Skip to content

Instantly share code, notes, and snippets.

@NirantK

NirantK/pascal_pandas.ipynb

Created March 27, 2018 01:15
Show Gist options
  • Save NirantK/e5fd67ef7a504ba032cbc02ffbad6358 to your computer and use it in GitHub Desktop.
Save NirantK/e5fd67ef7a504ba032cbc02ffbad6358 to your computer and use it in GitHub Desktop.
Download ZIP
A quick way to get the bounding boxes in fastai csv format ready for bounding box regression using Pandas.
Display the source blob
Display the rendered blob
Raw
pascal_pandas.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['images', 'type', 'annotations', 'categories'])\n"
]
}
],
"source": [
"with open(\"../PASCAL_VOC/pascal_train2007.json\") as i:\n",
" d = json.load(i)\n",
"\n",
"print(d.keys())\n",
" \n",
"categories = pd.DataFrame(d['categories'])\n",
"annotations = pd.DataFrame(d['annotations'])\n",
"images = pd.DataFrame(d['images'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>height</th>\n",
" <th>id</th>\n",
" <th>width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000012.jpg</td>\n",
" <td>333</td>\n",
" <td>12</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000017.jpg</td>\n",
" <td>364</td>\n",
" <td>17</td>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000023.jpg</td>\n",
" <td>500</td>\n",
" <td>23</td>\n",
" <td>334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000026.jpg</td>\n",
" <td>333</td>\n",
" <td>26</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000032.jpg</td>\n",
" <td>281</td>\n",
" <td>32</td>\n",
" <td>500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height id width\n",
"0 000012.jpg 333 12 500\n",
"1 000017.jpg 364 17 480\n",
"2 000023.jpg 500 23 334\n",
"3 000026.jpg 333 26 500\n",
"4 000032.jpg 281 32 500"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"images.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>supercategory</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>aeroplane</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>bicycle</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>bird</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>boat</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>bottle</td>\n",
" <td>none</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name supercategory\n",
"0 1 aeroplane none\n",
"1 2 bicycle none\n",
"2 3 bird none\n",
"3 4 boat none\n",
"4 5 bottle none"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categories.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>bbox</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>ignore</th>\n",
" <th>image_id</th>\n",
" <th>iscrowd</th>\n",
" <th>segmentation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34104</td>\n",
" <td>[155, 96, 196, 174]</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>[[155, 96, 155, 270, 351, 270, 351, 96]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13110</td>\n",
" <td>[184, 61, 95, 138]</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>[[184, 61, 184, 199, 279, 199, 279, 61]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81326</td>\n",
" <td>[89, 77, 314, 259]</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>[[89, 77, 89, 336, 403, 336, 403, 77]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>64227</td>\n",
" <td>[8, 229, 237, 271]</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>[[8, 229, 8, 500, 245, 500, 245, 229]]</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29505</td>\n",
" <td>[229, 219, 105, 281]</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>[[229, 219, 229, 500, 334, 500, 334, 219]]</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" area bbox category_id id ignore image_id iscrowd \\\n",
"0 34104 [155, 96, 196, 174] 7 1 0 12 0 \n",
"1 13110 [184, 61, 95, 138] 15 2 0 17 0 \n",
"2 81326 [89, 77, 314, 259] 13 3 0 17 0 \n",
"3 64227 [8, 229, 237, 271] 2 4 0 23 0 \n",
"4 29505 [229, 219, 105, 281] 2 5 0 23 0 \n",
"\n",
" segmentation \n",
"0 [[155, 96, 155, 270, 351, 270, 351, 96]] \n",
