-
Notifications
You must be signed in to change notification settings - Fork 15
/
index.html
817 lines (696 loc) · 43.8 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width">
<title>OpenAI CLIP Image Search in JavaScript (Using ONNX Web Runtime)</title>
<script src="enable-threads.js"></script>
<script src="./vips/vips.js"></script>
</head>
<body>
<style>
body * {
font-family: monospace;
}
</style>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/ort.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script> <!-- NOTE: tfjs is currently only used for image preprocessing stuff. -->
<div>
<h1 style="font-size:1rem;">Sort/search images using OpenAI's CLIP in your browser</h1>
<p>This web app sorts/searches through images in a directory on your computer using OpenAI's CLIP model, and the new File System Access API. <a href="https://github.com/josephrocca/clip-image-sorter">Here's the Github repo</a> for this web app, and <a href="https://github.com/josephrocca/openai-clip-js">here's the Github repo</a> for the web-ported CLIP models. Feel free to open an issue if you have any questions about this demo, but note that I'm not actively adding any more features right now.</p>
<p>All processing happens in your browser, on your device - i.e. your images are <b>not</b> uploaded to a server for processing.</p>
<p id="browserCompatibilityWarning" style="padding:0.25rem; background:rgb(255, 227, 160); display:none;"><b>Heads Up</b>: Your browser is missing one or more features (File System Access API and/or credentialless COEP) that are needed for this demo to work. As of writing, it works in Chromium-based browsers like Chrome and Edge. Other browsers like Firefox and Safari are often a bit slower in implementing cutting-edge features.</p>
<script>
if(!self.crossOriginIsolated || !window.showDirectoryPicker) {
browserCompatibilityWarning.style.display = "";
}
</script>
<hr>
<div id="modelNameSelectCtn" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 1:</b> Choose model:
<select onchange="window.MODEL_NAME=this.value;">
<option value="clip_vit_32">CLIP ViT-B/32 (recommended)</option>
<option value="clip_vit_32_uint8">CLIP ViT-B/32 (quantized - inaccurate embeddings)</option>
<option value="lit_b16b">LiT B16B</option>
</select>
</div>
<div id="initCtnEl" style="padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 2:</b> Download and initialize the models.
<br>
Download image model: <progress id="imageModelLoadingProgressBarEl" value="0"></progress> <span id="imageModelLoadingMbEl"></span>
<br>
Download text model: <progress id="textModelLoadingProgressBarEl" value="0"></progress> <span id="textModelLoadingMbEl"></span>
<br>
Initialize workers: <progress id="workerInitProgressBarEl" value="0"></progress>
<div style="display:none;"> <!-- more workers (dividing threads between them) doesn't seem to make things faster -->
Number of image embedding workers/threads: <input id="numThreadsEl" type="range" min="1" max="4" value="1" oninput="numThreadsDisplayEl.textContent=this.value"> <span id="numThreadsDisplayEl"></span> <script>numThreadsEl.max = navigator.hardwareConcurrency; numThreadsDisplayEl.textContent=numThreadsEl.value;</script>
</div>
<br>
<button id="initWorkersBtn" onclick="modelNameSelectCtn.style.pointerEvents='none'; modelNameSelectCtn.style.opacity=0.5; initializeWorkers()">initialize workers</button>
</div>
<div id="pickDirCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 3:</b> Pick a directory of images (images in subdirectories will be included).
<br>
<button id="pickDirectoryBtn" onclick="pickDirectory({source:'local'})">pick directory</button> or <button id="useRedditImagesBtn" onclick="pickDirectory({source:'reddit'})">use ~200k reddit images</button> (remove nsfw:<input id="removeRedditNsfwEl" type="checkbox" checked>)
<br>
<div id="redditLoadProgressCtn" style="display:none;">Download progress: <progress id="redditProgressBarEl" value="0"></progress> <span id="redditProgressMbEl"></span></div>
<div id="existingEmbeddingsProgressCtn" style="display:none;">Loading existing embeddings: <span id="existingEmbeddingsLoadedEl">none</span></div>
</div>
<div id="computeEmbeddingsCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 4:</b> Compute image embeddings. <span style="opacity:0.5;">(they will be saved as <ModelName>_embeddings.tsv in the selected directory)</span>
<br>
<button id="computeEmbeddingsBtn" onclick="computeImageEmbeddings(); this.disabled=true;">compute image embeddings</button>
<br>
<span id="computeEmbeddingsProgressEl">0</span> images embedded (<span id="computeEmbeddingsSpeedEl">?</span> ms per image) <span id="preexistingEmbeddingsEl"></span>
</div>
<div id="existingEmbeddingsFoundCtnEl" style="display:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 5:</b> <b>Existing embeddings found.</b>
<br>
Only needed if you've added or changed images: <button onclick="existingEmbeddingsFoundCtnEl.style.display='none'; computeEmbeddingsCtnEl.style.display=''; disableCtn(searchCtnEl); computeEmbeddingsBtn.click()">(re)compute image embeddings</button>
<input id="onlyEmbedNewImagesCheckbox" type="checkbox" checked> Only new images?
