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from __future__ import absolute_import, division, print_function, unicode_literals
import scipy.ndimage
from scipy.sparse.linalg import spsolve
from scipy import sparse
import scipy.io as sio
import numpy as np
from PIL import Image
import copy
import cv2
import os
import argparse
def sub2ind(pi, pj, imgH, imgW):
return pj + pi * imgW
def Poisson_blend_img(imgTrg, imgSrc_gx, imgSrc_gy, holeMask, gradientMask=None, edge=None):
imgH, imgW, nCh = imgTrg.shape
if not isinstance(gradientMask, np.ndarray):
gradientMask = np.zeros((imgH, imgW), dtype=np.float32)
if not isinstance(edge, np.ndarray):
edge = np.zeros((imgH, imgW), dtype=np.float32)
# Initialize the reconstructed image
imgRecon = np.zeros((imgH, imgW, nCh), dtype=np.float32)
# prepare discrete Poisson equation
A, b, UnfilledMask = solvePoisson(holeMask, imgSrc_gx, imgSrc_gy, imgTrg,
gradientMask, edge)
# Independently process each channel
for ch in range(nCh):
# solve Poisson equation
x = scipy.sparse.linalg.lsqr(A, b[:, ch])[0]
imgRecon[:, :, ch] = x.reshape(imgH, imgW)
# Combined with the known region in the target
holeMaskC = np.tile(np.expand_dims(holeMask, axis=2), (1, 1, nCh))
imgBlend = holeMaskC * imgRecon + (1 - holeMaskC) * imgTrg
# while((UnfilledMask * edge).sum() != 0):
# # Fill in edge pixel
# pi = np.expand_dims(np.where((UnfilledMask * edge) == 1)[0], axis=1) # y, i
# pj = np.expand_dims(np.where((UnfilledMask * edge) == 1)[1], axis=1) # x, j
#
# for k in range(len(pi)):
# if pi[k, 0] - 1 >= 0:
# if (UnfilledMask * edge)[pi[k, 0] - 1, pj[k, 0]] == 0:
# imgBlend[pi[k, 0], pj[k, 0], :] = imgBlend[pi[k, 0] - 1, pj[k, 0], :]
# UnfilledMask[pi[k, 0], pj[k, 0]] = 0
# continue
# if pi[k, 0] + 1 <= imgH - 1:
# if (UnfilledMask * edge)[pi[k, 0] + 1, pj[k, 0]] == 0:
# imgBlend[pi[k, 0], pj[k, 0], :] = imgBlend[pi[k, 0] + 1, pj[k, 0], :]
# UnfilledMask[pi[k, 0], pj[k, 0]] = 0
# continue
# if pj[k, 0] - 1 >= 0:
# if (UnfilledMask * edge)[pi[k, 0], pj[k, 0] - 1] == 0:
# imgBlend[pi[k, 0], pj[k, 0], :] = imgBlend[pi[k, 0], pj[k, 0] - 1, :]
# UnfilledMask[pi[k, 0], pj[k, 0]] = 0
# continue
# if pj[k, 0] + 1 <= imgW - 1:
# if (UnfilledMask * edge)[pi[k, 0], pj[k, 0] + 1] == 0:
# imgBlend[pi[k, 0], pj[k, 0], :] = imgBlend[pi[k, 0], pj[k, 0] + 1, :]
# UnfilledMask[pi[k, 0], pj[k, 0]] = 0
return imgBlend, UnfilledMask
def solvePoisson(holeMask, imgSrc_gx, imgSrc_gy, imgTrg,
gradientMask, edge):
# UnfilledMask indicates the region that is not completed
UnfilledMask_topleft = copy.deepcopy(holeMask)
UnfilledMask_bottomright = copy.deepcopy(holeMask)
# Prepare the linear system of equations for Poisson blending
imgH, imgW = holeMask.shape
N = imgH * imgW
# Number of unknown variables
numUnknownPix = holeMask.sum()
# 4-neighbors: dx and dy
dx = [1, 0, -1, 0]
dy = [0, 1, 0, -1]
# 3
# |
# 2 -- * -- 0
# |
# 1
#
# Initialize (I, J, S), for sparse matrix A where A(I(k), J(k)) = S(k)
I = np.empty((0, 1), dtype=np.float32)
J = np.empty((0, 1), dtype=np.float32)
S = np.empty((0, 1), dtype=np.float32)
# Initialize b
b = np.empty((0, 3), dtype=np.float32)
# Precompute unkonwn pixel position
pi = np.expand_dims(np.where(holeMask == 1)[0], axis=1) # y, i
