ref: 7ec7a33a081aeeb53fed1a8d87e4cbd189152527
dir: /tools/3D-Reconstruction/MotionEST/SearchSmooth.py/
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import numpy.linalg as LA
from Util import MSE
from MotionEST import MotionEST
"""Search & Smooth Model with Adapt Weights"""
class SearchSmoothAdapt(MotionEST):
"""
Constructor:
cur_f: current frame
ref_f: reference frame
blk_sz: block size
wnd_size: search window size
beta: neigbor loss weight
max_iter: maximum number of iterations
metric: metric to compare the blocks distrotion
"""
def __init__(self, cur_f, ref_f, blk_size, search, max_iter=100):
self.search = search
self.max_iter = max_iter
super(SearchSmoothAdapt, self).__init__(cur_f, ref_f, blk_size)
"""
get local diffiencial of refernce
"""
def getRefLocalDiff(self, mvs):
m, n = self.num_row, self.num_col
localDiff = [[] for _ in xrange(m)]
blk_sz = self.blk_sz
for r in xrange(m):
for c in xrange(n):
I_row = 0
I_col = 0
#get ssd surface
count = 0
center = self.cur_yuv[r * blk_sz:(r + 1) * blk_sz,
c * blk_sz:(c + 1) * blk_sz, 0]
ty = np.clip(r * blk_sz + int(mvs[r, c, 0]), 0, self.height - blk_sz)
tx = np.clip(c * blk_sz + int(mvs[r, c, 1]), 0, self.width - blk_sz)
target = self.ref_yuv[ty:ty + blk_sz, tx:tx + blk_sz, 0]
for y, x in {(ty - blk_sz, tx), (ty + blk_sz, tx)}:
if 0 <= y < self.height - blk_sz and 0 <= x < self.width - blk_sz:
nb = self.ref_yuv[y:y + blk_sz, x:x + blk_sz, 0]
I_row += np.sum(np.abs(nb - center)) - np.sum(
np.abs(target - center))
count += 1
I_row //= (count * blk_sz * blk_sz)
count = 0
for y, x in {(ty, tx - blk_sz), (ty, tx + blk_sz)}:
if 0 <= y < self.height - blk_sz and 0 <= x < self.width - blk_sz:
nb = self.ref_yuv[y:y + blk_sz, x:x + blk_sz, 0]
I_col += np.sum(np.abs(nb - center)) - np.sum(
np.abs(target - center))
count += 1
I_col //= (count * blk_sz * blk_sz)
localDiff[r].append(
np.array([[I_row * I_row, I_row * I_col],
[I_col * I_row, I_col * I_col]]))
return localDiff
"""
add smooth constraint
"""
def smooth(self, uvs, mvs):
sm_uvs = np.zeros(uvs.shape)
blk_sz = self.blk_sz
for r in xrange(self.num_row):
for c in xrange(self.num_col):
nb_uv = np.array([0.0, 0.0])
for i, j in {(r - 1, c), (r + 1, c), (r, c - 1), (r, c + 1)}:
if 0 <= i < self.num_row and 0 <= j < self.num_col:
nb_uv += uvs[i, j] / 6.0
else:
nb_uv += uvs[r, c] / 6.0
for i, j in {(r - 1, c - 1), (r - 1, c + 1), (r + 1, c - 1),
(r + 1, c + 1)}:
if 0 <= i < self.num_row and 0 <= j < self.num_col:
nb_uv += uvs[i, j] / 12.0
else:
nb_uv += uvs[r, c] / 12.0
ssd_nb = self.block_dist(r, c, self.blk_sz * nb_uv)
mv = mvs[r, c]
ssd_mv = self.block_dist(r, c, mv)
alpha = (ssd_nb - ssd_mv) / (ssd_mv + 1e-6)
M = alpha * self.localDiff[r][c]
P = M + np.identity(2)
inv_P = LA.inv(P)
sm_uvs[r, c] = np.dot(inv_P, nb_uv) + np.dot(
np.matmul(inv_P, M), mv / blk_sz)
return sm_uvs
def block_matching(self):
self.search.motion_field_estimation()
def motion_field_estimation(self):
self.localDiff = self.getRefLocalDiff(self.search.mf)
#get matching results