</div>
<div id="searchCtnEl" style="opacity:0.5; pointer-events:none; padding:0.5rem; background:lightgrey; margin:0.5rem;">
<b>Step 6:</b> Enter a search term or <button id="searchWithImagesBtn" style="font-size:80%;margin-bottom: 0.25rem;" onclick="loadImagesAsQuery()">search with image(s)</button>
<br>
<input id="searchTextEl" style="width:500px;" value="" placeholder="Enter search text here..." onclick="clearImageQueryIfNeeded()" onkeyup="if(event.which==13) searchSort()">
<button id="searchBtn" onclick="searchSort()">search</button> max results: <input id="maxResultsEl" type="number" value="100" style="width:50px;"> skip first: <input title="skip over this many of the most relevant results" id="skipFirstResultsNumEl" type="number" value="0" style="width:50px;"> score-based visibility: <button onclick="scoreBasedVisibilityCtnEl.style.display=''; this.remove();">enable</button>
<div id="searchNoteEl" style="font-size:80%;"></div>
<div id="scoreBasedVisibilityCtnEl" style="display:none;">
<textarea id="scoreBasedVisibilityRulesInputEl" style="width:100%; height:180px; white-space:nowrap;" placeholder="mouse hover here to show examples of syntax - one filter rule per line" title="skip:>0.25:blue cat skip:<0.21:elephant"></textarea>
</div>
</div>
</div>
<hr>
<b>Results</b> <span id="skippedCountEl"></span> <span style="opacity:0.5;">(hover over images for cosine similarities)</span>
<div id="resultsEl" style="margin-top:1rem; min-height:100vh;"><span style="opacity:0.5;">Click the search button to compute the results.</span></div>
<script>
/////////////
// STEP 1 //
/////////////
window.MODEL_NAME = "clip_vit_32";
window.modelData = {
clip_vit_32: {
image: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-image-vit-32-${quantized ? "uint8" : "float32"}.onnx`,
embed: async function(blob, session) {
let mode = "bicubicResizeAndCenterCrop";
if(window.debugImageTransformMode) mode = window.debugImageTransformMode;
let rgbData = await getRgbData(blob, mode);
const feeds = {input: new ort.Tensor('float32', rgbData, [1,3,224,224])};
const results = await session.run(feeds);
const embedVec = results["output"].data; // Float32Array
return embedVec;
}
},
text: {
modelUrl: (quantized) => `https://huggingface.co/rocca/openai-clip-js/resolve/main/clip-text-vit-32-${quantized ? "uint8" : "float32-int32"}.onnx`,
embed: async function(text, session) {
if(!window.textTokenizerClip) {
let Tokenizer = (await import("https://deno.land/x/[email protected]/mod.js")).default;
window.textTokenizerClip = new Tokenizer();
}
let textTokens = window.textTokenizerClip.encodeForCLIP(text);
textTokens = Int32Array.from(textTokens);
const feeds = {input: new ort.Tensor('int32', textTokens, [1, 77])};
const results = await session.run(feeds);
return [...results["output"].data];
},
}
},
lit_b16b: {
image: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_images.onnx',
embed: async function(blob, session) {
// TODO: Maybe remove tf from this code so you can remove the whole tfjs dependency
blob = await bicubicResizeAndCenterCrop(blob);
let inputImg = new Image();
await new Promise(r => inputImg.onload=r, inputImg.src=URL.createObjectURL(blob));
let img = tf.browser.fromPixels(inputImg);
img = tf.sub(tf.div(tf.expandDims(img), 127.5), 1);
let float32RgbData = img.dataSync();
const feeds = {'images': new ort.Tensor('float32', float32RgbData, [1,224,224,3])};
const results = await session.run(feeds);
return results["Identity_1:0"].data;
},
},
text: {
modelUrl: () => 'https://huggingface.co/rocca/lit-web/resolve/main/embed_text_tokens.onnx',
embed: async function(text, session) {
if(!window.bertTextTokenizerLit) {
window.bertTextTokenizerLit = await import("./bert-text-tokenizer.js").then(m => new m.BertTokenizer());
await window.bertTextTokenizerLit.load();
}
let textTokens = window.bertTextTokenizerLit.tokenize(text);
textTokens.unshift(101); // manually put CLS token at the start
textTokens.length = 16;
textTokens = [...textTokens.slice(0, 16)].map(e => e == undefined ? 0 : e); // pad with zeros to length of 16
textTokens = Int32Array.from(textTokens);
const feeds = {'text_tokens': new ort.Tensor('int32', textTokens, [1,16])};
const results = await session.run(feeds);
return [...results["Identity_1:0"].data];
}
}
},
};
let imageWorkers = [];
let onnxImageSessions = [];
let onnxTextSession;
let textTokenizer;
async function initializeWorkers() {
initWorkersBtn.disabled = true;
numThreadsEl.disabled = true;
let useQuantizedModel = false;
if(MODEL_NAME.endsWith("_uint8")) {
MODEL_NAME = MODEL_NAME.replace(/_uint8$/g, "");
useQuantizedModel = true;
}
let imageOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].image.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
imageModelLoadingProgressBarEl.value = ratio;
imageModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let textOnnxBlobPromise = downloadBlobWithProgress(window.modelData[MODEL_NAME].text.modelUrl(useQuantizedModel), function(e) {
let ratio = e.loaded / e.total;
textModelLoadingProgressBarEl.value = ratio;
textModelLoadingMbEl.innerHTML = Math.round(ratio*e.total/1e6)+" MB";
});
let [imageOnnxBlob, textOnnxBlob] = await Promise.all([imageOnnxBlobPromise, textOnnxBlobPromise])
let imageModelUrl = window.URL.createObjectURL(imageOnnxBlob);
