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from __future__ import division, print_function
%matplotlib inline


Note: This example has been significantly expanded and enhanced. The new, recommended version is located here. We retain this version intact as it was the exact example used in the scikit-image paper.

# Panorama stitching¶

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import numpy as np
import matplotlib.pyplot as plt
from skimage import io, transform
from skimage.color import rgb2gray
from skdemo import imshow_all

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ic = io.ImageCollection('../images/pano/DFM_*')


The ImageCollection class provides an easy way of loading and representing multiple images. Images are not read from disk until accessed.

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imshow_all(ic[0], ic[1])


Credit: Photographs taken in Petra, Jordan by François Malan

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image0 = rgb2gray(ic[0][:, 500:500+1987, :])
image1 = rgb2gray(ic[1][:, 500:500+1987, :])

image0 = transform.rescale(image0, 0.25)
image1 = transform.rescale(image1, 0.25)

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imshow_all(image0, image1)


For this demo, we estimate a projective transformation that relates the two images. Since the outer parts of these photographs do not comform well to such a model, we select only the central parts. To further speed up the demonstration, images are downscaled to 25% of their original size.

# 1. Feature detection and matching¶

"Oriented FAST and rotated BRIEF" features are detected in both images:

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from skimage.feature import ORB, match_descriptors

orb = ORB(n_keypoints=1000, fast_threshold=0.05)

orb.detect_and_extract(image0)
keypoints1 = orb.keypoints
descriptors1 = orb.descriptors

orb.detect_and_extract(image1)
keypoints2 = orb.keypoints
descriptors2 = orb.descriptors

matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)

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from skimage.feature import plot_matches

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
plot_matches(ax, image0, image1, keypoints1, keypoints2, matches12)
ax.axis('off');


Each feature yields a binary descriptor; those are used to find the putative matches shown. Many false matches are observed.

# 2. Transform estimation¶

To filter matches, we apply RANdom SAMple Consensus (RANSAC), a common method of rejecting outliers. This iterative process estimates transformation models based on randomly chosen subsets of matches, finally selecting the model which corresponds best with the majority of matches.

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from skimage.transform import ProjectiveTransform
from skimage.measure import ransac
from skimage.feature import plot_matches

# Select keypoints from the source (image to be registered)
# and target (reference image)
src = keypoints2[matches12[:, 1]][:, ::-1]
dst = keypoints1[matches12[:, 0]][:, ::-1]

model_robust, inliers = ransac((src, dst), ProjectiveTransform,
min_samples=4, residual_threshold=2)

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fig, ax = plt.subplots(1, 1, figsize=(15, 15))
plot_matches(ax, image0, image1, keypoints1, keypoints2, matches12[inliers])
ax.axis('off');


Note how most of the false matches have now been rejected.

# 3. Warping¶

Next, we want to produce the panorama itself. The first step is to find the shape of the output image, by taking considering the extents of all warped images.

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from skimage.transform import SimilarityTransform

r, c = image1.shape[:2]

# Note that transformations take coordinates in (x, y) format,
# not (row, column), in order to be consistent with most literature
corners = np.array([[0, 0],
[0, r],
[c, 0],
[c, r]])

# Warp the image corners to their new positions
warped_corners = model_robust(corners)

# Find the extents of both the reference image and the warped
# target image
all_corners = np.vstack((warped_corners, corners))

corner_min = np.min(all_corners, axis=0)
corner_max = np.max(all_corners, axis=0)

output_shape = (corner_max - corner_min)
output_shape = np.ceil(output_shape[::-1])


Warp the images according to the estimated transformation model. Values outside the input images are set to -1 to distinguish the "background".

A shift is added to make sure that both images are visible in their entirety. Note that warp takes the inverse mapping as an input.

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from skimage.color import gray2rgb
from skimage.exposure import rescale_intensity
from skimage.transform import warp

offset = SimilarityTransform(translation=-corner_min)

image0_ = warp(image0, offset.inverse,
output_shape=output_shape, cval=-1)

image1_ = warp(image1, (model_robust + offset).inverse,
output_shape=output_shape, cval=-1)


An alpha channel is now added to the warped images before they are merged together:

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def add_alpha(image, background=-1):
"""Add an alpha layer to the image.

The alpha layer is set to 1 for foreground and 0 for background.
"""
return np.dstack((gray2rgb(image), (image != background)))

merged = (image0_alpha + image1_alpha)
alpha = merged[..., 3]

# The summed alpha layers give us an indication of how many
# images were combined to make up each pixel.  Divide by the
# number of images to get an average.
merged /= np.maximum(alpha, 1)[..., np.newaxis]
merged = merged[..., :3]

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imshow_all(image0_alpha, image1_alpha, merged)


Note that, while the columns are well aligned, the color intensity is not well matched between images.

# 4. Blending¶

To blend images smoothly we make use of the open source package Enblend, which in turn employs multi-resolution splines and Laplacian pyramids [1, 2].

[1] P. Burt and E. Adelson. "A Multiresolution Spline With Application to Image Mosaics". ACM Transactions on Graphics, Vol. 2, No. 4, October 1983. Pg. 217-236. [2] P. Burt and E. Adelson. "The Laplacian Pyramid as a Compact Image Code". IEEE Transactions on Communications, April 1983.

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plt.imsave('/tmp/frame0.tif', image0_alpha)
plt.imsave('/tmp/frame1.tif', image1_alpha)

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%%bash

enblend /tmp/frame*.tif -o /tmp/pano.tif

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pano = io.imread('/tmp/pano.tif')

plt.figure(figsize=(10, 10))
plt.imshow(pano)
plt.axis('off');


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%reload_ext load_style