"1 [[184, 61, 184, 199, 279, 199, 279, 61]] \n",
"2 [[89, 77, 89, 336, 403, 336, 403, 77]] \n",
"3 [[8, 229, 8, 500, 245, 500, 245, 229]] \n",
"4 [[229, 219, 229, 500, 334, 500, 334, 219]] "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"annotations.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>area</th>\n",
" <th>bbox</th>\n",
" <th>category_id</th>\n",
" <th>id_x</th>\n",
" <th>ignore</th>\n",
" <th>image_id</th>\n",
" <th>iscrowd</th>\n",
" <th>segmentation</th>\n",
" <th>id_y</th>\n",
" <th>name</th>\n",
" <th>supercategory</th>\n",
" <th>file_name</th>\n",
" <th>height</th>\n",
" <th>id</th>\n",
" <th>width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>34104</td>\n",
" <td>[155, 96, 196, 174]</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>[[155, 96, 155, 270, 351, 270, 351, 96]]</td>\n",
" <td>7</td>\n",
" <td>car</td>\n",
" <td>none</td>\n",
" <td>000012.jpg</td>\n",
" <td>333</td>\n",
" <td>12</td>\n",
" <td>500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13110</td>\n",
" <td>[184, 61, 95, 138]</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>[[184, 61, 184, 199, 279, 199, 279, 61]]</td>\n",
" <td>15</td>\n",
" <td>person</td>\n",
" <td>none</td>\n",
" <td>000017.jpg</td>\n",
" <td>364</td>\n",
" <td>17</td>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>81326</td>\n",
" <td>[89, 77, 314, 259]</td>\n",
" <td>13</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>0</td>\n",
" <td>[[89, 77, 89, 336, 403, 336, 403, 77]]</td>\n",
" <td>13</td>\n",
" <td>horse</td>\n",
" <td>none</td>\n",
" <td>000017.jpg</td>\n",
" <td>364</td>\n",
" <td>17</td>\n",
" <td>480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>64227</td>\n",
" <td>[8, 229, 237, 271]</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>[[8, 229, 8, 500, 245, 500, 245, 229]]</td>\n",
" <td>2</td>\n",
" <td>bicycle</td>\n",
" <td>none</td>\n",
" <td>000023.jpg</td>\n",
" <td>500</td>\n",
" <td>23</td>\n",
" <td>334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>29505</td>\n",
" <td>[229, 219, 105, 281]</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" <td>[[229, 219, 229, 500, 334, 500, 334, 219]]</td>\n",
" <td>2</td>\n",
" <td>bicycle</td>\n",
" <td>none</td>\n",
" <td>000023.jpg</td>\n",
" <td>500</td>\n",
" <td>23</td>\n",
" <td>334</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" area bbox category_id id_x ignore image_id iscrowd \\\n",
"0 34104 [155, 96, 196, 174] 7 1 0 12 0 \n",
"1 13110 [184, 61, 95, 138] 15 2 0 17 0 \n",
"2 81326 [89, 77, 314, 259] 13 3 0 17 0 \n",
"3 64227 [8, 229, 237, 271] 2 4 0 23 0 \n",
"4 29505 [229, 219, 105, 281] 2 5 0 23 0 \n",
"\n",
" segmentation id_y name supercategory \\\n",
"0 [[155, 96, 155, 270, 351, 270, 351, 96]] 7 car none \n",
"1 [[184, 61, 184, 199, 279, 199, 279, 61]] 15 person none \n",
"2 [[89, 77, 89, 336, 403, 336, 403, 77]] 13 horse none \n",
"3 [[8, 229, 8, 500, 245, 500, 245, 229]] 2 bicycle none \n",
"4 [[229, 219, 229, 500, 334, 500, 334, 219]] 2 bicycle none \n",
"\n",
" file_name height id width \n",
"0 000012.jpg 333 12 500 \n",
"1 000017.jpg 364 17 480 \n",
"2 000017.jpg 364 17 480 \n",
"3 000023.jpg 500 23 334 \n",
"4 000023.jpg 500 23 334 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = (annotations\n",
" .merge(categories, how='left', left_on='category_id', right_on='id')\n",
" .merge(images, how='left', left_on='image_id', right_on='id'))\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>area</th>\n",
" <th>bbox</th>\n",
" <th>image_id</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000012.jpg</td>\n",
" <td>34104</td>\n",
" <td>[155, 96, 196, 174]</td>\n",
" <td>12</td>\n",
" <td>car</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000017.jpg</td>\n",
" <td>81326</td>\n",
" <td>[89, 77, 314, 259]</td>\n",
" <td>17</td>\n",
" <td>horse</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000023.jpg</td>\n",
" <td>111101</td>\n",
" <td>[2, 1, 241, 461]</td>\n",
" <td>23</td>\n",
" <td>person</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000026.jpg</td>\n",
