test finish, add python mat

IceClear · Nov 30, 2021 · 0018f12 · 0018f12
1 parent 6dc849c
commit 0018f12
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Showing 4 changed files with 9 additions and 6 deletions.
diff --git a/IL-NIQE.py b/IL-NIQE.py
@@ -245,7 +245,7 @@ def ilniqe(img, mu_pris_param, cov_pris_param, gaussian_window, principleVectors
     nanConst = 2000
 
     if resize:
-        img = cv2.resize(img, (normalizedWidth, normalizedWidth),interpolation=cv2.INTER_AREA)
+        img = cv2.resize(img, (normalizedWidth, normalizedWidth), interpolation=cv2.INTER_AREA)
     h, w, _ = img.shape
 
     num_block_h = math.floor(h / block_size_h)
@@ -437,6 +437,9 @@ def calculate_ilniqe(img, crop_border, input_order='HWC', num_cpus=3, resize=Tru
 
     ilniqe_result = ilniqe(img, mu_pris_param, cov_pris_param, gaussian_window, principleVectors, meanOfSampleData, resize)
 
+    if isinstance(ilniqe_result, complex) and ilniqe_result.imag == 0:
+        ilniqe_result = ilniqe_result.real
+
     return ilniqe_result
 
 if __name__ == '__main__':

diff --git a/README.md b/README.md
@@ -20,7 +20,7 @@ You can also train your own model via training.m in the Matlab version. But the
 
 ## Results
 
-|Image|IL-NIQE (using official .mat, Matlab/Python)|IL-NIQE (using .mat trained in python, Matlab/Python)|IL-NIQE (w/o imresize) (Matlab/Python)|Time(sec) (Matlab/Python)|
+|Image|IL-NIQE (using official .mat) (Matlab/Python)|IL-NIQE (using .mat trained in python) (Python)|IL-NIQE (w/o imresize) (Matlab/Python)|Time(sec) (Matlab/Python)|
 |:-|:-|:-|:-|:-|
 |pepper_0.png|29.1422 / 27.3655|28.1166|38.7078 / 38.9319|9.9567 / 103.4350|
 |pepper_1.png|36.9637 / 39.0683|38.7309|36.6869 / 37.0163|9.7487 / 90.1218|

diff --git a/python_templateModel.mat b/python_templateModel.mat
diff --git a/train.py b/train.py
@@ -376,10 +376,10 @@ def train(data_path):
     cov_prisparam = np.cov(prisparam_no_nan, rowvar=False)
 
     templateModel = []
-    templateModel.append(np.real(mu_prisparam) if isinstance(mu_prisparam,complex) else mu_prisparam)
-    templateModel.append(np.real(cov_prisparam) if isinstance(cov_prisparam,complex) else cov_prisparam)
-    templateModel.append(np.real(meanOfSampleData) if isinstance(meanOfSampleData,complex) else meanOfSampleData)
-    templateModel.append(np.real(principleVectors) if isinstance(principleVectors,complex) else principleVectors)
+    templateModel.append(mu_prisparam)
+    templateModel.append(cov_prisparam)
+    templateModel.append(meanOfSampleData)
+    templateModel.append(principleVectors)
 
     scipy.io.savemat('./python_templateModel.mat', {'templateModel':[templateModel]})