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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "continuous-captain",
"metadata": {},
"outputs": [],
"source": [
"cd .."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "closing-bishop",
"metadata": {},
"outputs": [],
"source": [
"import dlib\n",
"import glob\n",
"import os\n",
"from tqdm import tqdm\n",
"from utils.alignment import align_face"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "false-healthcare",
"metadata": {},
"outputs": [],
"source": [
"images_path = '/disk2/danielroich/Sandbox/Data/Images/barcelona'\n",
"SHAPE_PREDICTOR_PATH = 'pretrained_models/shape_predictor_68_face_landmarks.dat'\n",
"IMAGE_SIZE = 1024"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "coordinate-australia",
"metadata": {},
"outputs": [],
"source": [
"predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "accurate-allowance",
"metadata": {},
"outputs": [],
"source": [
"os.chdir(images_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "australian-yellow",
"metadata": {},
"outputs": [],
"source": [
"images_names = glob.glob(f'*')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "secure-concentrate",
"metadata": {},
"outputs": [],
"source": [
"images_names"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "basic-pickup",
"metadata": {},
"outputs": [],
"source": [
"aligned_images = []\n",
"for image_name in tqdm(images_names):\n",
" try:\n",
" aligned_image = align_face(filepath=f'{images_path}/{image_name}',\n",
" predictor=predictor, output_size=IMAGE_SIZE)\n",
" aligned_images.append(aligned_image)\n",
" except Exception as e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "textile-extraction",
"metadata": {},
"outputs": [],
"source": [
"os.makedirs(f'{images_path}/aligned', exist_ok=True)\n",
"os.makedirs(f'{images_path}/aligned/0', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "civic-dictionary",
"metadata": {},
"outputs": [],
"source": [
"for image, name in zip(aligned_images,images_names):\n",
" real_name = name.split('.')[0]\n",
" try:\n",
" image.save(f'{images_path}/aligned/0/{real_name}.jpeg')\n",
" except Exception as e:\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "tough-celebrity",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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