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Image Stacking

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• Author: Diego Inácio
• GitHub: github.com/diegoinacio
• Notebook: stacking.ipynb

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Implementation of solution to image stacking and statistical blending.

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import imageio
import numpy as np
import scipy.stats as stats
import glob

from _utils import *

1. Sample

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sample = imageio.imread('../_data/SEQ01-32bits/sample.0001.exr')

1.1. Sample analysis

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print('sample 0001')
summary(sample)

sample 0001
                          R               G               B
       min:          0.0294          0.0204          0.0148
  1st Quar:          0.1513          0.1422          0.1306
    median:          0.2340          0.2164          0.1900
      mean:          0.3025          0.2680          0.2191
  3rd Quar:          0.3858          0.3417          0.2785
       max:         71.7715         47.6547         24.3402
     sigma:          0.3659          0.2595          0.1554

histogram(sample, interval=[0, 1])

1.2. Colorspace transformation

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Transform from linear to sRGB usign gamma correction $\large \gamma = 2.2$.

sample_sRGB = sample**(1/2.2)

histogram(sample_sRGB, interval=[0, 1])

2. Stacking

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def stackRead(pathname):
    '''
    pathname defined by "glob" pattern.
    i.e.: "directory/sequence_folder/image_*.jpg"
    '''
    # List of image in pathname folder
    SEQ_IMG = glob.glob(pathname)
    n = len(SEQ_IMG)
    # sample for stack definition
    sample = imageio.imread(SEQ_IMG[0])
    # x and y are the dimensions
    # c is the number of channels
    y, x, c = sample.shape
    # define stack
    stack = np.zeros((n, y, x, c), dtype=sample.dtype)
    # image stacking
    for FILE in SEQ_IMG:
        index = SEQ_IMG.index(FILE)
        stack[index] = imageio.imread(FILE)
    # output
    return stack

stack = stackRead('../_data/SEQ01-32bits/sample.*.exr')

panel(stack**(1/2.2), (3, 1),
      interval=[0, 1],
      dims=(1200, 400),
      texts=['{:04}'.format(i + 1) for i in range(10)])