84 lines (83 with data), 2.5 kB
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"output = []\n",
"\n",
"with open('trains.txt') as filenames:\n",
" reader = csv.reader(filenames)\n",
" for filename in reader:\n",
" with open('bounding_boxes/' + filename[0][:-4] + '.txt') as bb:\n",
" line = bb.readline().strip().split()\n",
" while line:\n",
" minx = line[0].split('.')[0]\n",
" miny = line[1].split('.')[0] \n",
" maxx = line[2].split('.')[0] \n",
" maxy = line[3].split('.')[0] \n",
" output.append(filename[0][:-4] +'.jpg'+',1,'+minx+','+miny+','+maxx+','+maxy)\n",
" line = bb.readline().strip().split()\n",
"with open('trainlabels.csv', '+w') as labels:\n",
" for i in output:\n",
" labels.write(i + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"output = []\n",
"with open('testimages.txt') as filenames:\n",
" reader = csv.reader(filenames)\n",
" for filename in reader:\n",
" with open('bounding_boxes/' + filename[0][:-4] + '.txt') as bb:\n",
" line = bb.readline().strip().split()\n",
" while line:\n",
" minx = line[0].split('.')[0]\n",
" miny = line[1].split('.')[0] \n",
" maxx = line[2].split('.')[0] \n",
" maxy = line[3].split('.')[0] \n",
" output.append(filename[0][:-4]+'.jpg'+ ',1,'+minx+','+miny+','+maxx+','+maxy)\n",
" line = bb.readline().strip().split()\n",
"with open('testlabels.csv', '+w') as labels:\n",
" for i in output:\n",
" labels.write(i + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}