├── LICENSE ├── README.md ├── app.py ├── apps ├── __init__.py ├── annotator.py ├── config.py └── uploader.py ├── assets ├── bWLwgP.css ├── jquery-3.3.1.min.js ├── lasso-polygon.png ├── lasso.png └── rect.png ├── demo └── demo.gif ├── index.py └── requirements.txt /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Sean P. Leary 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # dash-image-annotator 2 | 3 | Image Annotator Web-app using Plotly-Dash, MongoDB, and MLflow 4 | 5 | ## Demo: 6 | ![dash-image-annotator demo](demo/demo.gif) 7 | 8 | ## Main Task List: 9 | 10 | - [x] multi-page web-app 11 | - [x] image uploader to external MongoDB 12 | - [x] ingest selected images 13 | - [x] ingest zipped images 14 | - [x] annotator 15 | - [x] query/display images from external MongoDB 16 | - [x] manual annotation (save to external MongoDB) 17 | - [x] box 18 | - [x] editable 19 | - [x] free-hand lasso 20 | - [x] opened/closed 21 | - [x] polygon-lasso (Ctrl+MouseClick) 22 | - [x] opened/closed 23 | - [x] DeepLearning model prediction (pyfunc models on MLflow server) 24 | - [x] one image at a time 25 | - [x] batch process 26 | - [ ] Dockerfile 27 | - [ ] Tests 28 | - [ ] Jupyter example notebooks: 29 | - [ ] database interaction 30 | - [ ] create train/validation splits with masks 31 | - [ ] upload images with pre-existing masks 32 | 33 | ## Built With: 34 | 35 | 1. python (version=3.6.7) 36 | 2. [Plotly-Dash](https://dash.plot.ly/) 37 | 3. [Visdcc](https://github.com/jimmybow/visdcc) 38 | 4. [MongoDB/GridFS](https://api.mongodb.com/python/current/api/gridfs/index.html) 39 | 5. [MLflow](https://mlflow.org/) 40 | 6. [Pillow](https://pillow.readthedocs.io/en/stable/) 41 | 42 | ## Step-By-Step Guide: 43 | 44 | 1. `$ conda create -n dash_img_app python=3.6.7` 45 | 2. `$ pip install -r requirements.txt` 46 | 3. set env variables shown in apps/config.py 47 | 1. MLFLOW_URI variable is optional 48 | 4. `$ pyhton index.py` 49 | 5. click **Navigate to uploader** link 50 | 1. drag and drop or select images or zipped images 51 | 6. click **Navigate to annotator** link 52 | 7. **select number of images** 53 | 8. **select annotation_label_name** 54 | 1. optional annotation label-name keyword filter 55 | 2. add **!** to keyword for logical NOT 56 | 9. click **Download** button 57 | 1. **Select Images** datatable will be populated with filenames in MongoDB 58 | 10. click a row's radio button in **Select Images** datatable to display images 59 | 11. Select manual annoation buttons for **box**. **free-hand lasso**, and **polygon-lasso** 60 | 1. additional button info: 61 | 1. **polygon-lasso** requires the `Ctrl` keyboard button to be held down continuously for each mouse click. release `Ctrl` button upon completion 62 | 2. only **boxes** can be edited 63 | 1. toggle **Edit boxes True** after drawing boxes to make them editable 64 | 2. boxes will appear dotted while editable 65 | 3. toggle **Edit boxes False** to stop edit mode 66 | 2. toggle **Close lasso** to connect starting and ending points with a staight line 67 | 3. toggle **Open lasso** to not connect starting and ending points 68 | 12. annotation label names can be set in two ways: 69 | 1. edit **Select/Edit Labels** datatable labels and colors 70 | 1. select a row's radio button before drawing annotation 71 | 2. click the legend to edit a lable name for a trace and `Enter` on keyboard 72 | 13. traces can be hidden by clicking on their respective legends 73 | 14. hidden traces can be removed by clicking the **Remove Hidden Traces** button 74 | 15. annotations can be saved to the MongoDB by clicking the **Save** button 75 | 76 | TODO: add mlflow model instructions 77 | 78 | ### References: 79 | 80 | 1. [Example: Upload and Download Files with Plotly Dash](https://docs.faculty.ai/user-guide/apps/examples/dash_file_upload_download.html) 81 | 2. [Visdcc](https://github.com/jimmybow/visdcc) 82 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import dash 5 | 6 | 7 | app = dash.Dash(__name__) 8 | server = app.server 9 | app.config.suppress_callback_exceptions = True 10 | -------------------------------------------------------------------------------- /apps/__init__.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | -------------------------------------------------------------------------------- /apps/annotator.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import re 5 | import base64 6 | import datetime 7 | import urllib.parse 8 | from io import BytesIO, StringIO 9 | import pandas as pd 10 | import numpy as np 11 | from PIL import Image as PImage 12 | from nested_lookup import nested_lookup 13 | 14 | from pymongo import MongoClient 15 | from bson.objectid import ObjectId 16 | import gridfs 17 | 18 | import mlflow.pyfunc 19 | import mlflow 20 | 21 | import dash 22 | from dash.dependencies import Input, Output, State 23 | import dash_core_components as dcc 24 | import dash_html_components as html 25 | import dash_table 26 | import dash_daq as daq 27 | import plotly.graph_objs as go 28 | import visdcc 29 | 30 | from apps import config 31 | from app import app 32 | 33 | 34 | client = MongoClient(config.MONGODB_CONNECT_STRING) 35 | db = client[config.MONGODB_DATABASE] 36 | fs = gridfs.GridFS(db) 37 | 38 | 39 | if config.MLFLOW_URI is not None: 40 | mlflow.tracking.set_tracking_uri(config.MLFLOW_URI) 41 | 42 | 43 | df_select_init = pd.DataFrame() 44 | df_select_init['filename'] = [[]] * 1 45 | df_select_init['_id'] = [[]] * 1 46 | df_select_init['content_type'] = [[]] * 1 47 | df_select_init['comments'] = [[]] * 1 48 | df_labels_init = pd.DataFrame(config.DEFAULT_LABELS) 49 | 50 | 51 | layout = html.Div([ 52 | html.Div([ 53 | html.Div([ 54 | dcc.Store(id='store-shapes'), 55 | 56 | dcc.Store(id='polygon-data'), 57 | 58 | visdcc.Run_js(id='javascript-ctrl-click', run="$('#graph-image').Graph()"), 59 | visdcc.Run_js(id='javascript-ctrl-keyup', run="$('#graph-image').Graph()"), 60 | visdcc.Run_js(id='javascript-drag-color', run="$('#graph-image').Graph()"), 61 | 62 | html.Br(), 63 | 64 | html.Div([ 65 | html.Button( 66 | id='button-rect', 67 | n_clicks=0, 68 | title='rectangle marquee tool', 69 | style={ 70 | 'background-image': 'url(../assets/rect.png)', 71 | 'background-repeat': 'no-repeat', 72 | 'background-size': 'contain', 73 | 'background-position': 'center', 74 | 'height': '50px', 75 | 'width': '50px' 76 | } 77 | ), 78 | html.Button( 79 | id='button-lasso', 80 | n_clicks=0, 81 | title='lasso tool', 82 | style={ 83 | 'background-image': 'url(../assets/lasso.png)', 84 | 