CODEWITHSUNDEEP
pythonimage-processingpycharmmappingshearsort
Gustav Jakobsen
Gustav Jakobsen

Reputation: 13

Shear an image in python using Bx and By for forward and backward mapping

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)

Upvotes: 1

Views: 1345

Answers (1)

user8190410
user8190410

Reputation: 1314

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()

Upvotes: 0

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