├── LICENSE.txt
├── README.md
├── docs
    └── search
    │   ├── asset-manifest.json
    │   ├── favicon.png
    │   ├── ffhq-dataset-v1-processed.json
    │   ├── index.html
    │   ├── manifest.json
    │   ├── precache-manifest.d5e29510a2069c4ce4aaafff8ab19b02.js
    │   ├── service-worker.js
    │   └── static
    │       ├── css
    │           ├── main.48ff51cb.chunk.css
    │           └── main.48ff51cb.chunk.css.map
    │       └── js
    │           ├── 2.f9cc10ee.chunk.js
    │           ├── 2.f9cc10ee.chunk.js.map
    │           ├── main.3ccdce75.chunk.js
    │           ├── main.3ccdce75.chunk.js.map
    │           ├── runtime~main.e5de1b36.js
    │           └── runtime~main.e5de1b36.js.map
├── download_ffhq.py
├── ffhq-piecharts.png
└── ffhq-teaser.png


/LICENSE.txt:
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 1 | Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces,
 2 | originally created as a benchmark for generative adversarial networks (GAN):
 3 | 
 4 |     A Style-Based Generator Architecture for Generative Adversarial Networks
 5 |     Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)
 6 |     http://stylegan.xyz/paper
 7 | 
 8 | The individual images were published in Flickr by their respective authors
 9 | under either Creative Commons BY 2.0, Creative Commons BY-NC 2.0,
10 | Public Domain Mark 1.0, Public Domain CC0 1.0, or U.S. Government Works
11 | license. All of these licenses allow free use, redistribution, and adaptation
12 | for non-commercial purposes. However, some of them require giving appropriate
13 | credit to the original author, as well as indicating any changes that were
14 | made to the images. The license and original author of each image are
15 | indicated in the metadata.
16 | 
17 |     https://creativecommons.org/licenses/by/2.0/
18 |     https://creativecommons.org/licenses/by-nc/2.0/
19 |     https://creativecommons.org/publicdomain/mark/1.0/
20 |     https://creativecommons.org/publicdomain/zero/1.0/
21 |     http://www.usa.gov/copyright.shtml
22 | 
23 | The dataset itself (including JSON metadata, download script, and
24 | documentation) is made available under Creative Commons BY-NC-SA 4.0 license
25 | by NVIDIA Corporation. You can use, redistribute, and adapt it for
26 | non-commercial purposes, as long as you (a) give appropriate credit by
27 | citing our paper, (b) indicate any changes that you've made, and
28 | (c) distribute any derivative works under the same license.
29 | 
30 |     https://creativecommons.org/licenses/by-nc-sa/4.0/
31 | 


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/README.md:
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  1 | ## Flickr-Faces-HQ Dataset (FFHQ)
  2 | ![Python 3.6](https://img.shields.io/badge/python-3.6-green.svg?style=plastic)
  3 | ![License CC](https://img.shields.io/badge/license-CC-green.svg?style=plastic)
  4 | ![Format PNG](https://img.shields.io/badge/format-PNG-green.svg?style=plastic)
  5 | ![Resolution 1024×1024](https://img.shields.io/badge/resolution-1024×1024-green.svg?style=plastic)
  6 | ![Images 70000](https://img.shields.io/badge/images-70,000-green.svg?style=plastic)
  7 | 
  8 | ![Teaser image](./ffhq-teaser.png)
  9 | 
 10 | Flickr-Faces-HQ (FFHQ) is a high-quality image dataset of human faces, originally created as a benchmark for generative adversarial networks (GAN):
 11 | 
 12 | > **A Style-Based Generator Architecture for Generative Adversarial Networks**<br>
 13 | > Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA)<br>
 14 | > https://arxiv.org/abs/1812.04948
 15 | 
 16 | The dataset consists of 70,000 high-quality PNG images at 1024&times;1024 resolution and contains considerable variation in terms of age, ethnicity and image background. It also has good coverage of accessories such as eyeglasses, sunglasses, hats, etc. The images were crawled from [Flickr](https://www.flickr.com/), thus inheriting all the biases of that website, and automatically aligned and cropped using [dlib](http://dlib.net/). Only images under permissive licenses were collected. Various automatic filters were used to prune the set, and finally [Amazon Mechanical Turk](https://www.mturk.com/) was used to remove the occasional statues, paintings, or photos of photos.
 17 | 
 18 | Please note that this dataset is not intended for, and should not be used for, development or improvement of facial recognition technologies.  For business inquiries, please visit our website and submit the form: [NVIDIA Research Licensing](https://www.nvidia.com/en-us/research/inquiries/)
 19 | 
 20 | ## Licenses
 21 | 
 22 | The individual images were published in Flickr by their respective authors under either [Creative Commons BY 2.0](https://creativecommons.org/licenses/by/2.0/), [Creative Commons BY-NC 2.0](https://creativecommons.org/licenses/by-nc/2.0/), [Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/), [Public Domain CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/), or [U.S. Government Works](http://www.usa.gov/copyright.shtml) license. All of these licenses allow **free use, redistribution, and adaptation for non-commercial purposes**. However, some of them require giving **appropriate credit** to the original author, as well as **indicating any changes** that were made to the images. The license and original author of each image are indicated in the metadata.
 23 | 
 24 | * [https://creativecommons.org/licenses/by/2.0/](https://creativecommons.org/licenses/by/2.0/)
 25 | * [https://creativecommons.org/licenses/by-nc/2.0/](https://creativecommons.org/licenses/by-nc/2.0/)
 26 | * [https://creativecommons.org/publicdomain/mark/1.0/](https://creativecommons.org/publicdomain/mark/1.0/)
 27 | * [https://creativecommons.org/publicdomain/zero/1.0/](https://creativecommons.org/publicdomain/zero/1.0/)
 28 | * [http://www.usa.gov/copyright.shtml](http://www.usa.gov/copyright.shtml)
 29 | 
 30 | The dataset itself (including JSON metadata, download script, and documentation) is made available under [Creative Commons BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license by NVIDIA Corporation. You can **use, redistribute, and adapt it for non-commercial purposes**, as long as you (a) give appropriate credit by **citing our paper**, (b) **indicate any changes** that you've made, and (c) distribute any derivative works **under the same license**.
 31 | 
 32 | * [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/)
 33 | 
 34 | ## Overview
 35 | 
 36 | All data is hosted on Google Drive:
 37 | 
 38 | | Path | Size | Files | Format | Description
 39 | | :--- | :--: | ----: | :----: | :----------
 40 | | [ffhq-dataset](https://drive.google.com/open?id=1u2xu7bSrWxrbUxk-dT-UvEJq8IjdmNTP) | 2.56 TB | 210,014 | | Main folder
 41 | | &boxvr;&nbsp; [ffhq-dataset-v2.json](https://drive.google.com/open?id=16N0RV4fHI6joBuKbQAoG34V_cQk7vxSA) | 255 MB | 1 | JSON | Metadata including copyright info, URLs, etc.
