Benjamin_Loison/Robust_image_source_identification_on_modern_smartphones
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#63: use scipy.stats.pearsonr

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Benjamin Loison 2024-05-13 12:51:26 +02:00
parent 1a910d2e2c
commit 5e6702e007
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1 changed files with 2 additions and 2 deletions
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datasets/raise/attribute_source_camera.py
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@ -7,6 +7,7 @@ from utils import denoise, iterativeMean, getColorChannel, escapeFilePath, Color
import sys
import os
import random
import scipy
sys.path.insert(0, '../../algorithms/distance/')
@ -138,8 +139,7 @@ for computeExtremes in tqdm(([True] if minColor is None or maxColor is None else
#plt.imsave(f'{escapeFilePath(actualCamera)}_{cameraTestingImageIndex}.png', cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex])
# Loop over each camera to compute closeness between the considered testing image noise and the estimated PRNUs of the various cameras.
for camera in IMAGES_CAMERAS_FOLDER:
distance = abs(np.corrcoef(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].flatten(), camerasIterativeMean[camera].mean.flatten()) - 1)
#distance = rmsDiffNumpy(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex], camerasIterativeMean[camera].mean)
distance = abs(scipy.stats.pearsonr(cameraTestingImagesNoise[actualCamera][cameraTestingImageIndex].flatten(), camerasIterativeMean[camera].mean.flatten()).statistic - 1)
print(f'{cameraTestingImageIndex=} {camera=} {actualCamera=} {distance=}')
if minimalDistance is None or distance < minimalDistance:
minimalDistance = distance