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lesolorzanov/mergiwild.ipynb

Last active May 31, 2019 17:39
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mergiwild.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"import IPython.display\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"images\n",
"info\n",
"annotations\n",
"categories\n",
"images\n",
"info\n",
"annotations\n",
"categories\n"
]
}
],
"source": [
"f=\"/home/leslie/Documents/Uppsala/courses/DLreadingCourse/iwildstuff/iWildCam_2019_iNat_Idaho/iWildCam_2019_iNat_Idaho.json\"\n",
"with open(f) as json_file: \n",
" idahodata = json.load(json_file)\n",
" for p in idahodata:\n",
" print(p)\n",
" \n",
"f=\"/home/leslie/Documents/Uppsala/courses/DLreadingCourse/iwildstuff/iWildCam_2019_CCT.json\"\n",
"with open(f) as json_file: \n",
" cctdata = json.load(json_file)\n",
" for p in cctdata:\n",
" print(p)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25263\n",
"25263\n",
"23\n",
"196157\n",
"196157\n",
"23\n"
]
}
],
"source": [
"idaho_images_df=pd.DataFrame(idahodata[\"images\"])\n",
"idaho_annotations_df=pd.DataFrame(idahodata[\"annotations\"])\n",
"idaho_categories_df=pd.DataFrame(idahodata[\"categories\"])\n",
"\n",
"cct_images_df=pd.DataFrame(cctdata[\"images\"])\n",
"cct_annotations_df=pd.DataFrame(cctdata[\"annotations\"])\n",
"cct_categories_df=pd.DataFrame(cctdata[\"categories\"])\n",
"\n",
"print(len(idaho_images_df))\n",
"print(len(idaho_annotations_df))\n",
"print(len(idaho_categories_df))\n",
"print(len(cct_images_df))\n",
"print(len(cct_annotations_df))\n",
"print(len(cct_categories_df))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-idaho_images_df\n",
"['file_name', 'height', 'id', 'inat_year', 'license', 'rights_holder', 'width']\n",
"\n",
"\n",
"-idaho_annotations_df\n",
"['category_id', 'id', 'image_id', 'inat_species']\n",
"\n",
"\n",
"-idaho_categories_df\n",
"['id', 'inat_species_list', 'name']\n",
"\n",
"\n",
"-cct_images_df\n",
"['date_captured', 'file_name', 'frame_num', 'height', 'id', 'location', 'rights_holder', 'seq_id', 'seq_num_frames', 'width']\n",
"\n",
"\n",
"-cct_annotations_df\n",
"['category_id', 'id', 'image_id']\n",
"\n",
"\n",
"-cct_categories_df\n",
"['id', 'name']\n",
"\n",
"\n"
]
}
],
"source": [
"print(\"-idaho_images_df\")\n",
"s= [str(x) for x in list(idaho_images_df)] \n",
"print(s);print(\"\\n\")\n",
"\n",
"print(\"-idaho_annotations_df\")\n",
"s= [str(x) for x in list(idaho_annotations_df)] \n",
"print(s);print(\"\\n\")\n",
" \n",
"print(\"-idaho_categories_df\")\n",
"s= [str(x) for x in list(idaho_categories_df)] \n",
"print(s);print(\"\\n\")\n",
"\n",
"print(\"-cct_images_df\")\n",
"s= [str(x) for x in list(cct_images_df)] \n",
"print(s);print(\"\\n\")\n",
" \n",
"print(\"-cct_annotations_df\")\n",
"s= [str(x) for x in list(cct_annotations_df)] \n",
"print(s);print(\"\\n\")\n",
"\n",
"print(\"-cct_categories_df\")\n",
"s= [str(x) for x in list(cct_categories_df)] \n",
"print(s);print(\"\\n\")\n",
"\n",
"keepids=['file_name', 'height', 'id', 'width']\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Idaho dataset"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idahocols=idaho_images_df.columns\n",
"for c in idahocols:\n",
" if c not in keepids:\n",
" idaho_images_df=idaho_images_df.drop(c,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>image_id</th>\n",
" <th>width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/6e...</td>\n",
" <td>532</td>\n",
" <td>1</td>\n",
" <td>800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/dc...</td>\n",
" <td>533</td>\n",
" <td>2</td>\n",
" <td>800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height image_id width\n",
"0 train_val2017/Mammalia/Marmota flaviventris/6e... 532 1 800\n",
"1 train_val2017/Mammalia/Marmota flaviventris/dc... 533 2 800"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idaho_images_df.rename(columns={\"id\":\"image_id\"},inplace=True)\n",
"idaho_images_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>image_id</th>\n",
" <th>inat_species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Marmota flaviventris</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>Marmota flaviventris</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category_id id image_id inat_species\n",
"0 3 1 1 Marmota flaviventris\n",
"1 3 2 2 Marmota flaviventris"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idaho_annotations_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>image_id</th>\n",
" <th>width</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/6e...</td>\n",
" <td>532</td>\n",
" <td>1</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/dc...</td>\n",
" <td>533</td>\n",
" <td>2</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height image_id width \\\n",
"0 train_val2017/Mammalia/Marmota flaviventris/6e... 532 1 800 \n",
"1 train_val2017/Mammalia/Marmota flaviventris/dc... 533 2 800 \n",
"\n",
" category_id id \n",
"0 3 1 \n",
"1 3 2 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idaho_merged=pd.merge(idaho_images_df,idaho_annotations_df,on=\"image_id\")\n",
"idaho_merged=idaho_merged.drop(\"inat_species\",axis=1)\n",
"idaho_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>image_id</th>\n",
" <th>width</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>dataset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/6e...</td>\n",
" <td>532</td>\n",
" <td>1</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/dc...</td>\n",
" <td>533</td>\n",
" <td>2</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height image_id width \\\n",
"0 train_val2017/Mammalia/Marmota flaviventris/6e... 532 1 800 \n",
"1 train_val2017/Mammalia/Marmota flaviventris/dc... 533 2 800 \n",
"\n",
" category_id id dataset \n",
"0 3 1 idaho \n",
"1 3 2 idaho "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idaho_merged.insert(6, \"dataset\", \"idaho\")\n",
"idaho_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>image_id</th>\n",
" <th>width</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>dataset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/6e...</td>\n",
" <td>532</td>\n",
" <td>1</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/dc...</td>\n",
" <td>533</td>\n",
" <td>2</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height image_id width \\\n",
"0 train_val2017/Mammalia/Marmota flaviventris/6e... 532 1 800 \n",
"1 train_val2017/Mammalia/Marmota flaviventris/dc... 533 2 800 \n",
"\n",
" category_id id dataset \n",
"0 3 1 idaho \n",
"1 3 2 idaho "
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(idaho_merged)\n",
"idaho_merged.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CCT dataset"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cctcols=cct_images_df.columns\n",
"for c in cctcols:\n",
" if c not in keepids:\n",
" print(c)\n",
" cct_images_df=cct_images_df.drop(c,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>file_name</th>\n",
" <th>height</th>\n",
" <th>image_id</th>\n",
" <th>width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1494</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>2048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1494</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>2048</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height \\\n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg 1494 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b.jpg 1494 \n",
"\n",
" image_id width \n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b 2048 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b 2048 "
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cct_images_df.rename(columns={\"id\":\"image_id\"},inplace=True)\n",
"cct_images_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>category_id</th>\n",
" <th>id</th>\n",
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" <tbody>\n",
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"text/plain": [
" category_id id \\\n",
"0 19 2a545480-cbf1-11e8-819c-970a9450cdbc \n",
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"\n",