 42 | | &boxvr;&nbsp; [images1024x1024](https://drive.google.com/open?id=1tZUcXDBeOibC6jcMCtgRRz67pzrAHeHL) | 89.1 GB | 70,000 | PNG | Aligned and cropped images at 1024&times;1024
 43 | | &boxvr;&nbsp; [thumbnails128x128](https://drive.google.com/open?id=1tg-Ur7d4vk1T8Bn0pPpUSQPxlPGBlGfv) | 1.95 GB | 70,000 | PNG | Thumbnails at 128&times;128
 44 | | &boxvr;&nbsp; [in-the-wild-images](https://drive.google.com/open?id=1ZX7QOy6LZuTLTnsOtQk-kmKq2-69l5hu) | 955 GB | 70,000 | PNG | Original images from Flickr
 45 | | &boxvr;&nbsp; [tfrecords](https://drive.google.com/open?id=1LTBpJ0W_WLjqza3zdayligS8Dh1V1gA6) | 273 GB | 9 | tfrecords | Multi-resolution data for [StyleGAN](https://github.com/NVlabs/stylegan) and [StyleGAN2](https://github.com/NVlabs/stylegan2)
 46 | | &boxur;&nbsp; [zips](https://drive.google.com/open?id=1WocxvZ4GEZ1DI8dOz30aSj2zT6pkATYS) | 1.28 TB | 4 | ZIP | Contents of each folder as a ZIP archive.
 47 | 
 48 | High-level statistics:
 49 | 
 50 | ![Pie charts](./ffhq-piecharts.png)
 51 | 
 52 | For use cases that require separate training and validation sets, we have appointed the first 60,000 images to be used for training and the remaining 10,000 for validation. In the [StyleGAN paper](https://arxiv.org/abs/1812.04948), however, we used all 70,000 images for training.
 53 | 
 54 | We have explicitly made sure that there are no duplicate images in the dataset itself. However, please note that the `in-the-wild` folder may contain multiple copies of the same image in cases where we extracted several different faces from the same image.
 55 | 
 56 | ## Download script
 57 | 
 58 | You can either grab the data directly from Google Drive or use the provided [download script](./download_ffhq.py). The script makes things considerably easier by automatically downloading all the requested files, verifying their checksums, retrying each file several times on error, and employing multiple concurrent connections to maximize bandwidth.
 59 | 
 60 | ```
 61 | > python download_ffhq.py -h
 62 | usage: download_ffhq.py [-h] [-j] [-s] [-i] [-t] [-w] [-r] [-a]
 63 |                         [--num_threads NUM] [--status_delay SEC]
 64 |                         [--timing_window LEN] [--chunk_size KB]
 65 |                         [--num_attempts NUM]
 66 | 
 67 | Download Flickr-Face-HQ (FFHQ) dataset to current working directory.
 68 | 
 69 | optional arguments:
 70 |   -h, --help            show this help message and exit
 71 |   -j, --json            download metadata as JSON (254 MB)
 72 |   -s, --stats           print statistics about the dataset
 73 |   -i, --images          download 1024x1024 images as PNG (89.1 GB)
 74 |   -t, --thumbs          download 128x128 thumbnails as PNG (1.95 GB)
 75 |   -w, --wilds           download in-the-wild images as PNG (955 GB)
 76 |   -r, --tfrecords       download multi-resolution TFRecords (273 GB)
 77 |   -a, --align           recreate 1024x1024 images from in-the-wild images
 78 |   --num_threads NUM     number of concurrent download threads (default: 32)
 79 |   --status_delay SEC    time between download status prints (default: 0.2)
 80 |   --timing_window LEN   samples for estimating download eta (default: 50)
 81 |   --chunk_size KB       chunk size for each download thread (default: 128)
 82 |   --num_attempts NUM    number of download attempts per file (default: 10)
 83 |   --random-shift SHIFT  standard deviation of random crop rectangle jitter
 84 |   --retry-crops         retry random shift if crop rectangle falls outside image (up to 1000
 85 |                         times)
 86 |   --no-rotation         keep the original orientation of images
 87 |   --no-padding          do not apply blur-padding outside and near the image borders
 88 |   --source-dir DIR      where to find already downloaded FFHQ source data
 89 | ```
 90 | 
 91 | ```
 92 | > python ..\download_ffhq.py --json --images
 93 | Downloading JSON metadata...
 94 | \ 100.00% done  2/2 files  0.25/0.25 GB   43.21 MB/s  ETA: done
 95 | Parsing JSON metadata...
 96 | Downloading 70000 files...
 97 | | 100.00% done  70001/70001 files  89.19 GB/89.19 GB  59.87 MB/s  ETA: done
 98 | ```
 99 | 
100 | The script also serves as a reference implementation of the automated scheme that we used to align and crop the images. Once you have downloaded the in-the-wild images with `python download_ffhq.py --wilds`, you can run `python download_ffhq.py --align` to reproduce exact replicas of the aligned 1024&times;1024 images using the facial landmark locations included in the metadata.
101 | 
102 | ### Reproducing the unaligned FFHQ
103 | 
104 | To reproduce the "unaligned FFHQ" dataset as used in the [Alias-Free Generative Adversarial Networks](https://arxiv.org/abs/2106.12423) paper, use the following options:
105 | 
106 | ```
107 | python download_ffhq.py \
108 |     --source-dir <path/to/downloaded/ffhq> \
109 |     --align --no-rotation --random-shift 0.2 --no-padding --retry-crops
110 | ```
111 | 
112 | 
113 | ## Metadata
114 | 
115 | The `ffhq-dataset-v2.json` file contains the following information for each image in a machine-readable format:
116 | 
117 | ```
118 | {
119 |   "0": {                                                 # Image index
120 |     "category": "training",                              # Training or validation
121 |     "metadata": {                                        # Info about the original Flickr photo:
122 |       "photo_url": "https://www.flickr.com/photos/...",  # - Flickr URL
123 |       "photo_title": "DSCF0899.JPG",                     # - File name
124 |       "author": "Jeremy Frumkin",                        # - Author
125 |       "country": "",                                     # - Country where the photo was taken
126 |       "license": "Attribution-NonCommercial License",    # - License name
127 |       "license_url": "https://creativecommons.org/...",  # - License detail URL
128 |       "date_uploaded": "2007-08-16",                     # - Date when the photo was uploaded to Flickr
129 |       "date_crawled": "2018-10-10"                       # - Date when the photo was crawled from Flickr
130 |     },
131 |     "image": {                                           # Info about the aligned 1024x1024 image:
132 |       "file_url": "https://drive.google.com/...",        # - Google Drive URL
133 |       "file_path": "images1024x1024/00000/00000.png",    # - Google Drive path
134 |       "file_size": 1488194,                              # - Size of the PNG file in bytes
135 |       "file_md5": "ddeaeea6ce59569643715759d537fd1b",    # - MD5 checksum of the PNG file
136 |       "pixel_size": [1024, 1024],                        # - Image dimensions
137 |       "pixel_md5": "47238b44dfb87644460cbdcc4607e289",   # - MD5 checksum of the raw pixel data
138 |       "face_landmarks": [...]                            # - 68 face landmarks reported by dlib
139 |     },
140 |     "thumbnail": {                                       # Info about the 128x128 thumbnail:
141 |       "file_url": "https://drive.google.com/...",        # - Google Drive URL
142 |       "file_path": "thumbnails128x128/00000/00000.png",  # - Google Drive path
143 |       "file_size": 29050,                                # - Size of the PNG file in bytes
144 |       "file_md5": "bd3e40b2ba20f76b55dc282907b89cd1",    # - MD5 checksum of the PNG file
145 |       "pixel_size": [128, 128],                          # - Image dimensions
146 |       "pixel_md5": "38d7e93eb9a796d0e65f8c64de8ba161"    # - MD5 checksum of the raw pixel data
147 |     },
148 |     "in_the_wild": {                                     # Info about the in-the-wild image:
149 |       "file_url": "https://drive.google.com/...",        # - Google Drive URL
150 |       "file_path": "in-the-wild-images/00000/00000.png", # - Google Drive path
151 |       "file_size": 3991569,                              # - Size of the PNG file in bytes
152 |       "file_md5": "1dc0287e73e485efb0516a80ce9d42b4",    # - MD5 checksum of the PNG file
153 |       "pixel_size": [2016, 1512],                        # - Image dimensions
154 |       "pixel_md5": "86b3470c42e33235d76b979161fb2327",   # - MD5 checksum of the raw pixel data
155 |       "face_rect": [667, 410, 1438, 1181],               # - Axis-aligned rectangle of the face region
156 |       "face_landmarks": [...],                           # - 68 face landmarks reported by dlib
157 |       "face_quad": [...]                                 # - Aligned quad of the face region
158 |     }
159 |   },
160 |   ...
