init-GrandPrix+dataset_v1-15kps
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227
Grand_Prix/Supervisely/Bbox from Pose.ipynb
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227
Grand_Prix/Supervisely/Bbox from Pose.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": "07bdc0da",
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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 json\n",
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"import numpy as np\n",
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"from tqdm.notebook import tqdm\n",
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"path_dataset = r'D:\\GrandPrix\\Grand-Prix'"
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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": 2,
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"id": "ca890aa0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def find_image(id):\n",
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" for row in coco['images']:\n",
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" if row['id'] == id:\n",
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" return row"
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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": "b8135031",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ltrb_from_cloud(cloud_2d, imgSize, expansion=0.1):\n",
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" height, width = imgSize\n",
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" ltrb = np.round((cloud_2d[:, 0].min(), cloud_2d[:, 1].min(),\n",
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" cloud_2d[:, 0].max(), cloud_2d[:, 1].max())).astype(int)\n",
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" \n",
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" if expansion > 0:\n",
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" dx = np.round((ltrb[2]-ltrb[0])*expansion/2)\n",
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" dy = np.round((ltrb[3]-ltrb[1])*expansion/2)\n",
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" ltrb += np.array([-dx, -2*dy, dx, dy], dtype=int)\n",
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" \n",
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" ltrb[[0,2]] = np.clip(ltrb[[0,2]], 0, width)\n",
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" ltrb[[1,3]] = np.clip(ltrb[[1,3]], 0, height)\n",
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" \n",
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" return ltrb\n",
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"\n",
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"def ltrb2ltwh(ltrb):\n",
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" return np.array([ltrb[0], ltrb[1], ltrb[2]-ltrb[0], ltrb[3]-ltrb[1]], dtype=int)"
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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": "79762c08",
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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, 'GrandPrix_COCO.json'), 'r') as file:\n",
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" coco = json.load(file)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a1cfe0e2",
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"metadata": {},
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"source": [
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"### Bboxes from pose"
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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": "f9440b54",
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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": "758138a872a9485885387a5ffe5a9696",
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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%| | 0/148 [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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"for obj in tqdm(coco['annotations']):\n",
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" keypoints_2d = np.array(obj['keypoints']).reshape((-1, 3))[:, :2]\n",
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" id, width, height, file_name = find_image(obj['image_id']).values()\n",
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" \n",
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" bbox_ltrb = ltrb_from_cloud(keypoints_2d, (height, width), 0.4)\n",
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" bbox_ltwh = ltrb2ltwh(bbox_ltrb).tolist()\n",
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" obj['bbox'] = bbox_ltwh\n",
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" obj['area'] = bbox_ltwh[2]*bbox_ltwh[3]"
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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": "dfcb675b",
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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, 'GrandPrix_COCO.json'), 'w') as file:\n",
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" json.dump(coco, file)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8ff698d",
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"metadata": {},
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"source": [
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"### Split COCO json to train/val"
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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": "3aec7a2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"import copy\n",
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"\n",
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"def split_coco_json(coco, test_size=0.2, random_state=0):\n",
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" \n",
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" train_idx, test_idx = train_test_split([i['id'] for i in coco['images']],\n",
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" test_size=test_size, random_state=random_state)\n",
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"\n",
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"\n",
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" train = copy.deepcopy(coco)\n",
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" test = copy.deepcopy(coco)\n",
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"\n",
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" test['images'] = [x for x in coco['images'] if x['id'] in test_idx]\n",
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" train['images'] = [x for x in coco['images'] if x['id'] in train_idx]\n",
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"\n",
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" test['annotations'] = [x for x in coco['annotations'] if x['image_id'] in test_idx]\n",
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" train['annotations'] = [x for x in coco['annotations'] if x['image_id'] in train_idx]\n",
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" return train, test"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1cdd1a3c",
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"metadata": {},
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"source": [
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"### Create new splited dataset"
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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": "aafd54fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"train, test = split_coco_json(coco, 0.1, random_state=77)"
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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": 9,
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"id": "f6af98fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"test_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in test['images']]\n",
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"train_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in train['images']]\n",
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"\n",
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"import shutil\n",
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"path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Drivecast\\place6D_Nurburgring\\Grand_Prix\\Training\\GrandPrix_dataset_v1_15'\n",
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"\n",
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"path_train_img = os.path.join(path_new_dataset, 'images', 'train')\n",
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"path_test_img = os.path.join(path_new_dataset, 'images', 'val')\n",
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"path_ann = os.path.join(path_new_dataset, 'annotations')\n",
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"\n",
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"os.makedirs(path_train_img, exist_ok=True)\n",
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"os.makedirs(path_test_img, exist_ok=True)\n",
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"os.makedirs(path_ann, exist_ok=True)\n",
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"\n",
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"with open(os.path.join(path_ann, 'train.json'), 'w') as file:\n",
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" json.dump(train, file)\n",
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" \n",
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"with open(os.path.join(path_ann, 'val.json'), 'w') as file:\n",
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" json.dump(test, file)\n",
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"\n",
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"for path in train_path_images:\n",
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" shutil.copy(path, path_train_img)\n",
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"\n",
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"for path in test_path_images:\n",
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" shutil.copy(path, path_test_img)"
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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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