ref: 76cdfe2d732188847002d4bcbde3ed0964d77bb0
dir: /tools/3D-Reconstruction/MotionEST/Exhaust.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
"""Exhaust Search:"""
class Exhaust(MotionEST):
"""
Constructor:
cur_f: current frame
ref_f: reference frame
blk_sz: block size
wnd_size: search window size
metric: metric to compare the blocks distrotion
"""
def __init__(self, cur_f, ref_f, blk_size, wnd_size, metric=MSE):
self.name = 'exhaust'
self.wnd_sz = wnd_size
self.metric = metric
super(Exhaust, self).__init__(cur_f, ref_f, blk_size)
"""
search method:
cur_r: start row
cur_c: start column
"""
def search(self, cur_r, cur_c):
min_loss = self.block_dist(cur_r, cur_c, [0, 0], self.metric)
cur_x = cur_c * self.blk_sz
cur_y = cur_r * self.blk_sz
ref_x = cur_x
ref_y = cur_y
#search all validate positions and select the one with minimum distortion
for y in xrange(cur_y - self.wnd_sz, cur_y + self.wnd_sz):
for x in xrange(cur_x - self.wnd_sz, cur_x + self.wnd_sz):
if 0 <= x < self.width - self.blk_sz and 0 <= y < self.height - self.blk_sz:
loss = self.block_dist(cur_r, cur_c, [y - cur_y, x - cur_x],
self.metric)
if loss < min_loss:
min_loss = loss
ref_x = x
ref_y = y
return ref_x, ref_y
def motion_field_estimation(self):
for i in xrange(self.num_row):
for j in xrange(self.num_col):
ref_x, ref_y = self.search(i, j)
self.mf[i, j] = np.array(
[ref_y - i * self.blk_sz, ref_x - j * self.blk_sz])
"""Exhaust with Neighbor Constraint"""
class ExhaustNeighbor(MotionEST):
"""
Constructor:
cur_f: current frame
ref_f: reference frame
blk_sz: block size
wnd_size: search window size
beta: neigbor loss weight
metric: metric to compare the blocks distrotion
"""
def __init__(self, cur_f, ref_f, blk_size, wnd_size, beta, metric=MSE):
self.name = 'exhaust + neighbor'
self.wnd_sz = wnd_size
self.beta = beta
self.metric = metric
super(ExhaustNeighbor, self).__init__(cur_f, ref_f, blk_size)
self.assign = np.zeros((self.num_row, self.num_col), dtype=np.bool)
"""
estimate neighbor loss:
cur_r: current row
cur_c: current column
mv: current motion vector
"""
def neighborLoss(self, cur_r, cur_c, mv):
loss = 0
#accumulate difference between current block's motion vector with neighbors'
for i, j in {(-1, 0), (1, 0), (0, 1), (0, -1)}:
nb_r = cur_r + i
nb_c = cur_c + j
if 0 <= nb_r < self.num_row and 0 <= nb_c < self.num_col and self.assign[
nb_r, nb_c]:
loss += LA.norm(mv - self.mf[nb_r, nb_c])
return loss
"""
search method:
cur_r: start row
cur_c: start column
"""
def search(self, cur_r, cur_c):
dist_loss = self.block_dist(cur_r, cur_c, [0, 0], self.metric)
nb_loss = self.neighborLoss(cur_r, cur_c, np.array([0, 0]))
min_loss = dist_loss + self.beta * nb_loss
cur_x = cur_c * self.blk_sz
cur_y = cur_r * self.blk_sz
ref_x = cur_x
ref_y = cur_y
#search all validate positions and select the one with minimum distortion
# as well as weighted neighbor loss
for y in xrange(cur_y - self.wnd_sz, cur_y + self.wnd_sz):
for x in xrange(cur_x - self.wnd_sz, cur_x + self.wnd_sz):
if 0 <= x < self.width - self.blk_sz and 0 <= y < self.height - self.blk_sz:
dist_loss = self.block_dist(cur_r, cur_c, [y - cur_y, x - cur_x],
