Update app.py

app.py

@@ -5,58 +5,116 @@ from huggingface_hub import from_pretrained_keras
 
 model = from_pretrained_keras("GIanlucaRub/doubleResFinal")
 def double_res(input_image):
-    input_height = input_image.shape[0] 
-    input_width = input_image.shape[1] 
+    input_height = input_image.shape[0]
+    input_width = input_image.shape[1]
     height = ceil(input_height/128)
     width = ceil(input_width/128)
-    expanded_input_image = np.zeros((128*height, 128*width,3), dtype=np.uint8)
+    expanded_input_image = np.zeros((128*height, 128*width, 3), dtype=np.uint8)
     np.copyto(expanded_input_image[0:input_height, 0:input_width], input_image)
-    
-    output_image = np.zeros((128*height*2, 128*width*2,3), dtype=np.float32)
 
+    output_image = np.zeros((128*height*2, 128*width*2, 3), dtype=np.float32)
+    
+    to_predict = []
     for i in range(height):
         for j in range(width):
-            temp_slice = expanded_input_image[i*128:(i+1)*128, j*128:(j+1)*128]/255
-            upsampled_slice = model.predict(temp_slice[np.newaxis, ...]) 
-            np.copyto(output_image[i*256:(i+1)*256, j*256:(j+1)*256], upsampled_slice[0])
-            if i!= 0 and j!= 0 and i != height-1 and j!=width-1:
+            temp_slice = expanded_input_image[i *
+                                              128:(i+1)*128, j*128:(j+1)*128]/255
+            to_predict.append(temp_slice)
+
 # removing inner borders
-                right_slice = expanded_input_image[i*128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
-                right_upsampled_slice = model.predict(right_slice[np.newaxis, ...]) 
-                resized_right_slice = right_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[i*256+64:(i+1)*256-64, (j+1)*256-64:(j+1)*256+64], resized_right_slice)
-                
-                left_slice = expanded_input_image[i*128:(i+1)*128, j*128-64:(j)*128+64]/255
-                left_upsampled_slice = model.predict(left_slice[np.newaxis, ...]) 
-                resized_left_slice = left_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[i*256+64:(i+1)*256-64, j*256-64:j*256+64], resized_left_slice)
-                
-                upper_slice = expanded_input_image[(i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
-                upper_upsampled_slice = model.predict(upper_slice[np.newaxis, ...]) 
-                resized_upper_slice = upper_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[(i+1)*256-64:(i+1)*256+64, j*256+64:(j+1)*256-64], resized_upper_slice)
+
+    for i in range(height):
+        for j in range(width):        
+            if i != 0 and j != 0 and i != height-1 and j != width-1:
+                right_slice = expanded_input_image[i *
+                                                   128:(i+1)*128, (j+1)*128-64:(j+1)*128+64]/255
+                to_predict.append(right_slice)
+    
+
+                left_slice = expanded_input_image[i *
+                                                  128:(i+1)*128, j*128-64:(j)*128+64]/255
+                to_predict.append(left_slice)
+    
+
+                upper_slice = expanded_input_image[(
+                    i+1)*128-64:(i+1)*128+64, j*128:(j+1)*128]/255
+                to_predict.append(upper_slice)
                 
-                lower_slice = expanded_input_image[i*128-64:i*128+64, j*128:(j+1)*128]/255
-                lower_upsampled_slice = model.predict(lower_slice[np.newaxis, ...]) 
-                resized_lower_slice = lower_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[i*256-64:i*256+64, j*256+64:(j+1)*256-64], resized_lower_slice)
 
+                lower_slice = expanded_input_image[i *
+                                                   128-64:i*128+64, j*128:(j+1)*128]/255
+                to_predict.append(lower_slice)
+    # removing angles
+
+                lower_right_slice = expanded_input_image[i *
+                                                         128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255
+                to_predict.append(lower_right_slice)
 
-# removing angles
-                lower_right_slice = expanded_input_image[i*128-64:i*128+64, (j+1)*128-64:(j+1)*128+64]/255
-                lower_right_upsampled_slice = model.predict(lower_right_slice[np.newaxis, ...]) 
-                resized_lower_right_slice = lower_right_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[i*256-64:i*256+64,  (j+1)*256-64:(j+1)*256+64], resized_lower_right_slice)
+                lower_left_slice = expanded_input_image[i *
+                                                        128-64:i*128+64, j*128-64:j*128+64]/255
+                to_predict.append(lower_left_slice)
+                
+# predicting all images at once    
+    predicted = model.predict(np.array(to_predict))
+    counter = 0
+    for i in range(height):
+        for j in range(width):
+            np.copyto(output_image[i*256:(i+1)*256, j *
+                      256:(j+1)*256], predicted[counter])
+            counter+=1
+    
                 
-                lower_left_slice = expanded_input_image[i*128-64:i*128+64, j*128-64:j*128+64]/255
-                lower_left_upsampled_slice = model.predict(lower_left_slice[np.newaxis, ...]) 
-                resized_lower_left_slice = lower_left_upsampled_slice[0][64:192,64:192]
-                np.copyto(output_image[i*256-64:i*256+64,  j*256-64:j*256+64], resized_lower_left_slice)
+
+    for i in range(height):
+        for j in range(width):        
+            if i != 0 and j != 0 and i != height-1 and j != width-1:            
+                right_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_right_slice = right_upsampled_slice[64:192, 64:192]
+                np.copyto(output_image[i*256+64:(i+1)*256-64,
+                          (j+1)*256-64:(j+1)*256+64], resized_right_slice)
+    
                 
+    
 
+                left_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_left_slice = left_upsampled_slice[64:192, 64:192]
+                np.copyto(output_image[i*256+64:(i+1)*256-64,
+                          j*256-64:j*256+64], resized_left_slice)
                 
+    
+
+                upper_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_upper_slice = upper_upsampled_slice[64:192, 64:192]
+                np.copyto(output_image[(i+1)*256-64:(i+1)*256+64,
+                          j*256+64:(j+1)*256-64], resized_upper_slice)
                 
-    resized_output_image = output_image[0:input_height*2,0:input_width*2]
+    
+
+                lower_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_lower_slice = lower_upsampled_slice[64:192, 64:192]
+                np.copyto(output_image[i*256-64:i*256+64,
+                          j*256+64:(j+1)*256-64], resized_lower_slice)
+
+
+
+                lower_right_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_lower_right_slice = lower_right_upsampled_slice[64:192, 64:192]        
+                np.copyto(output_image[i*256-64:i*256+64,  (j+1)
+                          * 256-64:(j+1)*256+64], resized_lower_right_slice)
+
+
+                lower_left_upsampled_slice = predicted[counter]
+                counter+=1
+                resized_lower_left_slice = lower_left_upsampled_slice[64:192, 64:192]
+                np.copyto(
+                    output_image[i*256-64:i*256+64,  j*256-64:j*256+64], resized_lower_left_slice)
+
+    resized_output_image = output_image[0:input_height*2, 0:input_width*2]
     return resized_output_image
 
 demo = gr.Interface(