图像金字塔

  Fu Xianjun. All Rights Reserved

图像金字塔是由一幅图像的多个不同分辨率的子图所构成的图像合集。

图像金字塔是由一系列以金字塔形状排列的、自底向上分辨率逐渐降低的图像集合。

高斯金字塔

import cv2 
import numpy as np
#高斯金字塔
def pyramid_demo(image,level):
    temp = image.copy()
    pyramid_images = []
    for i in range(level):
        dst = cv2.pyrDown(temp)
        pyramid_images.append(dst)
        cv2.imshow("pyramid_down_"+str(i), dst)
        temp = dst.copy()
    return pyramid_images[level-1]
src = cv2.imread("lena.jpg")
cv2.imshow("input image", src)
pyramid_demo(src,4)
cv2.waitKey(0)
cv2.destroyAllWindows()

 拉普拉斯金字塔

import cv2
import numpy as np
# 拉普拉斯金字塔构建
G0 = cv2.imread("lena.bmp")
cv2.imshow("input image",G0)
G1=cv2.pyrDown(G0)
G2=cv2.pyrDown(G1)
G3=cv2.pyrDown(G2)
G4=cv2.pyrDown(G3)
L0 = cv2.subtract(G0,cv2.pyrUp(G1))
L1 = cv2.subtract(G1,cv2.pyrUp(G2))
L2 = cv2.subtract(G2,cv2.pyrUp(G3))
L3 = cv2.subtract(G3,cv2.pyrUp(G4))
cv2.imshow("G1",G1)
cv2.imshow("G2",G2)
cv2.imshow("G3",G3)
cv2.imshow("G4",G4)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 使用拉普拉斯金字塔恢复高分辨图片
l3=cv2.pyrUp(G4)
l2=cv2.pyrUp(l3)
l1=cv2.pyrUp(l2)
l0=cv2.pyrUp(l1)
G00=L0+cv2.pyrUp(G1)
cv2.imshow("l0",l0)
cv2.imshow("G00",G00)
cv2.imshow("input image",G0)
cv2.waitKey(0)
cv2.destroyAllWindows()

 金字塔的应用

import cv2
import numpy as np
A = cv2.imread('apple.png')
A = cv2.resize(A,(256,256),interpolation=cv2.INTER_CUBIC) 
B = cv2.imread('orange.png')
B = cv2.resize(B,(256,256),interpolation=cv2.INTER_CUBIC)
# 生成高斯金字塔
G = A.copy()
gpA = [G]
for i in range(5):
    G = cv2.pyrDown(G)
    gpA.append(G)
    
G = B.copy()
gpB = [G]
for i in range(5):
    G = cv2.pyrDown(G)
    gpB.append(G)
# 产生Laplacian金字塔
lpA = [gpA[5]]
for i in range(5,0,-1):
    GE = cv2.pyrUp(gpA[i])
    L = cv2.subtract(gpA[i-1],GE)
    lpA.append(L)

lpB = [gpB[5]]
for i in range(5,0,-1):
    GE = cv2.pyrUp(gpB[i])
    L = cv2.subtract(gpB[i-1],GE)
    lpB.append(L)
# 合并
LS = []
for la,lb in zip(lpA,lpB):
    rows,cols,dpt = la.shape
    ls = np.hstack((la[:,0:cols//2], lb[:,cols//2:]))
    LS.append(ls)
# 重新构建图像
ls_ = LS[0]
for i in range(1,6):
    ls_ = cv2.pyrUp(ls_)
    ls_ = cv2.add(ls_, LS[i])
# 连接
real = np.hstack((A[:,:cols//2],B[:,cols//2:]))
cv2.imshow("apple",A)
cv2.imshow("orange",B)
cv2.imshow("LS",ls_)
cv2.imshow("Real",real)
cv2.waitKey()
cv2.destroyAllWindows()

 ROI在了解

import cv2
src=cv2.imread("lena.jpg")
cv2.imshow("first_image", src)
face = src[100:200, 100:200]    #选择200:300行、200:400列区域作为截取对象
gray = cv2.cvtColor(face, cv2.COLOR_RGB2GRAY)  #生成的的灰度图是单通道图像
backface = cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)  #将单通道图像转换为三通道RGB灰度图，因为只有三通道的backface才可以赋给三通道的src
src[100:200, 100:200] = backface
cv2.imshow("face", src)
cv2.waitKey(0)
cv2.destroyAllWindows()

泛洪填充 

import cv2 as cv
import numpy as np
# 彩色图像填充
def fill_color_demo(src,seedx,seedy):
    img_copy = src.copy()
    h, w, ch = src.shape
    mask = np.zeros([h+2, w+2], np.uint8)
    cv.floodFill(img_copy, mask, (seedx, seedy), (0, 255, 0),(50, 50, 50), (100, 100, 100), cv.FLOODFILL_FIXED_RANGE)
    cv.imshow("color_demo", img_copy)

src = cv.imread('AM.png')
fill_color_demo(src,140,140)
cv.waitKey()
cv.destroyAllWindows()