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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import menpo.io as mio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets say you have this file structure on disk:\n",
"```\n",
"image01.jpg\n",
"image01.01.ljson\n",
"image02.jpg\n",
"image02.01.ljson\n",
"image02.02.ljson\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def import_multiple_landmarks(path):\n",
" # path will be e.g. .foo/bar/image01.jpg\n",
" # as a pathlib.Path object\n",
" # \n",
" # Find all LJSON paths for this image following\n",
" # our pattern above\n",
" lm_paths = path.parent.glob(path.stem + '.*.ljson')\n",
" # return a mapping from numbers to paths\n",
" return {p.name.split('.')[1]:p for p in lm_paths}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"images = mio.import_images('./my/dir', landmark_resolver=import_multiple_landmarks)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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