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? It just 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 that you can achieve with image warping:

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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()