datasets/raise/merge_single_color_channel_images_according_to_bayer_filter.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python
 
+from PIL import Image
 from utils import Color, mergeSingleColorChannelImagesAccordingToBayerFilter
 import matplotlib.pyplot as plt
 import numpy as np
@@ -14,4 +15,4 @@ def getImageByColor(color):
 singleColorChannelImages = {color: getImageByColor(color) for color in Color}
 multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
 
-plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)
+plt.imsave(PREFIX + 'multiple_colors.png', multipleColorsImage)

datasets/raise/merge_with_same_color_scale_single_color_channel_images_according_to_bayer_filter.py

@@ -1,36 +0,0 @@
-#!/usr/bin/env python
-
-from utils import Denoiser, Color, mergeSingleColorChannelImagesAccordingToBayerFilter
-import matplotlib.pyplot as plt
-import numpy as np
-
-PREFIX = 'means/mean_rafael_230424_'
-
-def getImageByColor(color):
-    filePath = PREFIX + f'{color}.npy'
-    image = np.load(filePath)
-    return image
-
-multipleColorsImageDenoisers = {}
-
-DENOISERS = [
-    Denoiser.BILATERAL,
-    Denoiser.WAVELET,
-    Denoiser.MEAN,
-]
-
-for denoiser in DENOISERS:
-    singleColorChannelImages = {color: getImageByColor(color) for color in Color}
-    multipleColorsImage = mergeSingleColorChannelImagesAccordingToBayerFilter(singleColorChannelImages)
-    multipleColorsImageDenoisers[denoiser] = multipleColorsImage
-
-def getExtreme(extreme):
-    vExtreme = extreme(list(multipleColorsImageDenoisers.values()))
-    #print(vExtreme)
-    return vExtreme
-
-vMin, vMax = [getExtreme(extreme) for extreme in [np.min, np.max]]
-
-for denoiser in DENOISERS:
-    multipleColorsImage = multipleColorsImageDenoisers[denoiser]
-    plt.imsave(PREFIX + f'{denoiser}_multiple_colors.png', multipleColorsImage)