フレンズ画像を生成させてみたい-画像収集編+DCGAN ref: https://qiita.com/knok/items/3f3c1d3eef4b435ed37e

file0.txt

# -*- coding: utf-8 -*-

from bs4 import BeautifulSoup
import urllib.request
import urllib.parse
import time
import os

wait_sec = 5

start_url = 'https://kemono-friends.gamerch.com/%E3%82%AD%E3%83%A3%E3%83%A9%E4%B8%80%E8%A6%A7'
base_url = 'https://kemono-friends.gamerch.com'
output_dir = 'save'

html = urllib.request.urlopen(start_url)
soup = BeautifulSoup(html, 'lxml')
elems = soup.select('a[href^="/"]')
vals = []
for e in elems:
    p = e.parent
    if p.name == 'td':
        vals.append(e)
#import pdb; pdb.set_trace()
time.sleep(wait_sec)

for v in vals:
    c = v['href']
    page_url = base_url + urllib.parse.quote(c)
    html = urllib.request.urlopen(page_url)
    soup = BeautifulSoup(html, 'lxml')
    imgs = soup.select('a[href$=".jpg"]')
    for i in imgs:
        jpg_url = i['href']
        fname = os.path.basename(jpg_url)
        fname = os.path.join(output_dir, fname)
        if os.path.exists(fname):
            #import pdb; pdb.set_trace()
            print("skip %s" % fname)
            continue
        with open(fname, "wb") as f:
            raw_img = urllib.request.urlopen(jpg_url).read()
            f.write(raw_img)
        #
        print("%s saved" % fname)
        time.sleep(wait_sec)

file1.txt

$ for i in $(seq 1 82); do \
 wget https://umabi.jp/kemono-friends/shindan/asset/img/result/character/$i.png; \
 sleep 5; done

file2.txt

#!/bin/sh

INDIR=$1
OUTDIR=$2
SIZE=32x32

mkdir -p $OUTDIR

FILES=$(cd $INDIR; ls *)

for fname in $FILES
do
    convert $INDIR/$fname -resize $SIZE \
        \( +clone -alpha opaque -fill white -colorize 100% \) +swap \
        -geometry +0+0 -compose Over -composite -alpha off \
        -gravity center -extent $SIZE \
         $OUTDIR/$fname
done

file3.txt

$ python dataset/cifar10like.py --data-dir /path/to/32x32img image.npz

file4.txt

$ python c10like-train.py -g 0 \ # c10like-train.py: 任意の32x32カラー画像を訓練するスクリプト
 --image-npz image.npz -o result-c10like-dcgan \ # result-c10like-dcgan に出力
 --algorithm dcgan --adam_alpha 0.0001 --adam_beta1 0.5 \
 --adam_beta2 0.9 --snapshot-iter 2000

file5.txt

$ cat result-c10like-dcgan/log
[
    {
        "loss_dis": 0.039919644594192505,
        "loss_gen": 9.003257751464844,
        "epoch": 78,
        "iteration": 100,
        "elapsed_time": 56.77760434150696
    },
    {
        "loss_dis": 0.0010271351784467697,
        "loss_gen": 9.35523509979248,
        "epoch": 156,
        "iteration": 200,
        "elapsed_time": 87.36827898025513
    }
]