pj = np.expand_dims(np.where(holeMask == 1)[1], axis=1) # x, j
pind = sub2ind(pi, pj, imgH, imgW)
# |--------------------|
# | y (i) |
# | x (j) * |
# | |
# |--------------------|
# p[y, x]
qi = np.concatenate((pi + dy[0],
pi + dy[1],
pi + dy[2],
pi + dy[3]), axis=1)
qj = np.concatenate((pj + dx[0],
pj + dx[1],
pj + dx[2],
pj + dx[3]), axis=1)
# Handling cases at image borders
validN = (qi >= 0) & (qi <= imgH - 1) & (qj >= 0) & (qj <= imgW - 1)
qind = np.zeros((validN.shape), dtype=np.float32)
qind[validN] = sub2ind(qi[validN], qj[validN], imgH, imgW)
e_start = 0 # equation counter start
e_stop = 0 # equation stop
# 4 neighbors
I, J, S, b, e_start, e_stop = constructEquation(0, validN, holeMask, gradientMask, edge, imgSrc_gx, imgSrc_gy, imgTrg, pi, pj, pind, qi, qj, qind, I, J, S, b, e_start, e_stop)
I, J, S, b, e_start, e_stop = constructEquation(1, validN, holeMask, gradientMask, edge, imgSrc_gx, imgSrc_gy, imgTrg, pi, pj, pind, qi, qj, qind, I, J, S, b, e_start, e_stop)
I, J, S, b, e_start, e_stop = constructEquation(2, validN, holeMask, gradientMask, edge, imgSrc_gx, imgSrc_gy, imgTrg, pi, pj, pind, qi, qj, qind, I, J, S, b, e_start, e_stop)
I, J, S, b, e_start, e_stop = constructEquation(3, validN, holeMask, gradientMask, edge, imgSrc_gx, imgSrc_gy, imgTrg, pi, pj, pind, qi, qj, qind, I, J, S, b, e_start, e_stop)
nEqn = len(b)
# Construct the sparse matrix A
A = sparse.csr_matrix((S[:, 0], (I[:, 0], J[:, 0])), shape=(nEqn, N))
# Check connected pixels
for ind in range(0, len(pi), 1):
ii = pi[ind, 0]
jj = pj[ind, 0]
# check up (3)
if ii - 1 >= 0:
if UnfilledMask_topleft[ii - 1, jj] == 0 and gradientMask[ii - 1, jj] == 0:
UnfilledMask_topleft[ii, jj] = 0
# check left (2)
if jj - 1 >= 0:
if UnfilledMask_topleft[ii, jj - 1] == 0 and gradientMask[ii, jj - 1] == 0:
UnfilledMask_topleft[ii, jj] = 0
for ind in range(len(pi) - 1, -1, -1):
ii = pi[ind, 0]
jj = pj[ind, 0]
# check bottom (1)
if ii + 1 <= imgH - 1:
if UnfilledMask_bottomright[ii + 1, jj] == 0 and gradientMask[ii, jj] == 0:
UnfilledMask_bottomright[ii, jj] = 0
# check right (0)
if jj + 1 <= imgW - 1:
if UnfilledMask_bottomright[ii, jj + 1] == 0 and gradientMask[ii, jj] == 0:
UnfilledMask_bottomright[ii, jj] = 0
UnfilledMask = UnfilledMask_topleft * UnfilledMask_bottomright
return A, b, UnfilledMask
def constructEquation(n, validN, holeMask, gradientMask, edge, imgSrc_gx, imgSrc_gy, imgTrg, pi, pj, pind, qi, qj, qind, I, J, S, b, e_start, e_stop):
# Pixel that has valid neighbors
validNeighbor = validN[:, n]
# Change the out-of-boundary value to 0, in order to run edge[y,x]
# in the next line. It won't affect anything as validNeighbor is saved already
qi_tmp = copy.deepcopy(qi)
qj_tmp = copy.deepcopy(qj)
qi_tmp[np.invert(validNeighbor), n] = 0
qj_tmp[np.invert(validNeighbor), n] = 0
NotEdge = (edge[pi[:, 0], pj[:, 0]] == 0) * (edge[qi_tmp[:, n], qj_tmp[:, n]] == 0)
# Have gradient
if n == 0:
HaveGrad = gradientMask[pi[:, 0], pj[:, 0]] == 0
elif n == 2:
HaveGrad = gradientMask[pi[:, 0], pj[:, 0] - 1] == 0
elif n == 1:
HaveGrad = gradientMask[pi[:, 0], pj[:, 0]] == 0
elif n == 3:
HaveGrad = gradientMask[pi[:, 0] - 1, pj[:, 0]] == 0
