Duplicate from GIanlucaRub/DoubleResolution

Co-authored-by: Gianluca Ruberto <[email protected]>

.gitattributes

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README.md

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+---
+title: DoubleResolution
+emoji: 🐢
+colorFrom: red
+colorTo: red
+sdk: gradio
+sdk_version: 3.15.0
+app_file: app.py
+pinned: false
+duplicated_from: GIanlucaRub/DoubleResolution
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import numpy as np
+from math import ceil
+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]
+    height = ceil(input_height/128)
+    width = ceil(input_width/128)
+    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)
+    
+    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
+            to_predict.append(temp_slice)
+
+# removing inner borders
+
+    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
+                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)
+
+                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    
+    completed = False
+    n = 16
+    while not completed:
+        try:
+            print("attempting with "+ str(n))
+            predicted = model.predict(np.array(to_predict),batch_size = n)
+            completed = True
+            print("completed with "+ str(n))
+        except:
+            print("attempt with " + str(n) + " failed")
+            n += -1
+            if n <= 0:
+                n = 1
+    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
+    
+                
+
+    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)
+                
+    
+
+                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(
+    fn=double_res,
+    title="Double picture resolution",
+    description="Upload a picture and get the horizontal and vertical resolution doubled (4x pixels)",
+    allow_flagging="never",
+    inputs=[
+        gr.inputs.Image(type="numpy")
+        ],
+    outputs=gr.Image(type="numpy"))
+
+demo.launch()
+

requirements.txt

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+numpy
+tensorflow