Add analyze_bayer_filter_mean_denoiser.py

datasets/raise/analyze_bayer_filter_mean_denoiser.py

@@ -0,0 +1,44 @@
+import numpy as np
+from utils import Color
+import os
+from tqdm import tqdm
+import rawpy
+import matplotlib.pyplot as plt
+
+'''
+for color in Color:
+    #if color == 'green_bottom':
+    #    continue
+    print(color)
+'''
+
+os.chdir('flat-field/NEF')
+
+firstBayerFilterOccurrenceImages = []
+
+for fileName in tqdm(os.listdir()):
+    with rawpy.imread(fileName) as raw:
+        colorDesc = raw.color_desc.decode('ascii')
+        assert colorDesc == 'RGBG'
+        assert np.array_equal(raw.raw_pattern, np.array([[0, 1], [3, 2]], dtype = np.uint8))
+        # RG
+        # GB
+        firstBayerFilterOccurrenceImage = raw.raw_image_visible.copy()[:2, :2]
+        firstBayerFilterOccurrenceImages += [firstBayerFilterOccurrenceImage]
+
+firstBayerFilterOccurrenceImages = np.array(firstBayerFilterOccurrenceImages)
+print(rawImageVisible)
+
+NUMBER_OF_COLORS = 3
+HEX_COLOR = '#' + '%02x' * NUMBER_OF_COLORS
+COLOR_BASE = 256
+
+def getColor(colorIndex):
+    return HEX_COLOR % tuple((255 if colorIndex == colorIndexTmp else 0) for colorIndexTmp in range(NUMBER_OF_COLORS))
+
+#X, Y = np.histogram(firstBayerFilterOccurrenceImages[:,0,0])
+#plt.plot(X, Y)
+for colorIndex, (colorY, colorX) in enumerate([(0, 0), (0, 1), (1, 1)]):
+    X = firstBayerFilterOccurrenceImages[:, colorY, colorX] - np.mean(firstBayerFilterOccurrenceImages, axis = 0)[colorY, colorX]
+    plt.hist(X, bins = len(set(X)), color = getColor(colorIndex), alpha = 0.3)
+plt.show()