Agfdhyk

I have this problem i would like to have solved. I need to shear an image using forward mapping and then shear it back using backward mapping. The code works if I delete the backMapping but not with it added. Here is my code, any help is appreciated!

import cv2

import numpy as np



img = cv2. imread("Lena2.jpg")



rows, cols, c = img.shape



Bx = 0.2

By = 0.3



def forMap (img,Bx,By):

  rows = img.shape[0]

     cols = img.shape[1]

       imgForward = np.ndarray(shape = (int(cols + rows*By), int(rows + cols*Bx),3))



       for row in range(rows):

         for col in range(cols):

           np.matmul(imgForward,np.array([[rows],[cols]]))

             imgForward[int (row+col*By), int(col+row*Bx)] = img[row,col]/255



        return imgForward



def backMap (img, Bx, By):

  n = int(1/(1-Bx*By))

  rows = img.shape[0]

  cols = img.shape[1]

  imgBackwards = np.ndarray(shape = img.shape);



  for row in range(rows):

    for col in range(cols):

        backCol = int (n*(col+row*Bx))

        backRow = int (n*(col+row*By))

        np.matmul(imgBackwards,np.array([[rows],[cols]]))

        imgBackwards[int(backRow+backCol*By), int(backCol + backRow*Bx)] = img[row,col]



forMap(img, Bx, By)

BackMapping = (backMap(img, Bx, By))



cv2.imshow("original image", img)

cv2.imshow("Forward Mapping", forMap)

cv2.imshow("Backward mapping", backMap)

cv2.waitKey(0)

Forward Mapping:

order of shape should be (num of rows, num of cols, channnels), so it becomes
imgForward = np.ndarray(shape=(int(rows + cols*Bx),int(cols + rows*By),3))

No need of this line np.matmul(imgForward,np.array([[rows],[cols]]))

Then, you have to copy all the 3 channels at new position

imgForward[int(row+col*Bx), int(col+row*By),:] = img[row,col,:]

Backward Mapping

Only you need to change int(row+col*Bx), int(col+row*By) with int(row-col*Bx), int(col-row*By)

So your code becomes

import cv2

import numpy as np



img = cv2. imread('one.jpg')

rows, cols, c = img.shape



Bx = 0.2

By = 0.3



def forMap (img,Bx,By):

    rows = img.shape[0]

    cols = img.shape[1]

    imgForward = np.zeros((int(rows + cols*Bx),int(cols + rows*By),3), dtype=np.ubyte)

    for row in range(rows):

        for col in range(cols):

            #np.matmul(imgForward,np.array([[rows],[cols]]))

            imgForward[int(row+col*Bx), int(col+row*By),:] = img[row,col,:]



    return imgForward



def backMap (img, Bx, By):

    rows = img.shape[0]

    cols = img.shape[1]

    imgBackwards = np.zeros(shape=img.shape, dtype=np.ubyte);



    for row in range(rows):

        for col in range(cols):

            backCol = int (col-row*By)

            backRow = int (row-col*Bx)

            #np.matmul(imgBackwards,np.array([[rows],[cols],3]))

            imgBackwards[backRow, backCol, :] = img[row,col,:]

    return imgBackwards



fimg = forMap(img, Bx, By)

bimg = backMap(fimg, Bx, By)



cv2.imshow("original image", img)

cv2.imshow("Forward Mapping", fimg)

cv2.imshow("Backward mapping", bimg)

cv2.waitKey(0)

cv2.destroyAllWindows()