161 | }
162 | ```
163 | 
164 | ## Acknowledgements
165 | 
166 | We thank Jaakko Lehtinen, David Luebke, and Tuomas Kynk&auml;&auml;nniemi for in-depth discussions and helpful comments; Janne Hellsten, Tero Kuosmanen, and Pekka J&auml;nis for compute infrastructure and help with the code release.
167 | 
168 | We also thank Vahid Kazemi and Josephine Sullivan for their work on automatic face detection and alignment that enabled us to collect the data in the first place:
169 | 
170 | > **One Millisecond Face Alignment with an Ensemble of Regression Trees**<br>
171 | > Vahid Kazemi, Josephine Sullivan<br>
172 | > Proc. CVPR 2014<br>
173 | > https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Kazemi_One_Millisecond_Face_2014_CVPR_paper.pdf
174 | 
175 | ## Privacy
176 | 
177 | When collecting the data, we were careful to only include photos that &ndash; to the best of our knowledge &ndash; were intended for free use and redistribution by their respective authors. That said, we are committed to protecting the privacy of individuals who do not wish their photos to be included.
178 | 
179 | To find out whether your photo is included in the Flickr-Faces-HQ dataset, please [click this link](https://nvlabs.github.io/ffhq-dataset/search/) to search the dataset with your Flickr username.
180 | 
181 | To get your photo removed from the Flickr-Faces-HQ dataset:
182 | 
183 | 1. Go to Flickr and do one of the following:
184 |     - Tag the photo with `no_cv` to indicate that you do not wish it to be used for computer vision research.
185 |     - Change the license of the photo to `None` (All rights reserved) or any Creative Commons license with `NoDerivs` to indicate that you do not want it to be redistributed.
186 |     - Make the photo private, i.e., only visible to you and your friends/family.
187 |     - Get the photo removed from Flickr altogether.
188 | 2. Contact [researchinquiries@nvidia.com](mailto:researchinquiries@nvidia.com). Please include your Flickr username in the email.
189 | 3. We will check the status of all photos from the particular user and update the dataset accordingly.
190 | 


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 2 |   "short_name": "React App",
 3 |   "name": "Create React App Sample",
 4 |   "icons": [
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 6 |       "src": "favicon.ico",
 7 |       "sizes": "64x64 32x32 24x24 16x16",
 8 |       "type": "image/x-icon"
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10 |   ],
11 |   "start_url": ".",
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 1 | /**
 2 |  * Welcome to your Workbox-powered service worker!
 3 |  *
 4 |  * You'll need to register this file in your web app and you should
 5 |  * disable HTTP caching for this file too.
 6 |  * See https://goo.gl/nhQhGp
 7 |  *
 8 |  * The rest of the code is auto-generated. Please don't update this file
 9 |  * directly; instead, make changes to your Workbox build configuration
10 |  * and re-run your build process.
11 |  * See https://goo.gl/2aRDsh
12 |  */
13 | 
14 | importScripts("https://storage.googleapis.com/workbox-cdn/releases/3.6.3/workbox-sw.js");
15 | 
16 | importScripts(
17 |   "/ffhq-dataset/search/precache-manifest.d5e29510a2069c4ce4aaafff8ab19b02.js"
18 | );
19 | 
20 | workbox.clientsClaim();
21 | 
22 | /**
23 |  * The workboxSW.precacheAndRoute() method efficiently caches and responds to
24 |  * requests for URLs in the manifest.
25 |  * See https://goo.gl/S9QRab
26 |  */
27 | self.__precacheManifest = [].concat(self.__precacheManifest || []);
28 | workbox.precaching.suppressWarnings();
29 | workbox.precaching.precacheAndRoute(self.__precacheManifest, {});
30 | 
31 | workbox.routing.registerNavigationRoute("/ffhq-dataset/search/index.html", {
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34 | });
35 | 


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extracted by mini-css-extract-plugin\nmodule.exports = {\"App\":\"App_App__2gdlJ\",\"App-link\":\"App_App-link__2b3Co\",\"loading\":\"App_loading__fEdJi\",\"resultContainer\":\"App_resultContainer__3Z2SS\",\"flickrUrl\":\"App_flickrUrl__Z9Hlv\",\"details\":\"App_details__d3YpQ\",\"ellipsis\":\"App_ellipsis__2pdXV\",\"search\":\"App_search__2F63v\"};","\nimport React, { Component } from 'react';\nimport styles from './App.module.css';\n\ninterface Meta {\n  url: string;\n  author: string;\n  title: string;\n  account: string;\n}\n\ninterface Dataset {\n  [id: string]: Meta;\n}\n\ninterface SearchFormProps {\n  onSubmit: (v: string) => void;\n}\n\ninterface SearchFormState {\n  value: string;\n}\n\nclass SearchForm extends Component<SearchFormProps, SearchFormState> {\n  state = { value: '' }\n\n  ref = React.createRef<HTMLInputElement>();\n\n  handleSubmit = (e: React.FormEvent) => {\n    e.preventDefault();\n    const val = this.state.value.trim();\n    if (val !== '') {\n      this.props.onSubmit(val);\n    }\n    if (this.ref.current) {\n      this.ref.current.blur();\n    }\n  }\n\n  handleInputChanged = (e: React.ChangeEvent<HTMLInputElement>) => {\n    this.setState({ value: e.target.value });\n  }\n\n  render() {\n    const inputStyle = {\n      marginLeft: '10px',\n    }\n\n    return (\n      <form className={styles.search} onSubmit={this.handleSubmit}>\n        <label>Flickr login or display name:\n          <input\n            ref={this.ref}\n            value={this.state.value}\n            style={inputStyle} type='text'\n            onChange={this.handleInputChanged}\n          />\n          <button style={{marginLeft: '3px'}} type='submit'>Search</button>\n        </label>\n      </form>\n    )\n  }\n}\n\nfunction FlickrUrl(props: { account: string }) {\n  const { account } = props;\n  return (\n    <a className={styles.flickrUrl} href={`https://www.flickr.com/photos/${account}`}>{account}</a>\n  )\n}\n\nfunction SearchResult(props: Meta & {ffhqId: string}) {\n  return (\n    <div className={styles.resultContainer}>\n      <a href={props.url}>{props.title ? props.title : 'Untitled'}</a><br />\n      <div className={styles.details}>Author: {props.author}, Flickr: <FlickrUrl account={props.account} />, Ffhq Id: {props.ffhqId}</div>\n    </div>\n  )\n}\n\nfunction SearchResultList (props: {\n  ids: string[];\n  searchString: string;\n  dataset: Dataset | null;\n}) {\n  const dataset = props.dataset;\n  if (dataset == null) {\n    return null;\n  }\n\n  const maxResults = 3000;\n  const limitedIds = props.ids.slice(0, maxResults);\n\n  const resultRows = limitedIds.map(id => {\n    return (\n      <SearchResult key={id} {...dataset[id] } ffhqId={id} />\n    )\n  });\n\n  return (\n    <div style={{marginTop: '16px'}}>\n      {limitedIds.length != 0 ?\n        <span>\n          <h3>Results</h3>\n          {limitedIds.length < props.ids.length ? `Your search term '${props.searchString}' produced a large number of results.  