HD-CelebA-Cropper

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HD-CelebA-Cropper

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To obtain high resolution face images from CelebA

HD-CelebA-Cropper 최신버젼 다운로드

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// repository documentation

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# HD CelebA Cropper

[CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset provides an aligned set `img_align_celeba.zip`. However, the size of each aligned image is **218x178**, so the faces cropped from such images would be even smaller!

Here we provide a code to obtain **higher resolution** face images, by cropping the faces from the original unaligned images via 68 landmarks.

***We also use a [deep image quality assessment](https://github.com/dmaniry/deepIQA) method to evaluate and rank the cropped image quality in [scores.txt](scores.txt), lower score the better.***

## Cropped Faces (512x512)

***Notice***: There are still some low resolution cropped faces since the corresponding original images are low resolution.

## Usage

- Prerequisites

- OpenCV

- Python 3.6

- Dataset

- [CelebA](http://openaccess.thecvf.com/content_iccv_2015/papers/Liu_Deep_Learning_Face_ICCV_2015_paper.pdf)-unaligned (10.2GB, higher quality than the aligned data)

- download the dataset

- img_celeba.7z (move to **./data/img_celeba.7z**): [Google Drive](https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg) or [Baidu Netdisk](https://pan.baidu.com/s/1CRxxhoQ97A5qbsKO7iaAJg) (password rp0s)

- annotations.zip (move to **./data/annotations.zip**): [Google Drive](https://drive.google.com/file/d/1xd-d1WRnbt3yJnwh5ORGZI3g-YS-fKM9/view?usp=sharing)

- unzip the data

```console
            7z x ./data/img_celeba.7z/img_celeba.7z.001 -o./data/

unzip ./data/annotations.zip -d ./data/
            ```

- Cropping Examples

- 512x512 + lanczos4 + jpg

```console
        python align.py --crop_size_h 512 --crop_size_w 512 --order 4 --save_format jpg --n_worker 32
        ```

- 512x512 + lanczos4 + png + larger face in the image (by setting `face_factor`, default is 0.45)

```console
        python align.py --crop_size_h 512 --crop_size_w 512 --order 4 --save_format png --face_factor 0.6 --n_worker 32
        ```

- 384x384 + bicubic + jpg + smaller face in the image (by setting `face_factor`, default is 0.45)

```console
        python align.py --crop_size_h 384 --crop_size_w 384 --order 3 --save_format jpg --face_factor 0.3 --n_worker 32
        ```

- Notice

- `order`

- 0: INTER_NEAREST

- 1: INTER_LINEAR

- 2: INTER_AREA

- 3: INTER_CUBIC

- 4: INTER_LANCZOS4

- 5: INTER_LANCZOS4

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