ref: e436930c39acaf71fed5ca830dcac6878de95b9d
dir: /tools/3D-Reconstruction/MotionEST/HornSchunck.py/
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import numpy.linalg as LA
from scipy.ndimage.filters import gaussian_filter
from scipy.sparse import csc_matrix
from scipy.sparse.linalg import inv
from MotionEST import MotionEST
"""Horn & Schunck Model"""
class HornSchunck(MotionEST):
"""
constructor:
cur_f: current frame
ref_f: reference frame
blk_sz: block size
alpha: smooth constrain weight
sigma: gaussian blur parameter
"""
def __init__(self, cur_f, ref_f, blk_sz, alpha, sigma, max_iter=100):
super(HornSchunck, self).__init__(cur_f, ref_f, blk_sz)
self.cur_I, self.ref_I = self.getIntensity()
#perform gaussian blur to smooth the intensity
self.cur_I = gaussian_filter(self.cur_I, sigma=sigma)
self.ref_I = gaussian_filter(self.ref_I, sigma=sigma)
self.alpha = alpha
self.max_iter = max_iter
self.Ix, self.Iy, self.It = self.intensityDiff()
"""
Build Frame Intensity
"""
def getIntensity(self):
cur_I = np.zeros((self.num_row, self.num_col))
ref_I = np.zeros((self.num_row, self.num_col))
#use average intensity as block's intensity
for i in xrange(self.num_row):
for j in xrange(self.num_col):
r = i * self.blk_sz
c = j * self.blk_sz
cur_I[i, j] = np.mean(self.cur_yuv[r:r + self.blk_sz, c:c + self.blk_sz,
0])
ref_I[i, j] = np.mean(self.ref_yuv[r:r + self.blk_sz, c:c + self.blk_sz,
0])
return cur_I, ref_I
"""
Get First Order Derivative
"""
def intensityDiff(self):
Ix = np.zeros((self.num_row, self.num_col))
Iy = np.zeros((self.num_row, self.num_col))
It = np.zeros((self.num_row, self.num_col))
sz = self.blk_sz
for i in xrange(self.num_row - 1):
for j in xrange(self.num_col - 1):
"""
Ix:
(i ,j) <--- (i ,j+1)
(i+1,j) <--- (i+1,j+1)
"""
count = 0
for r, c in {(i, j + 1), (i + 1, j + 1)}:
if 0 <= r < self.num_row and 0 < c < self.num_col:
Ix[i, j] += (
self.cur_I[r, c] - self.cur_I[r, c - 1] + self.ref_I[r, c] -
self.ref_I[r, c - 1])
count += 2
Ix[i, j] /= count
"""
Iy:
(i ,j) (i ,j+1)
^ ^
| |
(i+1,j) (i+1,j+1)
"""
count = 0
for r, c in {(i + 1, j), (i + 1, j + 1)}:
if 0 < r < self.num_row and 0 <= c < self.num_col:
Iy[i, j] += (
self.cur_I[r, c] - self.cur_I[r - 1, c] + self.ref_I[r, c] -
self.ref_I[r - 1, c])
count += 2
Iy[i, j] /= count
count = 0
#It:
for r in xrange(i, i + 2):
for c in xrange(j, j + 2):
if 0 <= r < self.num_row and 0 <= c < self.num_col:
It[i, j] += (self.ref_I[r, c] - self.cur_I[r, c])
count += 1
It[i, j] /= count
return Ix, Iy, It
"""
Get weighted average of neighbor motion vectors
for evaluation of laplacian
"""
def averageMV(self):
avg = np.zeros((self.num_row, self.num_col, 2))
"""
1/12 --- 1/6 --- 1/12
| | |
1/6 --- -1/8 --- 1/6
| | |
1/12 --- 1/6 --- 1/12
"""
