add best psnr from n2v source

utils.py

@@ -6,6 +6,7 @@ import warnings
 import pywt
 from skimage.filters import gaussian
 from skimage.transform import rescale
+from csbdeep.utils.utils import normalize_minmse
 
 # warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
 # warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)
@@ -280,12 +281,45 @@ def PSNR(gt, pred, range_=4095.0):
 
     psnr_indices = np.zeros((gt.shape[0]), dtype='float32')
     for z in range(gt.shape[0]):
-        mse = np.mean((gt[z] - pred[z])**2)
-        psnr = 20 * np.log10((range_)/np.sqrt(mse))
-        psnr_indices[z] = psnr
+        # mse = np.mean((gt[z] - pred[z])**2)
+        # psnr = 20 * np.log10((range_)/np.sqrt(mse))
+        psnr_indices[z] = best_PSNR(gt[z], pred[z], range_)
 
     return psnr_indices
 
+def PSNR_single(gt, img, range):
+    """
+    Compute Peak Signal-to-Noise Ratio.
+
+    Parameters:
+        gt: np.array
+            The ground truth target image.
+        img: np.array
+            The image of interest.
+        range: float
+            Intensity range e.g. gt.max() - gt.min() used for the PSNR
+            computation.
+    """
+    mse = np.mean(np.square(gt - img))
+    return 20 * np.log10(range) - 10 * np.log10(mse)
+
+def best_PSNR(gt, img, range):
+    """
+    Compute best Peak Signal-to-Noise Ratio by normalizing img such that
+    MSE is minimized to the gt image.
+
+    Parameters:
+        gt: np.array
+            The ground truth target image.
+        img: np.array
+            The image of interest.
+        range: float
+            Intensity range e.g. gt.max() - gt.min() used for the PSNR
+            computation.
+    """
+    img_n = normalize_minmse(img, gt)
+    return PSNR_single(gt, img_n, range=range)
+
 def MSE(gt, pred):
     """
     TODO add description
@@ -310,4 +344,4 @@ def MAE(gt, pred):
     for z in range(gt.shape[0]):
         mae_values[z] = np.mean((gt[z] - pred[z]))
     
-    return mae_values
+    return mae_values
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