" image_id \n",
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"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cct_annotations_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cct_merged=pd.merge(cct_images_df,cct_annotations_df,on=\"image_id\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false,
"hide_input": false
},
"outputs": [
{
"data": {
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" file_name height \\\n",
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" image_id width category_id \\\n",
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"1 2a5455b6-cbf1-11e8-819c-970a9450cdbc "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cct_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cct_merged.insert(6, \"dataset\", \"cct\")"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <th>height</th>\n",
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" <th>width</th>\n",
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" <th>id</th>\n",
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" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1494</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>2048</td>\n",
" <td>19</td>\n",
" <td>2a545480-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>cct</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1494</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>2048</td>\n",
" <td>19</td>\n",
" <td>2a5455b6-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>cct</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height \\\n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg 1494 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b.jpg 1494 \n",
"\n",
" image_id width category_id \\\n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b 2048 19 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b 2048 19 \n",
"\n",
" id dataset \n",
"0 2a545480-cbf1-11e8-819c-970a9450cdbc cct \n",
"1 2a5455b6-cbf1-11e8-819c-970a9450cdbc cct "
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cct_merged.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Append them"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>image_id</th>\n",
" <th>width</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>dataset</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/6e...</td>\n",
" <td>532</td>\n",
" <td>1</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>train_val2017/Mammalia/Marmota flaviventris/dc...</td>\n",
" <td>533</td>\n",
" <td>2</td>\n",
" <td>800</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>idaho</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name height image_id width \\\n",
"0 train_val2017/Mammalia/Marmota flaviventris/6e... 532 1 800 \n",
"1 train_val2017/Mammalia/Marmota flaviventris/dc... 533 2 800 \n",
"\n",
" category_id id dataset \n",
"0 3 1 idaho \n",
"1 3 2 idaho "
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extra_df=idaho_merged.append(cct_merged, ignore_index=True)\n",
"extra_df.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get values corresponding to complementary classes\n",
" This is from the idaho dataset only!"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idaho_needed=[2,5,6,7,9,12,15,20,21,22] #in idaho labels are still numbers\n",
"#idaho_needed=[\"2\",\"5\",\"6\",\"7\",\"9\",\"12\",\"15\",\"20\",\"21\",\"22\"]\n",
"try_anno_from_these=idaho_merged.loc[idaho_merged[\"category_id\"].isin(idaho_needed)]\n",
"try_anno_from_these.to_csv(\"/home/leslie/Documents/Uppsala/courses/DLreadingCourse/iwildstuff/idaho_necessary.csv\",index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## So, bad news, this dataset is messy AF.\n",
"\n",
"CCT bboxes are actually the bounding boxes of what they call iWild. CCT without bboxes is what they call CCT big. and CCT small has no json or csv. Basically CCT small is useless.\n",
"\n",
"bboxes are of the type: [x, y, width, height]"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"images\n",
"info\n",
"annotations\n",
"categories\n"
]
}
],
"source": [
