Correct Image wuthout PRNU rendering and precise that it is the first image

datasets/fake/generate_dataset.py

@@ -14,7 +14,7 @@ from tqdm import tqdm
 
 IMAGE_SIZE = 64
 NUMBER_OF_PHONES = 1#0
-NUMBER_OF_IMAGES_PER_PHONE = 100
+NUMBER_OF_IMAGES_PER_PHONE = 10_000
 # Compared to images being 1.
 PRNU_FACTOR = 0.1
 
@@ -43,26 +43,32 @@ def showImageWithMatplotlib(npArray):
     plt.imshow(npArray)
     plt.show()
 
-fig, axs = plt.subplots(1, 4)
+NUMBER_OF_ROWS = 5
+NUMBER_OF_COLUMNS = 3
+fig, axes = plt.subplots(NUMBER_OF_ROWS, NUMBER_OF_COLUMNS)
 fig.suptitle('Single PRNU estimation with images being Gaussian noise')
 
 prnusPil = [toPilImage(prnu) for prnu in prnus]
-#showImageWithMatplotlib(prnus[0])
-axs[0].set_title('Actual PRNU')
-axs[0].imshow(prnus[0])
+MAIN_AXIS_ROW_INDEX = 1
+mainAxis = axes[MAIN_AXIS_ROW_INDEX]
+mainAxis[0].set_title('Actual PRNU')
+mainAxis[0].imshow(prnus[0])
 
-axs[1].set_title('Image without PRNU')
-axs[1].imshow(imagesWithPrnu[0][0])
+mainAxis[1].set_title('First image without PRNU')
+mainAxis[1].imshow(imagesWithoutPrnu[0][0])
 
-imagesWithPrnuPil = [[toPilImage(imageWithPrnu) for imageWithPrnu in imagesWithPrnu[phoneIndex]] for phoneIndex in range(NUMBER_OF_PHONES)]
-#showImageWithMatplotlib(imagesWithPrnu[0][0])
-axs[2].set_title(f'First image with PRNU\nRMS with image without PRNU: {round(rmsDiffNumpy(imagesWithPrnu[0][0], imagesWithoutPrnu[0][0]), 4)}')
-axs[2].imshow(imagesWithPrnu[0][0])
+assert NUMBER_OF_IMAGES_PER_PHONE >= 10 ** (NUMBER_OF_ROWS - 1), 'Try to use more images than generated!'
 
-imagesWithPrnuPil0Mean = np.array(imagesWithPrnuPil[0]).mean(axis = 0)
-#showImageWithMatplotlib(imagesWithPrnuPil0Mean)
-axs[3].set_title(f'Mean of images with PRNU\ni.e. estimated PRNU\nRMS with actual PRNU: {round(rmsDiffNumpy(imagesWithPrnuPil0Mean, prnus[0]), 4)}')
-axs[3].imshow(imagesWithPrnuPil0Mean)
+for rowIndex, numberOfImages in enumerate([10 ** power for power in range(NUMBER_OF_ROWS)]):
+    imagesWithPrnuPil0Mean = np.array(imagesWithPrnu[0][:numberOfImages]).mean(axis = 0)
+    title = (f'Mean of first $N$ images with PRNU\ni.e. estimated PRNU\nRMS with actual PRNU\n\n' if rowIndex == 0 else '') + f'$N$ = {numberOfImages:,}, $RMS$ = {round(rmsDiffNumpy(imagesWithPrnuPil0Mean, prnus[0]), 4)}'
+    axes[rowIndex][2].set_title(title)
+    axes[rowIndex][2].imshow(imagesWithPrnuPil0Mean)
+
+for columnIndex in range(NUMBER_OF_COLUMNS):
+    for axisIndex in range(NUMBER_OF_ROWS):
+        if axisIndex != MAIN_AXIS_ROW_INDEX:
+            axes[axisIndex][columnIndex].axis('off')
 
 plt.tight_layout()
 plt.show()