├── LICENSE ├── README.md ├── __init__.py ├── assets ├── create_index.png ├── search.svg ├── sort_by_frames.gif ├── sort_by_video.gif └── video_semantic_search.gif └── fiftyone.yml /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 🎥 FiftyOne + Twelve Labs Plugin 2 | 3 | Bring multimodal video intelligence into your computer vision workflows with **FiftyOne** and **Twelve Labs**. 4 | 5 | This plugin lets you generate rich video embeddings (visual, audio, OCR, conversation) using the Twelve Labs API and organize them into a **clip-level FiftyOne dataset** for analysis, search, and iteration. 6 | 7 | > ⚡ Ideal for building your own retrieval pipelines, video QA systems, or semantic labeling tools on top of real clip-level understanding. 8 | 9 | ![semantic_video_search](https://github.com/danielgural/semantic_video_search/blob/main/assets/video_semantic_search.gif) 10 | 11 | --- 12 | 13 | ## ✨ Key Features 14 | 15 | - 🧠 Generate multimodal embeddings from full videos 16 | - 🔄 Automatically split videos into meaningful **clips** 17 | - 📦 Store results in a new FiftyOne dataset with clip-level granularity 18 | - 🔍 Run **semantic search** over your indexed videos using prompts 19 | - 🔐 Uses secure secrets (`TL_API_KEY`) for easy API access 20 | 21 | --- 22 | 23 | ## 📦 Installation 24 | 25 | Install the plugin directly in FiftyOne: 26 | 27 | ```bash 28 | fiftyone plugins download https://github.com/danielgural/semantic_video_search 29 | ``` 30 | 31 | --- 32 | 33 | ## 🧩 Plugin Operators 34 | 35 | ### `create_twelve_labs_embeddings` 36 | 37 | Generate embeddings for your videos via the [Twelve Labs API](https://twelvelabs.io). Videos are automatically split into clips, and the resulting dataset contains embeddings from selected modalities: 38 | 39 | - `visual` 40 | - `audio` 41 | 42 | Each sample afterwards contains a [TemporalDetection](https://docs.voxel51.com/user_guide/using_datasets.html#temporal-detection) correlating to its embeddings. Turn your dataset into clips with [to_clips](https://docs.voxel51.com/user_guide/using_views.html#clip-views) to use as a normal embeddings! (More below!) 43 | 44 | > ☑️ Recommended to run as a **delegated operator** due to processing time. 45 | 46 | --- 47 | 48 | ### `create_twelve_labs_index` 49 | 50 | Creates a searchable **Twelve Labs index** from your embedded clips. Choose your index name and embedding types. You can build indexes from: 51 | 52 | - Entire dataset 53 | - Current view 54 | - Selected samples 55 | 56 | Note, this builds the index in Twelve Labs! 57 | 58 | --- 59 | 60 | ### `twelve_labs_index_search` 61 | 62 | Query your Twelve Labs index using a **natural language prompt**, and return results sorted by relevance. You can select one or more modalities to match (e.g., visual + audio + OCR). 63 | 64 | Use this to semantically explore your video data while keeping data in Twelve Labs! 65 | 66 | --- 67 | 68 | ## 🔐 Environment Setup 69 | 70 | You'll need a Twelve Labs API Key. 71 | 72 | ```bash 73 | export TL_API_KEY= 74 | ``` 75 | 76 | You can also securely store it in the FiftyOne App as a **plugin secret**. 77 | 78 | --- 79 | 80 | ## 🔁 Example Workflow 81 | 82 | 1. **Generate clip-level embeddings** 83 | Run `create_twelve_labs_embeddings` on a video dataset 84 | → Creates a new dataset with embedded clips for more embedding awesomeness! 85 | 86 | 2. **Index your clips** 87 | Run `create_twelve_labs_index` on the clip dataset 88 | → Builds a searchable index with selected modalities that stays in Twelve Labs 89 | 90 | 3. **Search your videos** 91 | Use `twelve_labs_index_search` with a prompt 92 | → View most relevant clips inside FiftyOne! 