一部にピントを合わせた画像の生成(Python)

import cv2
import numpy as np

def GaussianMask(sizex,sizey, sigma=50, center=None):
    x = np.arange(0, sizex, 1, float)
    y = np.arange(0, sizey, 1, float)
    x,y=np.meshgrid(x,y)
    #y = x[:,np.newaxis]

    if center is None:
        x0 = sizex // 2
        y0 = sizey // 2
    else:
        x0 = center[0]
        y0 = center[1]

    return np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / sigma**2)

#画像を読み込み
imgdir="test.png"
img = cv2.imread(imgdir)

#画像サイズを取得
H,W = img.shape[:2]

#Gaussianマスクを作製(画像サイズ、ガウシアンの標準偏差、中央点)
mask = GaussianMask(W,H,100,(800,200))

#maskを3次元に拡張
alpha = np.repeat(mask[:, :, np.newaxis], 3, axis=2)

#平滑化をかけた画像
blured = cv2.GaussianBlur(img, (21,21), 11)

#平滑化した画像と元画像をalphaの割合で結合
blended = cv2.convertScaleAbs(img*alpha + blured*(1-alpha))

#画像を保存
cv2.imwrite("blended.jpg",blended)

#描画
cv2.imshow("blended", blended)
cv2.waitKey();cv2.destroyAllWindows()

import cv2
import numpy as np

def alphaBlend(img1, img2, mask):
    """ 
    alphaBlend img1 and img 2 (of CV_8UC3) with mask (CV_8UC1 or CV_8UC3)
    """
    if mask.ndim==3 and mask.shape[-1] == 3:
        alpha = mask/255.0
    else:
        alpha = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)/255.0
    blended = cv2.convertScaleAbs(img1*alpha + img2*(1-alpha))
    return blended

#画像の読み込み
imgdir="test.png"
img = cv2.imread(imgdir)

H,W = img.shape[:2]

mask = np.zeros((H,W), np.uint8)

#mask shape is circle
cv2.circle(mask,
           (800, 200), #center
           100,        #radius
           (255,255,255), -1, cv2.LINE_AA)

mask = cv2.GaussianBlur(mask, (21,21),11 )

blured = cv2.GaussianBlur(img, (21,21), 11)

blended1 = alphaBlend(img, blured, mask)
#blended2 = alphaBlend(img, blured, 255- mask)

# 画像を保存
cv2.imwrite("blended1.png",blended1)
#cv2.imwrite("blended2.png",blended2)

cv2.imshow("blened1", blended1);
#cv2.imshow("blened2", blended2);
cv2.waitKey();cv2.destroyAllWindows()

import cv2
import numpy as np

#画像の場所
imgdir="test.png"
img = cv2.imread(imgdir) # Read in image

height = img.shape[0] # Get the dimensions
width = img.shape[1]

# Define mask
mask = 255*np.ones((height,width), dtype='uint8')

# Draw circle at x = 800, y = 200 of radius 100 and fill this in with 0
cv2.circle(mask, (800, 200), 100, 0, -1)    

# Apply distance transform to mask
out = cv2.distanceTransform(mask, cv2.DIST_L2, 3)

# Define scale factor
scale_factor = 10

# Create output image that is the same as the original
filtered = img.copy() 

# Create floating point copy for precision
img_float = img.copy().astype('float')

# Number of channels
if len(img_float.shape) == 3:
  num_chan = img_float.shape[2]
else:
  # If there is a single channel, make the images 3D with a singleton
  # dimension to allow for loop to work properly
  num_chan = 1
  img_float = img_float[:,:,None]
  filtered = filtered[:,:,None]

# For each pixel in the input...
for y in range(height):
  for x in range(width):

    # If distance transform is 0, skip
    if out[y,x] == 0.0:
      continue

    # Calculate M = d / S
    mask_val = np.ceil(out[y,x] / scale_factor)

    # If M is too small, set the mask size to the smallest possible value
    if mask_val <= 3:
      mask_val = 3

    # Get beginning and ending x and y coordinates for neighbourhood
    # and ensure they are within bounds
    beginx = x-int(mask_val/2)
    if beginx < 0:
      beginx = 0

    beginy = y-int(mask_val/2)
    if beginy < 0:
      beginy = 0

    endx = x+int(mask_val/2)
    if endx >= width:
      endx = width-1

    endy = y+int(mask_val/2)
    if endy >= height:
      endy = height-1

    # Get the coordinates of where we need to grab pixels
    xvals = np.arange(beginx, endx+1)
    yvals = np.arange(beginy, endy+1)
    (col_neigh,row_neigh) = np.meshgrid(xvals, yvals)
    col_neigh = col_neigh.astype('int')
    row_neigh = row_neigh.astype('int')

    # Get the pixels now
    # For each channel, do the foveation
    for j in range(num_chan):
      chan = img_float[:,:,j]
      pix = chan[row_neigh, col_neigh].ravel()

      # Calculate the average and set it to be the output
      filtered[y,x,j] = int(np.mean(pix))

# Remove singleton dimension if required for display and saving
if num_chan == 1:
  filtered = filtered[:,:,0]

# Save the image
cv2.imwrite("Output.png",filtered)

# Show the image
cv2.imshow('Output', filtered)
cv2.waitKey(0)
cv2.destroyAllWindows()