84 lines
3.9 KiB
Python
Executable File
84 lines
3.9 KiB
Python
Executable File
#!/usr/bin/env python
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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, iterativeMean, getColorChannel, escapeFilePath, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, rescaleRawImageForDenoiser, updateExtremes, saveNpArray
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import sys
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import os
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import random
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sys.path.insert(0, '../../algorithms/distance/')
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from rms_diff import rmsDiffNumpy
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DENOISER = 'wavelet'
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IMAGES_CAMERAS_FOLDER = {
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'RAISE': 'flat-field/nef',
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'Rafael 23/04/24': 'rafael/230424',
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}
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TRAINING_PORTION = 0.5
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setting = ','.join([escapeFilePath(imageCameraFolder) for imageCameraFolder in IMAGES_CAMERAS_FOLDER]) + f'_{DENOISER}'
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imagesCamerasFileNames = {camera: os.listdir(imageCameraFolder) for camera, imageCameraFolder in IMAGES_CAMERAS_FOLDER.items()}
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random.seed(0)
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# To not have a bias (chronological for instance) when split to make training and testing sets.
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for camera in IMAGES_CAMERAS_FOLDER:
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#print(imagesCamerasFileNames[camera][:3])
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random.shuffle(imagesCamerasFileNames[camera])
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#print(imagesCamerasFileNames[camera][:3])
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#exit(1)
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minimumNumberOfImagesCameras = 10#min([len(imagesCamerasFileNames[camera]) for camera in IMAGES_CAMERAS_FOLDER])
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#print(minimumNumberOfImagesCameras)
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#exit(1)
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for camera in IMAGES_CAMERAS_FOLDER:
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IMAGES_CAMERAS_FOLDER[camera] = IMAGES_CAMERAS_FOLDER[camera][:minimumNumberOfImagesCameras]
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numberOfCameras = len(IMAGES_CAMERAS_FOLDER)
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camerasIterativeMean = [iterativeMean() for _ in range(numberOfCameras)]
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minColor = None
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maxColor = None
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accuracy = []
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returnSingleColorChannelImage = lambda singleColorChannelImage, _minColor, _maxColor: singleColorChannelImage
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for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else []) + [False], 'Compute extremes'):
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rescaleIfNeeded = returnSingleColorChannelImage if computeExtremes else rescaleRawImageForDenoiser
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for cameraImageIndex in tqdm(range(minimumNumberOfImagesCameras * TRAINING_PORTION), 'Camera image index'):
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for cameraIndex, camera in enumerate(tqdm(IMAGES_CAMERAS_FOLDER, 'Camera')):
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imageFileName = imagesCamerasFileNames[camera][cameraImageIndex]
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imageFilePath = f'{IMAGES_CAMERAS_FOLDER[camera]}/{imageFileName}'
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singleColorChannelImages = {color: rescaleIfNeeded(getColorChannel(imageFilePath, color), minColor, maxColor) for color in Color}
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multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
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if computeExtremes:
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minColor, maxColor = updateExtremes(multipleColorsImage, minColor, maxColor)
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continue
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singleColorChannelDenoisedImages = {color: denoise(singleColorChannelImages[color], DENOISER) for color in Color}
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multipleColorsDenoisedImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelDenoisedImages)
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imagePrnuEstimateNpArray = multipleColorsImage - multipleColorsDenoisedImage
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cameraIterativeMean = subgroupsIterativeMean[cameraIndex]
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cameraIterativeMean.add(imagePrnuEstimateNpArray)
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if cameraIndex == numberOfCameras - 1:
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rms = rmsDiffNumpy(subgroupIterativeMean.mean, subgroupsIterativeMean[1 - cameraIndex].mean)
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rmss += [rms]
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if computeExtremes:
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print(f'{minColor=} {maxColor=}')
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for camera in range(IMAGES_CAMERAS_FOLDER):
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plt.imsave(f'{setting}_estimated_prnu_subgroup_{escapeFilePath(camera)}.png', (subgroupsIterativeMean[cameraIndex].mean))
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plt.title(f'Accuracy of camera source attribution thanks to a given number of images to estimate PRNUs with {DENOISER} denoiser')
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plt.xlabel('Number of images to estimate PRNU')
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plt.ylabel('Accuracy of camera source attribution')
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plt.plot(accuracy)
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internalTitle = f'{setting}_accuracy_of_camera_source_attribution'
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saveNpArray(internalTitle, accuracy)
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plt.savefig(f'{internalTitle}.svg')
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