Logo Questions Linux Laravel Mysql Ubuntu Git Menu
 

Image transformation in OpenCV

Tags:

This question is related to this question: How to remove convexity defects in sudoku square

I was trying to implement nikie's answer in Mathematica to OpenCV-Python. But i am stuck at the final step of procedure.

ie I got the all intersection points in square like below:

enter image description here

Now, i want to transform this into a perfect square of size (450,450) as given below:

enter image description here

(Never mind the brightness difference of two images).

Question: How can i do this in OpenCV-Python? I am using cv2 version.

like image 899
Abid Rahman K Avatar asked Apr 28 '12 14:04

Abid Rahman K


People also ask

What is image transformation in image processing?

In image processing, image transformation can be defined as having control on its dimensional points to edit the images by moving them into three dimensional or two-dimensional space. Next in the article will perform some basic transformations of 2D images.

What is affine transformation OpenCV?

What is an Affine Transformation? A transformation that can be expressed in the form of a matrix multiplication (linear transformation) followed by a vector addition (translation). From the above, we can use an Affine Transformation to express: Rotations (linear transformation)


1 Answers

Apart from etarion's suggestion, you could also use the remap function. I wrote a quick script to show how you can do this. As you see coding this is really easy in Python. This is the test image:

distorted image

and this is the result after warping:

warped image

And here is the code:

import cv2 from scipy.interpolate import griddata import numpy as np  grid_x, grid_y = np.mgrid[0:149:150j, 0:149:150j] destination = np.array([[0,0], [0,49], [0,99], [0,149],                   [49,0],[49,49],[49,99],[49,149],                   [99,0],[99,49],[99,99],[99,149],                   [149,0],[149,49],[149,99],[149,149]]) source = np.array([[22,22], [24,68], [26,116], [25,162],                   [64,19],[65,64],[65,114],[64,159],                   [107,16],[108,62],[108,111],[107,157],                   [151,11],[151,58],[151,107],[151,156]]) grid_z = griddata(destination, source, (grid_x, grid_y), method='cubic') map_x = np.append([], [ar[:,1] for ar in grid_z]).reshape(150,150) map_y = np.append([], [ar[:,0] for ar in grid_z]).reshape(150,150) map_x_32 = map_x.astype('float32') map_y_32 = map_y.astype('float32')  orig = cv2.imread("tmp.png") warped = cv2.remap(orig, map_x_32, map_y_32, cv2.INTER_CUBIC) cv2.imwrite("warped.png", warped) 

I suppose you can google and find what griddata does. In short, it does interpolation and here we use it to convert sparse mappings to dense mappings as cv2.remap requires dense mappings. We just need to convert to the values to float32 as OpenCV complains about the float64 type. Please let me know how it goes.

Update: If you don't want to rely on Scipy, one way is to implement the 2d interpolation function in your code, for example, see the source code of griddata in Scipy or a simpler one like this http://inasafe.readthedocs.org/en/latest/_modules/engine/interpolation2d.html which depends only on numpy. Though, I'd suggest to use Scipy or another library for this, though I see why requiring only CV2 and numpy may be better for a case like this. I'd like to hear how your final code solves Sudokus.

like image 99
fireant Avatar answered Dec 29 '22 11:12

fireant