let textModelUrl = window.URL.createObjectURL(textOnnxBlob);
let numImageWorkers = Number(numThreadsEl.value);
// Inference latency is about 5x faster with wasm threads, but this requires these headers: https://web.dev/coop-coep/ I'm using this as a hack (in enable-threads.js) since Github pages doesn't allow setting headers: https://github.com/gzuidhof/coi-serviceworker
if(self.crossOriginIsolated) {
ort.env.wasm.numThreads = Math.ceil(navigator.hardwareConcurrency / numImageWorkers) / 2; // divide by two to utilise only half the CPU's threads because trying to use all the cpu's threads actually makes it slower
}
workerInitProgressBarEl.max = numImageWorkers + 2; // +2 because of text model and bpe library
let imageModelExecutionProviders = ["wasm"]; // webgl is not compatible with this model (need to tweak conversion data/op types)
for(let i = 0; i < numImageWorkers; i++) {
let session = await ort.InferenceSession.create(imageModelUrl, { executionProviders: imageModelExecutionProviders });
onnxImageSessions.push(session);
imageWorkers.push({
session,
busy: false,
});
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
}
console.log("Image model loaded.");
onnxTextSession = await ort.InferenceSession.create(textModelUrl, { executionProviders: ["wasm"] }); // webgl is not compatible with this model (need to tweak conversion data/op types)
console.log("Text model loaded.");
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
window.URL.revokeObjectURL(imageModelUrl);
window.URL.revokeObjectURL(textModelUrl);
window.vips = await Vips(); // for bicubicly resizing images (since that's what CLIP expects)
window.vips.EMBIND_AUTOMATIC_DELETELATER = false;
workerInitProgressBarEl.value = Number(workerInitProgressBarEl.value) + 1;
disableCtn(initCtnEl);
enableCtn(pickDirCtnEl);
}
/////////////
// STEP 2 //
/////////////
let directoryHandle;
let embeddingsFileHandle;
let embeddings;
let dataSource;
async function pickDirectory(opts={}) {
dataSource = opts.source;
if(dataSource === "local") {
if(!window.showDirectoryPicker) return alert("Your browser does not support some modern features (specifically, File System Access API) required to use this web app. Please try again with a Chromium-based browser, like Chrome or Edge.");
directoryHandle = await window.showDirectoryPicker({mode: 'readwrite'});
embeddingsFileHandle = await directoryHandle.getFileHandle(`${window.MODEL_NAME}_embeddings.tsv`, {create:true});
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
pickDirectoryBtn.textContent = "Loading...";
}
let redditEmbeddingsBlob;
if(dataSource === "reddit") {
if(window.MODEL_NAME !== "clip_vit_32") return alert("Sorry, there are only pre-computed Reddit image embeddings for the CLIP ViT-B/32 model at the moment.");
if(!removeRedditNsfwEl.checked && !confirm("Are you sure you'd like to see NSFW Reddit images?")) return;
if(removeRedditNsfwEl.checked) alert("Note that NSFW images are filtered from Reddit using CLIP, and CLIP can make mistakes, so some NSFW images may still be shown.");
pickDirectoryBtn.disabled = true;
useRedditImagesBtn.disabled = true;
useRedditImagesBtn.textContent = "Loading...";
redditLoadProgressCtn.style.display = "";
redditEmbeddingsBlob = await downloadBlobWithProgress("https://huggingface.co/datasets/rocca/top-reddit-posts/resolve/main/clip_embeddings_top_50_images_per_subreddit.tsv.gz", function(e) {
let ratio = e.loaded / e.total;
redditProgressBarEl.value = ratio;
redditProgressMbEl.innerHTML = Math.round(ratio*213)+" MB";
});
}
try {
existingEmbeddingsProgressCtn.style.display = "";
embeddings = {};
let file, opts;
if(dataSource === "local") {
file = await embeddingsFileHandle.getFile();
opts = {};
}
if(dataSource === "reddit") {
file = redditEmbeddingsBlob;
opts = {decompress:"gzip"};
}
let i = 0;
for await (let line of makeTextFileLineIterator(file, opts)) {
if(!line || !line.trim()) continue; // <-- to skip final new line (not sure if this is needed)
let [filePath, embeddingVec] = line.split("\t");
embeddings[filePath] = JSON.parse(embeddingVec);
i++;
if(i % 1000 === 0) {
existingEmbeddingsLoadedEl.innerHTML = i;
await sleep(10);
}
}
} catch(e) {
embeddings = undefined;
console.log("No existing embedding found, or the embeddings file was corrupted:", e);
existingEmbeddingsProgressCtn.style.display = "none";
}
pickDirectoryBtn.textContent = "Done.";
useRedditImagesBtn.textContent = "Done.";
disableCtn(pickDirCtnEl);
enableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
if(embeddings && Object.keys(embeddings).length > 0) {
computeEmbeddingsCtnEl.style.display = "none";
existingEmbeddingsFoundCtnEl.style.display = "";
}
if(dataSource === "reddit") {
disableCtn(existingEmbeddingsFoundCtnEl);
}
}
/////////////
// STEP 3 //
/////////////
let totalEmbeddingsCount = 0;
let imagesEmbedded;
let recentEmbeddingTimes = []; // how long each embed took in ms, newest at end
let recomputeAllEmbeddings;
let imagesBeingProcessedNow = 0;
let needToSaveEmbeddings = false;
async function computeImageEmbeddings() {
imagesEmbedded = 0;
totalEmbeddingsCount = Object.keys(embeddings).length;
recomputeAllEmbeddings = !onlyEmbedNewImagesCheckbox.checked;
let gotSomeExistingEmbeddings = totalEmbeddingsCount > 0;
if(onlyEmbedNewImagesCheckbox.checked && gotSomeExistingEmbeddings) {
