Update app.py

app.py

@@ -123,23 +123,28 @@ net.cuda()
 
 print('Model successfully loaded!')
 
-def check_image_quality(image):
-    # Convert the image to grayscale
-    gray_image = np.array(image.convert("L"))
+#######  Image quality checking func ######################
+
+#def check_image_quality(image):
+    ### Convert the image to grayscale
+    ##gray_image = np.array(image.convert("L"))
     
-    # Check for under/over-exposure using histogram
-    hist = exposure.histogram(gray_image)
-    low_exposure = hist[0][:5].sum() > 0.5 * hist[0].sum()  # Significant pixels in dark range
-    high_exposure = hist[0][-5:].sum() > 0.5 * hist[0].sum()  # Significant pixels in bright range
+    ### Check for under/over-exposure using histogram
+    #hist = exposure.histogram(gray_image)
+    #low_exposure = hist[0][:5].sum() > 0.5 * hist[0].sum()  # Significant pixels in dark range
+    #high_exposure = hist[0][-5:].sum() > 0.5 * hist[0].sum()  # Significant pixels in bright range
     
-    # Check sharpness using Laplacian variance
-    sharpness = cv2.Laplacian(np.array(image), cv2.CV_64F).var()
-    low_sharpness = sharpness < 70  # Threshold for sharpness
+    ### Check sharpness using Laplacian variance
+    #sharpness = cv2.Laplacian(np.array(image), cv2.CV_64F).var()
+    #low_sharpness = sharpness < 70  # Threshold for sharpness
     
-    # Check overall quality
-    if low_exposure or high_exposure or low_sharpness:
-        return False  # Image quality is insufficient
-    return True  # Image quality is sufficient
+    ### Check overall quality
+    #if low_exposure or high_exposure or low_sharpness:
+        #return False  # Image quality is insufficient
+    #return True  # Image quality is sufficient
+
+############################################################
+
 
 # Functions for face and mouth region
 def get_face_region(image):
@@ -278,9 +283,9 @@ def process_image(uploaded_image):
 
 # Gradio Interface
 def show_results(uploaded_image):
-    # Perform quality check
-    if not check_image_quality(uploaded_image):
-        return None, None, "Not_Allowed"
+    ### Perform quality check
+    #if not check_image_quality(uploaded_image):
+        #return None, None, "Not_Allowed"
 
     # If quality is acceptable, continue with processing
     aged_image_good_teeth, aged_image_bad_teeth, predicted_age, target_age = process_image(uploaded_image)