Images Transformation

image_matching.py

#!/usr/bin/python2

from __future__ import print_function
from __future__ import division
from __future__ import absolute_import

import os
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
from skimage import transform


path = ''  # TODO Fill path
file1 = 'LM15_aussen_mark_4x.tiff'
file2 = 'heatmap2.tiff'
output_filename = 'test.png'

# Load images
img1 = io.imread(os.path.join(path, file1))
img2 = io.imread(os.path.join(path, file2))

# Show images
fig, ax = plt.subplots(ncols=3, figsize=(15, 6))
ax[0].imshow(img1, picker=True)
ax[0].set_title('Image 1')
ax[1].imshow(img2, picker=True)
ax[1].set_title('Image 2')
ax[2].set_title('Reconstructed Image 1')
plt.tight_layout()

# Points data structure
img_points = {
    0: [],  # Image 1
    1: [],  # Image 2
}


def on_click(event):
    # Save points in subplot list
    point = [event.mouseevent.xdata, event.mouseevent.ydata]
    subplot = np.where(ax == event.artist.axes)[0][0]
    img_points[subplot].append(point)
    
    # Print and add label in image
    print('New event at x={} y={} subplot={}'.format(point[0], point[1], subplot))
    ax[subplot].scatter(point[0], point[1], color='red', s=20)  
    ax[subplot].text(point[0] + 12, point[1] + 12, str(len(img_points[subplot])), color='red', fontsize=13)
    
    # Calculate transformation, show and save image
    if len(img_points[0]) > 2 and len(img_points[0]) == len(img_points[1]):
        transformation = transform.estimate_transform('similarity', np.array(img_points[0]), np.array(img_points[1]))
        reconstructed_img = transform.warp(img2, transformation, output_shape=img1.shape)
        ax[2].cla()  # Clear subplot
        ax[2].imshow(reconstructed_img)
        ax[2].set_xlim(0, reconstructed_img.shape[1])  # Limits are somehow not set automatically
        ax[2].set_ylim(reconstructed_img.shape[0], 0)
        io.imsave(os.path.join(path, output_filename), reconstructed_img)
    else:
        print('Need either more points or the same number of points on each image.')
    
    plt.show()


fig.canvas.mpl_connect('pick_event', on_click)
plt.show()