Showing only the first ${maxResults} hits.` : null}\n          {resultRows}\n        </span> :\n        <h3>No results.</h3>}\n    </div>\n  )\n}\n\nfunction Loading() {\n  return <p className={styles.loading}>Loading</p>;\n}\n\nfunction grepAccountFromUrl(url: string) {\n  const reg = /^http[s]?:\\/\\/www\\.flickr\\.com\\/photos\\/([^\\/]+)\\/.*/;\n  const m = url.match(reg);\n  if (m !== null && m[1] !== undefined) {\n    return m[1];\n  }\n  throw new Error('Invalid input json -- should be able to match account name from flickr URL.');\n}\n\nfunction processDataset(data: any): Dataset {\n  const res: Dataset = {};\n  Object.keys(data).forEach((key: string) => {\n    const m: Meta = data[key];\n    const account = grepAccountFromUrl(m.url)\n    res[key] = {\n      ...m,\n      account\n    };\n  });\n  return res;\n}\n\ninterface AppState {\n  loading: boolean;\n  searchString: string;\n  dataset: Dataset | null;\n  searchResults: string[];\n}\n\nclass App extends Component<{}, AppState> {\n  state = {\n    loading: true,\n    searchString: '',\n    dataset: null,\n    searchResults: []\n  }\n\n  componentDidMount() {\n    fetch('ffhq-dataset-v1-processed.json')\n      .then(resp => resp.json())\n      .then(data => {\n        this.setState({\n          dataset: processDataset(data),\n          loading: false\n        });\n      });\n  }\n\n  searchItems (searchString: string) {\n    if (!this.state.dataset) {\n      return;\n    }\n\n    const dataset: Dataset = this.state.dataset!;\n\n    const ids: string[] = [];\n    const searchTerm = searchString.toLowerCase();\n\n    Object.keys(dataset).forEach((key: string) => {\n      const img = dataset[key];\n      const author = img.author.toLowerCase();\n      const account = img.account.toLowerCase();\n\n      if (account == searchTerm) {\n        ids.push(key);\n        return;\n      }\n\n      if (author.indexOf(searchTerm) != -1) {\n        ids.push(key);\n        return;\n      }\n    });\n\n    // Sort results by Flickr account name\n    ids.sort((keyA: string, keyB: string) => {\n      const imgA = dataset[keyA];\n      const imgB = dataset[keyB];\n      return imgA.account.localeCompare(imgB.account);\n    });\n\n    this.setState({\n      searchResults: ids\n    })\n  }\n\n  handleSetSearchString = (value: string) => {\n    this.setState({ searchString: value });\n    this.searchItems(value);\n  }\n\n  render() {\n    return (\n      <div className={styles.App}>\n        <h1>Flickr-Faces-HQ Dataset (FFHQ)</h1>\n        <p>Want to know if your images are included in the FFHQ dataset?  Use this tool to list images in the FFHQ for a given Flickr login or display name.</p>\n        <p>For more information, see <a href='https://stylegan.xyz/ffhq'>FFHQ dataset</a>.</p>\n        <SearchForm onSubmit={this.handleSetSearchString} />\n        {this.state.loading && <Loading />}\n        {this.state.searchString != '' &&\n          <SearchResultList\n            ids={this.state.searchResults}\n            dataset={this.state.dataset}\n            searchString={this.state.searchString}\n          />}\n      </div>\n    );\n  }\n}\n\nexport default App;\n","// This optional code is used to register a service worker.\n// register() is not called by default.\n\n// This lets the app load faster on subsequent visits in production, and gives\n// it offline capabilities. However, it also means that developers (and users)\n// will only see deployed updates on subsequent visits to a page, after all the\n// existing tabs open on the page have been closed, since previously cached\n// resources are updated in the background.\n\n// To learn more about the benefits of this model and instructions on how to\n// opt-in, read https://bit.ly/CRA-PWA\n\nconst isLocalhost = Boolean(\n  window.location.hostname === 'localhost' ||\n    // [::1] is the IPv6 localhost address.\n    window.location.hostname === '[::1]' ||\n    // 127.0.0.1/8 is considered localhost for IPv4.\n    window.location.hostname.match(\n      /^127(?:\\.(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$/\n    )\n);\n\nexport function register(config) {\n  if (process.env.NODE_ENV === 'production' && 'serviceWorker' in navigator) {\n    // The URL constructor is available in all browsers that support SW.\n    const publicUrl = new URL(process.env.PUBLIC_URL, window.location.href);\n    if (publicUrl.origin !== window.location.origin) {\n      // Our service worker won't work if PUBLIC_URL is on a different origin\n      // from what our page is served on. This might happen if a CDN is used to\n      // serve assets; see https://github.com/facebook/create-react-app/issues/2374\n      return;\n    }\n\n    window.addEventListener('load', () => {\n      const swUrl = `${process.env.PUBLIC_URL}/service-worker.js`;\n\n      if (isLocalhost) {\n        // This is running on localhost. Let's check if a service worker still exists or not.\n        checkValidServiceWorker(swUrl, config);\n\n        // Add some additional logging to localhost, pointing developers to the\n        // service worker/PWA documentation.\n        navigator.serviceWorker.ready.then(() => {\n          console.log(\n            'This web app is being served cache-first by a service ' +\n              'worker. To learn more, visit https://bit.ly/CRA-PWA'\n          );\n        });\n      } else {\n        // Is not localhost. Just register service worker\n        registerValidSW(swUrl, config);\n      }\n    });\n  }\n}\n\nfunction registerValidSW(swUrl, config) {\n  navigator.serviceWorker\n    .register(swUrl)\n    .then(registration => {\n      registration.onupdatefound = () => {\n        const installingWorker = registration.installing;\n        if (installingWorker == null) {\n          return;\n        }\n        installingWorker.onstatechange = () => {\n          if (installingWorker.state === 'installed') {\n            if (navigator.serviceWorker.controller) {\n              // At this point, the updated precached content has been fetched,\n              // but the previous service worker will still serve the older\n              // content until all client tabs are closed.\n              console.log(\n                'New content is available and will be used when all ' +\n                  'tabs for this page are closed. See https://bit.ly/CRA-PWA.'\n              );\n\n              // Execute callback\n              if (config && config.onUpdate) {\n                config.onUpdate(registration);\n              }\n            } else {\n              // At this point, everything has been precached.\n              // It's the perfect time to display a\n              // \"Content is cached for offline use.\" message.