20190730_图像混合_opencv_python

今天学习了 图像的混合

教程上的代码很简单，但是绝对运行不出来

教程名称：OpenCV-Python 中文教程

 #图像融合
 import cv2
 import numpy as np
 import matplotlib.pyplot as plt

 imgO = cv2.imread(r"C:\Users\lijin\Desktop\opencvImg\opencvSource\orange.png")
 imgA = cv2.imread(r"C:\Users\lijin\Desktop\opencvImg\opencvSource\apple.png")

 cv2.imshow('apple', imgA)
 cv2.imshow('orange', imgO)

 print(imgO.shape) #高y--rows, 宽x--cols
 print(imgA.shape)

 imgO = cv2.resize(imgO, (300, 300))
 imgA = cv2.resize(imgA, (300, 300))  #居然在这里错了，浪费了一下午

 print(imgO.shape) #高y--rows, 宽x--cols
 print(imgA.shape)

 #Orange Operation
 #gaussian pyramid list for Orange
 gOperator = imgO.copy()
 gpO = [gOperator]
 for i in range(6):
     gOperator = cv2.pyrDown(gOperator)
     #cv2.imshow(test[i], gOperator)
     gpO.append(gOperator)  #add to list
     #cv2.imshow(testx[i], gpO[i])

 #laplacian pyramid list for Orange
 lpO = [gpO[6]]
 for i in range(6, 0, -1):
     lOperator = cv2.pyrUp(gpO[i]) #this operator is for gpO[i -1]
     #print(lOperator.shape)
     #print(gpO[i - 1].shape)
     rows, cols = gpO[i - 1].shape[:2]

     #必须要加这一步，使得尺寸相等
     lOperator = cv2.resize(lOperator, (cols, rows))

     lOperator = cv2.subtract(gpO[i - 1], lOperator)
     lpO.append(lOperator)  #add to list

 #Apple Operation
 #gaussian pyramid list for Apple
 gOperator = imgA.copy()
 gpA = [gOperator]
 for i in range(6):
     gOperator = cv2.pyrDown(gOperator)
     gpA.append(gOperator)  #add to list

 #laplacian pyramid list for Apple
 lpA = [gpA[6]]
 for i in range(6, 0, -1):
     lOperator = cv2.pyrUp(gpA[i]) #this operator is for gpO[i -1]

     rows, cols = gpA[i - 1].shape[:2]

     #必须要加这一步，使得尺寸相等
     lOperator = cv2.resize(lOperator, (cols, rows))

     lOperator = cv2.subtract(gpA[i - 1], lOperator)
     lpA.append(lOperator)   #add to list

 for i in range(7):
     print('lpO size: ', lpO[i].shape)
     print('lpA size: ', lpA[i].shape)

 #add left part of Apple and right part of Orange in each level
 #numpy.hstack(tup)
 #take a sequence of arrays and stack them horizontally
 #to make a single array
 combinedList = []
 for appleLeft, orangeRight in zip(lpA, lpO):
     rows, cols, dpt = appleLeft.shape

     #cv2.imshow('appleLeft ', appleLeft)
     #cv2.imshow('orangeRight ', orangeRight)
     #print('appleLeft.shape ', appleLeft.shape)
     #print('orangeRight.shape ', orangeRight.shape)

     combineElement = np.hstack((appleLeft[:, 0:cols//2], orangeRight[:, cols//2:])) #0:cols//2 不包括右边，cols//2: 包括左边
     combinedList.append(combineElement)
     print('appleLeft.shape   ',appleLeft.shape)
     print('orangeRight.shape ',orangeRight.shape)
     print('combineElement.shape', combineElement.shape)

 print('combinedList[1].shape ', combinedList[1].shape)
 print('combinedList[2].shape ', combinedList[2].shape)
 #"""
 #reconstruct
 combinedElement_ = combinedList[0]  #从 a (5, 5, 3)，o (5, 5, 3) 开始
 for i in range(1, 6):   #combinedList[1].shape  (10, 10, 3)
     combinedElement_ = cv2.pyrUp(combinedElement_)   #combinedList[1].shape  (10, 10, 3) #combinedList[2].shape  (19, 19, 3)
     #print('combinedElement_.shape...', combinedElement_.shape)
     #print('combinedList.shape...', combinedList[i].shape)
     rows, cols = combinedList[i].shape[:2]
     combinedElement_ = cv2.resize(combinedElement_,(cols, rows))
     combinedElement_ = cv2.add(combinedElement_, combinedList[i])  #combinedList[1].shape  (10, 10, 3) 

 #reconstruct
 pyramidBlending = combinedList[0]
 for i in range(1, 7):
     pyramidBlending = cv2.pyrUp(pyramidBlending)

     rows, cols = combinedList[i].shape[:2]
     pyramidBlending = cv2.resize(pyramidBlending,(cols, rows))

     pyramidBlending = cv2.add(pyramidBlending, combinedList[i])

 #image with direct cnnecting each half
 cv2.imshow('appleL', imgA[:, : cols // 2])
 cv2.imshow('orangeR', imgO[ : , cols // 2 : ])
 directBlending = np.hstack((imgA[:, : cols // 2], imgO[:, cols // 2 : ]))

 cv2.imshow("directBlending", directBlending)
 cv2.imshow("pyramidBlending", pyramidBlending)

 #"""

 cv2.waitKey(0)
 cv2.destroyAllWindows()

在调试代码的时候，需要不停的使用 resize() 函数在对两个生成图像做加减法时进行调整，不然会出现两图大小不一而无法运算的情况。

比如，如果没有这段代码

会出现的错误提示

所以要不停的使用 cv2.imshow(), img.shape 进行调试

这是最终的运行结果