"f=\"/home/leslie/Documents/Uppsala/courses/DLreadingCourse/iwildstuff/iWildCam_2019_CCT_Bboxes.json\"\n",
"with open(f) as json_file: \n",
" cctbboxdata = json.load(json_file)\n",
" for p in cctbboxdata:\n",
" print(p)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-cctbbox_images_df\n",
"['date_captured', 'file_name', 'frame_num', 'height', 'id', 'location', 'rights_holder', 'seq_id', 'seq_num_frames', 'width']\n",
"\n",
"\n",
"-cctbbox_annotations_df\n",
"['bbox', 'category_id', 'id', 'image_id']\n",
"\n",
"\n",
"-cctbbox_categories_df\n",
"['id', 'name']\n",
"\n",
"\n"
]
}
],
"source": [
"cctbbox_images_df=pd.DataFrame(cctbboxdata[\"images\"])\n",
"cctbbox_annotations_df=pd.DataFrame(cctbboxdata[\"annotations\"])\n",
"cctbbox_categories_df=pd.DataFrame(cctbboxdata[\"categories\"])\n",
"\n",
"print(\"-cctbbox_images_df\")\n",
"s= [str(x) for x in list(cctbbox_images_df)] \n",
"print(s);print(\"\\n\")\n",
"\n",
"print(\"-cctbbox_annotations_df\")\n",
"s= [str(x) for x in list(cctbbox_annotations_df)] \n",
"print(s);print(\"\\n\")\n",
" \n",
"print(\"-cctbbox_categories_df\")\n",
"s= [str(x) for x in list(cctbbox_categories_df)] \n",
"print(s);print(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date_captured</th>\n",
" <th>file_name</th>\n",
" <th>frame_num</th>\n",
" <th>height</th>\n",
" <th>image_id</th>\n",
" <th>location</th>\n",
" <th>rights_holder</th>\n",
" <th>seq_id</th>\n",
" <th>seq_num_frames</th>\n",
" <th>width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2011-05-13 23:43:18</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1</td>\n",
" <td>1494</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>33</td>\n",
" <td>Justin Brown</td>\n",
" <td>6f084ccc-5567-11e8-bc84-dca9047ef277</td>\n",
" <td>3</td>\n",
" <td>2048</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date_captured file_name frame_num \\\n",
"0 2011-05-13 23:43:18 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg 1 \n",
"\n",
" height image_id location rights_holder \\\n",
"0 1494 5998cfa4-23d2-11e8-a6a3-ec086b02610b 33 Justin Brown \n",
"\n",
" seq_id seq_num_frames width \n",
"0 6f084ccc-5567-11e8-bc84-dca9047ef277 3 2048 "
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cctbbox_images_df=cctbbox_images_df.rename(columns={\"id\":\"image_id\"})\n",
"cctbbox_images_df.head(1)\n"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bbox</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>image_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[499.2, 711.68, 353.28, 199.68]</td>\n",
" <td>19</td>\n",
" <td>2a545520-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[985.6, 783.36, 368.64, 230.4]</td>\n",
" <td>19</td>\n",
" <td>2a54562e-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bbox category_id \\\n",
"0 [499.2, 711.68, 353.28, 199.68] 19 \n",
"1 [985.6, 783.36, 368.64, 230.4] 19 \n",
"\n",
" id image_id \n",
"0 2a545520-cbf1-11e8-819c-970a9450cdbc 5998cfa4-23d2-11e8-a6a3-ec086b02610b \n",
"1 2a54562e-cbf1-11e8-819c-970a9450cdbc 588a679f-23d2-11e8-a6a3-ec086b02610b "
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cctbbox_annotations_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false,
"hide_input": true
},
"outputs": [],
"source": [
"#this was just to check that all images wuith bboxes are actually real files in the iwild folder\n",
"#ps: yes, they are\n",
"# arr=cctbbox_images_df[\"file_name\"].values\n",
"# location=\"/media/leslie/CBAEXT4/iwildcam_2019/train_images/\"\n",
"# existsIniWild=[]\n",
"# doesnotexistiniWild=[]\n",
"# for im in arr:\n",
"# exists = os.path.isfile(location+str(im))\n",
"# if(exists):\n",
"# existsIniWild.append(im)\n",
"# else:\n",
"# doesnotexistiniWild.append(im)\n",
"\n",
"# print(len(existsIniWild))\n",
"# print(len(doesnotexistiniWild))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cctbbox_annotations_df.iloc[0][\"bbox\"])"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#convert the bbox list into different columns\n",
"def bboxtocols(row,num):\n",
" bboxlist=list(row[\"bbox\"])\n",