self.metric)
nb_loss = self.neighborLoss(cur_r, cur_c, [y - cur_y, x - cur_x])
loss = dist_loss + self.beta * nb_loss
if loss < min_loss:
min_loss = loss
ref_x = x
ref_y = y
return ref_x, ref_y
def motion_field_estimation(self):
for i in xrange(self.num_row):
for j in xrange(self.num_col):
ref_x, ref_y = self.search(i, j)
self.mf[i, j] = np.array(
[ref_y - i * self.blk_sz, ref_x - j * self.blk_sz])
self.assign[i, j] = True
"""Exhaust with Neighbor Constraint and Feature Score"""
class ExhaustNeighborFeatureScore(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,
wnd_size,
beta=1,
max_iter=100,
metric=MSE):
self.name = 'exhaust + neighbor+feature score'
self.wnd_sz = wnd_size
self.beta = beta
self.metric = metric
self.max_iter = max_iter
super(ExhaustNeighborFeatureScore, self).__init__(cur_f, ref_f, blk_size)
self.fs = self.getFeatureScore()
"""
get feature score of each block
"""
def getFeatureScore(self):
fs = np.zeros((self.num_row, self.num_col))
for r in xrange(self.num_row):
for c in xrange(self.num_col):
IxIx = 0
IyIy = 0
IxIy = 0
#get ssd surface
for x in xrange(self.blk_sz - 1):
for y in xrange(self.blk_sz - 1):
ox = c * self.blk_sz + x
oy = r * self.blk_sz + y
Ix = self.cur_yuv[oy, ox + 1, 0] - self.cur_yuv[oy, ox, 0]
Iy = self.cur_yuv[oy + 1, ox, 0] - self.cur_yuv[oy, ox, 0]
IxIx += Ix * Ix
IyIy += Iy * Iy
IxIy += Ix * Iy
#get maximum and minimum eigenvalues
lambda_max = 0.5 * ((IxIx + IyIy) + np.sqrt(4 * IxIy * IxIy +
(IxIx - IyIy)**2))
lambda_min = 0.5 * ((IxIx + IyIy) - np.sqrt(4 * IxIy * IxIy +
(IxIx - IyIy)**2))
fs[r, c] = lambda_max * lambda_min / (1e-6 + lambda_max + lambda_min)
if fs[r, c] < 0:
fs[r, c] = 0
return fs
"""
do exhaust search
"""
def search(self, cur_r, cur_c):
min_loss = self.block_dist(cur_r, cur_c, [0, 0], self.metric)
cur_x = cur_c * self.blk_sz
cur_y = cur_r * self.blk_sz
ref_x = cur_x
ref_y = cur_y
#search all validate positions and select the one with minimum distortion
for y in xrange(cur_y - self.wnd_sz, cur_y + self.wnd_sz):
for x in xrange(cur_x - self.wnd_sz, cur_x + self.wnd_sz):
if 0 <= x < self.width - self.blk_sz and 0 <= y < self.height - self.blk_sz:
loss = self.block_dist(cur_r, cur_c, [y - cur_y, x - cur_x],
self.metric)
if loss < min_loss:
min_loss = loss
ref_x = x
ref_y = y
return ref_x, ref_y
"""
add smooth constraint
"""
def smooth(self, uvs, mvs):
sm_uvs = np.zeros(uvs.shape)
for r in xrange(self.num_row):
for c in xrange(self.num_col):
avg_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:
avg_uv += uvs[i, j] / 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:
avg_uv += uvs[i, j] / 12.0
sm_uvs[r, c] = (self.fs[r, c] * mvs[r, c] + self.beta * avg_uv) / (
self.beta + self.fs[r, c])
return sm_uvs
def motion_field_estimation(self):
#get matching results
mvs = np.zeros(self.mf.shape)
for r in xrange(self.num_row):
for c in xrange(self.num_col):
ref_x, ref_y = self.search(r, c)
mvs[r, c] = np.array([ref_y - r * self.blk_sz, ref_x - c * self.blk_sz])
#add smoothness constraint
uvs = np.zeros(self.mf.shape)
for _ in xrange(self.max_iter):
uvs = self.smooth(uvs, mvs)
self.mf = uvs