Simplify grayscale rendering

If it proceeds linearly to *covers the complete value range of the supplied data* then doing so is unnecessary and even if it is another not very different scale transformation then it is still fine for my goal.

algorithms/image_utils/image_utils.py

@@ -5,10 +5,11 @@ from matplotlib import pyplot as plt
 def randomGaussianImage(scale, size):
     return np.random.normal(loc = 0, scale = scale, size = size)
 
-def showImageWithMatplotlib(npArray, title = None):
+# `cmap` source: https://matplotlib.org/3.8.0/api/_as_gen/matplotlib.pyplot.imshow.html
+def showImageWithMatplotlib(npArray, title = None, cmap = 'viridis'):
     if title is not None:
         plt.title(title)
-    plt.imshow(npArray)
+    plt.imshow(npArray, cmap = cmap)
     plt.show()
 
 def toPilImage(npArray):

datasets/noise_free_test_images/estimate_prnu.py

@@ -62,5 +62,7 @@ for imageName in os.listdir(datasetPath):
 
 cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
 rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True)
-showImageWithMatplotlib(cameraPrnuEstimateNpArray, f'Camera PRNU estimate\nRMS with actual one: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f} (normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})')
+title = f'Camera PRNU estimate\nRMS with actual one: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray):.4f} (normalized RMS: {rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray, True):.4f})'
+#showImageWithMatplotlib(cameraPrnuEstimateNpArray, title)
 
+showImageWithMatplotlib(cameraPrnuEstimateNpArray, title, 'gray')