\n              console.log('Content is cached for offline use.');\n\n              // Execute callback\n              if (config && config.onSuccess) {\n                config.onSuccess(registration);\n              }\n            }\n          }\n        };\n      };\n    })\n    .catch(error => {\n      console.error('Error during service worker registration:', error);\n    });\n}\n\nfunction checkValidServiceWorker(swUrl, config) {\n  // Check if the service worker can be found. If it can't reload the page.\n  fetch(swUrl)\n    .then(response => {\n      // Ensure service worker exists, and that we really are getting a JS file.\n      const contentType = response.headers.get('content-type');\n      if (\n        response.status === 404 ||\n        (contentType != null && contentType.indexOf('javascript') === -1)\n      ) {\n        // No service worker found. Probably a different app. Reload the page.\n        navigator.serviceWorker.ready.then(registration => {\n          registration.unregister().then(() => {\n            window.location.reload();\n          });\n        });\n      } else {\n        // Service worker found. Proceed as normal.\n        registerValidSW(swUrl, config);\n      }\n    })\n    .catch(() => {\n      console.log(\n        'No internet connection found. App is running in offline mode.'\n      );\n    });\n}\n\nexport function unregister() {\n  if ('serviceWorker' in navigator) {\n    navigator.serviceWorker.ready.then(registration => {\n      registration.unregister();\n    });\n  }\n}\n","import React from 'react';\nimport ReactDOM from 'react-dom';\nimport './index.css';\nimport App from './App';\nimport * as serviceWorker from './serviceWorker';\n\nReactDOM.render(<App />, document.getElementById('root'));\n\n// If you want your app to work offline and load faster, you can change\n// unregister() to register() below. Note this comes with some pitfalls.\n// Learn more about service workers: https://bit.ly/CRA-PWA\nserviceWorker.unregister();\n"],"sourceRoot":""}


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/download_ffhq.py:
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  1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
  2 | #
  3 | # This work is licensed under the Creative Commons
  4 | # Attribution-NonCommercial-ShareAlike 4.0 International License.
  5 | # To view a copy of this license, visit
  6 | # http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to
  7 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
  8 | 
  9 | """Download Flickr-Faces-HQ (FFHQ) dataset to current working directory."""
 10 | 
 11 | import os
 12 | import sys
 13 | import requests
 14 | import html
 15 | import hashlib
 16 | import PIL.Image
 17 | import PIL.ImageFile
 18 | import numpy as np
 19 | import scipy.ndimage
 20 | import threading
 21 | import queue
 22 | import time
 23 | import json
 24 | import uuid
 25 | import glob
 26 | import argparse
 27 | import itertools
 28 | import shutil
 29 | from collections import OrderedDict, defaultdict
 30 | 
 31 | PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True # avoid "Decompressed Data Too Large" error
 32 | 
 33 | #----------------------------------------------------------------------------
 34 | 
 35 | json_spec = dict(file_url='https://drive.google.com/uc?id=16N0RV4fHI6joBuKbQAoG34V_cQk7vxSA', file_path='ffhq-dataset-v2.json', file_size=267793842, file_md5='425ae20f06a4da1d4dc0f46d40ba5fd6')
 36 | 
 37 | tfrecords_specs = [
 38 |     dict(file_url='https://drive.google.com/uc?id=1LnhoytWihRRJ7CfhLQ76F8YxwxRDlZN3', file_path='tfrecords/ffhq/ffhq-r02.tfrecords', file_size=6860000,      file_md5='63e062160f1ef9079d4f51206a95ba39'),
 39 |     dict(file_url='https://drive.google.com/uc?id=1LWeKZGZ_x2rNlTenqsaTk8s7Cpadzjbh', file_path='tfrecords/ffhq/ffhq-r03.tfrecords', file_size=17290000,     file_md5='54fb32a11ebaf1b86807cc0446dd4ec5'),
 40 |     dict(file_url='https://drive.google.com/uc?id=1Lr7Tiufr1Za85HQ18yg3XnJXstiI2BAC', file_path='tfrecords/ffhq/ffhq-r04.tfrecords', file_size=57610000,     file_md5='7164cc5531f6828bf9c578bdc3320e49'),
 41 |     dict(file_url='https://drive.google.com/uc?id=1LnyiayZ-XJFtatxGFgYePcs9bdxuIJO_', file_path='tfrecords/ffhq/ffhq-r05.tfrecords', file_size=218890000,    file_md5='050cc7e5fd07a1508eaa2558dafbd9ed'),
 42 |     dict(file_url='https://drive.google.com/uc?id=1Lt6UP201zHnpH8zLNcKyCIkbC-aMb5V_', file_path='tfrecords/ffhq/ffhq-r06.tfrecords', file_size=864010000,    file_md5='90bedc9cc07007cd66615b2b1255aab8'),
 43 |     dict(file_url='https://drive.google.com/uc?id=1LwOP25fJ4xN56YpNCKJZM-3mSMauTxeb', file_path='tfrecords/ffhq/ffhq-r07.tfrecords', file_size=3444980000,   file_md5='bff839e0dda771732495541b1aff7047'),
 44 |     dict(file_url='https://drive.google.com/uc?id=1LxxgVBHWgyN8jzf8bQssgVOrTLE8Gv2v', file_path='tfrecords/ffhq/ffhq-r08.tfrecords', file_size=13766900000,  file_md5='74de4f07dc7bfb07c0ad4471fdac5e67'),
 45 |     dict(file_url='https://drive.google.com/uc?id=1M-ulhD5h-J7sqSy5Y1njUY_80LPcrv3V', file_path='tfrecords/ffhq/ffhq-r09.tfrecords', file_size=55054580000,  file_md5='05355aa457a4bd72709f74a81841b46d'),
 46 |     dict(file_url='https://drive.google.com/uc?id=1M11BIdIpFCiapUqV658biPlaXsTRvYfM', file_path='tfrecords/ffhq/ffhq-r10.tfrecords', file_size=220205650000, file_md5='bf43cab9609ab2a27892fb6c2415c11b'),
 47 | ]
 48 | 
 49 | license_specs = {
 50 |     'json':      dict(file_url='https://drive.google.com/uc?id=1SHafCugkpMZzYhbgOz0zCuYiy-hb9lYX', file_path='LICENSE.txt',                    file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'),
 51 |     'images':    dict(file_url='https://drive.google.com/uc?id=1sP2qz8TzLkzG2gjwAa4chtdB31THska4', file_path='images1024x1024/LICENSE.txt',    file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'),
 52 |     'thumbs':    dict(file_url='https://drive.google.com/uc?id=1iaL1S381LS10VVtqu-b2WfF9TiY75Kmj', file_path='thumbnails128x128/LICENSE.txt',  file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'),
 53 |     'wilds':     dict(file_url='https://drive.google.com/uc?id=1rsfFOEQvkd6_Z547qhpq5LhDl2McJEzw', file_path='in-the-wild-images/LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'),
 54 |     'tfrecords': dict(file_url='https://drive.google.com/uc?id=1SYUmqKdLoTYq-kqsnPsniLScMhspvl5v', file_path='tfrecords/ffhq/LICENSE.txt',     file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'),
 55 | }
 56 | 
 57 | #----------------------------------------------------------------------------
 58 | 
 59 | def download_file(session, file_spec, stats, chunk_size=128, num_attempts=10, **kwargs):
 60 |     file_path = file_spec['file_path']
 61 |     file_url = file_spec['file_url']
 62 |     file_dir = os.path.dirname(file_path)
 63 |     tmp_path = file_path + '.tmp.' + uuid.uuid4().hex
 64 |     if file_dir:
 65 |         os.makedirs(file_dir, exist_ok=True)
 66 | 
 67 |     for attempts_left in reversed(range(num_attempts)):
 68 |         data_size = 0
 69 |         try:
 70 |             # Download.
