Add RMS computation

datasets/noise_free_test_images/estimate_prnu.py

@@ -14,6 +14,10 @@ sys.path.insert(0, '../../algorithms/context_adaptive_interpolator/')
 
 from context_adaptive_interpolator import contextAdaptiveInterpolator
 
+sys.path.insert(0, '../../algorithms/distance/')
+
+from rms_diff import rmsDiffNumpy
+
 from skimage.restoration import denoise_tv_chambolle
 
 datasetPath = 'no_noise_images'
@@ -25,7 +29,7 @@ IMAGE_SIZE_SHAPE = (469, 704)
 np.random.seed(0)
 
 #prnuNpArray = 255 * randomGaussianImage(scale = PRNU_FACTOR, size = IMAGE_SIZE_SHAPE)
-prnuPil = Image.open('prnu_4x4.png').convert('F')
+prnuPil = Image.open('prnu.png').convert('F')
 prnuNpArray = np.array(prnuPil) * PRNU_FACTOR
 
 def isIn256Range(x):
@@ -39,8 +43,8 @@ for imageName in os.listdir(datasetPath):
         imageWithoutPrnuPil = Image.open(imagePath).convert('F')
         imageWithoutPrnuNpArray = np.array(imageWithoutPrnuPil)
 
-        m = imageWithoutPrnuNpArray.shape[0] // 4
-        n = imageWithoutPrnuNpArray.shape[1] // 4
+        m = imageWithoutPrnuNpArray.shape[0] // 1
+        n = imageWithoutPrnuNpArray.shape[1] // 1
 
         imageWithoutPrnuNpArrayTiles = [imageWithoutPrnuNpArray[x : x + m, y : y + n] for x in range(0, imageWithoutPrnuNpArray.shape[0], m) for y in range(0, imageWithoutPrnuNpArray.shape[1], n)]
         for imageWithoutPrnuNpArrayTile in imageWithoutPrnuNpArrayTiles:
@@ -53,5 +57,7 @@ for imageName in os.listdir(datasetPath):
 
             imagesPrnuEstimateNpArray += [imagePrnuEstimateNpArray]
 
-showImageWithMatplotlib(np.array(imagesPrnuEstimateNpArray).mean(axis = 0))
+cameraPrnuEstimateNpArray = np.array(imagesPrnuEstimateNpArray).mean(axis = 0)
+rms = rmsDiffNumpy(cameraPrnuEstimateNpArray, prnuNpArray)
+showImageWithMatplotlib(cameraPrnuEstimateNpArray, f'Camera PRNU estimate\nRMS with actual one: {rms:.4f}')