新しい画像が古い画像に対してどれだけシフトおよび回転したかを見つける必要があるプロジェクトに取り組んでいます。fftを使って実装しようとしています。ただし、一部のケースでは機能し、他のケースでは失敗します。私が従う手順は次のとおりです。
- 2 つの画像を取得し、キャニー エッジ検出を実装する
- fftを使用してシフトを見つけ、シフトを削除します
- 画像を極領域に変換し、シフトを見つけて、答えをラジアンに変換します
正しい答えが得られる場合もありますが、シフトが (0,0) として、回転が 0 ラジアンとして得られる場合もあります。これが発生する可能性がある理由を提案してください。
コードは次のとおりです。
class Register:
'''
Class for registering images based on FFT. The usage is as follows:
>>> im0 = imread('image0.jpg', flatten = True)
>>> im1 = imread('image1.jpg', flatten = True)
>>> reg = Register(im0, im1)
>>> shift = reg.shift
>>> rotation = reg.theta
Note:
1. This image registration technique is not very reliable and
is valid only for small rotation
2. The class is very slow, since it depends on canny edge detection
module for finding edges.
'''
def __init__(self,imin0, imin1, PROCESSED = False):
'''
This method is used to execute all the routines required to get the
shift and the rotation
'''
# find edges to remove low frequency signals and suppress information
if PROCESSED:
im0 = imin0
im1 = imin1
else:
im0 = Canny(imin0, 0.85, 5).grad
im1 = Canny(imin1, 0.85, 5).grad
# A major drawback of this method is that it can operate only on square
# images. Hence we will make square image of any input image
im0 = self.createsquareim(self.clearBorder(im0))
im1 = self.createsquareim(self.clearBorder(im1))
self.shift = self.findShift(im0,im1)
imtrans = shift(im1, self.shift)
# Remove the shift in the image. This is mandatory before we find theta
impolar0 = self.makePolar(im0)
impolar1 = self.makePolar(imtrans)
self.index = self.findShift(impolar0, impolar1)[1]
self.theta = ((self.index*90.0)/impolar1.shape[0])
def clearBorder(self,im,width = 50, color = 255):
'''
A little house keeping to clear any border noise
'''
im[:,:width] = color
im[:,-width:] = color
im[:width,:] = color
im[-width:,:] = color
return im
def createsquareim(self, im):
"""
function createsquareim
input:numpy ndarray
output:numpy ndarray
The function takes in an image array and converts it into square
image by creating empty columns and rows.
"""
lenmax = max(im.shape[0],im.shape[1])
imout = zeros((lenmax,lenmax))
imout[:,:] = 255
imout[:im.shape[0],:im.shape[1]] = im
return imout
def findShift(self, im0, im1):
'''
This method is based on fft method of registering images.
'''
IM0 = fft2(im0)
IM1 = fft2(im1)
numer = IM0*conj(IM1)
denom = abs(IM0*IM1)
pulse_im = ifft2(numer/denom)
mag = abs(pulse_im)
x, y = where(mag == mag.max())
x = array(x.tolist()) # Issues with read only arrays
y = array(y.tolist())
X, Y = im0.shape
if x > X/2:
x -= X
if y > Y/2:
y -= Y
return [x[0], y[0]]
def makePolar(self, im):
'''
This method will convert the cartesian coordinates image
to polar coordinates image. The relation between the two
domains is
F(r,theta) = f(r*cos(theta),r*sin(theta))
To make the process fast, we are using map_coordinates function
'''
m, n = im.shape
r_max = hypot(m, n)
r_mat = zeros_like(im)
t_mat = zeros_like(im)
r_mat.T[:] = linspace(0, r_max, m)
t_mat[:] = linspace(0, pi/2, n)
x = r_mat*cos(t_mat)
y = r_mat*sin(t_mat)
imout = zeros_like(im)
imout = map_coordinates(im, [x, y], cval = 255)
return imout