 71 |             data_md5 = hashlib.md5()
 72 |             with session.get(file_url, stream=True) as res:
 73 |                 res.raise_for_status()
 74 |                 with open(tmp_path, 'wb') as f:
 75 |                     for chunk in res.iter_content(chunk_size=chunk_size<<10):
 76 |                         f.write(chunk)
 77 |                         data_size += len(chunk)
 78 |                         data_md5.update(chunk)
 79 |                         with stats['lock']:
 80 |                             stats['bytes_done'] += len(chunk)
 81 | 
 82 |             # Validate.
 83 |             if 'file_size' in file_spec and data_size != file_spec['file_size']:
 84 |                 raise IOError('Incorrect file size', file_path)
 85 |             if 'file_md5' in file_spec and data_md5.hexdigest() != file_spec['file_md5']:
 86 |                 raise IOError('Incorrect file MD5', file_path)
 87 |             if 'pixel_size' in file_spec or 'pixel_md5' in file_spec:
 88 |                 with PIL.Image.open(tmp_path) as image:
 89 |                     if 'pixel_size' in file_spec and list(image.size) != file_spec['pixel_size']:
 90 |                         raise IOError('Incorrect pixel size', file_path)
 91 |                     if 'pixel_md5' in file_spec and hashlib.md5(np.array(image)).hexdigest() != file_spec['pixel_md5']:
 92 |                         raise IOError('Incorrect pixel MD5', file_path)
 93 |             break
 94 | 
 95 |         except:
 96 |             with stats['lock']:
 97 |                 stats['bytes_done'] -= data_size
 98 | 
 99 |             # Handle known failure cases.
100 |             if data_size > 0 and data_size < 8192:
101 |                 with open(tmp_path, 'rb') as f:
102 |                     data = f.read()
103 |                 data_str = data.decode('utf-8')
104 | 
105 |                 # Google Drive virus checker nag.
106 |                 links = [html.unescape(link) for link in data_str.split('"') if 'export=download' in link]
107 |                 if len(links) == 1:
108 |                     if attempts_left:
109 |                         file_url = requests.compat.urljoin(file_url, links[0])
110 |                         continue
111 | 
112 |                 # Google Drive quota exceeded.
113 |                 if 'Google Drive - Quota exceeded' in data_str:
114 |                     if not attempts_left:
115 |                         raise IOError("Google Drive download quota exceeded -- please try again later")
116 | 
117 |             # Last attempt => raise error.
118 |             if not attempts_left:
119 |                 raise
120 | 
121 |     # Rename temp file to the correct name.
122 |     os.replace(tmp_path, file_path) # atomic
123 |     with stats['lock']:
124 |         stats['files_done'] += 1
125 | 
126 |     # Attempt to clean up any leftover temps.
127 |     for filename in glob.glob(file_path + '.tmp.*'):
128 |         try:
129 |             os.remove(filename)
130 |         except:
131 |             pass
132 | 
133 | #----------------------------------------------------------------------------
134 | 
135 | def choose_bytes_unit(num_bytes):
136 |     b = int(np.rint(num_bytes))
137 |     if b < (100 << 0): return 'B', (1 << 0)
138 |     if b < (100 << 10): return 'kB', (1 << 10)
139 |     if b < (100 << 20): return 'MB', (1 << 20)
140 |     if b < (100 << 30): return 'GB', (1 << 30)
141 |     return 'TB', (1 << 40)
142 | 
143 | #----------------------------------------------------------------------------
144 | 
145 | def format_time(seconds):
146 |     s = int(np.rint(seconds))
147 |     if s < 60: return '%ds' % s
148 |     if s < 60 * 60: return '%dm %02ds' % (s // 60, s % 60)
149 |     if s < 24 * 60 * 60: return '%dh %02dm' % (s // (60 * 60), (s // 60) % 60)
150 |     if s < 100 * 24 * 60 * 60: return '%dd %02dh' % (s // (24 * 60 * 60), (s // (60 * 60)) % 24)
151 |     return '>100d'
152 | 
153 | #----------------------------------------------------------------------------
154 | 
155 | def download_files(file_specs, num_threads=32, status_delay=0.2, timing_window=50, **download_kwargs):
156 | 
157 |     # Determine which files to download.
158 |     done_specs = {spec['file_path']: spec for spec in file_specs if os.path.isfile(spec['file_path'])}
159 |     missing_specs = [spec for spec in file_specs if spec['file_path'] not in done_specs]
160 |     files_total = len(file_specs)
161 |     bytes_total = sum(spec['file_size'] for spec in file_specs)
162 |     stats = dict(files_done=len(done_specs), bytes_done=sum(spec['file_size'] for spec in done_specs.values()), lock=threading.Lock())
163 |     if len(done_specs) == files_total:
164 |         print('All files already downloaded -- skipping.')
165 |         return
166 | 
167 |     # Launch worker threads.
168 |     spec_queue = queue.Queue()
169 |     exception_queue = queue.Queue()
170 |     for spec in missing_specs:
171 |         spec_queue.put(spec)
172 |     thread_kwargs = dict(spec_queue=spec_queue, exception_queue=exception_queue, stats=stats, download_kwargs=download_kwargs)
173 |     for _thread_idx in range(min(num_threads, len(missing_specs))):
174 |         threading.Thread(target=_download_thread, kwargs=thread_kwargs, daemon=True).start()
175 | 
176 |     # Monitor status until done.
