|
180 | 180 | "nested_radio_annotation_ndjson = {\n", |
181 | 181 | " \"name\": \"nested_radio_question\",\n", |
182 | 182 | " \"answer\": {\n", |
183 | | - " \"name\": \"first_radio_answer\"\n", |
184 | | - " },\n", |
185 | | - " \"classifications\": [{\n", |
186 | | - " \"name\": \"sub_radio_question\",\n", |
187 | | - " \"answer\": {\n", |
188 | | - " \"name\": \"first_sub_radio_answer\"\n", |
189 | | - " }\n", |
190 | | - " }]\n", |
| 183 | + " \"name\": \"first_radio_answer\",\n", |
| 184 | + " \"classifications\": [{\n", |
| 185 | + " \"name\": \"sub_radio_question\",\n", |
| 186 | + " \"answer\": {\n", |
| 187 | + " \"name\": \"first_sub_radio_answer\"\n", |
| 188 | + " }\n", |
| 189 | + " }]\n", |
| 190 | + " }\n", |
191 | 191 | "}\n", |
192 | 192 | "\n", |
193 | 193 | "\n", |
|
431 | 431 | "metadata": {}, |
432 | 432 | "source": [ |
433 | 433 | "# Identifying what values in the numpy array correspond to the mask annotation\n", |
434 | | - "color = (0, 0, 0)\n", |
435 | | - "\n", |
436 | | - "# convert a polygon to mask\n", |
437 | | - "im_height, im_width = 100,100 # need to provide the height and width of image\n", |
438 | | - "mask_data = lb_types.MaskData(arr=\n", |
439 | | - " polygon_annotation.value.draw(height=im_height,width=im_width,color=color))\n", |
440 | | - "\n", |
441 | | - "# convert a 2D array to 3D array\n", |
442 | | - "arr_2d = np.zeros((100,100), dtype=\"uint8\")\n", |
443 | | - "mask_data = lb_types.MaskData.from_2D_arr(arr_2d)\n", |
444 | | - "\n", |
445 | | - "# a 3D array where 3rd axis is RGB values\n", |
446 | | - "mask_data = lb_types.MaskData(arr=np.zeros([400,450,3],dtype=\"uint8\"))\n", |
| 434 | + "color = (255, 255, 255)\n", |
| 435 | + "mask_data = lb_types.MaskData(url=\"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\")\n", |
447 | 436 | "\n", |
448 | 437 | "# Python annotation\n", |
449 | 438 | "mask_annotation = lb_types.ObjectAnnotation(\n", |
|
455 | 444 | "mask_annotation_ndjson = {\n", |
456 | 445 | " \"name\": \"mask\",\n", |
457 | 446 | " \"classifications\": [],\n", |
458 | | - " \"mask\": {\"instanceURI\": \"https://storage.labelbox.com/cjhfn5y6s0pk507024nz1ocys%2F1d60856c-59b7-3060-2754-83f7e93e0d01-1?Expires=1666901963361&KeyName=labelbox-assets-key-3&Signature=t-2s2DB4YjFuWEFak0wxYqfBfZA\",\n", |
459 | | - " \"colorRGB\": (0, 0, 0)}\n", |
| 447 | + " \"mask\": {\"instanceURI\": \"https://storage.googleapis.com/labelbox-datasets/image_sample_data/raster_seg.png\",\n", |
| 448 | + " \"colorRGB\": (255, 255, 255)}\n", |
460 | 449 | "}" |
461 | 450 | ], |
462 | 451 | "cell_type": "code", |
|
746 | 735 | " options=[lb.Option(value=\"first_sub_radio_answer\")]),\n", |
747 | 736 | " ]),\n", |
748 | 737 | " lb.Tool(tool=lb.Tool.Type.POLYGON, name=\"polygon\"),\n", |
749 | | - " lb.Tool(tool=lb.Tool.Type.SEGMENTATION, name=\"mask\"),\n", |
| 738 | + " lb.Tool(tool=lb.Tool.Type.RASTER_SEGMENTATION, name=\"mask\"),\n", |
750 | 739 | " lb.Tool(tool=lb.Tool.Type.POINT, name=\"point\"),\n", |
751 | 740 | " lb.Tool(tool=lb.Tool.Type.LINE, name=\"polyline\"),\n", |
752 | 741 | " lb.Tool(tool=lb.Tool.Type.RELATIONSHIP, name=\"relationship\")\n", |
|
831 | 820 | { |
832 | 821 | "metadata": {}, |
833 | 822 | "source": [ |
834 | | - "labels = []\n", |
| 823 | + "label = []\n", |
835 | 824 | "annotations = [\n", |
836 | 825 | " radio_annotation,\n", |
837 | 826 | " nested_radio_annotation,\n", |
|
848 | 837 | " bbox_target,\n", |
849 | 838 | " relationship,\n", |
850 | 839 | "]\n", |
851 | | - "labels.append(\n", |
| 840 | + "label.append(\n", |
852 | 841 | " lb_types.Label(data=lb_types.ImageData(global_key=global_key),\n", |
853 | 842 | " annotations=annotations))" |
854 | 843 | ], |
|
867 | 856 | { |
868 | 857 | "metadata": {}, |
869 | 858 | "source": [ |
870 | | - "ndjson_labels = []\n", |
| 859 | + "ndjson_label = []\n", |
871 | 860 | "annotations = [\n", |
872 | 861 | " radio_annotation_ndjson,\n", |
873 | 862 | " nested_radio_annotation_ndjson,\n", |
|
890 | 879 | " \"globalKey\": global_key\n", |
891 | 880 | " },\n", |
892 | 881 | " })\n", |
893 | | - " ndjson_labels.append(annotation)" |
| 882 | + " ndjson_label.append(annotation)" |
894 | 883 | ], |
895 | 884 | "cell_type": "code", |
896 | 885 | "outputs": [], |
|
919 | 908 | " client=client,\n", |
920 | 909 | " project_id=project.uid,\n", |
921 | 910 | " name=\"mal_job\" + str(uuid.uuid4()),\n", |
922 | | - " predictions=labels\n", |
| 911 | + " predictions=label\n", |
923 | 912 | ")\n", |
924 | 913 | "upload_job.wait_until_done()\n", |
925 | 914 | "\n", |
|
948 | 937 | "# client = client, \n", |
949 | 938 | "# project_id = project.uid, \n", |
950 | 939 | "# name=\"label_import_job\"+str(uuid.uuid4()), \n", |
951 | | - "# labels=ndjson_labels)\n", |
| 940 | + "# labels=label)\n", |
952 | 941 | "\n", |
953 | 942 | "# print(\"Errors:\", upload_job.errors)\n", |
954 | | - "# print(\"Status of uploads: \", upload_job.statuses)\n", |
955 | | - "# print(\" \")" |
| 943 | + "# print(\"Status of uploads: \", upload_job.statuses)" |
956 | 944 | ], |
957 | 945 | "cell_type": "code", |
958 | 946 | "outputs": [], |
|
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