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styleGAN.py
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# -*- coding: utf-8 -*-
"""Untitled2.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1LmmjkZG437l3T2y_3NdcNOICqya7MOfU
"""
#!git clone https://github.com/NVlabs/stylegan.git
# cd stylegan
import config
import dnnlib.tflib as tflib
import dnnlib
import PIL.Image
import numpy as np
import pickle
import random
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
from PIL import Image
import matplotlib.pyplot as plt
from pylab import rcParams
# -*- coding: utf-8 -*-
"""Untitled2.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1LmmjkZG437l3T2y_3NdcNOICqya7MOfU
"""
#!git clone https://github.com/NVlabs/stylegan.git
# cd stylegan
tflib.init_tf()
url = "https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ"
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
_G, _D, Gs = pickle.load(f)
rnd = np.random.RandomState(200)
latents = rnd.randn(1, Gs.input_shape[1])
fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
images = Gs.run(
latents, None, truncation_psi=1, randomize_noise=True, output_transform=fmt
)
os.makedirs(config.result_dir, exist_ok=True)
png_filename = os.path.join(config.result_dir, "example200.png")
PIL.Image.fromarray(images[0], "RGB").save(png_filename)
rcParams["figure.figsize"] = 20, 20
# karras2019stylegan-ffhq-1024x1024.pkl
url_ffhq = "https://drive.google.com/uc?id=1MQywl0FNt6lHu8E_EUqnRbviagS7fbiJ"
_Gs_cache = dict()
synthesis_kwargs = dict(
output_transform=dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True),
minibatch_size=8,
)
def draw_style_mixing_figure(png, Gs, w, h, src_seeds, dst_seeds, style_ranges):
print(png)
src_latents = np.stack(
np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in src_seeds
)
dst_latents = np.stack(
np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in dst_seeds
)
src_dlatents = Gs.components.mapping.run(
src_latents, None
) # [seed, layer, component]
dst_dlatents = Gs.components.mapping.run(
dst_latents, None
) # [seed, layer, component]
src_images = Gs.components.synthesis.run(
src_dlatents, randomize_noise=False, **synthesis_kwargs
)
dst_images = Gs.components.synthesis.run(
dst_dlatents, randomize_noise=False, **synthesis_kwargs
)
canvas = PIL.Image.new(
"RGB", (w * (len(src_seeds) + 1), h * (len(dst_seeds) + 1)), "white"
)
for col, src_image in enumerate(list(src_images)):
canvas.paste(PIL.Image.fromarray(src_image, "RGB"), ((col + 1) * w, 0))
for row, dst_image in enumerate(list(dst_images)):
canvas.paste(PIL.Image.fromarray(dst_image, "RGB"), (0, (row + 1) * h))
row_dlatents = np.stack([dst_dlatents[row]] * len(src_seeds))
row_dlatents[:, style_ranges[row]] = src_dlatents[:, style_ranges[row]]
row_images = Gs.components.synthesis.run(
row_dlatents, randomize_noise=False, **synthesis_kwargs
)
for col, image in enumerate(list(row_images)):
canvas.paste(
PIL.Image.fromarray(image, "RGB"), ((col + 1) * w, (row + 1) * h)
)
canvas.save(png)
def load_Gs(url):
if url not in _Gs_cache:
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
_G, _D, Gs = pickle.load(f)
_Gs_cache[url] = Gs
return _Gs_cache[url]
# シード値を2つ選び，最適な毛の長さの方をsrc_seedsに，
# 最適な毛の模様，色の方をdst_seedsに格納する
# 毛の長さはリストから取り出したシード値のものをそのまま利用する
def generate_cat_fugure(color, variety, hair_length):
tflib.init_tf()
with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f:
_G, _D, Gs = pickle.load(f)
import pandas as pd
data = pd.read_csv("./catcsv.csv")
# whiteに色を入れる
color = data[data["color of hair"] == color]
pattern = color[color["pattern of hair"] == "mix"]
# とりあえずのmixを代入して変数を初期化
# Toraに猫の品種
if data["pattern of hair"].value_counts()[variety] != 0:
pattern = color[color["pattern of hair"] == variety]
# ユーザーの入力した毛の模様に対応するシード値があるのならpatternに代入
src_seed = pattern["seed"]
src_seed.values[0]
# これが最初に生成される画像の毛の模様，色の初期値に使われるシード値になる
# 模様と色はここまで
# 毛の長さ
length = data[data["length of hair"] == hair_length]
dst_seed = length["seed"]
dst_seed.values[0]
# これが最初に生成される画像の毛の長さ，ポーズに使われるシード値になる
draw_style_mixing_figure(
os.path.join(config.result_dir, "figure03-style-mixing15.png"),
load_Gs(url_ffhq),
w=256,
h=256,
src_seeds=[int(dst_seed.values[0])],
dst_seeds=[int(src_seed.values[0])],
style_ranges=[range(0, 4)] * 3 + [range(4, 8)] * 2 + [range(8, 14)] * 1,
)