COLOR_NAME": "LIGHT_BLUE", 196 | "COLOR": [ 197 | 102, 198 | 153, 199 | 216 200 | ], 201 | "BLOCK_NAME": "空色の羊毛", 202 | "BLOCK_ID": 35, 203 | "DATA": 3, 204 | "USE": true 205 | }, 206 | { 207 | "COLOR_NAME": "YELLOW", 208 | "COLOR": [ 209 | 229, 210 | 229, 211 | 51 212 | ], 213 | "BLOCK_NAME": "黄色の羊毛", 214 | "BLOCK_ID": 35, 215 | "DATA": 4, 216 | "USE": true 217 | }, 218 | { 219 | "COLOR_NAME": "LIME", 220 | "COLOR": [ 221 | 127, 222 | 204, 223 | 25 224 | ], 225 | "BLOCK_NAME": "黄緑色の羊毛", 226 | "BLOCK_ID": 35, 227 | "DATA": 5, 228 | "USE": true 229 | }, 230 | { 231 | "COLOR_NAME": "PINK", 232 | "COLOR": [ 233 | 242, 234 | 127, 235 | 165 236 | ], 237 | "BLOCK_NAME": "桃色の羊毛", 238 | "BLOCK_ID": 35, 239 | "DATA": 6, 240 | "USE": true 241 | }, 242 | { 243 | "COLOR_NAME": "GRAY", 244 | "COLOR": [ 245 | 76, 246 | 76, 247 | 76 248 | ], 249 | "BLOCK_NAME": "灰色の羊毛", 250 | "BLOCK_ID": 35, 251 | "DATA": 7, 252 | "USE": true 253 | }, 254 | { 255 | "COLOR_NAME": "SILVER", 256 | "COLOR": [ 257 | 153, 258 | 153, 259 | 153 260 | ], 261 | "BLOCK_NAME": "薄灰色の羊毛", 262 | "BLOCK_ID": 35, 263 | "DATA": 8, 264 | "USE": true 265 | }, 266 | { 267 | "COLOR_NAME": "CYAN", 268 | "COLOR": [ 269 | 76, 270 | 127, 271 | 153 272 | ], 273 | "BLOCK_NAME": "青緑色の羊毛", 274 | "BLOCK_ID": 35, 275 | "DATA": 9, 276 | "USE": true 277 | }, 278 | { 279 | "COLOR_NAME": "PURPLE", 280 | "COLOR": [ 281 | 127, 282 | 63, 283 | 178 284 | ], 285 | "BLOCK_NAME": "紫色の羊毛", 286 | "BLOCK_ID": 35, 287 | "DATA": 10, 288 | "USE": true 289 | }, 290 | { 291 | "COLOR_NAME": "BLUE", 292 | "COLOR": [ 293 | 51, 294 | 76, 295 | 178 296 | ], 297 | "BLOCK_NAME": "青色の羊毛", 298 | "BLOCK_ID": 35, 299 | "DATA": 11, 300 | "USE": true 301 | }, 302 | { 303 | "COLOR_NAME": "BROWN", 304 | "COLOR": [ 305 | 102, 306 | 76, 307 | 51 308 | ], 309 | "BLOCK_NAME": "茶色の羊毛", 310 | "BLOCK_ID": 35, 311 | "DATA": 12, 312 | "USE": true 313 | }, 314 | { 315 | "COLOR_NAME": "GREEN", 316 | "COLOR": [ 317 | 102, 318 | 127, 319 | 51 320 | ], 321 | "BLOCK_NAME": "緑色の羊毛", 322 | "BLOCK_ID": 35, 323 | "DATA": 13, 324 | "USE": true 325 | }, 326 | { 327 | "COLOR_NAME": "RED", 328 | "COLOR": [ 329 | 153, 330 | 51, 331 | 51 332 | ], 333 | "BLOCK_NAME": "赤色の羊毛", 334 | "BLOCK_ID": 35, 335 | "DATA": 14, 336 | "USE": true 337 | }, 338 | { 339 | "COLOR_NAME": "BLACK", 340 | "COLOR": [ 341 | 25, 342 | 25, 343 | 25 344 | ], 345 | "BLOCK_NAME": "黒色の羊毛", 346 | "BLOCK_ID": 35, 347 | "DATA": 15, 348 | "USE": true 349 | }, 350 | { 351 | "COLOR_NAME": "GOLD", 352 | "COLOR": [ 353 | 250, 354 | 238, 355 | 77 356 | ], 357 | "BLOCK_NAME": "金ブロック", 358 | "BLOCK_ID": 41, 359 | "DATA": 0, 360 | "USE": true 361 | }, 362 | { 363 | "COLOR_NAME": "DIAMOND", 364 | "COLOR": [ 365 | 92, 366 | 219, 367 | 213 368 | ], 369 | "BLOCK_NAME": "プリズマリンレンガ", 370 | "BLOCK_ID": 168, 371 | "DATA": 1, 372 | "USE": true 373 | }, 374 | { 375 | "COLOR_NAME": "LAPIS", 376 | "COLOR": [ 377 | 