# Boundary constraint
Boundary = holeMask[qi_tmp[:, n], qj_tmp[:, n]] == 0
valid = validNeighbor * NotEdge * HaveGrad * Boundary
J_tmp = pind[valid, :]
# num of equations: len(J_tmp)
e_stop = e_start + len(J_tmp)
I_tmp = np.arange(e_start, e_stop, dtype=np.float32).reshape(-1, 1)
e_start = e_stop
S_tmp = np.ones(J_tmp.shape, dtype=np.float32)
if n == 0:
b_tmp = - imgSrc_gx[pi[valid, 0], pj[valid, 0], :] + imgTrg[qi[valid, n], qj[valid, n], :]
elif n == 2:
b_tmp = imgSrc_gx[pi[valid, 0], pj[valid, 0] - 1, :] + imgTrg[qi[valid, n], qj[valid, n], :]
elif n == 1:
b_tmp = - imgSrc_gy[pi[valid, 0], pj[valid, 0], :] + imgTrg[qi[valid, n], qj[valid, n], :]
elif n == 3:
b_tmp = imgSrc_gy[pi[valid, 0] - 1, pj[valid, 0], :] + imgTrg[qi[valid, n], qj[valid, n], :]
I = np.concatenate((I, I_tmp))
J = np.concatenate((J, J_tmp))
S = np.concatenate((S, S_tmp))
b = np.concatenate((b, b_tmp))
# Non-boundary constraint
NonBoundary = holeMask[qi_tmp[:, n], qj_tmp[:, n]] == 1
valid = validNeighbor * NotEdge * HaveGrad * NonBoundary
J_tmp = pind[valid, :]
# num of equations: len(J_tmp)
e_stop = e_start + len(J_tmp)
I_tmp = np.arange(e_start, e_stop, dtype=np.float32).reshape(-1, 1)
e_start = e_stop
S_tmp = np.ones(J_tmp.shape, dtype=np.float32)
if n == 0:
b_tmp = - imgSrc_gx[pi[valid, 0], pj[valid, 0], :]
elif n == 2:
b_tmp = imgSrc_gx[pi[valid, 0], pj[valid, 0] - 1, :]
elif n == 1:
b_tmp = - imgSrc_gy[pi[valid, 0], pj[valid, 0], :]
elif n == 3:
b_tmp = imgSrc_gy[pi[valid, 0] - 1, pj[valid, 0], :]
I = np.concatenate((I, I_tmp))
J = np.concatenate((J, J_tmp))
S = np.concatenate((S, S_tmp))
b = np.concatenate((b, b_tmp))
S_tmp = - np.ones(J_tmp.shape, dtype=np.float32)
J_tmp = qind[valid, n, None]
I = np.concatenate((I, I_tmp))
J = np.concatenate((J, J_tmp))
S = np.concatenate((S, S_tmp))
return I, J, S, b, e_start, e_stop
def getUnfilledMask(holeMask, gradientMask):
# UnfilledMask indicates the region that is not completed
UnfilledMask_topleft = copy.deepcopy(holeMask)
UnfilledMask_bottomright = copy.deepcopy(holeMask)
# Get the shape information of the mask
imgH, imgW = holeMask.shape
# Precompute the unknown pixel position
pi = np.expand_dims(np.where(holeMask == 1)[0], axis=1)
pj = np.expand_dims(np.where(holeMask == 1)[1], axis=1)
# Check connected pixels
for ind in range(0, len(pi), 1):
ii = pi[ind, 0]
jj = pj[ind, 0]
# check up (3)
if ii - 1 >= 0:
if UnfilledMask_topleft[ii - 1, jj] == 0 and gradientMask[ii - 1, jj] == 0:
UnfilledMask_topleft[ii, jj] = 0
# check left (2)
if jj - 1 >= 0:
if UnfilledMask_topleft[ii, jj - 1] == 0 and gradientMask[ii, jj - 1] == 0:
UnfilledMask_topleft[ii, jj] = 0
for ind in range(len(pi) - 1, -1, -1):
ii = pi[ind, 0]
jj = pj[ind, 0]
# check bottom (1)
if ii + 1 <= imgH - 1:
if UnfilledMask_bottomright[ii + 1, jj] == 0 and gradientMask[ii, jj] == 0:
UnfilledMask_bottomright[ii, jj] = 0
# check right (0)
if jj + 1 <= imgW - 1:
if UnfilledMask_bottomright[ii, jj + 1] == 0 and gradientMask[ii, jj] == 0:
UnfilledMask_bottomright[ii, jj] = 0
UnfilledMask = UnfilledMask_topleft * UnfilledMask_bottomright
return UnfilledMask
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