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init-GrandPrix+dataset_v1-15kps

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Кирилл Шохин
2022-08-14 23:37:05 +03:00
parent 998aec2b80
commit 6e22f83077
166 changed files with 716 additions and 0 deletions
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Grand_Prix/Supervisely/ToCOCO.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "283f6e9c",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import glob\n",
"import json\n",
"from tqdm.notebook import tqdm\n",
"\n",
"path_dataset = r'D:\\GrandPrix\\Grand-Prix'\n",
"with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
" meta = json.load(j)\n",
"\n",
"imgs = glob.glob(path_dataset + '\\\\images\\\\img\\\\*', recursive=True)\n",
"anns = glob.glob(path_dataset + '\\\\images\\\\ann\\\\*', recursive=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7af948d4",
"metadata": {},
"outputs": [],
"source": [
"def label2hash(meta_json, last):\n",
" for clss in meta_json['classes']:\n",
" if clss['title'] == last['classTitle']:\n",
" meta_nodes = clss['geometry_config']['nodes']\n",
" label2hash = {}\n",
" for name in meta_nodes:\n",
" label2hash[meta_nodes[name]['label']] = name\n",
" return label2hash"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7ea0771e",
"metadata": {},
"outputs": [],
"source": [
"def annotations(meta_json, obj):\n",
" nodes = obj['nodes']\n",
" keypoints_2d = pd.DataFrame(columns=['x', 'y'])\n",
"\n",
" for i in range(1, len(nodes)+1):\n",
" keypoints_2d.loc[i] = nodes[label2hash(meta_json, obj)[str(i)]]['loc']\n",
"\n",
" keypoints_2d['v'] = 2\n",
" keypoints_2d[(keypoints_2d.x<3)&(keypoints_2d.y<3)] = 0\n",
" keypoints_2d = keypoints_2d.astype(float).round().astype(int)\n",
" return keypoints_2d"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "26b6abf4",
"metadata": {},
"outputs": [],
"source": [
"def ann_json(keypoints, img_id, obj):\n",
" \n",
" annotation = {\n",
" \"id\": obj['id'],\n",
" \"segmentation\": [],\n",
" \"num_keypoints\": len(keypoints),\n",
" \"area\": 0,\n",
" \"iscrowd\": 0,\n",
" \"image_id\": img_id,\n",
" \"bbox\": [],\n",
" \"category_id\": 1,\n",
" \"keypoints\": keypoints.values.flatten().tolist()}\n",
"\n",
" return annotation\n",
"\n",
"def img_json(ann, name, id):\n",
" height, width = ann['size'].values()\n",
" image = {\n",
" \"id\": id,\n",
" \"width\": width,\n",
" \"height\": height,\n",
" \"file_name\": name,\n",
" }\n",
" return image"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c4d21a7",
"metadata": {},
"outputs": [],
"source": [
"def skeleton_supervisely(meta):\n",
" meta_nodes = meta['classes'][0]['geometry_config']['nodes']\n",
" label2hash = {}\n",
" for name in meta_nodes:\n",
" label2hash[meta_nodes[name]['label']] = name\n",
"\n",
" skeleton_supervisely = []\n",
" for edge in meta['classes'][0]['geometry_config']['edges']:\n",
" skeleton_supervisely.append([meta_nodes[edge['src']]['label'], meta_nodes[edge['dst']]['label']])\n",
" return skeleton_supervisely"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "155be42d",
"metadata": {},
"outputs": [],
"source": [
"def ann_img_list(anns, imgs, meta):\n",
" annotations_list = []\n",
" image_list = []\n",
" for i in tqdm(range(len(anns))):\n",
"\n",
" with open(anns[i], 'r') as j:\n",
" ann = json.load(j)\n",
" \n",
" image_name = os.path.basename(anns[i])[:-5]\n",
" image = img_json(ann, image_name, i)\n",
" image_list.append(image)\n",
"\n",
" for obj in ann['objects']:\n",
" keypoints = annotations(meta, obj)\n",
" annotations_list.append(ann_json(keypoints, i, obj))\n",
" return image_list, annotations_list, meta"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "6593f677",
"metadata": {},
"outputs": [],
"source": [
"def COCO(image_list, annotations_list, meta):\n",
" num_kpts = len(meta['classes'][0]['geometry_config']['nodes'])\n",
" coco = {\n",
"\n",
" \"info\": {\n",
" \"description\": \"karusel Dataset\", \"version\": \"1.0\", \"keypoints\":num_kpts\n",
" },\n",
"\n",
" \"categories\": [\n",
" {\n",
" \"supercategory\": \"NurburgRing\",\n",
" \"id\": 1,\n",
" \"name\": \"GrandPrix\",\n",
" \"keypoints\": list(range(num_kpts)),\n",
" \"skeleton\": skeleton_supervisely(meta)\n",
" }\n",
" ]\n",
" }\n",
"\n",
" coco['images'] = image_list\n",
" coco['annotations'] = annotations_list\n",
" return coco"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "38880d13",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7d6cab432b5425894ec499e3216f3a3",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/148 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"coco_json = COCO(*ann_img_list(anns, imgs, meta))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "314565c2",
"metadata": {},
"outputs": [],
"source": [
"with open(os.path.join(path_dataset, 'GrandPrix_COCO.json'), 'w') as file:\n",
" json.dump(coco_json, file)"
]
}
],
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}