93 | 94 | --- 95 | 96 | ## 📚 Resources 97 | 98 | - [Twelve Labs API Docs](https://docs.twelvelabs.io/) 99 | - [FiftyOne Plugins Guide](https://docs.voxel51.com/plugins/using_plugins.html) 100 | - [Official Blog](https://voxel51.com/blog) 101 | 102 | 103 | ## Clip Dataset Conversion 104 | ``` 105 | import fiftyone.utils.video as fouv 106 | 107 | def create_clip_dataset( 108 | dataset: fo.Dataset, 109 | clip_field: str, 110 | new_dataset_name: str = "clips", 111 | overwrite: bool = True, 112 | viz: bool = False, 113 | sim: bool = False, 114 | ) -> fo.Dataset: 115 | clips = [] 116 | clip_view = dataset.to_clips(clip_field) 117 | clip_dataset = fo.Dataset(name=new_dataset_name,overwrite=overwrite) 118 | i = 0 119 | last_file = "" 120 | samples = [] 121 | for clip in clip_view: 122 | 123 | out_path = clip.filepath.split(".")[0] + f"_{i}.mp4" 124 | fpath = clip.filepath 125 | fouv.extract_clip(fpath, output_path=out_path, support=clip.support) 126 | clip.filepath = out_path 127 | samples.append(clip) 128 | clip.filepath = fpath 129 | if clip.filepath == last_file: 130 | i += 1 131 | else: 132 | i = 0 133 | last_file = clip.filepath 134 | clip_dataset.add_samples(samples) 135 | clip_dataset.add_sample_field("Twelve Labs Marengo-retrieval-27 Embeddings", fo.VectorField) 136 | clip_dataset.set_field("Twelve Labs Marengo-retrieval-27 Embeddings", clip_view.values("Twelve Labs Marengo-retrieval-27.embedding")) 137 | 138 | return clip_dataset 139 | ``` 140 | 141 | ## 🪪 License 142 | 143 | MIT 144 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | import fiftyone as fo 2 | import fiftyone.operators as foo 3 | from fiftyone.operators import types 4 | from fiftyone.brain import Similarity 5 | import time 6 | import requests 7 | import glob 8 | from pprint import pprint 9 | import os 10 | from twelvelabs import TwelveLabs 11 | 12 | 13 | class CreateTwelveLabsEmbeddings(foo.Operator): 14 | @property 15 | def config(self): 16 | return foo.OperatorConfig( 17 | name="create_twelve_labs_embeddings", 18 | label="Create Twelve Labs Embeddings", 19 | description="Store Twelve Labs embeddings on your videos in a new Clips dataset", 20 | dynamic=True, 21 | icon="/assets/search.svg", 22 | ) 23 | 24 | def resolve_input(self, ctx): 25 | inputs = types.Object() 26 | API_KEY = ctx.secret("TL_API_KEY") 27 | 28 | # API_URL = os.getenv("TWELVE_API_URL") 29 | # API_KEY = os.getenv("TWELVE_API_KEY") 30 | 31 | if API_KEY is None: 32 | inputs.view( 33 | "warning", 34 | types.Warning( 35 | label="Twelve Lab key undefined", 36 | description="Please define the enviroment variables TL_API_KEY and reload", 37 | ), 38 | ) 39 | 40 | target_view = get_target_view(ctx, inputs) 41 | inputs.view( 42 | "header", 43 | types.Header( 44 | label="Select modalities for embedding generation", 45 | description="Select one or more from the below to extract embeddings from your videos", 46 | divider=True, 47 | ), 48 | ) 49 | inputs.bool( 50 | "visual", 51 | label="visual", 52 | description="", 53 | view=types.CheckboxView(), 54 | ) 55 | inputs.bool( 56 | "audio", 57 | label="audio", 58 | description="Video must have audio to work!", 59 | view=types.CheckboxView(), 60 | ) 61 | 62 | inputs.view( 63 | "header2", 64 | types.Header( 65 | label="Advise delegating to avoid timeout", description="", divider=True 66 | ), 67 | ) 68 | _execution_mode(ctx, inputs) 69 | return types.Property(inputs) 70 | 71 | def resolve_delegation(self, ctx): 72 | return ctx.params.get("delegate", False) 73 | 74 | def execute(self, ctx): 75 | 76 | ctx.dataset.compute_metadata() 77 | target = ctx.params.get("target", None) 78 | target_view = _get_target_view(ctx, target) 79 | 80 | API_KEY = ctx.secret("TL_API_KEY") 81 | # API_KEY = os.getenv("TL_API_KEY") 82 | 83 | client = TwelveLabs(api_key=API_KEY) 84 | 85 | so = [] 86 | 87 | if ctx.params.get("visual"): 88 | so.append("visual") 89 | if ctx.params.get("audio"): 90 | so.append("audio") 91 | 92 | models = [{"name": "marengo2.7", "options": so}] 93 | 94 | videos = target_view 95 | for sample in videos: 96 | if sample.metadata.duration < 4 or sample.metadata.duration > 