preexistingEmbeddingsEl.innerHTML = `(loaded ${Object.keys(embeddings).length} existing embeddings)`;
}
if(recomputeAllEmbeddings || !gotSomeExistingEmbeddings) {
embeddings = {}; // <-- maps file path (relative to top/selected directory) to embedding
}
try {
await recursivelyProcessImagesInDir(directoryHandle);
await saveEmbeddings();
} catch(e) {
console.error(e);
alert(e.message);
}
disableCtn(computeEmbeddingsCtnEl);
enableCtn(searchCtnEl);
}
async function recursivelyProcessImagesInDir(dirHandle, currentPath="") {
for await (let [name, handle] of dirHandle) {
const {kind} = handle;
let path = `${currentPath}/${name}`;
if (handle.kind === 'directory') {
await recursivelyProcessImagesInDir(handle, path);
} else {
let isImage = /\.(png|jpg|jpeg|webp)$/.test(path);
if(!isImage) continue;
let alreadyGotEmbedding = !!embeddings[path];
if(alreadyGotEmbedding && !recomputeAllEmbeddings) continue;
if(needToSaveEmbeddings) {
await saveEmbeddings();
needToSaveEmbeddings = false;
}
while(imageWorkers.filter(w => !w.busy).length === 0) await sleep(1);
let worker = imageWorkers.filter(w => !w.busy)[0];
worker.busy = true;
imagesBeingProcessedNow++;
(async function() {
let startTime = Date.now();
let blob = await handle.getFile();
const embedVec = await modelData[MODEL_NAME].image.embed(blob, worker.session);
embeddings[path] = [...embedVec];
worker.busy = false;
imagesEmbedded++;
totalEmbeddingsCount++;
computeEmbeddingsProgressEl.innerHTML = imagesEmbedded;
let saveInterval = totalEmbeddingsCount > 50_000 ? 10_000 : 1000; // since saves take longer if there are lots of embeddings
if(imagesEmbedded % saveInterval === 0) {
needToSaveEmbeddings = true;
}
recentEmbeddingTimes.push(Date.now()-startTime);
if(recentEmbeddingTimes.length > 100) recentEmbeddingTimes = recentEmbeddingTimes.slice(-50);
if(recentEmbeddingTimes.length > 10) computeEmbeddingsSpeedEl.innerHTML = Math.round(recentEmbeddingTimes.slice(-20).reduce((a,v) => a+v, 0)/20);
imagesBeingProcessedNow--;
})();
}
}
while(imagesBeingProcessedNow > 0) await sleep(10);
}
/////////////
// STEP 4 //
/////////////
async function searchSort() {
searchBtn.disabled = true;
if(dataSource === "local") {
for(let imgEl of [...document.querySelectorAll("img")]) {
URL.revokeObjectURL(imgEl.src);
}
}
resultsEl.innerHTML = "Loading...";
await sleep(50);
searchNoteEl.innerHTMl = "";
skippedCountEl.innerHTML = "";
let searchTexts = [searchTextEl.value];
let extraLabels = [];
if(searchTexts[0].includes(";;;")) {
let parts = searchTexts[0].split(";;;").map(s => s.trim());
searchTexts[0] = parts[0];
extraLabels = parts.slice(1);
searchNoteEl.innerHTML = `<b>Note</b>: Your search text contained the special <b>;;;</b> delimiter, so the first part is used as the search ranking text, and the rest will be used as extra labels. Hover over an image to see the extra label scores - they don't affect ranking.`;
} else if(searchTexts[0].includes("|||")) {
searchTexts = searchTexts[0].split("|||").map(s => s.trim());
searchNoteEl.innerHTML = `<b>Note</b>: Your search text contained the special <b>|||</b> delimiter, so it will be split up into multiple searches and the centroid/mean embedding of all the searches will be used for the scoring.`;
}
let searchEmbedding;
if(imageQueryVectors) {
searchEmbedding = meanVector(imageQueryVectors);
} else {
let embeddings = [];
for(let text of searchTexts) {
embeddings.push(await modelData[MODEL_NAME].text.embed(text, onnxTextSession));
}
searchEmbedding = meanVector(embeddings);
}
let similarities = {}; // maps path to similarity score
console.log("searchEmbedding:", searchEmbedding);
let extraLabelsEmbeddings = await Promise.all(extraLabels.map(label => modelData[MODEL_NAME].text.embed(label, onnxTextSession)));
let extraLabelsSimilarities = {}; // maps path to object with label and similarity score
for(let [path, embedding] of Object.entries(embeddings)) {
similarities[path] = cosineSimilarity(searchEmbedding, embedding);
for(let i = 0; i < extraLabels.length; i++) {
let label = extraLabels[i];
if(!extraLabelsSimilarities[path]) extraLabelsSimilarities[path] = [];
extraLabelsSimilarities[path].push({
label,
similarity: cosineSimilarity(extraLabelsEmbeddings[i], embedding),
});
}
}
let similarityEntries = Object.entries(similarities).sort((a,b) => b[1]-a[1]).slice(0, 500000);
if(dataSource === "reddit" && removeRedditNsfwEl.checked) {
let nsfwTextEmbedding = await modelData[MODEL_NAME].text.embed(atob('cG9ybiBuYWtlZCBwZW5pcyB2YWdpbmEgbnVkZSBzZXggZGljayBwdXNzeSBzZXh1YWwgcG9ybm9ncmFwaGljIGFzcyBib29icw=='), onnxTextSession); // nsfw words (hidden with `btoa`)
let nsfwSimilarities = {};
for(let [path, similarity] of similarityEntries) {
let embedding = embeddings[path];
nsfwSimilarities[path] = cosineSimilarity(nsfwTextEmbedding, embedding);
}
similarityEntries = similarityEntries.filter(e => nsfwSimilarities[e[0]] < 0.2093);
}
let scoreBasedVisibilityRules = scoreBasedVisibilityRulesInputEl.value.trim().split("\n").map(s => s.trim()).filter(s => s);
scoreBasedVisibilityRules = scoreBasedVisibilityRules.map(r => {
let [type, threshold, ...text] = r.split(":").map(s => s.trim());
text = text.join(":").trim();
if(threshold[0] !== "<" && threshold[0] !== ">") {
alert("Invalid threshold in score-based visibility rule. There must be a < or > before the threshold value.")