177 |     bytes_unit, bytes_div = choose_bytes_unit(bytes_total)
178 |     spinner = '/-\\|'
179 |     timing = []
180 |     while True:
181 |         with stats['lock']:
182 |             files_done = stats['files_done']
183 |             bytes_done = stats['bytes_done']
184 |         spinner = spinner[1:] + spinner[:1]
185 |         timing = timing[max(len(timing) - timing_window + 1, 0):] + [(time.time(), bytes_done)]
186 |         bandwidth = max((timing[-1][1] - timing[0][1]) / max(timing[-1][0] - timing[0][0], 1e-8), 0)
187 |         bandwidth_unit, bandwidth_div = choose_bytes_unit(bandwidth)
188 |         eta = format_time((bytes_total - bytes_done) / max(bandwidth, 1))
189 | 
190 |         print('\r%s %6.2f%% done  %d/%d files  %-13s  %-10s  ETA: %-7s ' % (
191 |             spinner[0],
192 |             bytes_done / bytes_total * 100,
193 |             files_done, files_total,
194 |             '%.2f/%.2f %s' % (bytes_done / bytes_div, bytes_total / bytes_div, bytes_unit),
195 |             '%.2f %s/s' % (bandwidth / bandwidth_div, bandwidth_unit),
196 |             'done' if bytes_total == bytes_done else '...' if len(timing) < timing_window or bandwidth == 0 else eta,
197 |         ), end='', flush=True)
198 | 
199 |         if files_done == files_total:
200 |             print()
201 |             break
202 | 
203 |         try:
204 |             exc_info = exception_queue.get(timeout=status_delay)
205 |             raise exc_info[1].with_traceback(exc_info[2])
206 |         except queue.Empty:
207 |             pass
208 | 
209 | def _download_thread(spec_queue, exception_queue, stats, download_kwargs):
210 |     with requests.Session() as session:
211 |         while not spec_queue.empty():
212 |             spec = spec_queue.get()
213 |             try:
214 |                 download_file(session, spec, stats, **download_kwargs)
215 |             except:
216 |                 exception_queue.put(sys.exc_info())
217 | 
218 | #----------------------------------------------------------------------------
219 | 
220 | def print_statistics(json_data):
221 |     categories = defaultdict(int)
222 |     licenses = defaultdict(int)
223 |     countries = defaultdict(int)
224 |     for item in json_data.values():
225 |         categories[item['category']] += 1
226 |         licenses[item['metadata']['license']] += 1
227 |         country = item['metadata']['country']
228 |         countries[country if country else '<Unknown>'] += 1
229 | 
230 |     for name in [name for name, num in countries.items() if num / len(json_data) < 1e-3]:
231 |         countries['<Other>'] += countries.pop(name)
232 | 
233 |     rows = [[]] * 2
234 |     rows += [['Category', 'Images', '% of all']]
235 |     rows += [['---'] * 3]
236 |     for name, num in sorted(categories.items(), key=lambda x: -x[1]):
237 |         rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data))]]
238 | 
239 |     rows += [[]] * 2
240 |     rows += [['License', 'Images', '% of all']]
241 |     rows += [['---'] * 3]
242 |     for name, num in sorted(licenses.items(), key=lambda x: -x[1]):
243 |         rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data))]]
244 | 
245 |     rows += [[]] * 2
246 |     rows += [['Country', 'Images', '% of all', '% of known']]
247 |     rows += [['---'] * 4]
248 |     for name, num in sorted(countries.items(), key=lambda x: -x[1] if x[0] != '<Other>' else 0):
249 |         rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data)),
250 |             '%.2f' % (0 if name == '<Unknown>' else 100.0 * num / (len(json_data) - countries['<Unknown>']))]]
251 | 
252 |     rows += [[]] * 2
253 |     widths = [max(len(cell) for cell in column if cell is not None) for column in itertools.zip_longest(*rows)]
254 |     for row in rows:
255 |         print("  ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths)))
256 | 
257 | #----------------------------------------------------------------------------
258 | 
259 | def recreate_aligned_images(json_data, source_dir, dst_dir='realign1024x1024', output_size=1024, transform_size=4096, enable_padding=True, rotate_level=True, random_shift=0.0, retry_crops=False):
260 |     print('Recreating aligned images...')
261 | 
262 |     # Fix random seed for reproducibility
263 |     np.random.seed(12345)
264 |     # The following random numbers are unused in present implementation, but we consume them for reproducibility
265 |     _ = np.random.normal(0, 1, (len(json_data.values()), 2))
266 | 
267 |     if dst_dir:
268 |         os.makedirs(dst_dir, exist_ok=True)
269 |         shutil.copyfile('LICENSE.txt', os.path.join(dst_dir, 'LICENSE.txt'))
270 | 
271 |     for item_idx, item in enumerate(json_data.values()):
272 |         print('\r%d / %d ... ' % (item_idx, len(json_data)), end='', flush=True)
273 | 
274 |         # Parse landmarks.
275 |         # pylint: disable=unused-variable
276 |         lm = np.array(item['in_the_wild']['face_landmarks'])
277 |         lm_chin          = lm[0  : 17]  # left-right
278 |         lm_eyebrow_left  = lm[17 : 22]  # left-right
279 |         lm_eyebrow_right = lm[22 : 27]  # left-right
280 |         lm_nose          = lm[27 : 31]  # top-down
281 |         lm_nostrils      = lm[31 : 36]  # top-down
282 |         lm_eye_left      = lm[36 : 42]  # left-clockwise
283 |         lm_eye_right     = lm[42 : 48]  # left-clockwise
284 |         lm_mouth_outer   = lm[48 : 60]  # left-clockwise
285 |         lm_mouth_inner   = lm[60 : 68]  # left-clockwise
286 | 
287 |         # Calculate auxiliary vectors.
288 |         eye_left     = np.mean(lm_eye_left, axis=0)
289 |         eye_right    = np.mean(lm_eye_right, axis=0)
290 |         eye_avg      = (eye_left + eye_right) * 0.5
291 |         eye_to_eye   = eye_right - eye_left
292 |         mouth_left   = lm_mouth_outer[0]
293 |         mouth_right  = lm_mouth_outer[6]
294 |         mouth_avg    = (mouth_left + mouth_right) * 0.5
295 |         eye_to_mouth = mouth_avg - eye_avg
296 | 
297 |         # Choose oriented crop rectangle.
298 |         if rotate_level:
299 |             x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
300 |             x /= np.hypot(*x)
301 |             x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
302 |             y = np.flipud(x) * [-1, 1]
303 |             c0 = eye_avg + eye_to_mouth * 0.1
304 |         else:
305 |             x = np.array([1, 0], dtype=np.float64)
306 |             x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
307 |             y = np.flipud(x) * [-1, 1]
308 |             c0 = eye_avg + eye_to_mouth * 0.1
309 | 
310 |         # Load in-the-wild image.
311 |         src_file = os.path.join(source_dir, item['in_the_wild']['file_path'])
312 |         if not os.path.isfile(src_file):
313 |             print('\nCannot find source image. Please run "--wilds" before "--align".')
