python实现泊松图像融合

本文实例为大家分享了python实现泊松图像融合的具体代码，供大家参考，具体内容如下

```from __future__ import divisionimport numpy as np import scipy.fftpackimport scipy.ndimageimport cv2import matplotlib.pyplot as plt #sns.set(style="darkgrid")def DST(x):  """  Converts Scipy's DST output to Matlab's DST (scaling).  """  X = scipy.fftpack.dst(x,type=1,axis=0)  return X/2.0def IDST(X):  """  Inverse DST. Python -> Matlab  """  n = X.shape[0]  x = np.real(scipy.fftpack.idst(X,type=1,axis=0))  return x/(n+1.0)def get_grads(im):  """  return the x and y gradients.  """  [H,W] = im.shape  Dx,Dy = np.zeros((H,W),'float32'), np.zeros((H,W),'float32')  j,k = np.atleast_2d(np.arange(0,H-1)).T, np.arange(0,W-1)  Dx[j,k] = im[j,k+1] - im[j,k]  Dy[j,k] = im[j+1,k] - im[j,k]  return Dx,Dydef get_laplacian(Dx,Dy):  """  return the laplacian  """  [H,W] = Dx.shape  Dxx, Dyy = np.zeros((H,W)), np.zeros((H,W))  j,k = np.atleast_2d(np.arange(0,H-1)).T, np.arange(0,W-1)  Dxx[j,k+1] = Dx[j,k+1] - Dx[j,k]   Dyy[j+1,k] = Dy[j+1,k] - Dy[j,k]  return Dxx+Dyydef poisson_solve(gx,gy,bnd):  # convert to double:  gx = gx.astype('float32')  gy = gy.astype('float32')  bnd = bnd.astype('float32')  H,W = bnd.shape  L = get_laplacian(gx,gy)  # set the interior of the boundary-image to 0:  bnd[1:-1,1:-1] = 0  # get the boundary laplacian:  L_bp = np.zeros_like(L)  L_bp[1:-1,1:-1] = -4*bnd[1:-1,1:-1] /           + bnd[1:-1,2:] + bnd[1:-1,0:-2] /           + bnd[2:,1:-1] + bnd[0:-2,1:-1] # delta-x  L = L - L_bp  L = L[1:-1,1:-1]  # compute the 2D DST:  L_dst = DST(DST(L).T).T #first along columns, then along rows  # normalize:  [xx,yy] = np.meshgrid(np.arange(1,W-1),np.arange(1,H-1))  D = (2*np.cos(np.pi*xx/(W-1))-2) + (2*np.cos(np.pi*yy/(H-1))-2)  L_dst = L_dst/D  img_interior = IDST(IDST(L_dst).T).T # inverse DST for rows and columns  img = bnd.copy()  img[1:-1,1:-1] = img_interior  return imgdef blit_images(im_top,im_back,scale_grad=1.0,mode='max'):  """  combine images using poission editing.  IM_TOP and IM_BACK should be of the same size.  """  assert np.all(im_top.shape==im_back.shape)  im_top = im_top.copy().astype('float32')  im_back = im_back.copy().astype('float32')  im_res = np.zeros_like(im_top)  # frac of gradients which come from source:  for ch in xrange(im_top.shape[2]):    ims = im_top[:,:,ch]    imd = im_back[:,:,ch]    [gxs,gys] = get_grads(ims)    [gxd,gyd] = get_grads(imd)    gxs *= scale_grad    gys *= scale_grad    gxs_idx = gxs!=0    gys_idx = gys!=0    # mix the source and target gradients:    if mode=='max':      gx = gxs.copy()      gxm = (np.abs(gxd))>np.abs(gxs)      gx[gxm] = gxd[gxm]      gy = gys.copy()      gym = np.abs(gyd)>np.abs(gys)      gy[gym] = gyd[gym]      # get gradient mixture statistics:      f_gx = np.sum((gx[gxs_idx]==gxs[gxs_idx]).flat) / (np.sum(gxs_idx.flat)+1e-6)      f_gy = np.sum((gy[gys_idx]==gys[gys_idx]).flat) / (np.sum(gys_idx.flat)+1e-6)      if min(f_gx, f_gy) <= 0.35:        m = 'max'        if scale_grad > 1:          