return null;
}
if(type !== "skip") {
alert("Only the 'skip' type is supported. Your rule should start with 'skip:'.");
return null;
}
let direction = threshold[0] === "<" ? "lt" : "gt";
threshold = Number(threshold.slice(1));
return {type, direction, threshold, text};
});
scoreBasedVisibilityRules = scoreBasedVisibilityRules.filter(r => r !== null);
for(let rule of scoreBasedVisibilityRules) {
rule.embedding = await modelData[MODEL_NAME].text.embed(rule.text, onnxTextSession);
}
let resultHtml = "";
let numResults = 0;
let maxNumResults = Number(maxResultsEl.value);
let skipFirstResultsNum = Number(skipFirstResultsNumEl.value);
let skippedDueToVisibilityRulesCount = 0;
let skitFirstCount = 0;
for(let [path, score] of similarityEntries) {
let shouldSkip = false;
for(let rule of scoreBasedVisibilityRules) {
let similarity = cosineSimilarity(rule.embedding, embeddings[path]);
if(rule.type === "skip" && rule.direction === "gt" && similarity > rule.threshold) shouldSkip = true;
if(rule.type === "skip" && rule.direction === "lt" && similarity < rule.threshold) shouldSkip = true;
}
if(shouldSkip) {
skippedDueToVisibilityRulesCount++;
continue;
}
if(skitFirstCount < skipFirstResultsNum) {
skitFirstCount++;
continue;
}
if(dataSource === "local") {
let url;
try {
let handle = await getFileHandleByPath(path).catch(e => null);
url = URL.createObjectURL(await handle.getFile());
} catch(e) { console.warn("A file that we computed embeddings for has since been deleted."); }
if(!url) continue;
resultHtml += `<img src="${url}" style="max-height:400px;" title="${path}: ${score.toFixed(3)}
${(extraLabelsSimilarities[path] || []).map(o => `${o.label}: ${o.similarity.toFixed(3)}`).join("
")}" loading="lazy"/>`;
}
if(dataSource === "reddit") {
let imageUrl = `https://i.redd.it/${path.split("__")[1]}`;
let postUrl = `https://reddit.com/comments/${path.split("__")[0].split("/")[1]}`;
resultHtml += `<a href="${postUrl}" target="_blank"><img src="${imageUrl}" onload="this.style.height='';this.style.width='';this.style.border='';" style="max-height:400px; height:300px; width:300px; border:1px solid black;" title="${path}: ${score.toFixed(3)}
${(extraLabelsSimilarities[path] || []).map(o => `${o.label}: ${o.similarity.toFixed(3)}`).join("
")}" loading="lazy"/></a>`;
}
numResults++;
if(numResults >= maxNumResults) break;
}
if(skippedDueToVisibilityRulesCount > 0) {
skippedCountEl.innerHTML = `(skipped <b>${skippedDueToVisibilityRulesCount}</b> images due to visibility rules)`;
}
if(!resultHtml) {
resultsEl.innerHTML = "No results found after filtering NSFW.";
} else {
resultsEl.innerHTML = resultHtml;
}
searchBtn.disabled = false;
}
let imageQueryVectors = null;
async function clearImageQueryIfNeeded() {
if(imageQueryVectors !== null) {
imageQueryVectors = null;
searchTextEl.value = "";
searchWithImagesBtn.innerHTML = `search with image(s)`;
}
}
async function loadImagesAsQuery() {
let fileHandles = await window.showOpenFilePicker({multiple:true});
searchWithImagesBtn.innerHTML = "loading...";
imageQueryVectors = [];
for(let handle of fileHandles) {
let blob = await handle.getFile();
let embedVec = await modelData[MODEL_NAME].image.embed(blob, imageWorkers[0].session);
imageQueryVectors.push(embedVec);
}
searchWithImagesBtn.innerHTML = `using ${imageQueryVectors.length} images`;
searchTextEl.value = "<using image query, click here to use text instead>";
}
function addImagePathsToIgnoreList() {
}
/////////////////////////////
// FUNCTIONS / UTILITIES //
/////////////////////////////
function meanVector(vectors) {
const vectorLength = vectors[0].length;
const mean = Array(vectorLength).fill(0);
vectors.forEach(vector => {
vector.forEach((value, index) => {
mean[index] += value;
});
});
return mean.map(val => val / vectors.length);
};
async function getFileHandleByPath(path) {
let handle = directoryHandle;
let chunks = path.split("/").slice(1);
for(let i = 0; i < chunks.length; i++) {
let chunk = chunks[i];
if(i === chunks.length-1) {
handle = await handle.getFileHandle(chunk);
} else {
handle = await handle.getDirectoryHandle(chunk);
}
}
return handle;
}
async function getRgbData(blob, mode="bicubicResizeAndCenterCrop") {
// let blob = await fetch(imgUrl, {referrer:""}).then(r => r.blob());
let width = 224;
let height = 224;
let imageData;
if(mode === "defaultCanvasResizeAndCenterCrop") {
let img = await createImageBitmap(blob);
let canvas = new OffscreenCanvas(224, 224);
let ctx = canvas.getContext("2d");
// scale img to fit the shorter side to the canvas size
let scale = Math.max(canvas.width / img.width, canvas.height / img.height);
// compute new image dimensions that would maintain the original aspect ratio
let scaledW = img.width * scale;
let scaledH = img.height * scale;
// compute position to center the image
let posX = (canvas.width - scaledW) / 2;
let posY = (canvas.height - scaledH) / 2;
// draw the image centered and scaled on the canvas
ctx.drawImage(img, posX, posY, scaledW, scaledH);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else if(mode === "bicubicResizeAndCenterCrop") {
let resizedBlob = await bicubicResizeAndCenterCrop(blob);
let img = await createImageBitmap(resizedBlob);
let canvas;
if(window.document) {
canvas = document.createElement("canvas");
canvas.width = width;
canvas.height = height;
} else {
canvas = new OffscreenCanvas(width, height);
}
let ctx = canvas.getContext("2d");
ctx.drawImage(img, 0, 0);
// document.body.appendChild(canvas);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else if(mode === "defaultCanvasSquash") {
let img = await createImageBitmap(resizedBlob);
let canvas = new OffscreenCanvas(width, height);
let ctx = canvas.getContext("2d");
ctx.drawImage(img, 0, 0, canvas.width, canvas.height);
imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);
} else {
throw new Error("Invalid resize/crop type");
}
let rgbData = [[], [], []]; // [r, g, b]
// remove alpha and put into correct shape:
let d = imageData.data;
for(let i = 0; i < d.length; i += 4) {
let x = (i/4) % width;
let y = Math.floor((i/4) / width)
if(!rgbData[0][y]) rgbData[0][y] = [];
if(!rgbData[1][y]) rgbData[1][y] = [];
if(!rgbData[2][y]) rgbData[2][y] = [];
rgbData[0][y][x] = d[i+0]/255;
rgbData[1][y][x] = d[i+1]/255;
rgbData[2][y][x] = d[i+2]/255;
// From CLIP repo: Normalize(mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711))
rgbData[0][y][x] = (rgbData[0][y][x] - 0.48145466) / 0.26862954;
rgbData[1][y][x] = (rgbData[1][y][x] - 0.4578275) / 0.26130258;
rgbData[2][y][x] = (rgbData[2][y][x] - 0.40821073) / 0.27577711;
}
rgbData = Float32Array.from(rgbData.flat().flat());
return rgbData;
}
async function bicubicResizeAndCenterCrop(blob) {
let im1 = vips.Image.newFromBuffer(await blob.arrayBuffer());
// Resize so smallest side is 224px:
const scale = 224 / Math.min(im1.height, im1.width);
let im2 = im1.resize(scale, { kernel: vips.Kernel.cubic });
// crop to 224x224:
let left = (im2.width - 224) / 2;
let top = (im2.height - 224) / 2;
let im3 = im2.crop(left, top, 224, 224)
let outBuffer = new Uint8Array(im3.writeToBuffer('.png'));
im1.delete(), im2.delete(), im3.delete();
return new Blob([outBuffer], { type: 'image/png' });
}
function downloadBlobWithProgress(url, onProgress) {
return new Promise((res, rej) => {
var blob;
var xhr = new XMLHttpRequest();
xhr.open('GET', url, true);
xhr.responseType = 'arraybuffer';
xhr.onload = function(e) {
blob = new Blob([this.response]);
};
xhr.onprogress = onProgress;
xhr.onloadend = function(e){
res(blob);
}
xhr.send();
});
}
async function saveEmbeddings(opts={}) {
let writable = await embeddingsFileHandle.createWritable();
let textBatch = "";
let i = 0;
for(let [filePath, embeddingVec] of Object.entries(embeddings)) {
let vecString = opts.compress ? JSON.stringify(embeddingVec.map(n => n.toFixed(3))).replace(/"/g, "") : JSON.stringify(embeddingVec);
textBatch += `${filePath}\t${vecString}\n`;
i++;
if(i % 1000 === 0) {
await writable.write(textBatch);
textBatch = "";
}
}
await writable.write(textBatch);
await writable.close();
}
// Tweaked version of example from here: https://developer.mozilla.org/en-US/docs/Web/API/ReadableStreamDefaultReader/read
