File size: 4,059 Bytes
87a14dd |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 45,
"id": "8ed070be-f7e8-49dd-bc82-41bbf94c2a31",
"metadata": {},
"outputs": [],
"source": [
"from glob import glob\n",
"\n",
"path = \"/workspace/Archives/Training/*.json\"\n",
"\n",
"datasetFiles = sorted(glob(path))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "8f2aff86-c2dc-438c-95ea-badebbd02693",
"metadata": {},
"outputs": [],
"source": [
"from pascal_voc_writer import Writer\n",
"from tqdm import tqdm\n",
"import json\n",
"\n",
"for file in tqdm(datasetFiles):\n",
" try:\n",
" fileLoad = open(path+file, \"r\", encoding=\"utf8\")\n",
" fileLoad = json.load(fileLoad)\n",
" except:\n",
" print(file)\n",
" \n",
" for num in range(len(fileLoad['images'])):\n",
" fileName = str(fileLoad['images'][num]['file_name']).split(\".\")[0]\n",
" \n",
" image_w = fileLoad['images'][num]['width']\n",
" image_h = fileLoad['images'][num]['height']\n",
" \n",
" writer = Writer(database='X-ray_multi_object_recognition_data', \n",
" path=fileLoad['images'][num]['file_name'], \n",
" width=image_w, height=image_h, \n",
" depth=3, segmented=len(fileLoad['annotations']))\n",
" \n",
" for objectNum in range(len(fileLoad['annotations'])):\n",
" x, y, w, h = fileLoad['annotations'][objectNum]['bbox']\n",
" label = fileLoad['categories'][objectNum]['name']\n",
" \n",
" writer.addObject(label, x, y, x+w, y+h)\n",
" \n",
" writer.save(f'{fileName}.xml')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "7139d6cc-1e86-4797-8bf4-23ee33836259",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'images': [{'id': 0,\n",
" 'file_name': 'E3S690_20220810_012120_S_Pistol_002-001_1.png',\n",
" 'angle': 0,\n",
" 'height': 760,\n",
" 'width': 896}],\n",
" 'annotations': [{'id': 0,\n",
" 'image_id': 0,\n",
" 'iscrowd': 1,\n",
" 'category_id': 1,\n",
" 'bbox': [365.0, 222.0, 130.0, 232.0],\n",
" 'area': 13110.0,\n",
" 'segmentation': {'size': [760, 896],\n",
" 'counts': '_U_89mf0c0]Oc0E9O2N2000000001O01O01O001O01O01O00010O001O00010O00001O010O00001O01O01O00010O001O00010O00001O0100O1O100O3M4M>A>C1N2N1O2N2M^Og[OhMXd0U2m[OiMSd0V2P\\\\OhMoc0X2R\\\\OiMlc0X2U\\\\OgMic0[2W\\\\OfMgc0[2Y\\\\OeMec0\\\\2]\\\\OcMac0_2_\\\\ObMXc0f2h\\\\OZMQc0m2n\\\\OTMRc0l2n\\\\OTMRc0m2l\\\\OTMSc0m2m\\\\OSMTc0l2k\\\\OUMUc0l2i\\\\OUMWc0n2`\\\\OXM`c0a3100023O5H8F:F5KO000006J<D;E<C7I010O1N2M3L4N20000000aIW_Oo5m`0iIY_OU6Ua001O0002111J4M1RJa^Oa5_a0_Jo^OS5Ra0kJ]_Og4d`0XKj_OY4W`0gKW@k3Yb0B>B?A`0\\\\Od0\\\\Od0_O9G2N2N2N[VY9'}}],\n",
" 'categories': [{'id': 1, 'name': 'Pistol', 'supercategory': None}],\n",
" 'meta': [{'object_id': '002-001',\n",
" 'material_outside': 'X',\n",
" 'material_inside': 'Plastic',\n",
" 'size': '24',\n",
" 'unit': 'cm'}]}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = \"/workspace/Archives/Training/E3S690_20220810_012120_S_Pistol_002-001_1.json\"\n",
"\n",
"fileLoad = open(path, \"r\", encoding=\"utf8\")\n",
"json.load(fileLoad)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|