'background-repeat': 'no-repeat', 85 | 'background-size': 'contain', 86 | 'background-position': 'center', 87 | 'height': '50px', 88 | 'width': '50px' 89 | } 90 | ), 91 | html.Button( 92 | id='button-lasso-polygon', 93 | n_clicks=0, 94 | title='lasso-polygon tool (Ctrl+Click)', 95 | style={ 96 | 'background-image': 'url(../assets/lasso-polygon.png)', 97 | 'background-repeat': 'no-repeat', 98 | 'background-size': 'contain', 99 | 'background-position': 'center', 100 | 'height': '50px', 101 | 'width': '50px' 102 | } 103 | ), 104 | html.Div([ 105 | html.Div( 106 | id='booleanswitch-lasso-output', 107 | children='Close lasso', 108 | style={'textAlign':'center'} 109 | ), 110 | daq.BooleanSwitch( 111 | id='daq-booleanswitch-lasso', 112 | on=False 113 | ), 114 | html.Div( 115 | id='booleanswitch-edit-output', 116 | children='Edit off', 117 | style={'textAlign':'center'} 118 | ), 119 | daq.BooleanSwitch( 120 | id='daq-booleanswitch-edit', 121 | on=False 122 | ) 123 | ], style={'position':'relative', 'float':'right'}), 124 | ], style={'display':'inline-block'}), 125 | 126 | 127 | html.Div('Select Images', style={'color': 'blue', 'fontSize': 14}), 128 | html.Div([ 129 | dash_table.DataTable( 130 | id='datatable-filenames', 131 | columns=[{'name': i, 'id': i} for i in df_select_init.columns[0:-1]], 132 | style_cell={'textAlign': 'left'}, 133 | fill_width=True, 134 | data=df_select_init.to_dict('records'), 135 | editable=True, 136 | sort_action='native', 137 | sort_mode='multi', 138 | row_selectable='single', 139 | row_deletable=False, 140 | selected_rows=[], 141 | page_action='native', 142 | page_current=0, 143 | page_size=10 144 | ) 145 | ], style={'overflowX': 'scroll'}), 146 | html.Button( 147 | children='Remove Hidden Traces', 148 | id='button-remove-traces', 149 | title='remove hidden traces', 150 | n_clicks=0 151 | ), 152 | html.Button( 153 | children='Save', 154 | id='button-save', 155 | title='save meta-data to database', 156 | n_clicks=0 157 | ), 158 | html.Div(id='report-save', children=''), 159 | 160 | html.Br(), 161 | 162 | html.Div('Select/Edit Labels', style={'color': 'blue', 'fontSize': 14}), 163 | html.Div([ 164 | dash_table.DataTable( 165 | id='datatable-labels', 166 | columns=[ 167 | {'name': i, 'id': i, 'deletable': False} for i in df_labels_init.columns 168 | ], 169 | style_header={ 170 | 'backgroundColor': 'white', 171 | 'fontWeight': 'bold' 172 | }, 173 | style_cell={'textAlign': 'left'}, 174 | fill_width=True, 175 | data=df_labels_init.to_dict('records'), 176 | editable=True, 177 | sort_action='native', 178 | sort_mode='multi', 179 | row_selectable='single', 180 | row_deletable=False, 181 | selected_rows=[0] 182 | ) 183 | ], style={'maxHeight': '200', 'overflowY': 'scroll', 'overflowX': 'scroll'}), 184 | 185 | html.Br(), 186 | html.Br(), 187 | 188 | html.Button( 189 | children='Connect MLflow', 190 | id='button-mlflow-connect', 191 | title='connect to MLflow', 192 | n_clicks=0 193 | ), 194 | html.Div(children='select experiment'), 195 | dcc.Dropdown( 196 | id='dropdown-select-exp' 197 | ), 198 | 199 | html.Div(children='select run'), 200 | dcc.Dropdown( 201 | id='dropdown-select-run', 202 | style={'font-size': '13px'} 203 | ), 204 | 205 | html.Button( 206 | children='Batch Model', 207 | id='button-mlflow-batch', 208 | title='Batch Model', 209 | n_clicks=0 210 | ), 211 | html.Button( 212 | children='Single Selection Model', 213 | id='button-mlflow-single', 214 | title='Batch Model', 215 | n_clicks=0 216 | ), 217 | html.Div(id='report-model', children=''), 218 | 219 | html.Br(), 220 | 221 | html.Div( 222 | id='div-meta-download', 223 | children='Download Metadata from Database', 224 | style={'color': 'black', 'fontSize': 16} 225 | ), 226 | 227 | html.Div(children='select annotation_label_name'), 228 | dcc.Input(id='input-annotation-labels', type='text', value=''), 229 | 230 | html.Div(children='select number of images'), 231 | dcc.Input(id='input-num-imgs', type='number', value=1, min=1), 232 | 233 | html.Button( 234 | children='Download', 235 | id='button-download-meta', 236 | title='download images from db', 237 | n_clicks=0 238 | ), 239 | 240 | html.Div(children='select csv file with filenames'), 241 | html.Div(id='output-data-upload'), 242 | dcc.Upload( 243 | id='upload-data', 244 | children=html.Div([ 245 | 'Drag and Drop or ', 246 | html.A('Select File') 247 | ]), 248 | style={ 249 | 'width': '100%', 250 | 'height': '60px', 251 | 'lineHeight': '60px', 252 | 'borderWidth': '1px', 253 | 'borderStyle': 'dashed', 254 | 'borderRadius': '5px', 255 | 'textAlign': 'center', 256 | 'margin': '10px' 257 | }, 258 | 259 | multiple=True 260 | ), 261 | ], className='three columns'), 262 | 263 | html.Div([ 264 | dcc.Graph( 265 | id='graph-image', 266 | config={ 267 | 'editable': True, 268 | 'displayModeBar': True, 269 | 'showLink': False, 270 | 'displaylogo': False, 271 | 'edits': {'legendText':True, 'legendPosition':True}, 272 | 'modeBarButtonsToRemove': [ 273 | 'resetScale2d' 274 | ] 275 | }, 276 | figure={ 277 | 'data': [{'x':[], 'y':[], 'type':'scattergl'}], 278 | 'layout':{ 279 | 'xaxis':{ 280 | 'visible': False, 281 | 'range': [0, config.IMG_DISPLAY_HEIGHT] 282 | }, 283 | 'yaxis':{ 284 | 'visible': False, 285 | 'range': [0, config.IMG_DISPLAY_HEIGHT], 286 | 'scaleanchor': 'x' 287 | }, 288 | 'width': config.IMG_DISPLAY_HEIGHT, 289 | 'height': config.IMG_DISPLAY_HEIGHT, 290 | 'margin': {'l': 0, 'r': 0, 't': 0, 'b': 0}, 291 | 'clickmode': 'event', 292 | 'legend': {'x': 0, 'y': 1, 'font': {'size': 22}}, 293 | } 294 | } 295 | ) 296 | ], className='three columns') 297 | ], className='rows'), 298 | ]) 299 | 300 | 301 | @app.callback( 302 | [Output('datatable-filenames', 'data'), 303 | Output('datatable-filenames', 'columns')], 304 | [Input('button-download-meta', 'n_clicks')], 305 | [State('input-num-imgs', 'value'), 306 | State('input-annotation-labels', 'value'), 307 | State('output-data-upload', 'children')]) 308 | def query_db(n_clicks, num_imgs, filter_label, csv_data): 309 | '''Query mongodb database and fill datatable with filenames.''' 