314 |             return
315 |         img = PIL.Image.open(src_file)
316 | 
317 |         quad = np.stack([c0 - x - y, c0 - x + y, c0 + x + y, c0 + x - y])
318 |         qsize = np.hypot(*x) * 2
319 | 
320 |         # Keep drawing new random crop offsets until we find one that is contained in the image
321 |         # and does not require padding
322 |         if random_shift != 0:
323 |             for _ in range(1000):
324 |                 # Offset the crop rectange center by a random shift proportional to image dimension
325 |                 # and the requested standard deviation
326 |                 c = (c0 + np.hypot(*x)*2 * random_shift * np.random.normal(0, 1, c0.shape))
327 |                 quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
328 |                 crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
329 |                 if not retry_crops or not (crop[0] < 0 or crop[1] < 0 or crop[2] >= img.width or crop[3] >= img.height):
330 |                     # We're happy with this crop (either it fits within the image, or retries are disabled)
331 |                     break
332 |             else:
333 |                 # rejected N times, give up and move to next image
334 |                 # (does not happen in practice with the FFHQ data)
335 |                 print('rejected image')
336 |                 return
337 | 
338 |         # Shrink.
339 |         shrink = int(np.floor(qsize / output_size * 0.5))
340 |         if shrink > 1:
341 |             rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
342 |             img = img.resize(rsize, PIL.Image.ANTIALIAS)
343 |             quad /= shrink
344 |             qsize /= shrink
345 | 
346 |         # Crop.
347 |         border = max(int(np.rint(qsize * 0.1)), 3)
348 |         crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
349 |         crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
350 |         if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
351 |             img = img.crop(crop)
352 |             quad -= crop[0:2]
353 | 
354 |         # Pad.
355 |         pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
356 |         pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
357 |         if enable_padding and max(pad) > border - 4:
358 |             pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
359 |             img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
360 |             h, w, _ = img.shape
361 |             y, x, _ = np.ogrid[:h, :w, :1]
362 |             mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
363 |             blur = qsize * 0.02
364 |             img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
365 |             img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
366 |             img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
367 |             quad += pad[:2]
368 | 
369 |         # Transform.
370 |         img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
371 |         if output_size < transform_size:
372 |             img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
373 | 
374 |         # Save aligned image.
375 |         dst_subdir = os.path.join(dst_dir, '%05d' % (item_idx - item_idx % 1000))
376 |         os.makedirs(dst_subdir, exist_ok=True)
377 |         img.save(os.path.join(dst_subdir, '%05d.png' % item_idx))
378 | 
379 |     # All done.
380 |     print('\r%d / %d ... done' % (len(json_data), len(json_data)))
381 | 
382 | #----------------------------------------------------------------------------
383 | 
384 | def run(tasks, **download_kwargs):
385 |     if not os.path.isfile(json_spec['file_path']) or not os.path.isfile('LICENSE.txt'):
386 |         print('Downloading JSON metadata...')
387 |         download_files([json_spec, license_specs['json']], **download_kwargs)
388 | 
389 |     print('Parsing JSON metadata...')
390 |     with open(json_spec['file_path'], 'rb') as f:
391 |         json_data = json.load(f, object_pairs_hook=OrderedDict)
392 | 
393 |     if 'stats' in tasks:
394 |         print_statistics(json_data)
395 | 
396 |     specs = []
397 |     if 'images' in tasks:
398 |         specs += [item['image'] for item in json_data.values()] + [license_specs['images']]
399 |     if 'thumbs' in tasks:
400 |         specs += [item['thumbnail'] for item in json_data.values()] + [license_specs['thumbs']]
401 |     if 'wilds' in tasks:
402 |         specs += [item['in_the_wild'] for item in json_data.values()] + [license_specs['wilds']]
403 |     if 'tfrecords' in tasks:
404 |         specs += tfrecords_specs + [license_specs['tfrecords']]
405 | 
406 |     if len(specs):
407 |         print('Downloading %d files...' % len(specs))
408 |         np.random.shuffle(specs) # to make the workload more homogeneous
409 |         download_files(specs, **download_kwargs)
410 | 
411 |     if 'align' in tasks:
412 |         recreate_aligned_images(json_data, source_dir=download_kwargs['source_dir'], rotate_level=not download_kwargs['no_rotation'], random_shift=download_kwargs['random_shift'], enable_padding=not download_kwargs['no_padding'], retry_crops=download_kwargs['retry_crops'])
413 | 
414 | #----------------------------------------------------------------------------
415 | 
416 | def run_cmdline(argv):
417 |     parser = argparse.ArgumentParser(prog=argv[0], description='Download Flickr-Face-HQ (FFHQ) dataset to current working directory.')
418 |     parser.add_argument('-j', '--json',         help='download metadata as JSON (254 MB)', dest='tasks', action='append_const', const='json')
419 |     parser.add_argument('-s', '--stats',        help='print statistics about the dataset', dest='tasks', action='append_const', const='stats')
420 |     parser.add_argument('-i', '--images',       help='download 1024x1024 images as PNG (89.1 GB)', dest='tasks', action='append_const', const='images')
421 |     parser.add_argument('-t', '--thumbs',       help='download 128x128 thumbnails as PNG (1.95 GB)', dest='tasks', action='append_const', const='thumbs')
422 |     parser.add_argument('-w', '--wilds',        help='download in-the-wild images as PNG (955 GB)', dest='tasks', action='append_const', const='wilds')
423 |     parser.add_argument('-r', '--tfrecords',    help='download multi-resolution TFRecords (273 GB)', dest='tasks', action='append_const', const='tfrecords')
424 |     parser.add_argument('-a', '--align',        help='recreate 1024x1024 images from in-the-wild images', dest='tasks', action='append_const', const='align')
425 |     parser.add_argument('--num_threads',        help='number of concurrent download threads (default: 32)', type=int, default=32, metavar='NUM')
426 |     parser.add_argument('--status_delay',       help='time between download status prints (default: 0.2)', type=float, default=0.2, metavar='SEC')
427 |     parser.add_argument('--timing_window',      help='samples for estimating download eta (default: 50)', type=int, default=50, metavar='LEN')
428 |     parser.add_argument('--chunk_size',         help='chunk size for each download thread (default: 128)', type=int, default=128, metavar='KB')
429 |     parser.add_argument('--num_attempts',       help='number of download attempts per file (default: 10)', type=int, default=10, metavar='NUM')
430 |     parser.add_argument('--random-shift',       help='standard deviation of random crop rectangle jitter', type=float, default=0.0, metavar='SHIFT')
431 |     parser.add_argument('--retry-crops',        help='retry random shift if crop rectangle falls outside image (up to 1000 times)', dest='retry_crops', default=False, action='store_true')
432 |     parser.add_argument('--no-rotation',        help='keep the original orientation of images', dest='no_rotation', default=False, action='store_true')
433 |     parser.add_argument('--no-padding',         help='do not apply blur-padding outside and near the image borders', dest='no_padding', default=False, action='store_true')
434 |     parser.add_argument('--source-dir',         help='where to find already downloaded FFHQ source data', default='', metavar='DIR')
435 | 
436 |     args = parser.parse_args()
437 |     if not args.tasks:
438 |         print('No tasks specified. Please see "-h" for help.')
439 |         exit(1)
440 |     run(**vars(args))
441 | 
442 | #----------------------------------------------------------------------------
443 | 
444 | if __name__ == "__main__":
445 |     run_cmdline(sys.argv)
446 | 
447 | #----------------------------------------------------------------------------
448 | 


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