74, 378 | 128, 379 | 255 380 | ], 381 | "BLOCK_NAME": "ラピスラズリブロック", 382 | "BLOCK_ID": 22, 383 | "DATA": 0, 384 | "USE": true 385 | }, 386 | { 387 | "COLOR_NAME": "EMERALD", 388 | "COLOR": [ 389 | 0, 390 | 217, 391 | 58 392 | ], 393 | "BLOCK_NAME": "エメラルドブロック", 394 | "BLOCK_ID": 133, 395 | "DATA": 0, 396 | "USE": true 397 | }, 398 | { 399 | "COLOR_NAME": "OBSIDIAN", 400 | "COLOR": [ 401 | 129, 402 | 86, 403 | 49 404 | ], 405 | "BLOCK_NAME": "マツの木材", 406 | "BLOCK_ID": 5, 407 | "DATA": 1, 408 | "USE": true 409 | }, 410 | { 411 | "COLOR_NAME": "NETHERRACK", 412 | "COLOR": [ 413 | 112, 414 | 2, 415 | 0 416 | ], 417 | "BLOCK_NAME": "ネザーラック", 418 | "BLOCK_ID": 87, 419 | "DATA": 0, 420 | "USE": true 421 | }, 422 | { 423 | "COLOR_NAME": "WHITE_STAINED_HARDENED_CLAY", 424 | "COLOR": [ 425 | 209, 426 | 177, 427 | 161 428 | ], 429 | "BLOCK_NAME": "白色のテラコッタ", 430 | "BLOCK_ID": 159, 431 | "DATA": 0, 432 | "USE": true 433 | }, 434 | { 435 | "COLOR_NAME": "ORANGE_STAINED_HARDENED_CLAY", 436 | "COLOR": [ 437 | 159, 438 | 82, 439 | 36 440 | ], 441 | "BLOCK_NAME": "橙色のテラコッタ", 442 | "BLOCK_ID": 159, 443 | "DATA": 1, 444 | "USE": true 445 | }, 446 | { 447 | "COLOR_NAME": "MAGENTA_STAINED_HARDENED_CLAY", 448 | "COLOR": [ 449 | 149, 450 | 87, 451 | 108 452 | ], 453 | "BLOCK_NAME": "赤紫色のテラコッタ", 454 | "BLOCK_ID": 159, 455 | "DATA": 2, 456 | "USE": true 457 | }, 458 | { 459 | "COLOR_NAME": "LIGHT_BLUE_STAINED_HARDENED_CLAY", 460 | "COLOR": [ 461 | 112, 462 | 108, 463 | 138 464 | ], 465 | "BLOCK_NAME": "空色のテラコッタ", 466 | "BLOCK_ID": 159, 467 | "DATA": 3, 468 | "USE": true 469 | }, 470 | { 471 | "COLOR_NAME": "YELLOW_STAINED_HARDENED_CLAY", 472 | "COLOR": [ 473 | 186, 474 | 133, 475 | 36 476 | ], 477 | "BLOCK_NAME": "黄色のテラコッタ", 478 | "BLOCK_ID": 159, 479 | "DATA": 4, 480 | "USE": true 481 | }, 482 | { 483 | "COLOR_NAME": "LIME_STAINED_HARDENED_CLAY", 484 | "COLOR": [ 485 | 103, 486 | 117, 487 | 53 488 | ], 489 | "BLOCK_NAME": "黄緑色のテラコッタ", 490 | "BLOCK_ID": 159, 491 | "DATA": 5, 492 | "USE": true 493 | }, 494 | { 495 | "COLOR_NAME": "PINK_STAINED_HARDENED_CLAY", 496 | "COLOR": [ 497 | 160, 498 | 77, 499 | 78 500 | ], 501 | "BLOCK_NAME": "桃色のテラコッタ", 502 | "BLOCK_ID": 159, 503 | "DATA": 6, 504 | "USE": true 505 | }, 506 | { 507 | "COLOR_NAME": "GRAY_STAINED_HARDENED_CLAY", 508 | "COLOR": [ 509 | 57, 510 | 41, 511 | 35 512 | ], 513 | "BLOCK_NAME": "灰色のテラコッタ", 514 | "BLOCK_ID": 159, 515 | "DATA": 7, 516 | "USE": true 517 | }, 518 | { 519 | "COLOR_NAME": "SILVER_STAINED_HARDENED_CLAY", 520 | "COLOR": [ 521 | 135, 522 | 107, 523 | 98 524 | ], 525 | "BLOCK_NAME": "薄灰色のテラコッタ", 526 | "BLOCK_ID": 159, 527 | "DATA": 8, 528 | "USE": true 529 | }, 530 | { 531 | "COLOR_NAME": "CYAN_STAINED_HARDENED_CLAY", 532 | "COLOR": [ 533 | 87, 534 | 92, 535 | 92 536 | ], 537 | "BLOCK_NAME": "青緑色のテラコッタ", 538 | "BLOCK_ID": 159, 539 | "DATA": 9, 540 | "USE": true 541 | }, 542 | { 543 | "COLOR_NAME": "PURPLE_STAINED_HARDENED_CLAY", 544 | "COLOR": [ 545 | 122, 546 | 73, 547 | 88 548 | ], 549 | "BLOCK_NAME": "紫色のテラコッタ", 550 | "BLOCK_ID": 159, 551 | "DATA": 10, 552 | "USE": true 553 | }, 554 | { 555 | "COLOR_NAME": "BLUE_STAINED_HARDENED_CLAY", 556 | "COLOR": [ 557 | 76, 558 | 62, 559 | 92 560 | ], 561 | "BLOCK_NAME": "青色のテラコッタ", 562 | "BLOCK_ID": 159, 563 | "DATA": 11, 564 | "USE": true 565 | }, 566 | { 567 | "COLOR_NAME": "BROWN_STAINED_HARDENED_CLAY", 568 | "COLOR": [ 569 | 76, 570 | 50, 571 | 35 572 | ], 573 | "BLOCK_NAME": "茶色のテラコッタ", 574 | "BLOCK_ID": 159, 575 | "DATA": 12, 576 | "USE": true 577 | }, 578 | { 579 | "COLOR_NAME": "GREEN_STAINED_HARDENED_CLAY", 580 | "COLOR": [ 581 | 76, 582 | 82, 583 | 42 584 | ], 585 | "BLOCK_NAME": "緑色のテラコッタ", 586 | "BLOCK_ID": 159, 587 | "DATA": 13, 588 | "USE": true 589 | }, 590 | { 591 | "COLOR_NAME": "RED_STAINED_HARDENED_CLAY", 592 | "COLOR": [ 593 | 142, 594 | 60, 595 | 46 596 | ], 597 | "BLOCK_NAME": "赤色のテラコッタ", 598 | "BLOCK_ID": 159, 599 | "DATA": 14, 600 | "USE": true 601 | }, 602 | { 603 | "COLOR_NAME": "BLACK_STAINED_HARDENED_CLAY", 604 | "COLOR": [ 605 | 37, 606 | 22, 607 | 16 608 | ], 609 | "BLOCK_NAME": "黒色のテラコッタ", 610 | "BLOCK_ID": 159, 611 | "DATA": 15, 612 | "USE": true 613 | } 614 | ] -------------------------------------------------------------------------------- /convert_obj2schematic.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pywavefront" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "# parameters\n", 19 | "HEIGHT_MAX = 150\n", 20 | "WIDTH_MAX = 100\n", 21 | "OUTPUT_FILE = 'result.schematic'" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "# データ読み込み" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": null, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "data = 'data/sample_data.obj'" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": null, 43 | "metadata": {}, 44 | "outputs": [], 45 | "source": [ 46 | "scene = pywavefront.Wavefront(data)" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "len(scene.vertices)" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": null, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "import pandas as pd" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": null, 70 | "metadata": {}, 71 | "outputs": [], 72 | "source": [ 73 | "coordinates = ['x','y','z']\n", 74 | "colors = ['r','g','b']\n", 75 | "df = pd.DataFrame(scene.vertices,columns=coordinates + colors)\n", 76 | "df.describe()" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "# `(x,y,z)`座標を0起点に変換" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": null, 89 | "metadata": {}, 90 | "outputs": [], 91 | "source": [ 92 | "df[coordinates] = df[coordinates] - df[coordinates].apply(min)\n", 93 | "'''\n", 94 | "for coor in coordinates:\n", 95 | " df[coor] = df[coor] - min(df[coor])\n", 96 | "'''\n", 97 | "df[coordinates].describe()" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "# `(x,y,z)`座標の拡大倍率を決定する" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": null, 110 | "metadata": {}, 111 | "outputs": [], 112 | "source": [ 113 | "%%time\n", 114 | "max_series = df[coordinates].apply(max)\n", 115 | "max_val = max_series.max()\n", 116 | "max_idx = max_series.idxmax()\n", 117 | "'''\n", 118 | "for coor in coordinates:\n", 119 | " if max_val < max(df[coor]):\n", 120 | " max_val = max(df[coor])\n", 121 | " max_idx = coor\n", 122 | "'''\n", 123 | "print('col:',max_idx)\n", 124 | "print('max_val:',max_val)" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": null, 130 | "metadata": {}, 131 | "outputs": [], 132 | "source": [ 133 | "if max_idx in ['x','z']:\n", 134 | " zoom_ratio = WIDTH_MAX / max_val\n", 135 | "else:\n", 136 | " zoom_ratio = HEIGHT_MAX / max_val\n", 137 | "print('zoom_ratio:',zoom_ratio)" 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": null, 143 | "metadata": {}, 144 | "outputs": [], 145 | "source": [ 146 | "df[coordinates] = df[coordinates] * zoom_ratio\n", 147 | "#for coor in coordinates:\n", 148 | "# df[coor] = df[coor] * zoom_ratio\n", 149 | "df[coordinates].describe()" 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "# 外接矩形領域の終点" 157 | ] 158 | }, 159 | { 160 | "cell_type": "code", 161 | "execution_count": null, 162 | "metadata": {}, 163 | "outputs": [], 164 | "source": [ 165 | "bounds_e = df[coordinates].apply(max)\n", 166 | "print(bounds_e)" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "# 色情報を復元\n", 174 | "値が`1.0`の場合に最大値(`255`)となるように変換 \n", 175 | "※これであってるのかは不明" 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "execution_count": null, 181 | "metadata": {}, 182 | "outputs": [], 183 | "source": [ 184 | "COLOR_MAX = 255\n", 185 | "norm_colors = ['norm_r','norm_g','norm_b']\n", 186 | "df[norm_colors] = df[colors] * COLOR_MAX\n", 187 | "#for color in colors:\n", 188 | "# df[color] = df[color] * COLOR_MAX\n", 189 | "df[colors + norm_colors].describe()" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "# 座標値を整数に変換（切り捨て）" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": null, 202 | "metadata": {}, 203 | "outputs": [], 204 | "source": [ 205 | "import numpy as np\n", 206 | "df[coordinates] = df[coordinates].apply(np.floor).astype(int)\n", 207 | "df[coordinates].describe()" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "metadata": {}, 213 | "source": [ 214 | "# ボクセル位置を表す識別子用の列を追加" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": null, 220 | "metadata": {}, 221 | "outputs": [], 222 | "source": [ 223 | "def gen_coordinates_id(row):\n", 224 | " return ','.join([str(v) for v in row.values])" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": null, 230 | "metadata": {}, 231 | "outputs": [], 232 | "source": [ 233 | "%%time\n", 234 | "coor_id = 'coordinate_id'\n", 235 | "df[coor_id] = df[coordinates].apply(gen_coordinates_id,axis=1)\n", 