Image Processing #2 - Shift

Sample codes are in my Repo.


Move image to left, right, top, bottom.

Translation using cv2.warpAffine

import argparse
import cv2
import numpy as np


parser = argparse.ArgumentParser(description="OpenCV Example")
parser.add_argument("--file", type=str, required=True, help="filename of the input image to process")
parser.add_argument("--x", type=int, required=True, help="shift value of X direction")
parser.add_argument("--y", type=int, required=True, help="shift value of Y direction")
args = parser.parse_args()

img = cv2.imread(args.file, cv2.IMREAD_COLOR)
height, width, channels = img.shape
print("image   H:%d W:%d, Channel:%d"%(height, width, channels))
cv2.imshow('original', img)

translation_matrix = np.float32([ [1,0,args.x], [0,1,args.y] ])
img_translation = cv2.warpAffine(img, translation_matrix, (width, height))

cv2.imshow('translate', img_translation)

cv2.waitKey(0)
cv2.destroyAllWindows()

Run the code.

python exam2.py --file=biden.png --x=100 --y=0
image   H:727 W:320, Channel:3

If you give y value, the image surely moves y direction too.

Shift image using numpy.roll

This is the numpy.roll function.

numpy.roll

numpy.roll(a, shift, axis=None)[source]

Roll array elements along a given axis.

Elements that roll beyond the last position are re-introduced at the first.

Parameters:

a : array_like Input array. shift : int or tuple of ints The number of places by which elements are shifted. If a tuple, then axis must be a tuple of the same size, and each of the given axes is shifted by the corresponding number. If an int while axis is a tuple of ints, then the same value is used for all given axes. axis : int or tuple of ints, optional Axis or axes along which elements are shifted. By default, the array is flattened before shifting, after which the original shape is restored.

Returns:

res : ndarray Output array, with the same shape as a.

>>> x = np.arange(10)
>>> np.roll(x, 2)
array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])

As you can see, if you roll the numpy array to x direction, the shift out values are newly refilled in front.

import argparse
import cv2
import numpy as np


parser = argparse.ArgumentParser(description="OpenCV Example")
parser.add_argument("--file", type=str, required=True, help="filename of the input image to process")
parser.add_argument("--x", type=int, required=True, help="shift value of X direction")
parser.add_argument("--y", type=int, required=True, help="shift value of Y direction")
args = parser.parse_args()

img = cv2.imread(args.file, cv2.IMREAD_COLOR)
height, width, channels = img.shape
print("image   H:%d W:%d, Channel:%d"%(height, width, channels))
cv2.imshow('original', img)

# if args.x:
#     img = np.roll(img, args.x, axis = 1) #axis = 1: x direction
# if args.y:
#     img = np.roll(img, args.y, axis = 0) #axis = 0: y direction

img = np.roll(img, (args.x, args.y), axis = (1, 0))

cv2.imshow('roll', img)

cv2.waitKey(0)
cv2.destroyAllWindows()

Run the code.

python exam3.py --file=biden.png --x=100 --y=100
image   H:727 W:320, Channel:3

Shift image using numpy.roll + Fill zero cut off with shift

In the above example, we used numpy.roll function to shift the image. Now let's make fill the shift out region to black(0).

Numpy basic example

P[:,:2]( = P[:,0:2]) means first 2 rows of array and P[:2,:] ( = P[0:2,:]) means first 2 columns.
And P[-2:,:] means last 2 rows of array and P[-2:,:] means last 2 columns.
This code demonstrates  numpy to replace columns or rows of a matrix with a different value.

# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import numpy as np
P = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(P)


# %%
# X coordination shifting
#s = np.array([[0,0], [0,0], [0,0]])
P = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
s = np.full((3, 2), 0)
P[:, 0:2] = s
print(P)


# %%
# Y coordination shifting
#s = np.array([[0, 0, 0], [0, 0, 0]])
P = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
s = np.full((2, 3), 0)
P[0:2, :] = s
print(P)


# %%
# -X coordination shifting
#s = np.array([[0,0], [0,0], [0,0]])
P = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
s = np.full((3, 2), 0)
P[:, -2:] = s
print(P)


# %%
# -Y coordination shifting
#s = np.array([[0, 0, 0], [0, 0, 0]])
P = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
s = np.full((2, 3), 0)
P[-2:, :] = s
print(P)

Run the code.

python exam5.py
[[1 2 3]
 [4 5 6]
 [7 8 9]]
[[0 0 3]
 [0 0 6]
 [0 0 9]]
[[0 0 0]
 [0 0 0]
 [7 8 9]]
[[1 0 0]
 [4 0 0]
 [7 0 0]]
[[1 2 3]
 [0 0 0]
 [0 0 0]]

Image example

# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import numpy as np
import cv2
filename = 'F:\\study\\opencv\\biden.png'
img = cv2.imread(filename, cv2.IMREAD_COLOR)
height, width, channels = img.shape
print("image   H:%d W:%d, Channel:%d"%(height, width, channels))
#cv2.imshow('original', img)


# %%
#shift X direction 100 pixels
black = np.full((height, 100, channels), 0, dtype=np.uint8)
shiftX = np.roll(img, (100, 0), axis = (1, 0))
shiftX[:, 0:100,:] = black


# %%
#shift Y direction 100 pixels
black = np.full((100, width, channels), 0, dtype=np.uint8)
shiftY = np.roll(img, (0, 100), axis = (1, 0))
shiftY[0:100, : ,:] = black


# %%
cv2.imshow('roll-X', shiftX)
cv2.imshow('roll-Y', shiftY)

cv2.waitKey(0)
cv2.destroyAllWindows()


# %%

Run the code.

You can download the source codes here(https://github.com/raspberry-pi-maker/OpenCV)