310 | df_select = pd.DataFrame() 311 | search_list = [] 312 | if n_clicks > 0: 313 | if csv_data: 314 | df_filenames = pd.DataFrame(nested_lookup('data', csv_data)[0]) 315 | filenames = df_filenames[df_filenames.columns[0]].tolist() 316 | num_imgs = len(filenames) 317 | search_list.append({"filename": {"$in": filenames}}) 318 | 319 | if filter_label: 320 | if filter_label[0] == '!': 321 | regx = {"$not":re.compile(filter_label[1:], re.IGNORECASE)} 322 | else: 323 | regx = re.compile(filter_label, re.IGNORECASE) 324 | search_list.append({'metadata.dash_img_annotation.name': regx}) 325 | 326 | if csv_data or filter_label: 327 | search_request = {'$and': search_list} 328 | result = fs.find(search_request).sort('uploadDate', -1).limit(num_imgs) 329 | else: 330 | result = fs.find().sort('uploadDate', -1).limit(num_imgs) 331 | 332 | filename = [] 333 | id_ = [] 334 | content_type = [] 335 | comments = [] 336 | for item in result: 337 | filename.append(item.filename) 338 | id_.append(str(item._id)) 339 | content_type.append(item.content_type) 340 | comments.append(item.metadata['comments']) 341 | 342 | df_select['filename'] = filename 343 | df_select['content-type'] = content_type 344 | df_select['comments'] = comments 345 | df_select['_id'] = id_ 346 | else: 347 | df_select['filename'] = [[]] * 1 348 | df_select['content_type'] = [[]] * 1 349 | df_select['comments'] = [[]] * 1 350 | df_select['_id'] = [[]] * 1 351 | 352 | return df_select.to_dict('records'), [{'name': i, 'id': i} for i in df_select.columns[0:-1]] 353 | 354 | 355 | @app.callback( 356 | Output('datatable-filenames', 'style_data_conditional'), 357 | [Input('datatable-filenames', 'selected_rows')]) 358 | def highlight_rows_filenames(row_index): 359 | '''Filenames-datatable row is highlighted on user selection.''' 360 | if row_index: 361 | return [{ 362 | 'if': {'row_index': row_index[0]}, 363 | 'backgroundColor': '#808080', 364 | 'color': 'white' 365 | }] 366 | return [{ 367 | 'backgroundColor': 'white', 368 | 'color': 'black' 369 | }] 370 | 371 | 372 | @app.callback( 373 | Output('datatable-labels', 'style_data_conditional'), 374 | [Input('datatable-labels', 'selected_rows')]) 375 | def highlight_rows_labels(row_index): 376 | '''Labels-datatable row is highlighted on user selection.''' 377 | return [{ 378 | 'if': {'row_index': row_index[0]}, 379 | 'backgroundColor': '#808080', 380 | 'color': 'white' 381 | }] 382 | 383 | 384 | @app.callback( 385 | [Output('graph-image', 'figure'), 386 | Output('booleanswitch-edit-output', 'children'), 387 | Output('polygon-data', 'data')], 388 | [Input('datatable-filenames', 'selected_rows'), 389 | Input('graph-image', 'selectedData'), 390 | Input('datatable-labels', 'selected_rows'), 391 | Input('daq-booleanswitch-edit', 'on'), 392 | Input('button-rect', 'n_clicks'), 393 | Input('button-lasso', 'n_clicks'), 394 | Input('javascript-ctrl-click', 'event'), 395 | Input('javascript-ctrl-keyup', 'event'), 396 | Input('button-mlflow-single', 'n_clicks'), 397 | Input('button-remove-traces', 'n_clicks')], 398 | [State('dropdown-select-run', 'value'), 399 | State('datatable-filenames', 'data'), 400 | State('datatable-labels', 'data'), 401 | State('daq-booleanswitch-edit', 'on'), 402 | State('daq-booleanswitch-lasso', 'on'), 403 | State('store-shapes', 'data'), 404 | State('polygon-data', 'data'), 405 | State('input-annotation-labels', 'value'), 406 | State('graph-image', 'figure')]) 407 | def display_update_image( 408 | filename_row_index, 409 | selectedData, 410 | label_row_index, 411 | edit_switch_bool, 412 | rect_nclicks, 413 | lasso_nclicks, 414 | js_ctrlclick_evt, 415 | js_ctrlkeyup_evt, 416 | mlflow_nclicks, 417 | remove_traces_nclicks, 418 | dropdwn_run_id, 419 | file_data, 420 | label_data, 421 | edit_boxes, 422 | lasso_open, 423 | shape_data, 424 | polygon_data, 425 | filter_label, 426 | fig 427 | ): 428 | '''Display and update image w/ annotations.''' 429 | data = fig['data'] 430 | layout_ = fig['layout'] 431 | ctx = dash.callback_context 432 | trig_id = ctx.triggered[0]['prop_id'] 433 | 434 | if trig_id == 'datatable-filenames.selected_rows': 435 | if filename_row_index: 436 | df_select = pd.DataFrame(file_data) 437 | id_ = df_select.loc[filename_row_index[0], '_id'] 438 | out = fs.find_one({'_id':ObjectId(id_)}) 439 | img = PImage.open(BytesIO(out.read())) 440 | img_width, img_height = img.size 441 | scale_factor = (config.IMG_DISPLAY_HEIGHT)/img_height 442 | data = [{ 443 | 'x': [0, img_width], 444 | 'y': [0, img_height], 445 | 'mode': 'markers+lines', 446 | 'marker': {'opacity': 0, 'color': 'rgba(0,255,0,0)'}, 447 | 'showlegend': False, 448 | 'name': 'dummy', 449 | 'customdata': [{'shape_type':''}], 450 | 'type':'scattergl' 451 | }] 452 | 453 | if type(out.metadata) == dict: 454 | if 'dash_img_annotation' in out.metadata.keys(): 455 | data_list = out.metadata['dash_img_annotation'] 456 | if filter_label: 457 | for index, data_item in enumerate(data_list): 458 | if re.search(filter_label, data_item['name'], re.IGNORECASE): 459 | data_list[index]['visible'] = True 460 | else: 461 | data_list[index]['visible'] = 'legendonly' 462 | data = data + data_list 463 | 464 | images = [go.layout.Image( 465 | x=0, 466 | sizex=img_width, 467 | y=img_height, 468 | sizey=img_height, 469 | xref='x', 470 | yref='y', 471 | opacity=1.0, 472 | layer='below', 473 | sizing='stretch', 474 | source=img 475 | )] 476 | layout_['images'] = images 477 | layout_['width'] = img_width*scale_factor 478 | layout_['height'] = img_height*scale_factor 479 | layout_['xaxis']['range'] = [0, img_width] 480 | layout_['yaxis']['range'] = [0, img_height] 481 | layout_['xaxis']['visible'] = False 482 | layout_['yaxis']['visible'] = False 483 | layout_['yaxis']['scaleanchor'] = 'x' 484 | layout_['margin'] = {'l': 0, 'r': 0, 't': 0, 'b': 0} 485 | layout_['clickmode'] = 'event' 486 | 487 | elif trig_id == 'graph-image.selectedData': 488 | # user just drew a box or lasso 489 | df_labels = pd.DataFrame(label_data) 490 | # get the labelname from the table 491 | labelname = df_labels.loc[label_row_index[0], 'labels'] 492 | # get the labelcolor from the table 493 | labelcolor = df_labels.loc[label_row_index[0], 'colors'] 494 | # check selection type: Box or Lasso 495 | if 'range' in list(selectedData.keys()): 496 | x = selectedData['range']['x'] 497 | x = [x[0], x[1], x[1], x[0], x[0]] 498 | y = selectedData['range']['y'] 499 | y = [y[0], y[0], y[1], y[1], y[0]] 500 | showlegend = True 501 | name = labelname + ' -box' 502 | shape_type = 'box' 503 | elif 'lassoPoints' in list(selectedData.keys()): 504 | x = selectedData['lassoPoints']['x'] 505 | y = selectedData['lassoPoints']['y'] 506 | if lasso_open: 507 | name = labelname + ' -line' 508 | shape_type = 'line' 509 | else: 510 | x.append(x[0]) 511 | y.append(y[0]) 512 | name = labelname + ' -polygon' 513 | shape_type = 'polygon' 514 | showlegend = True 515 | 516 | for trace_dict in fig['data']: 517 | if 'selectedpoints' in trace_dict: 518 | del trace_dict['selectedpoints'] 519 | 520 | data = fig['data'] + [{ 521 | 'x': x, 522 | 'y': y, 523 | 'mode': 'markers+lines', 524 | 'marker': {'opacity': 1, 'color': labelcolor}, 525 | 'showlegend': True, 526 | 'name': name, 527 | 'customdata': [{'shape_type': shape_type}], 528 | 'hoverinfo': 'name', 529 | 'visible': True, 530 | 'line': {'color': labelcolor}, 531 | 'type': 'scattergl' 532 | }] 533 | 534 | layout_ = fig['layout'] 535 | 536 | if trig_id == 'daq-booleanswitch-edit.on': 537 | if edit_boxes: 538 | index_shapes = 0 539 | for index, data_item in enumerate(data): 540 | if (data_item['customdata'][0]['shape_type'] == 'box') \ 541 | and (len(data_item['x']) == 5): 542 | if index_shapes != 0: 543 | layout_['shapes'] = layout_['shapes'] + [ 544 | { 545 | 'type': 'rect', 546 | 'xref': 'x', 547 | 'yref': 'y', 548 | 'x0': data_item['x'][0], 549 | 'y0': data_item['y'][0], 550 | 'x1': data_item['x'][1], 551 | 'y1': data_item['y'][2], 552 | 'line': { 553 | 'color': data_item['line']['color'], 554 | 'dash': 'dot' 555 | } 556 | } 557 | ] 558 | else: 559 | layout_['shapes'] = [ 560 | { 561 | 'type': 'rect', 562 | 'xref': 'x', 563 | 'yref': 'y', 564 | 'x0': data_item['x'][0], 565 | 'y0': data_item['y'][0], 566 | 'x1': data_item['x'][1], 567 | 'y1': data_item['y'][2], 568 | 'line': { 569 | 'color': data_item['line']['color'], 570 | 'dash': 'dot' 571 | } 572 | } 573 | ] 574 | index_shapes = index_shapes + 1 575 | data[index]['visible'] = 'legendonly' 576 | else: 577 | index_shapes = 0 578 | for index, data_item in enumerate(data): 579 | if (data_item['customdata'][0]['shape_type'] == 'box') \ 580 | and (len(data_item['x']) == 5): 581 | if shape_data is None: 582 | data[index]['visible'] = True 583 | else: 584 | keys = [ 585 | f'shapes[{index_shapes}].x0', 586 | f'shapes[{index_shapes}].y0', 587 | f'shapes[{index_shapes}].x1', 588 | f'shapes[{index_shapes}].y1' 589 | ] 590 | if keys[0] in shape_data: 591 | data_item['x'][0] = shape_data[keys[0]] 592 | data_item['y'][0] = shape_data[keys[1]] 593 | data_item['x'][1] = shape_data[keys[2]] 594 | data_item['y'][2] = shape_data[keys[3]] 595 | data_item['x'][2] = data_item['x'][1] 596 | data_item['x'][3] = data_item['x'][0] 597 | data_item['x'][4] = data_item['x'][0] 598 | data_item['y'][1] = data_item['y'][0] 599 | data_item['y'][3] = data_item['y'][2] 600 | data_item['y'][4] = data_item['y'][0] 601 | index_shapes = index_shapes + 1 602 | data[index]['visible'] = True 603 | try: 604 | del layout_['shapes'] 605 | except KeyError: 606 | print("Key 'shape' not found") 607 | 608 | 609 | if trig_id == 'button-rect.n_clicks': 610 | layout_ = fig['layout'] 611 | layout_['dragmode'] = 'select' 612 | elif trig_id == 'button-lasso.n_clicks': 613 | layout_ = fig['layout'] 614 | layout_['dragmode'] = 'lasso' 615 | 616 | if trig_id == 'javascript-ctrl-click.event': 617 | df_labels = pd.DataFrame(label_data) 618 | # get the labelname from the table 619 | labelname = df_labels.loc[label_row_index[0], 'labels'] 620 | # get the labelcolor from the table 621 | labelcolor = df_labels.loc[label_row_index[0], 'colors'] 622 | name = labelname + ' -polygon' 623 | if lasso_open: 624 | name = labelname +' -line' 625 | shape_type = 'line' 626 | else: 627 | name = labelname + ' -polygon' 628 | shape_type = 'polygon' 629 | data_store = [{ 630 | 'x': [ctx.inputs['javascript-ctrl-click.event']['x']], 631 | 'y': [ctx.inputs['javascript-ctrl-click.event']['y']], 632 | 'mode': 'markers+lines', 633 | 'marker': {'opacity': 1, 'color': labelcolor}, 634 | 'showlegend': True, 635 | 'name': name, 636 | 'customdata': [{'shape_type': shape_type}], 637 | 'hoverinfo': 'name', 638 | 'visible': True, 639 | 'line': {'color': labelcolor}, 640 | 'type': 'scattergl' 641 | }] 642 | if ctx.states['polygon-data.data'] is None: 643 | if lasso_open: 644 | data = fig['data'] + data_store 645 | else: 646 | data_store[0]['x'] = data_store[0]['x'] + data_store[0]['x'] 647 | data_store[0]['y'] = data_store[0]['y'] + data_store[0]['y'] 648 | data = fig['data'] + data_store 649 | else: 650 | if lasso_open: 651 | data_store[0]['x'] = ctx.states['polygon-data.data'][0]['x'] \ 652 | + data_store[0]['x'] 653 | data_store[0]['y'] = ctx.states['polygon-data.data'][0]['y'] \ 654 | + data_store[0]['y'] 655 | else: 656 | data_store[0]['x'] = ctx.states['polygon-data.data'][0]['x'][:-1] \ 657 | + data_store[0]['x'] + [ctx.states['polygon-data.data'][0]['x'][0]] 658 | data_store[0]['y'] = ctx.states['polygon-data.data'][0]['y'][:-1] \ 659 | + data_store[0]['y'] + [ctx.states['polygon-data.data'][0]['y'][0]] 660 | 661 | data = fig['data'][:-1] + data_store 662 | 663 | else: 664 | data_store = None 665 | 666 | if trig_id == 'button-mlflow-single.n_clicks': 667 | # Load the model in 'python_function' format 668 | if mlflow_nclicks > 0: 669 | artifacts = mlflow.tracking.MlflowClient().list_artifacts(run_id=dropdwn_run_id) 670 | path = [artifact.path for artifact in artifacts if 'pyfunc' in artifact.path][0] 671 | df_select = pd.DataFrame(file_data) 672 | loaded_model = mlflow.pyfunc.load_model( 673 | model_uri=f'runs:/{dropdwn_run_id}/{path}' 674 | ) 675 | # Evaluate the model 676 | filename = df_select.loc[filename_row_index[0], 'filename'] 677 | up = urllib.parse.urlparse(layout_['images'][0]['source']) 678 | img_head, img_data = up.path.split(',', 1) 679 | img = PImage.open(BytesIO(base64.b64decode(img_data))) 680 | img_width, img_height = img.size 681 | df_img = pd.DataFrame( 682 | data=[base64.encodebytes(base64.b64decode(img_data))], 683 | columns=['image'] 684 | ) 685 | predictions = loaded_model.predict(df_img) 686 | # predictions_sampled = predictions[::10] 687 | df_labels = pd.DataFrame(label_data) 688 | labelcolor = df_labels.loc[label_row_index[0], 'colors'] 689 | run_dict = mlflow.tracking.MlflowClient().get_run(run_id=dropdwn_run_id).to_dictionary() 690 | name = run_dict['data']['tags']['mlflow.runName'] + ' -model' 691 | shape_type = 'model' 692 | model_trace = go.Scattergl( 693 | x=predictions['x'], 694 | y=img_height-predictions['y'], 695 | mode='markers', 696 | marker={'opacity': 1, 'color': labelcolor}, 697 | showlegend=True, 698 | name=name, 699 | customdata=[ 700 | { 701 | 'shape_type': shape_type, 702 | 'mlflow_path': path, 703 | 'mlflow_run_id': dropdwn_run_id 704 | } 705 | ], 706 | hoverinfo='name', 707 | visible=True 708 | ) 709 | data = fig['data'] + [model_trace] 710 | 711 | if (trig_id == 'button-remove-traces.n_clicks') and (remove_traces_nclicks > 0): 712 | data = fig['data'] 713 | checklist_dict = {'visible': 'legendonly', 'showlegend': False} 714 | indices = [] 715 | for index, data_item in enumerate(data): 716 | data_item_dict = {k: data_item.get(k, None) for k in checklist_dict} 717 | intersect_dict = set(checklist_dict.items()).intersection(set(data_item_dict.items())) 718 | if intersect_dict: 719 | indices = indices + [index] 720 | data = np.delete(data, indices).tolist() 721 | 722 | return go.Figure(data, layout_), f'Edit boxes {edit_boxes}', data_store 723 | 724 | 725 | @app.callback( 726 | Output('booleanswitch-lasso-output', 'children'), 727 | [Input('daq-booleanswitch-lasso', 'on')]) 728 | def lasso_boolen_switch(on): 729 | '''Reports if user selected closed/open lasso (closed lasso connects 1st, last pts).''' 