我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用numpy.fft.ifft2()。
def correlate(self, imgfft): #Very much related to the convolution theorem, the cross-correlation #theorem states that the Fourier transform of the cross-correlation of #two functions is equal to the product of the individual Fourier #transforms, where one of them has been complex conjugated: if self.imgfft is not 0 or imgfft.imgfft is not 0: imgcj = np.conjugate(self.imgfft) imgft = imgfft.imgfft prod = deepcopy(imgcj) for x in range(imgcj.shape[0]): for y in range(imgcj.shape[0]): prod[x][y] = imgcj[x][y] * imgft[x][y] cc = Corr( np.real(fft.ifft2(fft.fftshift(prod)))) # real image of the correlation # adjust to center cc.data = np.roll(cc.data, int(cc.data.shape[0] / 2), axis = 0) cc.data = np.roll(cc.data, int(cc.data.shape[1] / 2), axis = 1) else: raise FFTnotInit() return cc
def ift(self): return MyImage(np.real(fft.ifft2(fft.fftshift(self.ft))))
def ift(self): self.imgifft = MyImage(np.real(fft.ifft2(fft.fftshift(self.imgfft))))
def cross_corr(img1,img2,mask=None): '''Compute the autocorrelation of two images. Right now does not take mask into account. todo: take mask into account (requires extra calculations) input: img1: first image img2: second image mask: a mask array output: the autocorrelation of the two images (same shape as the correlated images) ''' #if(mask is not None): # img1 *= mask # img2 *= mask #img1_mean = np.mean( img1.flat ) #img2_mean = np.mean( img2.flat ) # imgc = fftshift( ifft2( # fft2(img1/img1_mean -1.0 )*np.conj(fft2( img2/img2_mean -1.0 ))).real ) #imgc = fftshift( ifft2( # fft2( img1/img1_mean )*np.conj(fft2( img2/img2_mean ))).real ) imgc = fftshift( ifft2( fft2( img1 )*np.conj(fft2( img2 ))).real ) #imgc /= (img1.shape[0]*img1.shape[1])**2 if(mask is not None): maskc = cross_corr(mask,mask) imgc /= np.maximum( 1, maskc ) return imgc
def extractFeatures(self, img): "A vector of 2n elements where n is the number of theta angles" "and 2 is the number of frequencies under consideration" filters = self.build_filters(img.shape[0],img.shape[1],5,(0.75,1.5),2,1,np.pi/2.0) fft_filters = [np.fft.fft2(i) for i in filters] img_fft = np.fft.fft2(img) a = img_fft * fft_filters s = [np.fft.ifft2(i) for i in a] k = [p.real for p in s] return k
def fft_convolve2d(self, x,y): """ 2D convolution, using FFT""" fr = fft2(x) fr2 = fft2(np.flipud(np.fliplr(y))) m,n = fr.shape cc = np.real(ifft2(fr*fr2)) cc = np.roll(cc, -m/2+1,axis=0) cc = np.roll(cc, -n/2+1,axis=1) return cc
def xcorr(imageA, imageB): FimageA = _fft.fft2(imageA) CFimageB = _np.conj(_fft.fft2(imageB)) return _fft.fftshift(_np.real(_fft.ifft2((FimageA * CFimageB)))) / _np.sqrt(imageA.size)
def ift2(G, dfx, dfy): Nx = G.shape[1] Ny = G.shape[0] return ifftshift(ifft2(ifftshift(G))) * Nx * Ny * dfx * dfy
def plot_crystal2D(): plt.figure() plt.subplot(221) plt.imshow(crystal, cmap='gray') plt.title('crystal') plt.axis('off') plt.subplot(223) test=crystal_filter2D*crystal_fourier plt.imshow(np.log10(abs(test)), cmap='gray') plt.title('log10 |FFT2| of crystal') plt.axis('off') plt.subplot(222) plt.imshow(crystal_skewed, cmap='gray') plt.xlabel(' x_s') plt.ylabel(' y_s') plt.title('crystal in skewed coordinates') plt.axis('off') plt.subplot(224) plt.imshow(np.log10(abs(fft_crystal_skewed)), cmap='gray') plt.xlabel(' 1/x_s') plt.ylabel(' 1/y_s') plt.title('log10|FFT| of crystal in skewed coordinates') plt.axis('off') # plot filtered 2D retrived crystal2D plt.figure fig,ax = plt.subplots(1) plt.imshow(abs((fft.ifft2(fft.ifftshift(test))))) #plot_crystal2D() #plt.figure() #plt.imshow(projectedCrystal) #plt.title('Orthoganl projection of 3D crystal into x-y-plane') #plt.xlabel(' x') #plt.ylabel(' y') #savefig('test.jpg') ########################plot 3D crystal in all plane cuts
