Newer
Older
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"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",
"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",
"## 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"
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Import and init\n",
"### 1.2 - Import"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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": null,
"metadata": {},
"outputs": [],
"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",
"\n",
"dataset_csv = f'{dataset_dir}/list_attr_celeba.csv'\n",
"dataset_img = f'{dataset_dir}/img_align_celeba'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Read and shuffle filenames catalog"
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
]
},
{
"cell_type": "code",
"execution_count": null,
"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": null,
"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",
"\n",
"# ---- Clusters location\n",
"\n",
"train_dir = f'{dataset_dir}/clusters.train'\n",
"test_dir = f'{dataset_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",
"<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
}
],
"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",
}
},
"nbformat": 4,
"nbformat_minor": 4
}