730 | if on: 731 | switch = 'Open lasso' 732 | else: 733 | switch = 'Close lasso' 734 | return switch 735 | 736 | 737 | @app.callback( 738 | Output('store-shapes', 'data'), 739 | [Input('graph-image', 'relayoutData')], 740 | [State('store-shapes', 'data')]) 741 | def store_shapes(relayoutData, stored_data): 742 | '''Temporary storage of user annotions in the browser.''' 743 | if stored_data is None: 744 | stored_data = {} 745 | if relayoutData is not None: 746 | if any('shapes' in key for key in list(relayoutData.keys())): 747 | layout_data = relayoutData 748 | for key in layout_data: 749 | stored_data[key] = layout_data[key] 750 | 751 | return stored_data 752 | 753 | 754 | @app.callback( 755 | [Output('javascript-ctrl-keyup', 'run'), 756 | Output('javascript-ctrl-click', 'run')], 757 | [Input('button-lasso-polygon', 'n_clicks')]) 758 | def javascript_event_listeners(n_clicks): 759 | '''Javascript event listeners for Ctrl+MouseClick and Ctrl+Keyup.''' 760 | if n_clicks < 1: return '', '' 761 | return ''' 762 | var target = document 763 | target.addEventListener('keyup', function(evt) { 764 | if (evt.key == 'Control') { 765 | setProps({ 766 | 'event': {'x':-999999, 767 | 'y':-999999 } 768 | }) 769 | console.log(evt) 770 | } 771 | }) 772 | console.log(this) 773 | ''', ''' 774 | var target = $('#graph-image')[0] 775 | target.addEventListener('click', function(evt) { 776 | if (evt.ctrlKey) { 777 | var xaxis = target._fullLayout.xaxis; 778 | var yaxis = target._fullLayout.yaxis; 779 | var l = target._fullLayout.margin.l; 780 | var t = target._fullLayout.margin.t; 781 | var offl = target.offsetLeft; 782 | var offt = target.offsetTop; 783 | var xInDataCoord = xaxis.p2c(evt.x - offl); 784 | var yInDataCoord = yaxis.p2c(evt.y - offt); 785 | setProps({ 786 | 'event': {'x':xInDataCoord, 787 | 'y':yInDataCoord } 788 | }) 789 | console.log(evt) 790 | } 791 | }) 792 | console.log(this) 793 | ''' 794 | 795 | 796 | @app.callback( 797 | Output('javascript-drag-color', 'run'), 798 | [Input('button-lasso', 'n_clicks'), 799 | Input('button-rect', 'n_clicks'), 800 | Input('datatable-labels', 'selected_rows')], 801 | [State('datatable-labels', 'data')]) 802 | def drag_color(n_clicks_lasso, n_clicks_rect, label_row_index, label_data): 803 | '''Javascript to maintain label color while drawing box and lasso.''' 804 | df_labels = pd.DataFrame(label_data) 805 | labelcolor = df_labels.loc[label_row_index[0], 'colors'] 806 | s = ''' 807 | var target = $('#graph-image')[0] 808 | target.on('plotly_selecting', function(eventData){{ 809 | Plotly.d3.selectAll('.select-line,.select-outline-1,.select-outline-2') 810 | .style('stroke', '{color}'); 811 | console.log(eventData); 812 | }}); 813 | 814 | console.log(this) 815 | ''' 816 | return s.format(color=labelcolor) 817 | 818 | 819 | def parse_contents(contents, filename, date): 820 | content_type, content_string = contents.split(',') 821 | decoded = base64.b64decode(content_string) 822 | try: 823 | if 'csv' in filename: 824 | # Assume that the user uploaded a CSV file 825 | df_input = pd.read_csv( 826 | StringIO(decoded.decode('utf-8'))) 827 | elif 'xls' in filename: 828 | # Assume that the user uploaded an excel file 829 | df_input = pd.read_excel(BytesIO(decoded)) 830 | except Exception as e: 831 | print(e) 832 | return html.Div([ 833 | 'There was an error processing this file.' 834 | ]) 835 | return html.Div([ 836 | html.H5(filename), 837 | html.H6(datetime.datetime.fromtimestamp(date)), 838 | dash_table.DataTable( 839 | data=df_input.to_dict('records'), 840 | #columns=[{'name': i, 'id': i} for i in df_input.columns], 841 | columns=[] 842 | ), 843 | html.Hr(), 844 | ]) 845 | 846 | 847 | @app.callback(Output('output-data-upload', 'children'), 848 | [Input('upload-data', 'contents')], 849 | [State('upload-data', 'filename'), 850 | State('upload-data', 'last_modified')]) 851 | def update_output(list_of_contents, list_of_names, list_of_dates): 852 | if list_of_contents is not None: 853 | children = [ 854 | parse_contents(c, n, d) for c, n, d in 855 | zip(list_of_contents, list_of_names, list_of_dates)] 856 | return children 857 | return [] 858 | 859 | 860 | @app.callback( 861 | Output('report-model', 'children'), 862 | [Input('button-mlflow-batch', 'n_clicks')], 863 | [State('datatable-filenames', 'data'), 864 | State('datatable-labels', 'data'), 865 | State('datatable-labels', 'selected_rows'), 866 | State('dropdown-select-run', 'value')]) 867 | def mlflow_batch(n_clicks, file_data, label_data, label_row_index, dropdwn_run_id): 868 | '''Apply MLflow model to all files in datatable.''' 869 | if (n_clicks > 0) and (config.MLFLOW_URI is not None): 870 | df_select = pd.DataFrame(file_data) 871 | artifacts = mlflow.tracking.MlflowClient().list_artifacts(run_id=dropdwn_run_id) 872 | path = [artifact.path for artifact in artifacts if 'pyfunc' in artifact.path][0] 873 | df_labels = pd.DataFrame(label_data) 874 | labelcolor = df_labels.loc[label_row_index[0], 'colors'] 875 | for index, row in df_select.iterrows(): 876 | # Evaluate the model 877 | filename = row['filename'] 878 | result = fs.get_last_version(filename) 879 | result_id = result._id 880 | # check if metadata model annotation already exists 881 | search_request = { 882 | '$and': 883 | [ 884 | {'_id': result_id}, 885 | {'metadata.dash_img_annotation.customdata.mlflow_run_id': dropdwn_run_id} 886 | ] 887 | } 888 | if not fs.exists(search_request): 889 | result_read = result.read() 890 | img = PImage.open(BytesIO(result_read)) 891 | img_width, img_height = img.size 892 | df_img = pd.DataFrame(data=[base64.encodebytes(result_read)], columns=['image']) 893 | loaded_model = mlflow.pyfunc.load_model( 894 | model_uri=f'runs:/{dropdwn_run_id}/{path}' 895 | ) 896 | predictions = loaded_model.predict(df_img) 897 | run_dict = mlflow.tracking.MlflowClient().get_run( 898 | run_id=dropdwn_run_id).to_dictionary() 