Image warping

Let's have some more fun with the images and see what else we can achieve. Projective transformations are pretty flexible, but they still impose some restrictions on how we can transform the points. What if we want to do something completely random? We need more control, right? As it so happens, we can do that as well. We just need to create our own mapping, and it's not that difficult. Following are a few effects you can achieve with image warping:

Here is the code to create these effects:

import cv2
import numpy as np
import math

img = cv2.imread('images/input.jpg', cv2.IMREAD_GRAYSCALE)
rows, cols = img.shape

#####################
# Vertical wave

img_output = np.zeros(img.shape, dtype=img.dtype)

for i in range(rows):
    for j in range(cols):
        offset_x = int(25.0 * math.sin(2 * 3.14 * i / 180))
        offset_y = 0
        if j+offset_x < rows:
            img_output[i,j] = img[i,(j+offset_x)%cols]
        else:
            img_output[i,j] = 0

cv2.imshow('Input', img)
cv2.imshow('Vertical wave', img_output)

#####################
# Horizontal wave

img_output = np.zeros(img.shape, dtype=img.dtype)

for i in range(rows):
    for j in range(cols):
        offset_x = 0
        offset_y = int(16.0 * math.sin(2 * 3.14 * j / 150))
        if i+offset_y < rows:
            img_output[i,j] = img[(i+offset_y)%rows,j]
        else:
            img_output[i,j] = 0

cv2.imshow('Horizontal wave', img_output)

#####################
# Both horizontal and vertical 

img_output = np.zeros(img.shape, dtype=img.dtype)

for i in range(rows):
    for j in range(cols):
        offset_x = int(20.0 * math.sin(2 * 3.14 * i / 150))
        offset_y = int(20.0 * math.cos(2 * 3.14 * j / 150))
        if i+offset_y < rows and j+offset_x < cols:
            img_output[i,j] = img[(i+offset_y)%rows,(j+offset_x)%cols]
        else:
            img_output[i,j] = 0

cv2.imshow('Multidirectional wave', img_output)

#####################
# Concave effect

img_output = np.zeros(img.shape, dtype=img.dtype)

for i in range(rows):
    for j in range(cols):
        offset_x = int(128.0 * math.sin(2 * 3.14 * i / (2*cols)))
        offset_y = 0
        if j+offset_x < cols:
            img_output[i,j] = img[i,(j+offset_x)%cols]
        else:
            img_output[i,j] = 0

cv2.imshow('Concave', img_output)

cv2.waitKey()