From 5e6702e007b708972421b356a5bed228fd530c04 Mon Sep 17 00:00:00 2001
From: Benjamin Loison <12752145+Benjamin-Loison@users.noreply.github.com>
Date: Mon, 13 May 2024 12:51:26 +0200
Subject: [PATCH] #63: use `scipy.stats.pearsonr`

---
 datasets/raise/attribute_source_camera.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/datasets/raise/attribute_source_camera.py b/datasets/raise/attribute_source_camera.py
index 018d72e..24f6b63 100755
--- a/datasets/raise/attribute_source_camera.py
+++ b/datasets/raise/attribute_source_camera.py
@@ -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