899 | name = run_dict['data']['tags']['mlflow.runName'] + ' -model' 900 | shape_type = 'model' 901 | model_trace = { 902 | 'x': predictions['x'].tolist(), 903 | 'y': (img_height-predictions['y']).tolist(), 904 | 'mode': 'markers', 905 | 'marker': {'opacity': 1, 'color': labelcolor}, 906 | 'showlegend': True, 907 | 'name': name, 908 | 'customdata': [ 909 | { 910 | 'shape_type': shape_type, 911 | 'mlflow_path': path, 912 | 'mlflow_run_id': dropdwn_run_id 913 | } 914 | ], 915 | 'hoverinfo': 'name', 916 | 'visible': True, 917 | 'type': 'scattergl' 918 | } 919 | 920 | if 'dash_img_annotation' in result.metadata: 921 | data = result.metadata['dash_img_annotation'] + [model_trace] 922 | else: 923 | data = [model_trace] 924 | 925 | db.fs.files.update_one( 926 | {'_id': result_id}, {'$set': {'metadata.dash_img_annotation': data}} 927 | ) 928 | return 'batch model completed' 929 | return '' 930 | 931 | 932 | @app.callback( 933 | Output('dropdown-select-exp', 'options'), 934 | [Input('button-mlflow-connect', 'n_clicks')]) 935 | def mflow_connect(n_clicks): 936 | '''Display list of MLflow experiments in datatable.''' 937 | if (n_clicks > 0) and (config.MLFLOW_URI is not None): 938 | mlflow_experiments = mlflow.tracking.MlflowClient().list_experiments() 939 | return [{'label': ex.name, 'value': ex.name} for ex in mlflow_experiments] 940 | return [] 941 | 942 | 943 | @app.callback( 944 | [Output('dropdown-select-run', 'options'), 945 | Output('dropdown-select-run', 'value')], 946 | [Input('dropdown-select-exp', 'value')], 947 | [State('graph-image', 'figure')]) 948 | def dropdwn_exp(exp_name, fig): 949 | '''MLflow experiments drop-down menu.''' 950 | if (exp_name is not None) and ('images' in list(fig['layout'].keys())): 951 | exp = mlflow.tracking.MlflowClient().get_experiment_by_name(name=exp_name) 952 | runs = mlflow.tracking.MlflowClient().list_run_infos(experiment_id=exp.experiment_id) 953 | mlflow_run_opts = [] 954 | for run in runs: 955 | tags = mlflow.tracking.MlflowClient().get_run(run.run_uuid).data.tags 956 | mlflow_run_opts = mlflow_run_opts + [ 957 | {'label': tags['mlflow.runName'], 'value': run.run_uuid} 958 | ] 959 | return mlflow_run_opts, mlflow_run_opts[0]['value'] 960 | return [], 'None' 961 | 962 | 963 | @app.callback( 964 | Output('report-save', 'children'), 965 | [Input('button-save', 'n_clicks')], 966 | [State('datatable-filenames', 'data'), 967 | State('datatable-filenames', 'selected_rows'), 968 | State('graph-image', 'figure')]) 969 | def save_metadata(n_clicks, file_data, filename_row_index, fig): 970 | '''Save annotation metadata to MongoDB.''' 971 | save_stmt = '' 972 | if (n_clicks > 0) and filename_row_index: 973 | df_select = pd.DataFrame(file_data) 974 | filename = df_select.loc[filename_row_index[0], 'filename'] 975 | id_ = df_select.loc[filename_row_index[0], '_id'] 976 | comments = df_select.loc[filename_row_index[0], 'comments'] 977 | data = fig['data'] 978 | metadata = {} 979 | #metadata['saved_time'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') 980 | metadata['comments'] = comments 981 | metadata['dash_img_annotation'] = data 982 | db.fs.files.update_one({'_id': ObjectId(id_)}, {'$set': {'metadata':metadata}}) 983 | save_stmt = 'saved metadata: ' + filename 984 | 985 | return save_stmt 986 | -------------------------------------------------------------------------------- /apps/config.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import os 5 | import sys 6 | 7 | 8 | # Host and Port of image-annotator/uploader web-app 9 | HOST = os.environ.get('HOST', '127.0.0.1') 10 | PORT = os.environ.get('PORT', 8050) 11 | 12 | 13 | # Optional. Example: http://127.0.0.1:5000 14 | MLFLOW_URI = os.environ.get('MLFLOW_URI') 15 | 16 | 17 | try: 18 | # Example: mongodb://user:password@xx.xxx.xxx.xx:port 19 | MONGODB_CONNECT_STRING = os.environ['MONGODB_CONNECT_STRING'] 20 | except KeyError: 21 | print('Please set the environment variable MONGODB_CONNECT_STRING') 22 | sys.exit(1) 23 | 24 | 25 | try: 26 | # Database name 27 | MONGODB_DATABASE = os.environ['MONGODB_DATABASE'] 28 | except KeyError: 29 | print('Please set the environment variable MONGODB_DATABASE') 30 | sys.exit(1) 31 | 32 | 33 | # Annotation labels 34 | DEFAULT_LABELS = { 35 | 'labels': ['label_1', 'label_2', 'label_3', 'label_4'], 36 | 'colors': ['rgb(255,0,0)', 'rgb(0,255,0)', 'rgb(0,0,255)', 'rgb(255,0,255)'] 37 | } 38 | 39 | 40 | # Set image display height in pixels 41 | # - images will be displayed with this height while maintaining the aspect ratio 42 | IMG_DISPLAY_HEIGHT = 512 43 | -------------------------------------------------------------------------------- /apps/uploader.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | import base64 5 | from io import BytesIO 6 | import zipfile 7 | from PIL import Image as PImage 8 | import dash_core_components as dcc 9 | import dash_html_components as html 10 | from dash.dependencies import Input, Output, State 11 | from pymongo import MongoClient 12 | import gridfs 13 | 14 | from apps import config 15 | from app import app 16 | 17 | 18 | client = MongoClient(config.MONGODB_CONNECT_STRING) 19 | db = client[config.MONGODB_DATABASE] 20 | fs = gridfs.GridFS(db) 21 | 22 | 23 | layout = html.Div( 24 | [ 25 | html.H1('Upload Images to MongoDB'), 26 | html.Div([ 27 | dcc.Input( 28 | id='input-comments', 29 | placeholder='Add comments', 30 | type='text', 31 | value='' 32 | ) 33 | ]), 34 | html.Br(), 35 | dcc.Upload( 36 | id='upload-data', 37 | children=html.Div( 38 | ['Drag and drop or click to select image(s) or zipped images to upload.'] 39 | ), 40 | style={ 41 | 'width': '100%', 42 | 'height': '100px', 43 | 'lineHeight': '100px', 44 | 'borderWidth': '1px', 45 | 'borderStyle': 'dashed', 46 | 'borderRadius': '5px', 47 | 'textAlign': 'center', 48 | 'margin': '10px', 49 | }, 50 | multiple=True, 51 | ), 52 | html.H2('File List'), 53 | html.Ul(id='file-list'), 54 | ], className='five columns', 55 | ) 56 | 57 | 58 | def extract_zip(data): 59 | input_zip = zipfile.ZipFile(BytesIO(data)) 60 | return {name: input_zip.read(name) for name in input_zip.namelist()} 61 | 62 | 63 | def save_file(name, content, comments): 64 | '''Decode and store a file uploaded with Plotly Dash.''' 65 | files = [] 66 | metadata = {} 67 | metadata['comments'] = comments 68 | filename = name.split('.')[0] 69 | data = content.encode('utf8').split(b';base64,')[1] 70 | data = base64.decodebytes(data) 71 | 72 | if name.split('.')[-1] == 'zip': 73 | zipped_contents = zipfile.ZipFile(BytesIO(data)) 74 | for _name in zipped_contents.namelist(): 75 | filename = _name.split('.')[0:-1] 76 | try: 77 | img = PImage.open(BytesIO(zipped_contents.read(_name))) 78 | content_type = img.get_format_mimetype() 79 | image_id = fs.put( 80 | data=BytesIO(zipped_contents.read(_name)).getvalue(), 81 | content_type=content_type, 82 | filename=filename[0], 83 | metadata=metadata 84 | ) 85 | files.append(_name) 86 | except: IOError 87 | else: 88 | img = PImage.open(BytesIO(data)) 89 | content_type = img.get_format_mimetype() 90 | 91 | image_id = fs.put( 92 | data=data, 93 | content_type=content_type, 94 | filename=filename, 95 | metadata=metadata 96 | ) 97 | files.append(name) 98 | return files 99 | 100 | 101 | @app.callback( 102 | Output('file-list', 'children'), 103 | [Input('upload-data', 'filename'), 104 | Input('upload-data', 'contents')], 105 | [State('input-comments', 'value')]) 106 | def update_output(uploaded_filenames, uploaded_file_contents, comments): 107 | '''Save uploaded files and regenerate the file list.''' 108 | files = [] 109 | if uploaded_filenames is not None and uploaded_file_contents is not None: 110 | for name, data in zip(uploaded_filenames, uploaded_file_contents): 111 | if name.split('.')[-1] != 'zip': 112 | _ = save_file(name, data, comments) 113 | files.append(name) 114 | else: 115 | files = save_file(name, data, comments) 116 | 117 | if not files: 118 | return [html.Li('No files yet!')] 119 | return [html.Li(filename) for filename in files] 120 | -------------------------------------------------------------------------------- /assets/bWLwgP.css: -------------------------------------------------------------------------------- 1 | /* Table of contents 2 | –––––––––––––––––––––––––––––––––––––––––––––––––– 3 | - Plotly.js 4 | - Grid 5 | - Base Styles 6 | - Typography 7 | - Links 8 | - Buttons 9 | - Forms 10 | - Lists 11 | - Code 12 | - Tables 13 | - Spacing 14 | - Utilities 15 | - Clearing 16 | - Media Queries 17 | */ 18 | 19 | /* PLotly.js 20 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 21 | /* plotly.js's modebar's z-index is 1001 by default 22 | * https://github.com/plotly/plotly.js/blob/7e4d8ab164258f6bd48be56589dacd9bdd7fded2/src/css/_modebar.scss#L5 23 | * In case a dropdown is above the graph, the dropdown's options 24 | * will be rendered below the modebar 25 | * Increase the select option's z-index 26 | */ 27 | 28 | /* This was actually not quite right - 29 | dropdowns were overlapping each other (edited October 26) 30 | 31 | .Select { 32 | z-index: 1002; 33 | }*/ 34 | 35 | /* Grid 36 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 37 | .container { 38 | position: relative; 39 | width: 100%; 40 | max-width: 960px; 41 | margin: 0 auto; 42 | padding: 0 20px; 43 | box-sizing: border-box; } 44 | .column, 45 | .columns { 46 | width: 100%; 47 | float: left; 48 | box-sizing: border-box; } 49 | 50 | /* For devices larger than 400px */ 51 | @media (min-width: 400px) { 52 | .container { 53 | width: 85%; 54 | padding: 0; } 55 | } 56 | 57 | /* For devices larger than 550px */ 58 | @media (min-width: 550px) { 59 | .container { 60 | width: 80%; } 61 | .column, 62 | .columns { 63 | margin-left: 4%; } 64 | .column:first-child, 65 | .columns:first-child { 66 | margin-left: 0; } 67 | 68 | .one.column, 69 | .one.columns { width: 4.66666666667%; } 70 | .two.columns { width: 13.3333333333%; } 71 | .three.columns { width: 22%; } 72 | .four.columns { width: 30.6666666667%; } 73 | .five.columns { width: 39.3333333333%; } 74 | .six.columns { width: 48%; } 75 | .seven.columns { width: 56.6666666667%; } 76 | .eight.columns { width: 65.3333333333%; } 77 | .nine.columns { width: 74.0%; } 78 | .ten.columns { width: 82.6666666667%; } 79 | .eleven.columns { width: 91.3333333333%; } 80 | .twelve.columns { width: 100%; margin-left: 0; } 81 | 82 | .one-third.column { width: 30.6666666667%; } 83 | .two-thirds.column { width: 65.3333333333%; } 84 | 85 | .one-half.column { width: 48%; } 86 | 87 | /* Offsets */ 88 | .offset-by-one.column, 89 | .offset-by-one.columns { margin-left: 8.66666666667%; } 90 | .offset-by-two.column, 91 | .offset-by-two.columns { margin-left: 17.3333333333%; } 92 | .offset-by-three.column, 93 | .offset-by-three.columns { margin-left: 26%; } 94 | .offset-by-four.column, 95 | .offset-by-four.columns { margin-left: 34.6666666667%; } 96 | .offset-by-five.column, 97 | .offset-by-five.columns { margin-left: 43.3333333333%; } 98 | .offset-by-six.column, 99 | .offset-by-six.columns { margin-left: 52%; } 100 | .offset-by-seven.column, 101 | .offset-by-seven.columns { margin-left: 60.6666666667%; } 102 | .offset-by-eight.column, 103 | .offset-by-eight.columns { margin-left: 69.3333333333%; } 104 | .offset-by-nine.column, 105 | .offset-by-nine.columns { margin-left: 78.0%; } 106 | .offset-by-ten.column, 107 | .offset-by-ten.columns { margin-left: 86.6666666667%; } 108 | .offset-by-eleven.column, 109 | .offset-by-eleven.columns { margin-left: 95.3333333333%; } 110 | 111 | .offset-by-one-third.column, 112 | .offset-by-one-third.columns { margin-left: 34.6666666667%; } 113 | .offset-by-two-thirds.column, 114 | .offset-by-two-thirds.columns { margin-left: 69.3333333333%; } 115 | 116 | .offset-by-one-half.column, 117 | .offset-by-one-half.columns { margin-left: 52%; } 118 | 119 | } 120 | 121 | 122 | /* Base Styles 123 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 124 | /* NOTE 125 | html is set to 62.5% so that all the REM measurements throughout Skeleton 126 | are based on 10px sizing. So basically 1.5rem = 15px :) */ 127 | html { 128 | font-size: 62.5%; } 129 | body { 130 | font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */ 131 | line-height: 1.6; 132 | font-weight: 400; 133 | font-family: "Open Sans", "HelveticaNeue", "Helvetica Neue", Helvetica, Arial, sans-serif; 134 | color: rgb(50, 50, 50); } 135 | 136 | 137 | /* Typography 138 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 139 | h1, h2, h3, h4, h5, h6 { 140 | margin-top: 0; 141 | margin-bottom: 0; 142 | font-weight: 300; } 143 | h1 { font-size: 4.5rem; line-height: 1.2; letter-spacing: -.1rem; margin-bottom: 2rem; } 144 | h2 { font-size: 3.6rem; line-height: 1.25; letter-spacing: -.1rem; margin-bottom: 1.8rem; margin-top: 1.8rem;} 145 | h3 { font-size: 3.0rem; line-height: 1.3; letter-spacing: -.1rem; margin-bottom: 1.5rem; margin-top: 1.5rem;} 146 | h4 { font-size: 2.6rem; line-height: 1.35; letter-spacing: -.08rem; margin-bottom: 1.2rem; margin-top: 1.2rem;} 147 | h5 { font-size: 2.2rem; line-height: 1.5; letter-spacing: -.05rem; margin-bottom: 0.6rem; margin-top: 0.6rem;} 148 | h6 { font-size: 2.0rem; line-height: 1.6; letter-spacing: 0; margin-bottom: 0.75rem; margin-top: 0.75rem;} 149 | 150 | p { 151 | margin-top: 0; } 152 | 153 | 154 | /* Blockquotes 155 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 156 | blockquote { 157 | border-left: 4px lightgrey solid; 158 | padding-left: 1rem; 159 | margin-top: 2rem; 160 | margin-bottom: 2rem; 161 | margin-left: 0rem; 162 | } 163 | 164 | 165 | /* Links 166 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 167 | a { 168 | color: #1EAEDB; 169 | text-decoration: underline; 170 | cursor: pointer;} 171 | a:hover { 172 | color: #0FA0CE; } 173 | 174 | 175 | /* Buttons 176 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 177 | .button, 178 | button, 179 | input[type="submit"], 180 | input[type="reset"], 181 | input[type="button"] { 182 | display: inline-block; 183 | height: 38px; 184 | padding: 0 30px; 185 | color: #555; 186 | text-align: center; 187 | font-size: 11px; 188 | font-weight: 600; 189 | line-height: 38px; 190 | letter-spacing: .1rem; 191 | text-transform: uppercase; 192 | text-decoration: none; 193 | white-space: nowrap; 194 | background-color: transparent; 195 | border-radius: 4px; 196 | border: 1px solid #bbb; 197 | cursor: pointer; 198 | box-sizing: border-box; } 199 | .button:hover, 200 | button:hover, 201 | input[type="submit"]:hover, 202 | input[type="reset"]:hover, 203 | input[type="button"]:hover, 204 | .button:focus, 205 | button:focus, 206 | input[type="submit"]:focus, 207 | input[type="reset"]:focus, 208 | input[type="button"]:focus { 209 | color: #333; 210 | border-color: #888; 211 | outline: 0; } 212 | .button.button-primary, 213 | button.button-primary, 214 | input[type="submit"].button-primary, 215 | input[type="reset"].button-primary, 216 | input[type="button"].button-primary { 217 | color: #FFF; 218 | background-color: #33C3F0; 219 | border-color: #33C3F0; } 220 | .button.button-primary:hover, 221 | button.button-primary:hover, 222 | input[type="submit"].button-primary:hover, 223 | input[type="reset"].button-primary:hover, 224 | input[type="button"].button-primary:hover, 225 | .button.button-primary:focus, 226 | button.button-primary:focus, 227 | input[type="submit"].button-primary:focus, 228 | input[type="reset"].button-primary:focus, 229 | input[type="button"].button-primary:focus { 230 | color: #FFF; 231 | background-color: #1EAEDB; 232 | border-color: #1EAEDB; } 233 | 234 | 235 | /* Forms 236 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 237 | input[type="email"], 238 | input[type="number"], 239 | input[type="search"], 240 | input[type="text"], 241 | input[type="tel"], 242 | input[type="url"], 243 | input[type="password"], 244 | textarea, 245 | select { 246 | height: 38px; 247 | padding: 6px 10px; /* The 6px vertically centers text on FF, ignored by Webkit */ 248 | background-color: #fff; 249 | border: 1px solid #D1D1D1; 250 | border-radius: 4px; 251 | box-shadow: none; 252 | box-sizing: border-box; 253 | font-family: inherit; 254 | font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/} 255 | /* Removes awkward default styles on some inputs for iOS */ 256 | input[type="email"], 257 | input[type="number"], 258 | input[type="search"], 259 | input[type="text"], 260 | input[type="tel"], 261 | input[type="url"], 262 | input[type="password"], 263 | textarea { 264 | -webkit-appearance: none; 265 | -moz-appearance: none; 266 | appearance: none; } 267 | textarea { 268 | min-height: 65px; 269 | padding-top: 6px; 270 | padding-bottom: 6px; } 271 | input[type="email"]:focus, 272 | input[type="number"]:focus, 273 | input[type="search"]:focus, 274 | input[type="text"]:focus, 275 | input[type="tel"]:focus, 276 | input[type="url"]:focus, 277 | input[type="password"]:focus, 278 | textarea:focus, 279 | select:focus { 280 | border: 1px solid #33C3F0; 281 | outline: 0; } 282 | label, 283 | legend { 284 | display: block; 285 | margin-bottom: 0px; } 286 | fieldset { 287 | padding: 0; 288 | border-width: 0; } 289 | input[type="checkbox"], 290 | input[type="radio"] { 291 | display: inline; } 292 | label > .label-body { 293 | display: inline-block; 294 | margin-left: .5rem; 295 | font-weight: normal; } 296 | 297 | 298 | /* Lists 299 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 300 | ul { 301 | list-style: circle inside; } 302 | ol { 303 | list-style: decimal inside; } 304 | ol, ul { 305 | padding-left: 0; 306 | margin-top: 0; } 307 | ul ul, 308 | ul ol, 309 | ol ol, 310 | ol ul { 311 | margin: 1.5rem 0 1.5rem 3rem; 312 | font-size: 90%; } 313 | li { 314 | margin-bottom: 1rem; } 315 | 316 | 317 | /* Tables 318 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 319 | table { 320 | border-collapse: collapse; 321 | } 322 | th, 323 | td { 324 | padding: 12px 15px; 325 | text-align: left; 326 | border-bottom: 1px solid #E1E1E1; } 327 | th:first-child, 328 | td:first-child { 329 | padding-left: 0; } 330 | th:last-child, 331 | td:last-child { 332 | padding-right: 0; } 333 | 334 | 335 | /* Spacing 336 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 337 | button, 338 | .button { 339 | margin-bottom: 0rem; } 340 | input, 341 | textarea, 342 | select, 343 | fieldset { 344 | margin-bottom: 0rem; } 345 | pre, 346 | dl, 347 | figure, 348 | table, 349 | form { 350 | margin-bottom: 0rem; } 351 | p, 352 | ul, 353 | ol { 354 | margin-bottom: 0.75rem; } 355 | 356 | /* Utilities 357 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 358 | .u-full-width { 359 | width: 100%; 360 | box-sizing: border-box; } 361 | .u-max-full-width { 362 | max-width: 100%; 363 | box-sizing: border-box; } 364 | .u-pull-right { 365 | float: right; } 366 | .u-pull-left { 367 | float: left; } 368 | 369 | 370 | /* Misc 371 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 372 | hr { 373 | margin-top: 3rem; 374 | margin-bottom: 3.5rem; 375 | border-width: 0; 376 | border-top: 1px solid #E1E1E1; } 377 | 378 | 379 | /* Clearing 380 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 381 | 382 | /* Self Clearing Goodness */ 383 | .container:after, 384 | .row:after, 385 | .u-cf { 386 | content: ""; 387 | display: table; 388 | clear: both; } 389 | 390 | /* Dash Undo Button */ 391 | ._dash-undo-redo { 392 | display: none; 393 | } 394 | 395 | /* Media Queries 396 | –––––––––––––––––––––––––––––––––––––––––––––––––– */ 397 | /* 398 | Note: The best way to structure the use of media queries is to create the queries 399 | near the relevant code. 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