#Part 1.4 
#Multiresolution blending computes a gentle seam between the
#two images #seperately at each band of frequencies, resulting in a 
#much smoother seam.
import scipy 
apple = skio.imread('apple.jpeg')
# apple = scipy.misc.imresize(apple, 0.25)
apple = apple/255.0
# apple = skimage.util.invert(apple) #need this for the grayscale version
apple_bw = skimage.color.rgb2gray(apple)

orange = skio.imread('orange.jpeg')
# orange = scipy.misc.imresize(orange, apple.shape)
orange = orange/255.0
# orange = skimage.util.invert(orange)
orange_bw = skimage.color.rgb2gray(orange)

def blend(im1, im2, mask, N):
  result = []
  mask_gaussians = gaussian_pyramid(mask, N)
  im1_laplacians = laplacian_pyramid(im1, N)
  im1_gaussians = gaussian_pyramid(im1, N)
  im2_laplacians = laplacian_pyramid(im2, N)
  im2_gaussians = gaussian_pyramid(im2, N)

  for i in range(N):
    mask1 = mask_gaussians[i]
    mask2 = 1-mask1
    
    band1 = im1_laplacians[i]
    band2 = im2_laplacians[i]
    if i == N-1: #last laplacian stack is the rest of the gaussian
      band1 = im1_gaussians[i]
      band2 = im2_gaussians[i]
    
    band1 = scipy.misc.imresize(band1, mask1.shape)
    band2 = scipy.misc.imresize(band2, mask2.shape)

    
    band1 = np.multiply(mask1, band1)
    band2 = np.multiply(mask2, band2)
    band_i = band1+band2
    
    result.append(band_i)
    
  blended = reduce(lambda a,b: a + b, result)
  return blended

def make_mask(image_size):
  left = np.ones((image_size[0], image_size[1]/2))
  right = np.zeros((image_size[0], image_size[1]/2))
  mask = np.concatenate([left, right], axis = 1)
  return mask
  
    
def blend_color(im1, im2, mask, N):
  #split the image into color channels
  im1_red = im1[:,:,0]
  im2_red = im2[:,:,0]
  
  im1_green = im1[:,:,1]
  im2_green = im2[:,:,1]
  
  im1_blue = im1[:,:,2]
  im2_blue = im2[:,:,2]
  
  red = blend(im1_red, im2_red, mask, N)
  green = blend(im1_green, im2_green, mask, N)
  blue = blend(im1_blue, im2_blue, mask, N)
  
  blended = np.dstack([red, green, blue])
  blended = np.clip(blended, 0, 1)

  return blended

mask = make_mask(apple.shape)
# irreg_mask = skio.imread('mask_s.jpg')/255.0
# irreg_mask = scipy.misc.imresize(irreg_mask, 0.25)

# oraple_bw = blend(apple_bw, orange_bw, mask, 5)
# plt.figure()
# plt.axis('off')
# plt.imshow(skimage.util.invert(oraple_bw))

# oraple_color = blend_color(apple, orange, mask, 5)
# plt.figure()
# # plt.axis('off')
# plt.imshow(oraple_color)