" return int(bboxlist[num]//2)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bbox</th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>image_id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>w</th>\n",
" <th>h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[499.2, 711.68, 353.28, 199.68]</td>\n",
" <td>19</td>\n",
" <td>2a545520-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>249</td>\n",
" <td>355</td>\n",
" <td>176</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[985.6, 783.36, 368.64, 230.4]</td>\n",
" <td>19</td>\n",
" <td>2a54562e-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>492</td>\n",
" <td>391</td>\n",
" <td>184</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bbox category_id \\\n",
"0 [499.2, 711.68, 353.28, 199.68] 19 \n",
"1 [985.6, 783.36, 368.64, 230.4] 19 \n",
"\n",
" id image_id \\\n",
"0 2a545520-cbf1-11e8-819c-970a9450cdbc 5998cfa4-23d2-11e8-a6a3-ec086b02610b \n",
"1 2a54562e-cbf1-11e8-819c-970a9450cdbc 588a679f-23d2-11e8-a6a3-ec086b02610b \n",
"\n",
" x y w h \n",
"0 249 355 176 99 \n",
"1 492 391 184 115 "
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#convert the bbox list into different columns\n",
"cctbbox_annotations_df[\"x\"]=cctbbox_annotations_df.apply(lambda row: bboxtocols(row,0), axis=1)\n",
"cctbbox_annotations_df[\"y\"]=cctbbox_annotations_df.apply(lambda row: bboxtocols(row,1), axis=1)\n",
"cctbbox_annotations_df[\"w\"]=cctbbox_annotations_df.apply(lambda row: bboxtocols(row,2), axis=1)\n",
"cctbbox_annotations_df[\"h\"]=cctbbox_annotations_df.apply(lambda row: bboxtocols(row,3), axis=1)\n",
"cctbbox_annotations_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category_id</th>\n",
" <th>id</th>\n",
" <th>image_id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>w</th>\n",
" <th>h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19</td>\n",
" <td>2a545520-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>249</td>\n",
" <td>355</td>\n",
" <td>176</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>19</td>\n",
" <td>2a54562e-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>492</td>\n",
" <td>391</td>\n",
" <td>184</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category_id id \\\n",
"0 19 2a545520-cbf1-11e8-819c-970a9450cdbc \n",
"1 19 2a54562e-cbf1-11e8-819c-970a9450cdbc \n",
"\n",
" image_id x y w h \n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b 249 355 176 99 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b 492 391 184 115 "
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#drop the bbox column\n",
"cctbbox_annotations_df=cctbbox_annotations_df.drop(\"bbox\",axis=1)\n",
"cctbbox_annotations_df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date_captured</th>\n",
" <th>file_name</th>\n",
" <th>frame_num</th>\n",
" <th>height</th>\n",
" <th>image_id</th>\n",
" <th>location</th>\n",
" <th>rights_holder</th>\n",
" <th>seq_id</th>\n",
" <th>seq_num_frames</th>\n",
" <th>width</th>\n",
" <th>category_id</th>\n",
" <th>dataset</th>\n",
" <th>id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>w</th>\n",
" <th>h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2011-05-13 23:43:18</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>1</td>\n",
" <td>1494</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>33</td>\n",
" <td>Justin Brown</td>\n",
" <td>6f084ccc-5567-11e8-bc84-dca9047ef277</td>\n",
" <td>3</td>\n",
" <td>2048</td>\n",
" <td>19</td>\n",
" <td>iwild</td>\n",
" <td>2a545520-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>249</td>\n",
" <td>355</td>\n",
" <td>176</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-03-17 03:48:44</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>2</td>\n",
" <td>1494</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>115</td>\n",
" <td>Justin Brown</td>\n",
" <td>6f12067d-5567-11e8-b3c0-dca9047ef277</td>\n",
" <td>3</td>\n",
" <td>2048</td>\n",
" <td>19</td>\n",
" <td>iwild</td>\n",
" <td>2a54562e-cbf1-11e8-819c-970a9450cdbc</td>\n",
" <td>492</td>\n",