7200: 97 | continue 98 | else: 99 | file_name = sample.filepath.split("/")[-1] 100 | file_path = sample.filepath 101 | 102 | task = client.embed.task.create( 103 | model_name="Marengo-retrieval-2.7", 104 | video_file=file_path, 105 | ) 106 | 107 | def on_task_update(task): 108 | print(f" Status={task.status}") 109 | 110 | task.wait_for_done(sleep_interval=5, callback=on_task_update) 111 | 112 | if task.status != "ready": 113 | raise RuntimeError(f"Indexing failed with status {task.status}") 114 | 115 | retrieved_task = task.retrieve( 116 | embedding_option=["visual-text", "audio"] 117 | ) 118 | i = 0 119 | dets = [] 120 | for segment in retrieved_task.video_embedding.segments: 121 | det = fo.TemporalDetection.from_timestamps( 122 | [segment.start_offset_sec, segment.end_offset_sec], 123 | label=f"segment_{i}", 124 | sample=sample, 125 | ) 126 | 127 | i += 1 128 | det.embedding = segment.embeddings_float 129 | dets.append(det) 130 | 131 | sample["Twelve Labs Marengo-retrieval-27"] = fo.TemporalDetections( 132 | detections=dets 133 | ) 134 | sample.save() 135 | return {} 136 | 137 | 138 | class TwelveLabsSemanticSearch(foo.Operator): 139 | @property 140 | def config(self): 141 | return foo.OperatorConfig( 142 | name="twelve_labs_semantic_search", 143 | label="Twelve Labs Semantic Search", 144 | description="Semantic video search using videos as context and local Twelve Labs Embeddings", 145 | dynamic=True, 146 | icon="/assets/search.svg", 147 | ) 148 | 149 | def resolve_input(self, ctx): 150 | inputs = types.Object() 151 | 152 | API_KEY = ctx.secret("TL_API_KEY") 153 | 154 | if API_KEY is None: 155 | inputs.view( 156 | "warning", 157 | types.Warning( 158 | label="Twelve Lab key undefined", 159 | description="Please define the enviroment variables TL_API_KEY and reload", 160 | ), 161 | ) 162 | else: 163 | target_view = get_target_view(ctx, inputs) 164 | client = TwelveLabs(api_key=API_KEY) 165 | indexes = client.index.list() 166 | 167 | if not any( 168 | field.startswith("Twelve Labs") 169 | for field in list(ctx.dataset.get_field_schema().keys()) 170 | ): 171 | inputs.view( 172 | "No Embeddings", 173 | types.Warning( 174 | label="No embeddings detected", 175 | description="Please run `create twelve labs embeddings` first in order to semantic search on your dataset!", 176 | ), 177 | ) 178 | else: 179 | 180 | inputs.str("prompt", label="Prompt", required=True) 181 | 182 | _execution_mode(ctx, inputs) 183 | 184 | return types.Property(inputs) 185 | 186 | def resolve_delegation(self, ctx): 187 | return ctx.params.get("delegate", False) 188 | 189 | def execute(self, ctx): 190 | 191 | API_KEY = ctx.secret("TL_API_KEY") 192 | 193 | assert API_KEY, "Env variable TL_API_KEY not defined." 194 | 195 | target = ctx.params.get("target", None) 196 | target_view = _get_target_view(ctx, target) 197 | 198 | client = TwelveLabs(api_key=API_KEY) 199 | 200 | prompt = ctx.params.get("prompt") 201 | 202 | res = client.embed.create( 203 | model_name="Marengo-retrieval-2.7", 204 | text=prompt, 205 | ) 206 | 207 | 208 | print(f"Found {len(video_ids)} videos") 209 | samples = [] 210 | view1 = target_view.select_by( 211 | "Twelve Labs " + index_name, video_ids, ordered=True 212 | ) 213 | print(f"Found {len(view1)} samples") 214 | start = [entry.start for entry in search_results.data] 215 | end = [entry.end for entry in search_results.data] 216 | if "results" in ctx.dataset.get_field_schema().keys(): 217 | ctx.dataset.delete_sample_field("results") 218 | 219 | i = 0 220 | for sample in view1: 221 | support = [ 222 | int(start[i] * sample.metadata.frame_rate) + 1, 223 | int(end[i] * sample.metadata.frame_rate) + 1, 224 | ] 225 | sample["results"] = fo.TemporalDetection( 226 | label=prompt, support=tuple(support) 227 | ) 228 | sample.save() 229 | 230 | view2 = view1.to_clips("results") 231 | ctx.trigger("set_view", {"view": view2._serialize()}) 232 | ctx.ops.set_view(view=view2) 233 | return {} 234 | 235 | 236 | class CreateTwelveLabsIndex(foo.Operator): 237 | @property 238 | def config(self): 239 | return foo.OperatorConfig( 240 | name="create_twelve_labs_index", 241 | label="Create Twelve Labs index", 242 | description="Create a Twelve Labs index backend", 243 | dynamic=True, 244 | icon="/assets/search.svg", 245 | ) 246 | 247 | def resolve_input(self, ctx): 248 | inputs = types.Object() 249 | API_KEY = ctx.secret("TL_API_KEY") 250 | 251 | # API_URL = os.getenv("TWELVE_API_URL") 252 | # API_KEY = os.getenv("TWELVE_API_KEY") 253 | 254 | if API_KEY is None: 255 | inputs.view( 256 | "warning", 257 | types.Warning( 258 | label="Twelve Lab key undefined", 259 | description="Please define the enviroment variables TL_API_KEY and reload", 260 | ), 261 | ) 262 | 263 | target_view = get_target_view(ctx, inputs) 264 | inputs.message( 265 | "Notice", 266 | label="Create Twelve Labs Index", 267 | description="Choose your index name and the modalities of embeddings you will use!", 268 | ) 269 | inputs.str("index_name", label="Index_Name", required=True) 270 | inputs.view( 271 | "header", 272 | types.Header( 273 | label="Select modalities for index", 274 | description="Select one or more from the below to extract embeddings from your videos", 275 | divider=True, 276 | ), 277 | ) 278 | inputs.bool( 279 | "visual", 280 | label="visual", 281 | description="", 282 | view=types.CheckboxView(), 283 | ) 284 | inputs.bool( 285 | "audio", 286 | label="audio", 287 | description="Video must have audio to work!", 288 | view=types.CheckboxView(), 289 | ) 290 | 291 | inputs.view( 292 | "header2", 293 | types.Header( 294 | label="Advise delegating to avoid timeout", description="", divider=True 295 | ), 296 | ) 297 | _execution_mode(ctx, inputs) 298 | return types.Property(inputs) 299 | 300 | def resolve_delegation(self, ctx): 301 | return ctx.params.get("delegate", False) 302 | 303 | def execute(self, ctx): 304 | ctx.dataset.compute_metadata() 305 | target = ctx.params.get("target", None) 306 | target_view = _get_target_view(ctx, target) 307 | 308 | API_KEY = ctx.secret("TL_API_KEY") 309 | # API_KEY = os.getenv("TL_API_KEY") 310 | 311 | INDEX_NAME = ctx.params.get("index_name") 312 | 313 | client = TwelveLabs(api_key=API_KEY) 314 | 315 | so = [] 316 | 317 | if ctx.params.get("visual"): 318 | so.append("visual") 319 | if ctx.params.get("audio"): 320 | so.append("audio") 321 | 322 | models = [{"name": "marengo2.7", "options": so}] 323 | 324 | index = client.index.create( 325 | name=INDEX_NAME, 326 | models=models, 327 | ) 328 | 329 | index_id = index.id 330 | 331 | videos = target_view 332 | for sample in videos: 333 | if sample.metadata.duration < 4 or sample.metadata.duration > 7200: 334 | continue 335 | else: 336 | file_name = sample.filepath.split("/")[-1] 337 | file_path = sample.filepath 338 | 339 | task = client.task.create(index_id=index_id, file=file_path) 340 | 341 | def on_task_update(task): 342 | print(f" Status={task.status}") 343 | 344 | task.wait_for_done(sleep_interval=5, callback=on_task_update) 345 | 346 | if task.status != "ready": 347 | raise RuntimeError(f"Indexing failed with status {task.status}") 348 | 349 | video_id = task.video_id 350 | 351 | sample["Twelve Labs " + INDEX_NAME] = video_id 352 | sample.save() 353 | return {} 354 | 355 | 356 | class TwelveLabsIndexSearch(foo.Operator): 357 | @property 358 | def config(self): 359 | return foo.OperatorConfig( 360 | name="twelve_labs_index_search", 361 | label="Twelve Labs Index Search", 362 | description="Semantic video search using videos as context and a Twelve Labs Index on the backend", 363 | dynamic=True, 364 | icon="/assets/search.svg", 365 | ) 366 | 367 | def resolve_input(self, ctx): 368 | inputs = types.Object() 369 | 370 | API_KEY = ctx.secret("TL_API_KEY") 371 | 372 | if API_KEY is None: 373 | inputs.view( 374 | "warning", 375 | types.Warning( 376 | label="Twelve Lab key undefined", 377 | description="Please define the enviroment variables TL_API_KEY and reload", 378 | ), 379 | ) 380 | else: 381 | target_view = get_target_view(ctx, inputs) 382 | client = TwelveLabs(api_key=API_KEY) 383 | indexes = client.index.list() 384 | 385 | if indexes == []: 386 | inputs.view( 387 | "No Index", 388 | types.Warning( 389 | label="No Indexes detected", 390 | description="Please run `create semantic video index` first in order to semantic search on your dataset!", 391 | ), 392 | ) 393 | else: 394 | 395 | vis_flag = False 396 | audio_flag = False 397 | 398 | index_info = {} 399 | indexes[0].models.root[0].options 400 | for index in indexes: 401 | if "visual" in index.models.root[0].options: 402 | vis_flag = True 403 | if "audio" in index.models.root[0].options: 404 | audio_flag = True 405 | index_info[index.name] = index.id 406 | 407 | choices = index_info.keys() 408 | choices_compare = [ 409 | x[12:] for x in ctx.dataset.get_field_schema().keys() 410 | ] # change if ever add more than Twelve Labs 411 | common_index = list(set(choices_compare) & set(choices)) 412 | if len(common_index) < 1: 413 | inputs.view( 414 | "warning2", 415 | types.Warning( 416 | label="Twelve Lab Video ID Missing From Sample", 417 | description="Samples need to have a Twelve Lab Video ID associated with them.\ 418 | They are found in a field called Twelve Labs (index name). If this is missing from your dataset,\ 419 | make sure your dataset is persisent and to avoid losing between runs", 420 | ), 421 | ) 422 | else: 423 | 424 | radio_group = types.RadioGroup() 425 | 426 | for choice in common_index: 427 | radio_group.add_choice(choice, label=choice) 428 | 429 | inputs.message( 430 | "Notice", 431 | label="Semantic Video Search", 432 | description="Search through your video dataset with a prompt. If you haven't yet \ 433 | generated a similarity index with Twelve Labs, run the creaet semantic video index \ 434 | operator first! Note, you can only search on modalities within your chosen index!", 435 | ) 436 | if len(choices) != len(common_index): 437 | inputs.view( 438 | "warning3", 439 | types.Warning( 440 | label="Only showing indexes that have Video IDs on the dataset.", 441 | description="To add video IDs, run create_semantic_video_index to regenerate the index. \ 442 | It will store the Video IDs in a field called Twelve Labs (index_name). If this is missing from your dataset,\ 443 | make sure your dataset is persisent and to avoid losing between runs", 444 | ), 445 | ) 446 | inputs.enum( 447 | "index_name", 448 | radio_group.values(), 449 | label="Pick an index", 450 | description="", 451 | view=types.DropdownView(), 452 | required=True, 453 | ) 454 | inputs.str("prompt", label="Prompt", required=True) 455 | 456 | inputs.view( 457 | "header", 458 | types.Header( 459 | label="Select modalities for search", 460 | description="Select one or more from the below to search through your videos. Note: your index must have this modality!", 461 | divider=True, 462 | ), 463 | ) 464 | if vis_flag: 465 | inputs.bool( 466 | "visual", 467 | label="visual", 468 | description="", 469 | view=types.CheckboxView(), 470 | ) 471 | if audio_flag: 472 | inputs.bool( 473 | "audio", 474 | label="audio", 475 | description="Video must have audio to work!", 476 | view=types.CheckboxView(), 477 | ) 478 | 479 | _execution_mode(ctx, inputs) 480 | 481 | return types.Property(inputs) 482 | 483 | def resolve_delegation(self, ctx): 484 | return ctx.params.get("delegate", False) 485 | 486 | def execute(self, ctx): 487 | 488 | API_KEY = ctx.secret("TL_API_KEY") 489 | 490 | assert API_KEY, "Env variable TL_API_KEY not defined." 491 | 492 | target = ctx.params.get("target", None) 493 | target_view = _get_target_view(ctx, target) 494 | 495 | client = TwelveLabs(api_key=API_KEY) 496 | 497 | index_name = ctx.params.get("index_name") 498 | 499 | indexes = client.index.list() 500 | for index in indexes: 501 | if index.name == index_name: 502 | index_id = index.id 503 | 504 | prompt = ctx.params.get("prompt") 505 | 506 | so = [] 507 | 508 | if ctx.params.get("visual"): 509 | so.append("visual") 510 | if ctx.params.get("audio"): 511 | so.append("audio") 512 | 513 | if len(so) >= 2: 514 | search_results = client.search.query( 515 | index_id=index_id, query_text=prompt, options=so, operator="and" 516 | ) 517 | else: 518 | search_results = client.search.query( 519 | index_id=index_id, query_text=prompt, options=so 520 | ) 521 | 522 | video_ids = [entry.video_id for entry in search_results.data] 523 | print(f"Found {len(video_ids)} videos") 524 | samples = [] 525 | view1 = target_view.select_by( 526 | "Twelve Labs " + index_name, video_ids, ordered=True 527 | ) 528 | print(f"Found {len(view1)} samples") 529 | start = [entry.start for entry in search_results.data] 530 | end = [entry.end for entry in search_results.data] 531 | if "results" in ctx.dataset.get_field_schema().keys(): 532 | ctx.dataset.delete_sample_field("results") 533 | 534 | i = 0 535 | for sample in view1: 536 | support = [ 537 | int(start[i] * sample.metadata.frame_rate) + 1, 538 | int(end[i] * sample.metadata.frame_rate) + 1, 539 | ] 540 | sample["results"] = fo.TemporalDetection( 541 | label=prompt, support=tuple(support) 542 | ) 543 | sample.save() 544 | 545 | view2 = view1.to_clips("results") 546 | ctx.trigger("set_view", {"view": view2._serialize()}) 547 | ctx.ops.set_view(view=view2) 548 | return {} 549 | 550 | 551 | def get_target_view(ctx, inputs): 552 | has_view = ctx.view != ctx.dataset.view() 553 | has_selected = bool(ctx.selected) 554 | default_target = None 555 | 556 | if has_view or has_selected: 557 | target_choices = types.RadioGroup(orientation="horizontal") 558 | target_choices.add_choice( 559 | "DATASET", 560 | label="Entire dataset", 561 | description="Process the entire dataset", 562 | ) 563 | 564 | if has_view: 565 | target_choices.add_choice( 566 | "CURRENT_VIEW", 567 | label="Current view", 568 | description="Process the current view", 569 | ) 570 | default_target = "CURRENT_VIEW" 571 | 572 | if has_selected: 573 | target_choices.add_choice( 574 | "SELECTED_SAMPLES", 575 | label="Selected samples", 576 | description="Process only the selected samples", 577 | ) 578 | default_target = "SELECTED_SAMPLES" 579 | 580 | inputs.enum( 581 | "target", 582 | target_choices.values(), 583 | default=default_target, 584 | required=True, 585 | label="Target view", 586 | view=target_choices, 587 | ) 588 | 589 | target = ctx.params.get("target", default_target) 590 | 591 | return _get_target_view(ctx, target) 592 | 593 | 594 | def _get_target_view(ctx, target): 595 | if target == "SELECTED_SAMPLES": 596 | return ctx.view.select(ctx.selected) 597 | 598 | if target == "DATASET": 599 | return ctx.dataset 600 | 601 | return ctx.view 602 | 603 | 604 | def get_twelve_id_from_name(INDEXES_URL, headers, INDEX_NAME): 605 | response = requests.get(INDEXES_URL, headers=headers) 606 | INDEX_ID = None 607 | for index in response.json()["data"]: 608 | if index["index_name"] == INDEX_NAME: 609 | INDEX_ID = index["_id"] 610 | return INDEX_ID 611 | 612 | 613 | _BRAIN_RUN_TYPES = { 614 | "similarity": Similarity, 615 | } 616 | 617 | 618 | def get_brain_key( 619 | ctx, 620 | inputs, 621 | label="Brain key", 622 | description="Select a brain key", 623 | run_type="similarity", 624 | show_default=True, 625 | ): 626 | type = _BRAIN_RUN_TYPES.get(run_type, None) 627 | brain_keys = ctx.dataset.list_brain_runs(type=type) 628 | 629 | if not brain_keys: 630 | message = "This dataset has no text similarity brain runs" 631 | 632 | warning = types.Warning( 633 | label=message, 634 | description="https://docs.voxel51.com/user_guide/brain.html", 635 | ) 636 | prop = inputs.view("warning", warning) 637 | prop.invalid = True 638 | 639 | return 640 | 641 | text_brain_keys = [] 642 | for brain_key in brain_keys: 643 | info = ctx.dataset.get_brain_info(brain_key) 644 | if info.config.supports_prompts: 645 | text_brain_keys.append(brain_key) 646 | if len(text_brain_keys) < 1: 647 | message = "This dataset has no text similarity brain runs" 648 | 649 | warning = types.Warning( 650 | label=message, 651 | description="https://docs.voxel51.com/user_guide/brain.html", 652 | ) 653 | prop = inputs.view("warning", warning) 654 | prop.invalid = True 655 | 656 | return 657 | 658 | choices = types.DropdownView() 659 | for brain_key in brain_keys: 660 | choices.add_choice(brain_key, label=brain_key) 661 | 662 | default = brain_keys[0] if show_default else None 663 | inputs.str( 664 | "brain_key", 665 | default=default, 666 | required=True, 667 | label=label, 668 | description=description, 669 | view=choices, 670 | ) 671 | 672 | return ctx.params.get("brain_key", None) 673 | 674 | 675 | def _execution_mode(ctx, inputs): 676 | delegate = ctx.params.get("delegate", False) 677 | 678 | if delegate: 679 | description = "Uncheck this box to execute the operation immediately" 680 | else: 681 | description = "Check this box to delegate execution of this task" 682 | 683 | inputs.bool( 684 | "delegate", 685 | default=False, 686 | required=True, 687 | label="Delegate execution?", 688 | description=description, 689 | view=types.CheckboxView(), 690 | ) 691 | 692 | if delegate: 693 | inputs.view( 694 | "notice", 695 | types.Notice( 696 | label=( 697 | "You've chosen delegated execution. Note that you must " 698 | "have a delegated operation service running in order for " 699 | "this task to be processed. See " 700 | "https://docs.voxel51.com/plugins/index.html#operators " 701 | "for more information. Also, dont forget to set enviroment variables in the delegated enviroment as well!" 702 | ) 703 | ), 704 | ) 705 | 706 | 707 | def register(plugin): 708 | plugin.register(TwelveLabsIndexSearch) 709 | plugin.register(CreateTwelveLabsEmbeddings) 710 | plugin.register(CreateTwelveLabsIndex) 711 | -------------------------------------------------------------------------------- /assets/create_index.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/danielgural/semantic_video_search/e722f40438a317808eb3ee6cb91e4d187c30fc5b/assets/create_index.png -------------------------------------------------------------------------------- /assets/search.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /assets/sort_by_frames.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/danielgural/semantic_video_search/e722f40438a317808eb3ee6cb91e4d187c30fc5b/assets/sort_by_frames.gif -------------------------------------------------------------------------------- /assets/sort_by_video.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/danielgural/semantic_video_search/e722f40438a317808eb3ee6cb91e4d187c30fc5b/assets/sort_by_video.gif -------------------------------------------------------------------------------- /assets/video_semantic_search.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/danielgural/semantic_video_search/e722f40438a317808eb3ee6cb91e4d187c30fc5b/assets/video_semantic_search.gif -------------------------------------------------------------------------------- /fiftyone.yml: -------------------------------------------------------------------------------- 1 | name: "@danielgural/semantic_video_search" 2 | description: Semantic Video Search 3 | version: 1.0.0 4 | fiftyone: 5 | version: "*" 6 | url: https://github.com/danielgural/semantic_video_search/blob/main/README.md 7 | license: Apache 2.0 8 | operators: 9 | - twelve_labs_index_search 10 | - create_twelve_labs_embeddings 11 | - create_twelve_labs_index 12 | secrets: 13 | - TL_API_KEY --------------------------------------------------------------------------------