{
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"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE4] - Preparation of the CelebA dataset\n",
"<!-- DESC --> Preparation of a clustered dataset, batchable\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
" - Formatting our dataset in cluster files, using batch mode\n",
" - Adapting a notebook for batch use\n",
"\n",
"\n",
"The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) contains about 200,000 images (202599,218,178,3). \n",
"\n",
"\n",
"## What we're going to do :\n",
" - Lire les images\n",
" - redimensionner et normaliser celles-ci,\n",
" - Constituer des clusters d'images en format npy\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Import and init\n",
"### 1.2 - Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The history saving thread hit an unexpected error (DatabaseError('database disk image is malformed')).History will not be written to the database.\n"
]
},
{
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"\n",
"FIDLE 2020 - Practical Work Module\n",
"Version : 0.5.0\n",
"Run time : Monday 2 March 2020, 09:47:54\n",
"TensorFlow version : 2.0.0\n",
"Keras version : 2.2.4-tf\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from skimage import io, transform\n",
"\n",
"import os,time,sys,json,glob\n",
"import csv\n",
"import math, random\n",
"\n",
"from importlib import reload\n",
"\n",
"sys.path.append('..')\n",
"import fidle.pwk as ooo\n",
"\n",
"ooo.init()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 - Directories and files :"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Well, we should be at HOME !\n",
"We are going to use: /home/pjluc/datasets/celeba\n"
]
}
],
"source": [
"place, dataset_dir = ooo.good_place( { 'GRICAD' : f'{os.getenv(\"SCRATCH_DIR\",\"\")}/PROJECTS/pr-fidle/datasets/celeba',\n",
" 'IDRIS' : f'{os.getenv(\"WORK\",\"\")}/datasets/celeba',\n",
" 'HOME' : f'{os.getenv(\"HOME\",\"\")}/datasets/celeba'} )\n",
"\n",
"dataset_csv = f'{dataset_dir}/origine/list_attr_celeba.csv'\n",
"dataset_img = f'{dataset_dir}/origine/img_align_celeba'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Read and shuffle filenames catalog"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"dataset_desc = pd.read_csv(dataset_csv, header=0)\n",
"dataset_desc = dataset_desc.reindex(np.random.permutation(dataset_desc.index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Save as clusters of n images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.2 - Cooking function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def read_and_save( dataset_img, dataset_desc, \n",
" cluster_size=1000, cluster_dir='./dataset_cluster', cluster_name='images',\n",
" image_size=(128,128)):\n",
" \n",
" def save_cluster(imgs,desc,cols,id):\n",
" file_img = f'{cluster_dir}/{cluster_name}-{id:03d}.npy'\n",
" file_desc = f'{cluster_dir}/{cluster_name}-{id:03d}.csv'\n",
" np.save(file_img, np.array(imgs))\n",
" df=pd.DataFrame(data=desc,columns=cols)\n",
" df.to_csv(file_desc, index=False)\n",
" return [],[],id+1\n",
" \n",
" start_time = time.time()\n",
" cols = list(dataset_desc.columns)\n",
"\n",
" # ---- Check if cluster files exist\n",
" #\n",
" if os.path.isfile(f'{cluster_dir}/images-000.npy'):\n",
" print('\\n*** Oops. There are already clusters in the target folder!\\n')\n",
" return 0,0\n",
" \n",
" # ---- Create cluster_dir\n",
" #\n",
" os.makedirs(cluster_dir, mode=0o750, exist_ok=True)\n",
" \n",
" # ---- Read and save clusters\n",
" #\n",
" imgs, desc, cluster_id = [],[],0\n",
" #\n",
" for i,row in dataset_desc.iterrows():\n",
" #\n",
" filename = f'{dataset_img}/{row.image_id}'\n",
" #\n",
" # ---- Read image, resize (and normalize)\n",
" #\n",
" img = io.imread(filename)\n",
" img = transform.resize(img, image_size)\n",
" #\n",
" # ---- Add image and description\n",
" #\n",
" imgs.append( img )\n",
" desc.append( row.values )\n",
" #\n",
" # ---- Progress bar\n",
" #\n",
" ooo.update_progress(f'Cluster {cluster_id:03d} :',len(imgs),cluster_size)\n",
" #\n",
" # ---- Save cluster if full\n",
" #\n",
" if len(imgs)==cluster_size:\n",
" imgs,desc,cluster_id=save_cluster(imgs,desc,cols, cluster_id)\n",
"\n",
" # ---- Save uncomplete cluster\n",
" if len(imgs)>0 : imgs,desc,cluster_id=save_cluster(imgs,desc,cols,cluster_id)\n",
"\n",
" duration=time.time()-start_time\n",
" return cluster_id,duration\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4.3 - Cluster building\n",
"Reading the 200,000 images can take a long time (>20 minutes)\n",
"200,000 images will be used for training (x_train), the rest for validation (x_test) \n",
"If the target folder is not empty, the construction is blocked. \n",
"\n",
"Image Sizes: 128x128 : 74 GB \n",
"Image Sizes: 192x160 : 138 GB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ---- Cluster size\n",
"\n",
"cluster_size_train = 10000\n",
"cluster_size_test = 10000\n",
"image_size = (128,128)\n",
"\n",
"# ---- To write dataset in the project place\n",
"#\n",
"output_dir = dataset_dir\n",
"\n",
"# ---- To write dataset in a test place\n",
"# For small tests only !\n",
"#\n",
"# output_dir = './data'\n",
"# ooo.mkdir(output_dir)\n",
"\n",
"# ---- Clusters location\n",
"\n",
"train_dir = f'{output_dir}/clusters.train'\n",
"test_dir = f'{output_dir}/clusters.test'\n",
"\n",
"# ---- x_train, x_test\n",
"#\n",
"n1,d1 = read_and_save(dataset_img, dataset_desc[:200000],\n",
" cluster_size = cluster_size_train, \n",
" cluster_dir = train_dir,\n",
" image_size = image_size )\n",
"\n",
"n2,d2 = read_and_save(dataset_img, dataset_desc[200000:],\n",
" cluster_size = cluster_size_test, \n",
" cluster_dir = test_dir,\n",
" image_size = image_size )\n",
" \n",
"print(f'\\n\\nDuration : {d1+d2:.2f} s or {ooo.hdelay(d1+d2)}')\n",
"print(f'Train clusters : {train_dir}')\n",
"print(f'Test clusters : {test_dir}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
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