236 | "df[coor_id]" 237 | ] 238 | }, 239 | { 240 | "cell_type": "markdown", 241 | "metadata": {}, 242 | "source": [ 243 | "# 各ボクセルの色の代表値を決める" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": null, 249 | "metadata": {}, 250 | "outputs": [], 251 | "source": [ 252 | "%%time\n", 253 | "from tqdm import tqdm\n", 254 | "typical_colors = {}\n", 255 | "for coor_id_val in tqdm(set(df[coor_id])):\n", 256 | " target = df[df[coor_id] == coor_id_val][colors + norm_colors]\n", 257 | " typical_colors[coor_id_val] = target.mean()\n", 258 | "#typical_colors" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": null, 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "voxel = np.zeros(list((bounds_e + 1).apply(np.ceil).values.astype(int)) + [3])\n", 268 | "voxel.shape" 269 | ] 270 | }, 271 | { 272 | "cell_type": "code", 273 | "execution_count": null, 274 | "metadata": { 275 | "tags": [ 276 | "outputPrepend", 277 | "outputPrepend", 278 | "outputPrepend", 279 | "outputPrepend", 280 | "outputPrepend", 281 | "outputPrepend", 282 | "outputPrepend", 283 | "outputPrepend", 284 | "outputPrepend", 285 | "outputPrepend" 286 | ] 287 | }, 288 | "outputs": [], 289 | "source": [ 290 | "for coor,color in tqdm(typical_colors.items()):\n", 291 | " x,y,z = [int(v) for v in coor.split(',')]\n", 292 | " voxel[x,y,z] = color[norm_colors].values" 293 | ] 294 | }, 295 | { 296 | "cell_type": "markdown", 297 | "metadata": {}, 298 | "source": [ 299 | "# 結果表示" 300 | ] 301 | }, 302 | { 303 | "cell_type": "code", 304 | "execution_count": null, 305 | "metadata": {}, 306 | "outputs": [], 307 | "source": [ 308 | "#%matplotlib inline\n", 309 | "#%matplotlib notebook\n", 310 | "import matplotlib.pyplot as plt\n", 311 | "import numpy as np\n", 312 | "from matplotlib.image import imread\n", 313 | "from mpl_toolkits.mplot3d import Axes3D" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": null, 319 | "metadata": {}, 320 | "outputs": [], 321 | "source": [ 322 | "fig = plt.figure(figsize=(8,20))\n", 323 | "ax = fig.add_subplot(111, projection='3d')\n", 324 | "for coor,color in tqdm(typical_colors.items()):\n", 325 | " x,y,z = [int(v) for v in coor.split(',')]\n", 326 | " ax.plot(x,z,y,marker='.',color=color[colors])\n", 327 | "#ax.scatter3D(np.ravel(x), np.ravel(z), np.ravel(y),s=10,marker='.')\n", 328 | "ax.set_title(\"result\")\n", 329 | "plt.show()" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": {}, 336 | "outputs": [], 337 | "source": [ 338 | "import plotly.plotly as py\n", 339 | "import plotly.graph_objs as go\n", 340 | "import os\n", 341 | "from plotly.offline import iplot, init_notebook_mode" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": null, 347 | "metadata": {}, 348 | "outputs": [], 349 | "source": [ 350 | "#import plotly.graph_objects as go\n", 351 | "import numpy as np\n", 352 | "\n", 353 | "# Helix equation\n", 354 | "t = np.linspace(0, 10, 50)\n", 355 | "x, y, z = np.cos(t), np.sin(t), t\n", 356 | "\n", 357 | "fig = go.Figure(data=[go.Scatter3d(x=df['x'], y=df['y'], z=df['z'],\n", 358 | " mode='markers')])\n", 359 | "fig.show()" 360 | ] 361 | }, 362 | { 363 | "cell_type": "markdown", 364 | "metadata": {}, 365 | "source": [ 366 | "# Schematicファイルに変換" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": null, 372 | "metadata": {}, 373 | "outputs": [], 374 | "source": [ 375 | "blocks = []\n", 376 | "data = []\n", 377 | "width,height,length = voxel.shape[:3]\n", 378 | "print(width,height,length)" 379 | ] 380 | }, 381 | { 382 | "cell_type": "code", 383 | "execution_count": null, 384 | "metadata": {}, 385 | "outputs": [], 386 | "source": [ 387 | "def _packing(schematic_obj):\n", 388 | " schematic_obj.root['Blocks'] = tag.ByteArray(blocks)\n", 389 | " schematic_obj.root['Data'] = tag.ByteArray(data)\n", 390 | " schematic_obj.root['Width'] = tag.Short(width)\n", 391 | " schematic_obj.root['Length'] = tag.Short(length)\n", 392 | " schematic_obj.root['Height'] = tag.Short(height)\n", 393 | "\n", 394 | "def _save_schematic(out_file,schematic_obj):\n", 395 | " schematic_obj.save(out_file)\n", 396 | "\n", 397 | "def output(out_path,schematic_obj):\n", 398 | " _packing(schematic_obj)\n", 399 | " _save_schematic(out_path,schematic_obj)" 400 | ] 401 | }, 402 | { 403 | "cell_type": "code", 404 | "execution_count": null, 405 | "metadata": {}, 406 | "outputs": [], 407 | "source": [ 408 | "def calc_nearest_block(obj_color,config):\n", 409 | " best = float('inf')\n", 410 | " for c in config:\n", 411 | " p_color = c['COLOR']\n", 412 | " diff = obj_color - p_color\n", 413 | " diff_dist = sum(diff ** 2)\n", 414 | " if best > diff_dist:\n", 415 | " best = diff_dist\n", 416 | " color_data = c\n", 417 | " if best == 0:\n", 418 | " break\n", 419 | " return color_data\n" 420 | ] 421 | }, 422 | { 423 | "cell_type": "code", 424 | "execution_count": null, 425 | "metadata": {}, 426 | "outputs": [], 427 | "source": [ 428 | "# 設定ファイルを読み込み\n", 429 | "import json\n", 430 | "with open('config/block_info.json', 'r', encoding='utf-8') as f:\n", 431 | " config = json.load(f)\n", 432 | "# config" 433 | ] 434 | }, 435 | { 436 | "cell_type": "markdown", 437 | "metadata": {}, 438 | "source": [ 439 | "## [Schematic File Format](https://minecraft.gamepedia.com/Schematic_file_format)\n", 440 | "\n", 441 | "> Indices for the Blocks and Data arrays are ordered YZX - that is, the X coordinate varies the fastest. \n", 442 | "\n", 443 | "> Blocks: Block IDs defining the terrain. 8 bits per block. Sorted by height (bottom to top) then length then width—the index of the block at X,Y,Z is (Y×length + Z)×width + X.\n", 444 | "\n", 445 | "`X`は`width`、`Y`は`height`、`Z`は`length`に対応する。 \n", 446 | "`Blocks`と`Data`は`X`方向への走査後に、`Z`方向にうつり、その後`Y`方向に移動する。" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": null, 452 | "metadata": { 453 | "tags": [ 454 | "outputPrepend", 455 | "outputPrepend", 456 | "outputPrepend", 457 | "outputPrepend", 458 | "outputPrepend", 459 | "outputPrepend", 460 | "outputPrepend", 461 | "outputPrepend", 462 | "outputPrepend", 463 | "outputPrepend", 464 | "outputPrepend", 465 | "outputPrepend", 466 | "outputPrepend", 467 | "outputPrepend", 468 | "outputPrepend", 469 | "outputPrepend", 470 | "outputPrepend", 471 | "outputPrepend", 472 | "outputPrepend", 473 | "outputPrepend", 474 | "outputPrepend", 475 | "outputPrepend", 476 | "outputPrepend", 477 | "outputPrepend", 478 | "outputPrepend", 479 | "outputPrepend", 480 | "outputPrepend", 481 | 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4940 | "outputPrepend", 4941 | "outputPrepend", 4942 | "outputPrepend", 4943 | "outputPrepend", 4944 | "outputPrepend", 4945 | "outputPrepend", 4946 | "outputPrepend", 4947 | "outputPrepend", 4948 | "outputPrepend", 4949 | "outputPrepend", 4950 | "outputPrepend", 4951 | "outputPrepend", 4952 | "outputPrepend", 4953 | "outputPrepend", 4954 | "outputPrepend", 4955 | "outputPrepend", 4956 | "outputPrepend", 4957 | "outputPrepend", 4958 | "outputPrepend", 4959 | "outputPrepend", 4960 | "outputPrepend", 4961 | "outputPrepend", 4962 | "outputPrepend", 4963 | "outputPrepend", 4964 | "outputPrepend", 4965 | "outputPrepend", 4966 | "outputPrepend", 4967 | "outputPrepend", 4968 | "outputPrepend", 4969 | "outputPrepend", 4970 | "outputPrepend" 4971 | ] 4972 | }, 4973 | "outputs": [], 4974 | "source": [ 4975 | "X,Y,Z = voxel.shape[:3]\n", 4976 | "for y in tqdm(range(Y)):\n", 4977 | " for z in range(Z):\n", 4978 | " for x in range(X): \n", 4979 | " if (voxel[x,y,z] == 0).all():\n", 4980 | " blocks.append(0)\n", 4981 | " data.append(0)\n", 4982 | " else:\n", 4983 | " color_data = calc_nearest_block(voxel[x,y,z],config)\n", 4984 | " blocks.append(color_data['BLOCK_ID'])\n", 4985 | " data.append(color_data['DATA'])" 4986 | ] 4987 | }, 4988 | { 4989 | "cell_type": "code", 4990 | "execution_count": null, 4991 | "metadata": {}, 4992 | "outputs": [], 4993 | "source": [ 4994 | "set(blocks)" 4995 | ] 4996 | }, 4997 | { 4998 | "cell_type": "code", 4999 | "execution_count": null, 5000 | "metadata": {}, 5001 | "outputs": [], 5002 | "source": [ 5003 | "from nbtlib import nbt,tag\n", 5004 | "schem = nbt.load('data/original')\n", 5005 | "out_path = os.path.join('output',OUTPUT_FILE)\n", 5006 | "os.makedirs('output',exist_ok=True)\n", 5007 | "output(out_path,schem)" 5008 | ] 5009 | }, 5010 | { 5011 | "cell_type": "code", 5012 | "execution_count": null, 5013 | "metadata": {}, 5014 | "outputs": [], 5015 | "source": [] 5016 | } 5017 | ], 5018 | "metadata": { 5019 | "language_info": { 5020 | "codemirror_mode": { 5021 | "name": "ipython", 5022 | "version": 3 5023 | }, 5024 | "file_extension": ".py", 5025 | "mimetype": "text/x-python", 5026 | "name": "python", 5027 | "nbconvert_exporter": "python", 5028 | "pygments_lexer": "ipython3", 5029 | "version": "3.7.7-final" 5030 | }, 5031 | "orig_nbformat": 2, 5032 | "kernelspec": { 5033 | "name": "python37764bitobj2schematiccondaddadbb272fa7485789f23bb0e8dbad92", 5034 | "display_name": "Python 3.7.7 64-bit ('obj2schematic': conda)" 5035 | } 5036 | }, 5037 | "nbformat": 4, 5038 | "nbformat_minor": 2 5039 | } -------------------------------------------------------------------------------- /empty_schematic: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/takecx/obj2schematic/0e7208e7317a0ca08ed9b2897be4de14091c9659/empty_schematic -------------------------------------------------------------------------------- /images/sample_obj_view.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/takecx/obj2schematic/0e7208e7317a0ca08ed9b2897be4de14091c9659/images/sample_obj_view.png -------------------------------------------------------------------------------- /images/sample_schematic_view.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/takecx/obj2schematic/0e7208e7317a0ca08ed9b2897be4de14091c9659/images/sample_schematic_view.png -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | appnope==0.1.0 2 | astroid==2.4.2 3 | attrs==19.3.0 4 | autopep8==1.5.3 5 | backcall==0.2.0 6 | bleach==3.1.5 7 | certifi==2020.6.20 8 | chardet==3.0.4 9 | cycler==0.10.0 10 | decorator==4.4.2 11 | defusedxml==0.6.0 12 | entrypoints==0.3 13 | hmmlearn==0.2.3 14 | idna==2.10 15 | importlib-metadata==1.7.0 16 | ipykernel==5.3.4 17 | ipython==7.16.1 18 | ipython-genutils==0.2.0 19 | ipywidgets==7.5.1 20 | isort==4.3.21 21 | jedi==0.17.2 22 | Jinja2==2.11.2 23 | jsonschema==3.2.0 24 | jupyter==1.0.0 25 | jupyter-client==6.1.6 26 | jupyter-console==6.1.0 27 | jupyter-core==4.6.3 28 | kiwisolver==1.2.0 29 | lazy-object-proxy==1.4.3 30 | MarkupSafe==1.1.1 31 | matplotlib==3.3.0 32 | mccabe==0.6.1 33 | mistune==0.8.4 34 | nbconvert==5.6.1 35 | nbformat==5.0.7 36 | nbtlib==1.7.0 37 | notebook==6.0.3 38 | numpy==1.19.1 39 | packaging==20.4 40 | pandas==1.0.5 41 | pandocfilters==1.4.2 42 | parso==0.7.1 43 | pexpect==4.8.0 44 | pickleshare==0.7.5 45 | Pillow==7.2.0 46 | plotly==3.10.0 47 | prometheus-client==0.8.0 48 | prompt-toolkit==3.0.5 49 | ptyprocess==0.6.0 50 | pycodestyle==2.6.0 51 | Pygments==2.6.1 52 | pylint==2.5.3 53 | pyparsing==2.4.7 54 | pyrsistent==0.16.0 55 | python-dateutil==2.8.1 56 | pytz==2020.1 57 | PyWavefront==1.3.2 58 | pyzmq==19.0.1 59 | qtconsole==4.7.5 60 | QtPy==1.9.0 61 | requests==2.24.0 62 | retrying==1.3.3 63 | Send2Trash==1.5.0 64 | six==1.15.0 65 | terminado==0.8.3 66 | testpath==0.4.4 67 | toml==0.10.1 68 | tornado==6.0.4 69 | tqdm==4.48.0 70 | traitlets==4.3.3 71 | typed-ast==1.4.1 72 | urllib3==1.25.10 73 | wcwidth==0.2.5 74 | webencodings==0.5.1 75 | widgetsnbextension==3.5.1 76 | wrapt==1.12.1 77 | zipp==3.1.0 78 | --------------------------------------------------------------------------------