" <td>391</td>\n",
" <td>184</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date_captured file_name frame_num \\\n",
"0 2011-05-13 23:43:18 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg 1 \n",
"1 2012-03-17 03:48:44 588a679f-23d2-11e8-a6a3-ec086b02610b.jpg 2 \n",
"\n",
" height image_id location rights_holder \\\n",
"0 1494 5998cfa4-23d2-11e8-a6a3-ec086b02610b 33 Justin Brown \n",
"1 1494 588a679f-23d2-11e8-a6a3-ec086b02610b 115 Justin Brown \n",
"\n",
" seq_id seq_num_frames width category_id \\\n",
"0 6f084ccc-5567-11e8-bc84-dca9047ef277 3 2048 19 \n",
"1 6f12067d-5567-11e8-b3c0-dca9047ef277 3 2048 19 \n",
"\n",
" dataset id x y w h \n",
"0 iwild 2a545520-cbf1-11e8-819c-970a9450cdbc 249 355 176 99 \n",
"1 iwild 2a54562e-cbf1-11e8-819c-970a9450cdbc 492 391 184 115 "
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merge images with annotations\n",
"cctbbox_merged=pd.merge(cctbbox_images_df,cctbbox_annotations_df,on=\"image_id\")\n",
"#create a column called dataset to know from which one it comes\n",
"cctbbox_merged.insert(11, \"dataset\", \"iwild\")\n",
"cctbbox_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"date_captured\n",
"frame_num\n",
"height\n",
"location\n",
"rights_holder\n",
"seq_id\n",
"seq_num_frames\n",
"width\n",
"id\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file_name</th>\n",
" <th>image_id</th>\n",
" <th>category_id</th>\n",
" <th>dataset</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>w</th>\n",
" <th>h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>19</td>\n",
" <td>iwild</td>\n",
" <td>249</td>\n",
" <td>355</td>\n",
" <td>176</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>19</td>\n",
" <td>iwild</td>\n",
" <td>492</td>\n",
" <td>391</td>\n",
" <td>184</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" file_name \\\n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b.jpg \n",
"\n",
" image_id category_id dataset x y w \\\n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b 19 iwild 249 355 176 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b 19 iwild 492 391 184 \n",
"\n",
" h \n",
"0 99 \n",
"1 115 "
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#drop all useless columns. Keep: category, image_id, file_name, dataset, x, y, w, h\n",
"keepids=[\"category_id\",\"image_id\",\"file_name\",\"dataset\",\"x\",\"y\",\"w\",\"h\"]\n",
"cctbboxcols=cctbbox_merged.columns\n",
"for c in cctbboxcols:\n",
" if c not in keepids:\n",
" print(c)\n",
" cctbbox_merged=cctbbox_merged.drop(c,axis=1)\n",
" \n",
"cctbbox_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>category_id</th>\n",
" <th>image_id</th>\n",
" <th>file_name</th>\n",
" <th>dataset</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>w</th>\n",
" <th>h</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>iwild</td>\n",
" <td>249</td>\n",
" <td>355</td>\n",
" <td>176</td>\n",
" <td>99</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>19</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b</td>\n",
" <td>588a679f-23d2-11e8-a6a3-ec086b02610b.jpg</td>\n",
" <td>iwild</td>\n",
" <td>492</td>\n",
" <td>391</td>\n",
" <td>184</td>\n",
" <td>115</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" category_id image_id \\\n",
"0 19 5998cfa4-23d2-11e8-a6a3-ec086b02610b \n",
"1 19 588a679f-23d2-11e8-a6a3-ec086b02610b \n",
"\n",
" file_name dataset x y w h \n",
"0 5998cfa4-23d2-11e8-a6a3-ec086b02610b.jpg iwild 249 355 176 99 \n",
"1 588a679f-23d2-11e8-a6a3-ec086b02610b.jpg iwild 492 391 184 115 "
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#now organize them as our colleague is expecting them\n",
"cctbbox_merged=cctbbox_merged[[\"category_id\",\"image_id\",\"file_name\",\"dataset\",\"x\",\"y\",\"w\",\"h\"]]\n",
"cctbbox_merged.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cctbbox_merged.to_csv(\"/home/leslie/Documents/Uppsala/courses/DLreadingCourse/iwildstuff/cctWithBboxesForiWild.csv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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