async function* makeTextFileLineIterator(blob, opts={}) {
const utf8Decoder = new TextDecoder("utf-8");
let stream = await blob.stream();
if(opts.decompress === "gzip") stream = stream.pipeThrough(new DecompressionStream("gzip"));
let reader = stream.getReader();
let {value: chunk, done: readerDone} = await reader.read();
chunk = chunk ? utf8Decoder.decode(chunk, {stream: true}) : "";
let re = /\r\n|\n|\r/gm;
let startIndex = 0;
while (true) {
let result = re.exec(chunk);
if (!result) {
if (readerDone) {
break;
}
let remainder = chunk.substr(startIndex);
({value: chunk, done: readerDone} = await reader.read());
chunk = remainder + (chunk ? utf8Decoder.decode(chunk, {stream: true}) : "");
startIndex = re.lastIndex = 0;
continue;
}
yield chunk.substring(startIndex, result.index);
startIndex = re.lastIndex;
}
if (startIndex < chunk.length) {
// last line didn't end in a newline char
yield chunk.substr(startIndex);
}
}
function cosineSimilarity(A, B) {
if(A.length !== B.length) throw new Error("A.length !== B.length");
let dotProduct = 0, mA = 0, mB = 0;
for(let i = 0; i < A.length; i++){
dotProduct += A[i] * B[i];
mA += A[i] * A[i];
mB += B[i] * B[i];
}
mA = Math.sqrt(mA);
mB = Math.sqrt(mB);
let similarity = dotProduct / (mA * mB);
return similarity;
}
function sleep(ms) {
return new Promise(r => setTimeout(r, ms));
}
function enableCtn(el) {
el.style.opacity = 1;
el.style.pointerEvents = "";
}
function disableCtn(el) {
el.style.opacity = 0.5;
el.style.pointerEvents = "none";
}
// From the PyTorch model running on CUDA:
// Text: "a portrait of an astronaut with the American flag"
// Embedding: [-1.6626e-01, 5.2277e-02, -1.5332e-01, 4.4946e-01, 2.0667e-01, -2.9565e-01, 4.0588e-02, -4.1016e-01, -1.5027e-01, 3.1934e-01, -6.9702e-02, -2.5488e-01, 1.2335e-01, -9.5337e-02, 2.4109e-01, -4.8950e-02, 2.6074e-01, 5.3835e-04, 2.1033e-01, 3.7012e-01, 4.5679e-01, 3.9795e-01, 3.1641e-01, 3.9551e-01, 1.3931e-02, -4.3060e-02, 4.8798e-02, 3.7158e-01, 1.1731e-01, -3.7256e-01, -2.7295e-01, 3.3130e-01, 5.4980e-01, -2.9816e-02, -2.5806e-01, -1.0016e-01, 8.0750e-02, -6.7139e-02, -2.4072e-01, 2.4353e-01, -3.2202e-01, -1.0327e-01, 1.1566e-01, 6.2646e-01, 1.8262e-01, 2.7539e-01, -1.1816e-01, 4.9512e-01, 8.9539e-02, 5.6299e-01, 2.1313e-01, -1.5625e-01, 1.9958e-01, -5.0049e-01, -2.5854e-01, -4.0430e-01, -1.1298e-01, -6.6338e-03, 2.5391e-01, -5.0629e-02, 2.2253e-01, -2.7295e-01, -5.8289e-03, -4.8804e-01, -7.7820e-02, -3.5187e-02, -3.7537e-02, 4.3213e-01, 3.8300e-02, 2.1045e-01, -3.0347e-01, -9.8999e-02, -1.7407e-01, 2.8882e-01, 1.1322e-01, -1.0883e-01, 1.7065e-01, -2.1191e-01, 1.7920e-01, -1.2805e-01, -4.6924e-01, 1.1957e-01, -1.1829e-01, -1.1902e-01, -2.4353e-01, -9.6008e-02, 2.2913e-01, -1.0948e-02, -1.5686e-01, -2.0483e-01, -2.4756e-01, 9.1125e-02, -9.5557e-01, -4.2511e-02, 4.6356e-02, 4.3481e-01, 2.3633e-01, -3.3252e-01, 3.7231e-01, -5.5695e-02, 7.1777e-02, -1.0370e-01, -2.1912e-01, -1.3733e-01, 1.2048e-01, 1.7151e-01, -1.2659e-01, 2.3523e-01, 2.6001e-01, -4.0381e-01, 1.1761e-01, -4.1626e-02, 1.0974e-01, -5.5206e-02, 4.9713e-02, 5.2197e-01, 3.9124e-02, 5.7959e-01, 9.9609e-02, -3.3740e-01, -2.7295e-01, -7.3389e-01, 1.0962e-01, -3.2178e-01, 6.5869e-01, 2.3460e-03, 2.6733e-02, 3.2471e-02, -2.4500e-01, 7.9041e-02, 1.5405e-01, -3.5547e-01, -1.5625e-01, 4.2695e+00, -1.6113e-01, 1.6467e-01, -3.8794e-01, 2.1545e-02, 1.5771e-01, 2.0068e-01, -3.4741e-01, 2.5244e-01, 1.2201e-01, -3.9795e-02, 3.2471e-01, 2.6562e-01, 1.2915e-01, 1.4465e-02, -2.1265e-01, -2.5055e-02, 5.6689e-01, -6.5125e-02, 1.1652e-01, -5.1025e-01, 1.0712e-01, 8.8867e-02, 1.6882e-01, -6.5125e-02, 4.5929e-02, 2.9517e-01, 6.5479e-01, -9.0881e-02, -4.6417e-02, -3.0713e-01, -5.1537e-03, 4.9390e-01, 5.4492e-01, -3.1812e-01, -6.0577e-02, 3.6591e-02, 7.6782e-02, -3.5181e-01, 4.9487e-01, -4.1040e-01, 4.3970e-01, 1.6553e-01, -3.7329e-01, 3.3594e-01, -1.9263e-01, -1.8225e-01, -1.9623e-02, -4.0454e-01, 1.6187e-01, 1.5259e-01, 2.5122e-01, 1.1993e-01, -8.4595e-02, -4.1016e-01, -1.8225e-01, 1.8555e-01, -3.9124e-02, 