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#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Power by Zongsheng Yue 2022-06-09 14:59:55
import torch
import random
import numpy as np
from einops import rearrange
def batch_inpainging_from_grad(im_in, mask, gradx, grady):
'''
Recovering from gradient for batch data (torch tensro).
Input:
im_in: N x c x h x w, torch tensor, masked image
mask: N x 1 x h x w, torch tensor
gradx, grady: N x c x h x w, torch tensor, image gradient
'''
im_out = torch.zeros_like(im_in.data)
for ii in range(im_in.shape[0]):
im_current, gradx_current, grady_current = [rearrange(x[ii,].cpu().numpy(), 'c h w -> h w c')
for x in [im_in, gradx, grady]]
mask_current = mask[ii, 0,].cpu().numpy()
out_current = inpainting_from_grad(im_current, mask_current, gradx_current, grady_current)
im_out[ii,] = torch.from_numpy(rearrange(out_current, 'h w c -> c h w')).to(
device=im_in.device,
dtype=im_in.dtype
)
return im_out
def inpainting_from_grad(im_in, mask, gradx, grady):
'''
Input:
im_in: h x w x c, masked image, numpy array
mask: h x w, image mask, 1 represents missing value
gradx: h x w x c, gradient along x-axis, numpy array
grady: h x w x c, gradient along y-axis, numpy array
Output:
im_out: recoverd image
'''
h, w = im_in.shape[:2]
counts_h = np.sum(1-mask, axis=0, keepdims=False)
counts_w = np.sum(1-mask, axis=1, keepdims=False)
if np.any(counts_h[1:-1,] == h):
idx = find_first_index(counts_h[1:-1,], h) + 1
im_out = fill_image_from_gradx(im_in, mask, gradx, idx)
elif np.any(counts_w[1:-1,] == w):
idx = find_first_index(counts_w[1:-1,], w) + 1
im_out = inpainting_from_grad(im_in.T, mask.T, gradx.T, idx)
else:
idx = random.choices(list(range(1,w-1)), k=1, weights=counts_h[1:-1])[0]
line = fill_line(im_in[:, idx, ], mask[:, idx,], grady[:, idx,])
im_in[:, idx,] = line
im_out = fill_image_from_gradx(im_in, mask, gradx, idx)
if im_in.ndim > mask.ndim:
mask = mask[:, :, None]
im_out = im_in + im_out * mask
return im_out
def fill_image_from_gradx(im_in, mask, gradx, idx):
init = np.zeros_like(im_in)
init[:, idx,] = im_in[:, idx,]
right = np.cumsum(init[:, idx:-1, ] + gradx[:, idx+1:, ], axis=1)
left = np.cumsum(
init[:, idx:0:-1, ] - gradx[:, idx:0:-1, ],
axis=1
)[:, ::-1]
center = im_in[:, idx, ][:, None] # h x 1 x 3
im_out = np.concatenate((left, center, right), axis=1)
return im_out
def fill_line(xx, mm, grad):
'''
Fill one line from grad.
Input:
xx: n x c array, masked vector
mm: (n,) array, mask, 1 represent missing value
grad: (n,) array
'''
n = xx.shape[0]
assert mm.sum() < n
if mm.sum() == 0:
return xx
else:
idx1 = find_first_index(mm, 1)
if idx1 == 0:
idx2 = find_first_index(mm, 0)
subx = xx[idx2::-1,].copy()
subgrad = grad[idx2::-1, ].copy()
subx -= subgrad
xx[:idx2,] = np.cumsum(subx, axis=0)[idx2-1::-1,]
mm[idx1:idx2,] = 0
else:
idx2 = find_first_index(mm[idx1:,], 0) + idx1
subx = xx[idx1-1:idx2-1,].copy()
subgrad = grad[idx1:idx2,].copy()
subx += subgrad
xx[idx1:idx2,] = np.cumsum(subx, axis=0)
mm[idx1:idx2,] = 0
return fill_line(xx, mm, grad)
def find_first_index(mm, value):
'''
Input:
mm: (n, ) array
value: scalar
'''
try:
out = next((idx for idx, val in np.ndenumerate(mm) if val == value))[0]
except StopIteration:
out = mm.shape[0]
return out
if __name__ == '__main__':
import sys
from pathlib import Path
sys.path.append(str(Path(__file__).resolve().parents[1]))
from utils import util_image
from datapipe.masks.train import process_mask
# mask_file_names = [x for x in Path('../lama/LaMa_test_images').glob('*mask*.png')]
mask_file_names = [x for x in Path('./testdata/inpainting/val/places/').glob('*mask*.png')]
file_names = [x.parents[0]/(x.stem.rsplit('_mask',1)[0]+'.png') for x in mask_file_names]
for im_path, mask_path in zip(file_names, mask_file_names):
im = util_image.imread(im_path, chn='rgb', dtype='float32')
mask = process_mask(util_image.imread(mask_path, chn='rgb', dtype='float32')[:, :, 0])
grad_dict = util_image.imgrad(im)
im_masked = im * (1 - mask[:, :, None])
im_recover = inpainting_from_grad(im_masked, mask, grad_dict['gradx'], grad_dict['grady'])
error_max = np.abs(im_recover -im).max()
print('Error Max: {:.2e}'.format(error_max))
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