Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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Uppercase constants

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Benjamin Loison 2024-05-03 02:43:56 +02:00
parent 4bea5e3a97
commit 82b14a8724
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1 changed files with 19 additions and 19 deletions
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38
datasets/raise/extract_noise.py
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@ -7,19 +7,19 @@ import csv
from utils import Color, denoise, iterativeMean, escapeFilePath, saveNpArray, getColorMeans, getImageNpArray
import matplotlib.pyplot as plt
imagesFolderPath = 'rafael/arw'
imagesFolderPathFileName = escapeFilePath(imagesFolderPath)
IMAGES_FOLDER_PATH = 'rafael/230424'
imagesFolderPathFileName = escapeFilePath(IMAGES_FOLDER_PATH)
# Among:
# `denoise` possible denoisers and `mean`.
denoiser = 'mean'
DENOISER = 'mean'
raiseNotFlatFields = False
RAISE_NOT_FLAT_FIELDS = False
# `[Color.RED, Color.GREEN_RIGHT, ...]` or `Color` or `[None]` for not raw images.
colors = [None]
COLORS = [None]
imagesFileNames = os.listdir(imagesFolderPath + ('/png' if raiseNotFlatFields else ''))
imagesFileNames = os.listdir(IMAGES_FOLDER_PATH + ('/png' if RAISE_NOT_FLAT_FIELDS else ''))
if raiseNotFlatFields:
if RAISE_NOT_FLAT_FIELDS:
files = {}
with open('RAISE_all.csv') as csvfile:
@ -28,7 +28,7 @@ if raiseNotFlatFields:
file = row['File'] + '.png'
files[file] = row
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{imagesFolderPath}/png/{imageFileName}').size == (4946, 3278)]
imagesFileNames = [imageFileName for imageFileName in tqdm(imagesFileNames, 'Filtering images') if files[imageFileName]['Device'] == 'Nikon D7000' and Image.open(f'{IMAGES_FOLDER_PATH}/png/{imageFileName}').size == (4946, 3278)]
# Among:
# - `None`
@ -47,11 +47,11 @@ minColor = None
maxColor = None
def getImageFilePath(imageFileName):
if raiseNotFlatFields:
if RAISE_NOT_FLAT_FIELDS:
imageFileName = imageFileName.replace('.png', '.NEF')
imageFilePath = f'{imagesFolderPath}/nef/{imageFileName}'
imageFilePath = f'{IMAGES_FOLDER_PATH}/nef/{imageFileName}'
else:
imageFilePath = f'{imagesFolderPath}/{imageFileName}'
imageFilePath = f'{IMAGES_FOLDER_PATH}/{imageFileName}'
return imageFilePath
# `color` is the actual color to estimate PRNU with.
@ -61,17 +61,17 @@ def treatImage(imageFileName, computeExtremes = False, color = None):
imageNpArray = getImageNpArray(imageFilePath, computeExtremes, color)
if imageNpArray is None:
return
if denoiser != 'mean':
imageDenoisedNpArray = denoise(imageNpArray, denoiser)
if DENOISER != 'mean':
imageDenoisedNpArray = denoise(imageNpArray, DENOISER)
else:
imageDenoisedNpArray = colorMeans[color]
imageNoiseNpArray = imageNpArray - imageDenoisedNpArray
estimatedPrnuIterativeMean.add(imageNoiseNpArray)
if (minColor is None or maxColor is None) and denoiser != 'mean':
if (minColor is None or maxColor is None) and DENOISER != 'mean':
# Assuming same intensity scale across color channels.
for imageFileName in tqdm(imagesFileNames, 'Computing extremes of images'):
for color in colors:
for color in COLORS:
treatImage(imageFileName, computeExtremes = True, color = color)
# To skip this step next time.
@ -79,19 +79,19 @@ if (minColor is None or maxColor is None) and denoiser != 'mean':
print(f'{minColor=}')
print(f'{maxColor=}')
if denoiser == 'mean':
colorMeans = getColorMeans(imagesFileNames, colors)
if DENOISER == 'mean':
colorMeans = getColorMeans(imagesFileNames, COLORS)
for color in Color:
colorMeans[color] = colorMeans[color]
fileName = f'mean_{imagesFolderPathFileName}_{color}'
# Then use `merge_single_color_channel_images_according_to_bayer_filter.py` to consider all color channels, instead of saving this single color channel as an image.
saveNpArray(fileName, colorMeans[color])
for color in colors:
for color in COLORS:
estimatedPrnuIterativeMean = iterativeMean()
for imageFileName in tqdm(imagesFileNames, f'Denoising images for color {color}'):
treatImage(imageFileName, color = color)
npArrayFilePath = f'mean_{imagesFolderPathFileName}_{denoiser}_{color}'
npArrayFilePath = f'mean_{imagesFolderPathFileName}_{DENOISER}_{color}'
saveNpArray(npArrayFilePath, estimatedPrnuIterativeMean.mean)