datasets/raise/fft/generate_vignetting.py

@@ -29,12 +29,12 @@ for y in tqdm(range(SHAPE[0])):
 def checkOriginalPoint(point):
     print(f'distance = {getDistance(*point)} intensity = {image[tuple(point)]}')
 
-#for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
-#    checkOriginalPoint(point)
+for point in [CENTER, [0, 0], [CENTER[0], int(CENTER[1] + getDistance(0, 0) / 2)]]:
+    checkOriginalPoint(point)
 
 pad = (SHAPE[1] - SHAPE[0]) // 2
 cropImage = image[:, pad:-pad]
 
 #plt.imshow(image, cmap = 'grey')
-plt.imsave('vignetting_odd.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
+plt.imsave('vignetting.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
 plt.show()

datasets/raise/fft/verify_dots.py

@@ -6,7 +6,7 @@ import sys
 
 sys.path.insert(0, '../')
 
-from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean, getPearsonCorrelationDistance
+from utils import isARawImage, Color, getColorChannel, mergeSingleColorChannelImagesAccordingToBayerFilter, iterativeMean
 
 FOLDERS = {
     'Rafael 23/04/24': '../rafael/230424',
@@ -34,15 +34,7 @@ def getEstimatedPrnu(folder):
         estimatedPrnu.add(imageMergedColorChannels - meanImages.mean)
     return estimatedPrnu.mean
 
-estimatedPrnus = {folderName: getEstimatedPrnu(folder) for folderName, folder in FOLDERS.items()}
+estimatedPrnu = getEstimatedPrnu(FOLDERS['Rafael 23/04/24'])
 
-# TODO: largest common crop
-
-print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["Rafael 23/04/24"])=}')
-print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE flat-field"])=}')
-print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["Rafael 23/04/24"])=}')
-print(f'{corr(estimatedPrnus["Rafael 23/04/24"], estimatedPrnus["RAISE not flat-field"])=}')
-print(f'{corr(estimatedPrnus["RAISE flat-field"], estimatedPrnus["RAISE not flat-field"])=}')
-
-plt.imshow(estimatedPrnus['RAISE flat-field'])
+plt.imshow(estimatedPrnu)
 plt.show()

datasets/raise/utils.py

@@ -10,7 +10,7 @@ import builtins as __builtin__
 from scipy.ndimage import gaussian_filter
 import os
 from skimage.restoration import estimate_sigma
-from scipy import fftpack, stats
+from scipy import fftpack
 import imageio
 
 class Color(Enum):
@@ -44,7 +44,7 @@ class Distance(Enum):
         return self.name.replace('_', '').title()
 
 def getPearsonCorrelationDistance(firstImage, secondImage):
-    pearsonCorrelation = stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
+    pearsonCorrelation = scipy.stats.pearsonr(firstImage.flatten(), secondImage.flatten()).statistic
     if pearsonCorrelation < 0:
         print('Negative Pearson correlation, investigate further!')
         exit(1)
@@ -161,6 +161,7 @@ def getColorChannel(imageFilePath, color):
 def escapeFilePath(filePath):
     return filePath.replace('/', '_')
 
+
 def getNewIndex(index, offset):
     newIndex = (index - offset) * 2 + offset
     return newIndex