Small changes

datasets/raise/benchmark_load_part_of_images.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-from utils import getColorChannel, Color
+from utils import getColorChannel, Color, isARawImage
 import os
 from tqdm import tqdm
 import numpy as np
@@ -32,7 +32,7 @@ def getNumpyFilePath(imageFilePath, color):
 for camera in tqdm(IMAGES_CAMERAS_FOLDER, 'Camera'):
     imagesCameraFolder = IMAGES_CAMERAS_FOLDER[camera]
     for file in tqdm(os.listdir(imagesCameraFolder), 'Image'):
-        if file.endswith('.NEF') or file.endswith('.ARW'):
+        if isARawImage(file):
             #print(file)
             imageFilePath = f'{imagesCameraFolder}/{file}'
             for color in Color:

datasets/raise/extract_4_color_channel_images_from_raw.py

@@ -15,21 +15,21 @@ def getImageFilePath(imageIndex):
 
 def getImageColorChannel(imageIndex, color):
     imageFilePath = getImageFilePath(imageIndex)
-    return getColorChannel(imageFilePath, color).astype(np.int32)
+    return getColorChannel(imageFilePath, color)
+
+def crop(image, interestingPosition, yRange, xRange):
+    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
 
 # Could leverage crop already here.
-colorChannels = [getImageColorChannel(imageIndex, COLOR) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
+colorChannels = [crop(getImageColorChannel(imageIndex, COLOR), INTERESTING_POSITION, Y_RANGE, X_RANGE).astype(np.int32) for imageIndex in tqdm(IMAGE_INDEX_RANGE)]
 meanColorChannels = np.mean(colorChannels, axis = 0)
 estimatedPrnus = [colorChannel - meanColorChannels for colorChannel in tqdm(colorChannels)]
 estimatedPrnu = np.mean(estimatedPrnus, axis = 0)
 print(estimatedPrnu.shape)
 
-def crop(image, interestingPosition, yRange, xRange):
-    return image[interestingPosition[0] - yRange: interestingPosition[0] + yRange, interestingPosition[1] - xRange: interestingPosition[1] + xRange]
-
 #colorChannel = crop(multipleColorsImage, ORIGINAL_INTERESTING_POSITION, Y_RANGE, X_RANGE)
 colorChannelToDisplay = estimatedPrnu
-colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
+#colorChannelToDisplay = crop(colorChannelToDisplay, INTERESTING_POSITION, Y_RANGE, X_RANGE)
 print(colorChannelToDisplay.shape)
 plt.imshow(colorChannelToDisplay)
 plt.show()

datasets/raise/fft/generate_vignetting.py

@@ -2,6 +2,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 from tqdm import tqdm
 
+# TODO: seems to avoid some intermediary values resulting in visible circles
+
 A = -1.076923838846692e-06
 B = 0.0010539307456559996
 C = 42
@@ -30,6 +32,9 @@ def 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.png', image * 2.55, cmap = 'grey', vmin = 0, vmax = 255)
 plt.show()

datasets/raise/fft/image_to_fourier.py

@@ -3,13 +3,15 @@ import numpy as np
 from scipy import fftpack
 import matplotlib.pyplot as plt
 
-image = np.array(Image.open('circle.png').convert('L'))# / 255
+IMAGE_FILE_NAME = 'manual_tests/horizontal_ellipse'
+image = np.array(Image.open(f'{IMAGE_FILE_NAME}.png').convert('L'))# / 255
 
 # fft of image
 fft1 = fftpack.fftshift(fftpack.fft2(image))
 
 # save the image
+#toDisplay = np.angle(fft1)
 toDisplay = np.log10(1 + abs(fft1))
 plt.imshow(toDisplay)
 plt.show()
-plt.imsave('ifft1.png', toDisplay)
+#plt.imsave(f'{IMAGE_FILE_NAME}_fft.png', toDisplay)

datasets/raise/find_consecutive_most_similar_images.py

@@ -1,6 +1,7 @@
 import os
-from utils import getColorChannel, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
+from utils import getColorChannel, Color, mergeSingleColorChannelImagesAccordingToBayerFilter, isARawImage
 from tqdm import tqdm
+import numpy as np
 
 folder = 'flat-field/NEF'
 
@@ -24,7 +25,9 @@ lastCorrelation = None
 highestCorrelation = None
 highestCorrelationFile = None
 
-for file in tqdm(sorted(os.listdir(folder)), 'File'):
+files = [file for file in sorted(os.listdir(folder)) if isARawImage(file)]
+
+for file in tqdm(files, 'File'):
     colorChannels = {}
     for color in Color:
         colorChannel = getColorChannel(f'{folder}/{file}', color)
@@ -35,7 +38,8 @@ for file in tqdm(sorted(os.listdir(folder)), 'File'):
         correlation = corr(mergedSingleColorChannelImage, lastMergedSingleColorChannelImage)
         if highestCorrelation is None or correlation > highestCorrelation:
             highestCorrelation = correlation
+            highestCorrelationFile = file
 
     lastMergedSingleColorChannelImage = mergedSingleColorChannelImage
 
-print(f'{highestCorrelationFile=} (and previous file) have a correlation of {highestCorrelation}')
+print(f'{highestCorrelationFile} (and previous file) have the highest correlation with a correlation of {highestCorrelation}')

datasets/raise/utils.py

@@ -148,7 +148,7 @@ def isARawImage(imageFilePath):
 def getColorChannel(imageFilePath, color):
     if isARawImage(imageFilePath):
         numpyFilePath = f'{imageFilePath}.{color}.npy'
-        if False and os.path.isfile(numpyFilePath):
+        if os.path.isfile(numpyFilePath):
             imageNpArray = np.load(numpyFilePath)
         else:
             with rawpy.imread(imageFilePath) as raw: