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waifu2x_transparent_2.py
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#!/usr/bin/env python3
import cv2
import numpy as np
import skimage.transform
from utils import do_imgs, floor_even, read_img, trim_img, untrim_img, write_img
from waifu2x import pad_size, run_img
model_filename = "./models/waifu2x/noise0_scale2x.onnx"
in_filenames = [
"./in.png",
]
out_suffix = "_waifu2x"
pre_blur = 0.5
pre_darken = False
pre_lighten = False
trim_eps = 1e-3
waifu2x_strength = 0.7
waifu2x_alpha = False
alpha_blur_scale = 0.7
alpha_blur_gamma = 1
alpha_blur_strength = 0.7
use_gpu = True
if use_gpu:
import cupy as cp
else:
cp = np
cp.asnumpy = lambda x: x
def cross_sum(a, out):
a = cp.pad(a, ((1, 1), (1, 1), (0, 0)))
out[:] = a[2:, 1:-1] + a[:-2, 1:-1] + a[1:-1, 2:] + a[1:-1, :-2]
def bleed_alpha(img, alpha, eps, max_iter=10**3, tol=1e-4):
img = cp.asarray(img)
alpha = cp.asarray(alpha)
assert alpha.ndim == 2
alpha = alpha[:, :, None]
mask_0 = alpha > eps
mask = mask_0.astype(img.dtype)
# TODO: np.maximum breaks type stability in numba
confidence = cp.maximum(alpha, eps).astype(img.dtype)
del alpha
img_new = cp.zeros_like(img)
for iter_count in range(max_iter):
cross_sum(mask * confidence * img, img_new)
cross_sum(mask * confidence, mask)
img_new /= cp.maximum(mask, 1e-7).astype(mask.dtype)
img_new = cp.where(mask_0, img, img_new)
norm = ((img_new - img) ** 2).max()
if norm < tol:
break
img, img_new = img_new, img
mask = (mask > 0).astype(mask.dtype)
img_new = cp.asnumpy(img_new)
return iter_count + 1, img_new
def do_blur(img):
if pre_blur:
img_blur = cv2.GaussianBlur(img, (0, 0), pre_blur)
else:
img_blur = img
if pre_darken:
assert pre_blur
assert not pre_lighten
img = np.minimum(img, img_blur)
elif pre_lighten:
assert pre_blur
img = np.maximum(img, img_blur)
else:
img = img_blur
return img
def convert_img(sess, in_filename, out_filename):
# Network input is BGR
img, alpha = read_img(in_filename, swap_rb=False, signed=False, return_alpha=True)
original_shape, (trim_t, trim_b, trim_l, trim_r) = trim_img(
img, alpha, trim_eps, pad=pad_size
)
trim_b = trim_t + floor_even(trim_b - trim_t)
trim_r = trim_l + floor_even(trim_r - trim_l)
img = img[trim_t:trim_b, trim_l:trim_r, :]
alpha = alpha[trim_t:trim_b, trim_l:trim_r]
iter_count, img = bleed_alpha(img, alpha, trim_eps)
print("Bleed alpha iter", iter_count)
img_out = do_blur(img)
img_out = skimage.transform.resize(img_out, (img.shape[0] // 2, img.shape[1] // 2))
img_out = run_img(sess, img_out)
img = (1 - waifu2x_strength) * img + waifu2x_strength * img_out
del img_out
alpha_blur = do_blur(alpha)
if waifu2x_alpha:
alpha_blur = skimage.transform.resize(
alpha_blur, (alpha.shape[0] // 2, alpha.shape[1] // 2)
)
alpha_blur = np.repeat(alpha_blur[:, :, None], 3, axis=2)
alpha_blur = run_img(sess, alpha_blur)
alpha_blur = alpha_blur.mean(axis=2)
else:
alpha_blur = skimage.transform.resize(
alpha_blur, tuple(int(alpha_blur_scale * x) for x in alpha.shape)
)
alpha_blur = skimage.transform.resize(alpha_blur, alpha.shape)
alpha_blur **= alpha_blur_gamma
alpha = (1 - alpha_blur_strength) * alpha + alpha_blur_strength * alpha_blur
del alpha_blur
img, alpha = untrim_img(
img, alpha, original_shape, (trim_t, trim_b, trim_l, trim_r)
)
# Network output is BGR
write_img(out_filename, img, alpha, swap_rb=False, signed=False)
if __name__ == "__main__":
do_imgs(convert_img, model_filename, in_filenames, out_suffix)