mvs = self.search.mf
#add smoothness constraint
uvs = mvs / self.blk_sz
for _ in xrange(self.max_iter):
uvs = self.smooth(uvs, mvs)
self.mf = uvs * self.blk_sz
"""Search & Smooth Model with Fixed Weights"""
class SearchSmoothFix(MotionEST):
"""
Constructor:
cur_f: current frame
ref_f: reference frame
blk_sz: block size
wnd_size: search window size
beta: neigbor loss weight
max_iter: maximum number of iterations
metric: metric to compare the blocks distrotion
"""
def __init__(self, cur_f, ref_f, blk_size, search, beta, max_iter=100):
self.search = search
self.max_iter = max_iter
self.beta = beta
super(SearchSmoothFix, self).__init__(cur_f, ref_f, blk_size)
"""
get local diffiencial of refernce
"""
def getRefLocalDiff(self, mvs):
m, n = self.num_row, self.num_col
localDiff = [[] for _ in xrange(m)]
blk_sz = self.blk_sz
for r in xrange(m):
for c in xrange(n):
I_row = 0
I_col = 0
#get ssd surface
count = 0
center = self.cur_yuv[r * blk_sz:(r + 1) * blk_sz,
c * blk_sz:(c + 1) * blk_sz, 0]
ty = np.clip(r * blk_sz + int(mvs[r, c, 0]), 0, self.height - blk_sz)
tx = np.clip(c * blk_sz + int(mvs[r, c, 1]), 0, self.width - blk_sz)
target = self.ref_yuv[ty:ty + blk_sz, tx:tx + blk_sz, 0]
for y, x in {(ty - blk_sz, tx), (ty + blk_sz, tx)}:
if 0 <= y < self.height - blk_sz and 0 <= x < self.width - blk_sz:
nb = self.ref_yuv[y:y + blk_sz, x:x + blk_sz, 0]
I_row += np.sum(np.abs(nb - center)) - np.sum(
np.abs(target - center))
count += 1
I_row //= (count * blk_sz * blk_sz)
count = 0
for y, x in {(ty, tx - blk_sz), (ty, tx + blk_sz)}:
if 0 <= y < self.height - blk_sz and 0 <= x < self.width - blk_sz:
nb = self.ref_yuv[y:y + blk_sz, x:x + blk_sz, 0]
I_col += np.sum(np.abs(nb - center)) - np.sum(
np.abs(target - center))
count += 1
I_col //= (count * blk_sz * blk_sz)
localDiff[r].append(
np.array([[I_row * I_row, I_row * I_col],
[I_col * I_row, I_col * I_col]]))
return localDiff
"""
add smooth constraint
"""
def smooth(self, uvs, mvs):
sm_uvs = np.zeros(uvs.shape)
blk_sz = self.blk_sz
for r in xrange(self.num_row):
for c in xrange(self.num_col):
nb_uv = np.array([0.0, 0.0])
for i, j in {(r - 1, c), (r + 1, c), (r, c - 1), (r, c + 1)}:
if 0 <= i < self.num_row and 0 <= j < self.num_col:
nb_uv += uvs[i, j] / 6.0
else:
nb_uv += uvs[r, c] / 6.0
for i, j in {(r - 1, c - 1), (r - 1, c + 1), (r + 1, c - 1),
(r + 1, c + 1)}:
if 0 <= i < self.num_row and 0 <= j < self.num_col:
nb_uv += uvs[i, j] / 12.0
else:
nb_uv += uvs[r, c] / 12.0
mv = mvs[r, c] / blk_sz
M = self.localDiff[r][c]
P = M + self.beta * np.identity(2)
inv_P = LA.inv(P)
sm_uvs[r, c] = np.dot(inv_P, self.beta * nb_uv) + np.dot(
np.matmul(inv_P, M), mv)
return sm_uvs
def block_matching(self):
self.search.motion_field_estimation()
def motion_field_estimation(self):
#get local structure
self.localDiff = self.getRefLocalDiff(self.search.mf)
#get matching results
mvs = self.search.mf
#add smoothness constraint
uvs = mvs / self.blk_sz
for _ in xrange(self.max_iter):
uvs = self.smooth(uvs, mvs)
self.mf = uvs * self.blk_sz