#59: Leverage iterativeMean
to not suffer of RAM quantity issue
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@ -2,7 +2,7 @@ from PIL import Image
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import numpy as np
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import matplotlib.pyplot as plt
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from tqdm import tqdm
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from utils import denoise
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from utils import denoise, iterativeMean
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from skimage import img_as_float
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import sys
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import os
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@ -12,7 +12,7 @@ sys.path.insert(0, '../../algorithms/distance/')
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from rms_diff import rmsDiffNumpy
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NUMBER_OF_SUBGROUPS = 1
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NUMBER_OF_SUBGROUPS = 2
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DENOISER = 'wavelet'
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IMAGES_FOLDER = 'flat-field/TIF'
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@ -24,7 +24,8 @@ numberOfImagesThresholds = range(numberOfImagesPerSubgroup, numberOfImagesPerSub
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subgroupsPrnuEstimatesNpArray = []
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for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
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imagesPrnuEstimateNpArray = []
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subgroupPrnuEstimateNpArray = []
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subgroupIterativeMean = iterativeMean()
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subgroupImagesFileNames = imagesFileNames[numberOfImagesPerSubgroup * subgroupIndex : numberOfImagesPerSubgroup * (subgroupIndex + 1)]
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for imageFileName in tqdm(subgroupImagesFileNames, f'Image of subgroup {subgroupIndex}'):
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imagePath = f'{IMAGES_FOLDER}/{imageFileName}'
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@ -33,11 +34,10 @@ for subgroupIndex in tqdm(range(NUMBER_OF_SUBGROUPS), 'Subgroup'):
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imagePrnuEstimateNpArray = imageNpArray - denoise(imageNpArray, DENOISER)
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imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
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subgroupPrnuEstimateNpArray = []
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# Not efficient mean computation.
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for numberOfImagesIndex, numberOfImages in enumerate(numberOfImagesThresholds):
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subgroupPrnuEstimateNpArray += [np.array(imagesPrnuEstimateNpArray[:numberOfImages]).mean(axis = 0)]
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plt.imsave('prnu.png', subgroupPrnuEstimateNpArray[-1])
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subgroupIterativeMean.add(imagePrnuEstimateNpArray)
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subgroupPrnuEstimateNpArray += [subgroupIterativeMean.mean]
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plt.imsave(f'prnu_subgroup_{subgroupIndex}.png', subgroupPrnuEstimateNpArray[-1])
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subgroupsPrnuEstimatesNpArray += [subgroupPrnuEstimateNpArray]
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rmss = []
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