2.5122e-01, -2.2949e-01, 5.3125e-01, -1.0504e-01, -2.1439e-02, -2.2559e-01, 5.9357e-02, -3.9160e-01, -3.3716e-01, -9.0393e-02, -1.7493e-01, -2.5952e-01, 2.3401e-01, 3.8013e-01, 1.2927e-01, 1.5491e-01, 1.1920e-01, -1.5906e-01, 5.7487e-03, 7.6172e-02, 1.5552e-01, 2.1790e-01, 9.9304e-02, -3.8025e-02, -1.1829e-01, -1.3293e-01, -2.2278e-01, -2.1472e-01, 3.0957e-01, -1.0254e-01, -2.0264e-01, -2.6840e-02, 8.8379e-02, -8.2092e-02, 1.3647e-01, -2.1399e-01, -2.5684e-01, 1.3745e-01, 6.1371e-02, -1.2988e-01, 6.2683e-02, 1.2964e-01, -3.3112e-02, 1.4111e-01, -2.6440e-01, 4.7379e-03, -1.0815e-01, -4.4971e-01, -3.5583e-02, 1.1469e-01, 6.7871e-02, -5.8350e-02, 1.0297e-01, -6.3086e-01, -8.3350e-01, 4.3481e-01, -1.7383e-01, -1.5491e-01, -5.5176e-01, 5.9766e-01, -2.6880e-01, 5.6976e-02, -2.6318e-01, -4.0466e-02, 2.4927e-01, -1.4893e-01, -2.0032e-01, -2.7515e-01, -1.2598e-01, 3.2440e-02, -1.2939e-01, 6.8018e-01, -3.6060e-01, -3.3496e-01, 9.8267e-02, -1.0010e-01, 1.9653e-01, -3.3032e-01, -2.0032e-01, 1.9116e-01, 8.2214e-02, 3.2397e-01, 1.3708e-01, 3.0899e-02, 1.5454e-01, 3.2422e-01, 1.0693e-01, 3.1714e-01, 3.7280e-01, -2.3401e-01, -5.0171e-02, 7.6758e-01, 2.2046e-01, 3.3997e-02, 1.0231e-02, -2.3999e-01, -1.3672e-01, 5.2002e-01, -6.5552e-02, 6.9275e-02, 2.3706e-01, -3.8257e-01, 1.4990e-01, -2.2656e-01, 4.3018e-01, -4.4678e-01, 2.5977e-01, 3.8208e-01, 9.5215e-02, -2.0239e-01, -2.3169e-01, -3.1396e-01, 3.2806e-02, -2.7905e-01, -1.0361e+00, 3.8147e-02, -1.0278e-01, 2.8369e-01, -3.8623e-01, -2.3132e-01, 1.5784e-01, 4.2734e+00, -1.5820e-01, -1.4758e-01, 2.0032e-01, -1.9678e-01, -7.5586e-01, 1.0712e-01, -2.1777e-01, -4.0552e-01, -1.0919e-01, 1.1932e-01, 7.3486e-01, -1.7700e-01, 5.0732e-01, -9.7351e-02, -4.7192e-01, 5.9052e-02, -7.0117e-01, -1.8774e-01, 2.4329e-01, 3.5083e-01, -2.3633e-01, 2.2205e-01, -2.4719e-03, 4.4507e-01, -1.1005e-01, 5.6494e-01, -1.4697e-01, -8.5022e-02, -2.6050e-01, -8.3679e-02, 3.0615e-01, 1.0181e-01, -2.2266e-01, -1.2054e-01, 1.0094e-02, -1.6382e-01, 3.7671e-01, 1.1371e-01, 8.8959e-03, -1.2866e-01, 7.5500e-02, -3.1177e-01, 9.0698e-02, 2.4139e-02, 2.7124e-01, 4.1382e-01, 9.3872e-02, -3.5815e-01, -1.0602e-01, -2.7637e-01, 1.5613e-01, 2.6025e-01, -1.3428e-01, -2.1509e-01, -3.0005e-01, 7.7515e-02, -2.2253e-01, -1.2634e-01, 1.0114e-01, 2.9395e-01, 4.2065e-01, -4.6425e-03, -6.3721e-01, -4.0308e-01, -5.1849e-02, -9.1309e-02, 1.0577e-01, -1.6800e-02, -4.4823e-03, 2.4231e-01, -1.3635e-01, 1.7041e-01, -9.9243e-02, -1.2439e-01, 1.5247e-01, 1.4717e-02, -1.6785e-01, -3.0615e-01, 2.6074e-01, 1.0938e-01, 4.9487e-01, 1.0529e-01, 3.1799e-02, 7.5928e-02, -1.1212e-01, -3.1201e-01, 5.8740e-01, -1.3171e-01, -1.1090e-01, 5.8887e-01, -1.1420e-01, -2.0056e-01, 1.0425e-01, 2.7710e-01, -5.8098e-03, 5.7324e-01, -1.4417e-01, 1.4575e-01, -2.7466e-01, -2.1313e-01, -1.7627e-01, 1.5466e-01, 3.8013e-01, -1.4612e-01, -2.7246e-01, -1.8604e-01, 1.0394e-01, 1.6016e-01, -1.1017e-01, 1.8140e-01, -3.0078e-01, 6.0303e-01, -1.3904e-01, 1.7322e-01, 2.2510e-01, 2.3303e-01, -5.0879e-01, -2.3462e-01, -2.0544e-01, -2.4768e-01, -2.4121e-01, -7.2754e-01, 7.2754e-01, -4.4312e-02, -1.4198e-02, -1.1475e-01, -1.3684e-01, -3.5278e-01, 1.2347e-01, -2.9602e-02, 4.0550e-03, 1.1951e-01, -6.5575e-03, -7.1228e-02, -3.8062e-01, 6.5125e-02, -1.6541e-01, -3.0289e-02, 8.6609e-02, -1.2134e-01, -1.2164e-01, 3.0319e-02, -1.5173e-01, -7.9834e-02, 1.4148e-01, 2.7319e-01, -2.1545e-01, -1.6382e-01, -2.9419e-01, -2.6611e-01, -7.5102e-04, 1.3135e-01, 9.8389e-02, 3.1812e-01, 5.5115e-02, -5.3253e-02, 4.3823e-02, 3.6957e-02, -1.3599e-01, 1.1023e-01, 9.8267e-02, 2.1643e-01, 9.3567e-02, 1.2718e-02, 1.6406e-01, -1.0338e-02, 1.9019e-01, 1.4392e-01, 3.3081e-02, 1.0138e-01, -1.6943e-01, 8.6136e-03, 1.4478e-01, 1.0941e-02, 1.3635e-01, -8.1543e-01, -3.4912e-01, 7.4959e-03, 2.1997e-01, -2.5681e-02, 2.3206e-01, 3.7622e-01, 3.6401e-01, -1.6357e-01, -2.0984e-01, -1.3220e-01, -6.7322e-02, 2.0117e-01, -4.7583e-01, 6.8054e-02, 2.2437e-01, 2.6709e-01, -5.4626e-02, -4.0741e-02, 5.2002e-02, -1.8872e-01, 3.1372e-01, -1.3574e-01, -2.6538e-01];
</script>
</body>
</html>