" <td>21824</td>\n",
" <td>[89, 124, 248, 88]</td>\n",
" <td>26</td>\n",
" <td>car</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000032.jpg</td>\n",
" <td>28832</td>\n",
" <td>[103, 77, 272, 106]</td>\n",
" <td>32</td>\n",
" <td>aeroplane</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name area bbox image_id name\n",
"0 000012.jpg 34104 [155, 96, 196, 174] 12 car\n",
"1 000017.jpg 81326 [89, 77, 314, 259] 17 horse\n",
"2 000023.jpg 111101 [2, 1, 241, 461] 23 person\n",
"3 000026.jpg 21824 [89, 124, 248, 88] 26 car\n",
"4 000032.jpg 28832 [103, 77, 272, 106] 32 aeroplane"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"largest_bbox = data.pivot_table(index='file_name', values='area', aggfunc=max).reset_index()\n",
"largest_bbox = largest_bbox.merge(data[['area', 'bbox', 'image_id', 'file_name', 'name']], how='left')\n",
"largest_bbox.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>area</th>\n",
" <th>bbox</th>\n",
" <th>image_id</th>\n",
" <th>name</th>\n",
" <th>bbox_new</th>\n",
" <th>bbox_str</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000012.jpg</td>\n",
" <td>34104</td>\n",
" <td>[155, 96, 196, 174]</td>\n",
" <td>12</td>\n",
" <td>car</td>\n",
" <td>[96, 155, 269, 350]</td>\n",
" <td>155 96 196 174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000017.jpg</td>\n",
" <td>81326</td>\n",
" <td>[89, 77, 314, 259]</td>\n",
" <td>17</td>\n",
" <td>horse</td>\n",
" <td>[77, 89, 335, 402]</td>\n",
" <td>89 77 314 259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000023.jpg</td>\n",
" <td>111101</td>\n",
" <td>[2, 1, 241, 461]</td>\n",
" <td>23</td>\n",
" <td>person</td>\n",
" <td>[1, 2, 461, 242]</td>\n",
" <td>2 1 241 461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000026.jpg</td>\n",
" <td>21824</td>\n",
" <td>[89, 124, 248, 88]</td>\n",
" <td>26</td>\n",
" <td>car</td>\n",
" <td>[124, 89, 211, 336]</td>\n",
" <td>89 124 248 88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000032.jpg</td>\n",
" <td>28832</td>\n",
" <td>[103, 77, 272, 106]</td>\n",
" <td>32</td>\n",
" <td>aeroplane</td>\n",
" <td>[77, 103, 182, 374]</td>\n",
" <td>103 77 272 106</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name area bbox image_id name \\\n",
"0 000012.jpg 34104 [155, 96, 196, 174] 12 car \n",
"1 000017.jpg 81326 [89, 77, 314, 259] 17 horse \n",
"2 000023.jpg 111101 [2, 1, 241, 461] 23 person \n",
"3 000026.jpg 21824 [89, 124, 248, 88] 26 car \n",
"4 000032.jpg 28832 [103, 77, 272, 106] 32 aeroplane \n",
"\n",
" bbox_new bbox_str \n",
"0 [96, 155, 269, 350] 155 96 196 174 \n",
"1 [77, 89, 335, 402] 89 77 314 259 \n",
"2 [1, 2, 461, 242] 2 1 241 461 \n",
"3 [124, 89, 211, 336] 89 124 248 88 \n",
"4 [77, 103, 182, 374] 103 77 272 106 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def bb_hw_pandas(x):\n",
" return [x[1], x[0], x[1]+x[3]-1, x[0]+x[2]-1]\n",
"\n",
"largest_bbox['bbox_new'] = largest_bbox['bbox'].apply(lambda x: bb_hw_pandas(x))\n",
"largest_bbox['bbox_str'] = largest_bbox['bbox'].apply(lambda x: ' '.join(str(y) for y in x))\n",
"largest_bbox.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>bbox_str</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>000012.jpg</td>\n",
" <td>155 96 196 174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>000017.jpg</td>\n",
" <td>89 77 314 259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>000023.jpg</td>\n",
" <td>2 1 241 461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>000026.jpg</td>\n",
" <td>89 124 248 88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>000032.jpg</td>\n",
" <td>103 77 272 106</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name bbox_str\n",
"0 000012.jpg 155 96 196 174\n",
"1 000017.jpg 89 77 314 259\n",
"2 000023.jpg 2 1 241 461\n",
"3 000026.jpg 89 124 248 88\n",
"4 000032.jpg 103 77 272 106"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f = \"bbox_dataset.csv\"\n",
"largest_bbox[['file_name', 'bbox_str']].to_csv(f, index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment