karussel_v2+new_model
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212
Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb
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212
Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "283f6e9c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import glob\n",
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"import json\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
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"with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
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" meta = json.load(j)\n",
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"\n",
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"imgs = glob.glob(path_dataset + '\\\\images\\\\img\\\\*', recursive=True)\n",
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"anns = glob.glob(path_dataset + '\\\\images\\\\ann\\\\*', recursive=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "7af948d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def label2hash(meta_json, last):\n",
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" for clss in meta_json['classes']:\n",
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" if clss['title'] == last['classTitle']:\n",
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" meta_nodes = clss['geometry_config']['nodes']\n",
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" label2hash = {}\n",
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" for name in meta_nodes:\n",
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" label2hash[meta_nodes[name]['label']] = name\n",
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" return label2hash"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "7ea0771e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def annotations(meta_json, obj):\n",
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" nodes = obj['nodes']\n",
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" keypoints_2d = pd.DataFrame(columns=['x', 'y'])\n",
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"\n",
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" for i in range(1, len(nodes)+1):\n",
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" keypoints_2d.loc[i] = nodes[label2hash(meta_json, obj)[str(i)]]['loc']\n",
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"\n",
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" keypoints_2d['v'] = 2\n",
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" keypoints_2d = keypoints_2d.astype(float).round().astype(int)\n",
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" return keypoints_2d[:24]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "26b6abf4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ann_json(keypoints, img_id, obj):\n",
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" \n",
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" annotation = {\n",
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" \"id\": obj['id'],\n",
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" \"segmentation\": [],\n",
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" \"num_keypoints\": len(keypoints),\n",
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" \"area\": 0,\n",
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" \"iscrowd\": 0,\n",
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" \"image_id\": img_id,\n",
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" \"bbox\": [],\n",
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" \"category_id\": 1,\n",
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" \"keypoints\": keypoints.values.flatten().tolist()}\n",
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"\n",
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" return annotation\n",
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"\n",
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"def img_json(ann, name, id):\n",
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" height, width = ann['size'].values()\n",
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" image = {\n",
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" \"id\": id,\n",
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" \"width\": width,\n",
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" \"height\": height,\n",
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" \"file_name\": name,\n",
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" }\n",
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" return image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "155be42d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ann_img_list(anns, imgs, meta):\n",
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" annotations_list = []\n",
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" image_list = []\n",
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" for i in tqdm(range(len(anns))):\n",
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"\n",
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" with open(anns[i], 'r') as j:\n",
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" ann = json.load(j)\n",
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" \n",
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" image_name = os.path.basename(anns[i])[:-5]\n",
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" image = img_json(ann, image_name, i)\n",
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" image_list.append(image)\n",
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"\n",
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" for obj in ann['objects']:\n",
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" keypoints = annotations(meta, obj)\n",
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" annotations_list.append(ann_json(keypoints, i, obj))\n",
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" return image_list, annotations_list"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "6593f677",
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"metadata": {},
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"outputs": [],
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"source": [
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"def COCO(image_list, annotations_list):\n",
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" coco = {\n",
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"\n",
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" \"info\": {\n",
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" \"description\": \"karusel Dataset\", \"version\": \"2.0\"\n",
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" },\n",
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"\n",
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" \"categories\": [\n",
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" {\n",
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" \"supercategory\": \"NurburgRing\",\n",
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" \"id\": 1,\n",
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" \"name\": \"karusel\",\n",
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" \"keypoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
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" 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24],\n",
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" \"skeleton\": [\n",
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" [1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7],[7, 8],[8, 9],[9, 10],[10, 11],\n",
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" [11, 12],[12, 13],[13, 14],[14, 15],[15, 16],[16, 17],[17, 18],[18, 19],[19, 20],\n",
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" [20, 21],[21, 22],[22, 23],[23, 24],[24, 1],[24, 3],[1, 5]\n",
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" ]\n",
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" }\n",
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" ]\n",
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" }\n",
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"\n",
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" coco['images'] = image_list\n",
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" coco['annotations'] = annotations_list\n",
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" return coco"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "38880d13",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "96021bfc91ce425dbef57ae348321005",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/448 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"coco_json = COCO(*ann_img_list(anns, imgs, meta))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "314565c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
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" json.dump(coco_json, file)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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