|
118 | 118 | "outputs": [], |
119 | 119 | "source": [ |
120 | 120 | "print('Downloading the data into `datasets` folder..')\n", |
121 | | - "url = 'https://privdatastorage.blob.core.windows.net/github/ipyplot/gear_images.zip'\n", |
| 121 | + "url = 'https://privdatastorage.blob.core.windows.net/github/ipyplot/gear_images.zip?sp=r&st=2022-03-15T07:56:45Z&se=2030-03-15T15:56:45Z&spr=https&sv=2020-08-04&sr=b&sig=%2BywohgAXA2E02DSVghZzHudlyvmM9kFRDydAfwtC9g8%3D'\n", |
122 | 122 | "urllib.request.urlretrieve(url, datasets_dir + zip_filename)\n", |
123 | 123 | "print(\"Done!\")" |
124 | 124 | ] |
|
242 | 242 | }, |
243 | 243 | "outputs": [], |
244 | 244 | "source": [ |
245 | | - "ipyplot.plot_class_tabs(images, labels, max_imgs_per_tab=5, img_width=150)" |
| 245 | + "ipyplot.plot_class_tabs(images, labels, max_imgs_per_tab=10, img_width=150)" |
246 | 246 | ] |
247 | 247 | }, |
248 | 248 | { |
|
282 | 282 | "\n", |
283 | 283 | "images_np = np.asarray(images)\n", |
284 | 284 | "\n", |
285 | | - "ipyplot.plot_images(images_np, max_images=5)" |
| 285 | + "ipyplot.plot_images(images_np, max_images=10)" |
286 | 286 | ] |
287 | 287 | }, |
288 | 288 | { |
|
310 | 310 | "images_df['images'] = images\n", |
311 | 311 | "images_df['labels'] = labels\n", |
312 | 312 | "\n", |
313 | | - "ipyplot.plot_images(images_df['images'], max_images=5)" |
| 313 | + "ipyplot.plot_images(images_df['images'], max_images=7)" |
314 | 314 | ] |
315 | 315 | }, |
316 | 316 | { |
|
352 | 352 | "\n", |
353 | 353 | "images_pil = [Image.open(image) for image in images]\n", |
354 | 354 | "\n", |
355 | | - "ipyplot.plot_images(images_pil, max_images=5)" |
| 355 | + "ipyplot.plot_images(images_pil, max_images=10)" |
356 | 356 | ] |
357 | 357 | }, |
358 | 358 | { |
|
377 | 377 | "\n", |
378 | 378 | "images_np = [np.asarray(image) for image in images_pil]\n", |
379 | 379 | "\n", |
380 | | - "ipyplot.plot_images(images_np, max_images=5)" |
| 380 | + "ipyplot.plot_images(images_np, max_images=10)" |
381 | 381 | ] |
382 | 382 | }, |
383 | 383 | { |
|
492 | 492 | "outputs": [], |
493 | 493 | "source": [ |
494 | 494 | "ipyplot.plot_class_tabs(\n", |
495 | | - " images, labels, max_imgs_per_tab=5, tabs_order=labels_list_filtered\n", |
| 495 | + " images, labels, max_imgs_per_tab=7, tabs_order=labels_list_filtered\n", |
496 | 496 | ")" |
497 | 497 | ] |
498 | 498 | }, |
|
535 | 535 | "end_time": "2020-10-20T19:38:37.022119Z", |
536 | 536 | "start_time": "2020-10-20T19:38:36.798582Z" |
537 | 537 | }, |
538 | | - "scrolled": true |
| 538 | + "scrolled": false |
539 | 539 | }, |
540 | 540 | "outputs": [], |
541 | 541 | "source": [ |
542 | 542 | "ignore_list = [\"boots\", \"axes\", \"helmets\", \"insulated_jackets\", \"hardshell_jackets\", \"tents\"]\n", |
543 | 543 | "\n", |
544 | 544 | "ipyplot.plot_class_representations(\n", |
545 | | - " images, labels, img_width=100, ignore_labels=ignore_list\n", |
| 545 | + " images, labels, img_width=150, ignore_labels=ignore_list\n", |
546 | 546 | ")" |
547 | 547 | ] |
548 | 548 | }, |
|
594 | 594 | }, |
595 | 595 | "outputs": [], |
596 | 596 | "source": [ |
597 | | - "ipyplot.plot_images(images, max_images=5, force_b64=True)" |
| 597 | + "ipyplot.plot_images(images, max_images=8, force_b64=True)" |
598 | 598 | ] |
| 599 | + }, |
| 600 | + { |
| 601 | + "cell_type": "code", |
| 602 | + "execution_count": null, |
| 603 | + "metadata": {}, |
| 604 | + "outputs": [], |
| 605 | + "source": [] |
599 | 606 | } |
600 | 607 | ], |
601 | 608 | "metadata": { |
602 | 609 | "kernelspec": { |
603 | | - "display_name": "Python [conda env:ipyplot_env] *", |
| 610 | + "display_name": "Python [conda env:kaggle]", |
604 | 611 | "language": "python", |
605 | | - "name": "conda-env-ipyplot_env-py" |
| 612 | + "name": "conda-env-kaggle-py" |
606 | 613 | }, |
607 | 614 | "language_info": { |
608 | 615 | "codemirror_mode": { |
|
614 | 621 | "name": "python", |
615 | 622 | "nbconvert_exporter": "python", |
616 | 623 | "pygments_lexer": "ipython3", |
617 | | - "version": "3.7.0" |
| 624 | + "version": "3.7.6" |
618 | 625 | } |
619 | 626 | }, |
620 | 627 | "nbformat": 4, |
|
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