for i in xrange(self.num_row):
for j in xrange(self.num_col):
for r, c in {(-1, 0), (1, 0), (0, -1), (0, 1)}:
if 0 <= i + r < self.num_row and 0 <= j + c < self.num_col:
avg[i, j] += self.mf[i + r, j + c] / 6.0
for r, c in {(-1, -1), (-1, 1), (1, -1), (1, 1)}:
if 0 <= i + r < self.num_row and 0 <= j + c < self.num_col:
avg[i, j] += self.mf[i + r, j + c] / 12.0
return avg
def motion_field_estimation(self):
count = 0
"""
u_{n+1} = ~u_n - Ix(Ix.~u_n+Iy.~v+It)/(IxIx+IyIy+alpha^2)
v_{n+1} = ~v_n - Iy(Ix.~u_n+Iy.~v+It)/(IxIx+IyIy+alpha^2)
"""
denom = self.alpha**2 + np.power(self.Ix, 2) + np.power(self.Iy, 2)
while count < self.max_iter:
avg = self.averageMV()
self.mf[:, :, 1] = avg[:, :, 1] - self.Ix * (
self.Ix * avg[:, :, 1] + self.Iy * avg[:, :, 0] + self.It) / denom
self.mf[:, :, 0] = avg[:, :, 0] - self.Iy * (
self.Ix * avg[:, :, 1] + self.Iy * avg[:, :, 0] + self.It) / denom
count += 1
self.mf *= self.blk_sz
def motion_field_estimation_mat(self):
row_idx = []
col_idx = []
data = []
N = 2 * self.num_row * self.num_col
b = np.zeros((N, 1))
for i in xrange(self.num_row):
for j in xrange(self.num_col):
"""(IxIx+alpha^2)u+IxIy.v-alpha^2~u IxIy.u+(IyIy+alpha^2)v-alpha^2~v"""
u_idx = i * 2 * self.num_col + 2 * j
v_idx = u_idx + 1
b[u_idx, 0] = -self.Ix[i, j] * self.It[i, j]
b[v_idx, 0] = -self.Iy[i, j] * self.It[i, j]
#u: (IxIx+alpha^2)u
row_idx.append(u_idx)
col_idx.append(u_idx)
data.append(self.Ix[i, j] * self.Ix[i, j] + self.alpha**2)
#IxIy.v
row_idx.append(u_idx)
col_idx.append(v_idx)
data.append(self.Ix[i, j] * self.Iy[i, j])
#v: IxIy.u
row_idx.append(v_idx)
col_idx.append(u_idx)
data.append(self.Ix[i, j] * self.Iy[i, j])
#(IyIy+alpha^2)v
row_idx.append(v_idx)
col_idx.append(v_idx)
data.append(self.Iy[i, j] * self.Iy[i, j] + self.alpha**2)
#-alpha^2~u
#-alpha^2~v
for r, c in {(-1, 0), (1, 0), (0, -1), (0, 1)}:
if 0 <= i + r < self.num_row and 0 <= j + c < self.num_col:
u_nb = (i + r) * 2 * self.num_col + 2 * (j + c)
v_nb = u_nb + 1
row_idx.append(u_idx)
col_idx.append(u_nb)
data.append(-1 * self.alpha**2 / 6.0)
row_idx.append(v_idx)
col_idx.append(v_nb)
data.append(-1 * self.alpha**2 / 6.0)
for r, c in {(-1, -1), (-1, 1), (1, -1), (1, 1)}:
if 0 <= i + r < self.num_row and 0 <= j + c < self.num_col:
u_nb = (i + r) * 2 * self.num_col + 2 * (j + c)
v_nb = u_nb + 1
row_idx.append(u_idx)
col_idx.append(u_nb)
data.append(-1 * self.alpha**2 / 12.0)
row_idx.append(v_idx)
col_idx.append(v_nb)
data.append(-1 * self.alpha**2 / 12.0)
M = csc_matrix((data, (row_idx, col_idx)), shape=(N, N))
M_inv = inv(M)
uv = M_inv.dot(b)
for i in xrange(self.num_row):
for j in xrange(self.num_col):
self.mf[i, j, 0] = uv[i * 2 * self.num_col + 2 * j + 1, 0] * self.blk_sz
self.mf[i, j, 1] = uv[i * 2 * self.num_col + 2 * j, 0] * self.blk_sz