m = 'blend'        return blit_images(im_top, im_back, scale_grad=1.5, mode=m)    elif mode=='src':      gx,gy = gxd.copy(), gyd.copy()      gx[gxs_idx] = gxs[gxs_idx]      gy[gys_idx] = gys[gys_idx]    elif mode=='blend': # from recursive call:      # just do an alpha blend      gx = gxs+gxd      gy = gys+gyd    im_res[:,:,ch] = np.clip(poisson_solve(gx,gy,imd),0,255)  return im_res.astype('uint8')def contiguous_regions(mask):  """  return a list of (ind0, ind1) such that mask[ind0:ind1].all() is  True and we cover all such regions  """  in_region = None  boundaries = []  for i, val in enumerate(mask):    if in_region is None and val:      in_region = i    elif in_region is not None and not val:      boundaries.append((in_region, i))      in_region = None  if in_region is not None:    boundaries.append((in_region, i+1))  return boundariesif __name__=='__main__':  """  example usage:  """  import seaborn as sns  im_src = cv2.imread('../f01006.jpg').astype('float32')  im_dst = cv2.imread('../f01006-5.jpg').astype('float32')  mu = np.mean(np.reshape(im_src,[im_src.shape[0]*im_src.shape[1],3]),axis=0)  # print mu  sz = (1920,1080)  im_src = cv2.resize(im_src,sz)  im_dst = cv2.resize(im_dst,sz)  im0 = im_dst[:,:,0] > 100  im_dst[im0,:] = im_src[im0,:]  im_dst[~im0,:] = 50  im_dst = cv2.GaussianBlur(im_dst,(5,5),5)  im_alpha = 0.8*im_dst + 0.2*im_src  # plt.imshow(im_dst)  # plt.show()  im_res = blit_images(im_src,im_dst)  import scipy  scipy.misc.imsave('orig.png',im_src[:,:,::-1].astype('uint8'))  scipy.misc.imsave('alpha.png',im_alpha[:,:,::-1].astype('uint8'))  scipy.misc.imsave('poisson.png',im_res[:,:,::-1].astype('uint8'))  im_actual_L = cv2.cvtColor(im_src.astype('uint8'),cv2.cv.CV_BGR2Lab)[:,:,0]  im_alpha_L = cv2.cvtColor(im_alpha.astype('uint8'),cv2.cv.CV_BGR2Lab)[:,:,0]  im_poisson_L = cv2.cvtColor(im_res.astype('uint8'),cv2.cv.CV_BGR2Lab)[:,:,0]  # plt.imshow(im_alpha_L)  # plt.show()  for i in xrange(500,im_alpha_L.shape[1],5):    l_actual = im_actual_L[i,:]#-im_actual_L[i,:-1]    l_alpha = im_alpha_L[i,:]#-im_alpha_L[i,:-1]    l_poisson = im_poisson_L[i,:]#-im_poisson_L[i,:-1]    with sns.axes_style("darkgrid"):      plt.subplot(2,1,2)      #plt.plot(l_alpha,label='alpha')      plt.plot(l_poisson,label='poisson')      plt.hold(True)      plt.plot(l_actual,label='actual')      plt.legend()      # find "text regions":      is_txt = ~im0[i,:]      t_loc = contiguous_regions(is_txt)      ax = plt.gca()      for b0,b1 in t_loc:        ax.axvspan(b0, b1, facecolor='red', alpha=0.1)    with sns.axes_style("white"):      plt.subplot(2,1,1)      plt.imshow(im_alpha[:,:,::-1].astype('uint8'))      plt.hold(True)      plt.plot([0,im_alpha_L.shape[0]-1],[i,i],'r')      plt.axis('image')      plt.show()  plt.subplot(1,3,1)  plt.imshow(im_src[:,:,::-1].astype('uint8'))  plt.subplot(1,3,2)  plt.imshow(im_alpha[:,:,::-1].astype('uint8'))  plt.subplot(1,3,3)    plt.imshow(im_res[:,:,::-1]) #cv2 reads in BGR  plt.show()