Create app.py

app.py

@@ -0,0 +1,75 @@
+import gradio as gr
+
+import os
+
+os.system('pip install voxelmorph -q')
+os.system('pip install tensorflow  -q')
+
+import sys
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+
+from codes import *
+
+## Print samples
+vxm_model_loaded = create_model(dim = 128)
+vxm_model_loaded.load_weights('./modelbest/')
+
+vxm_model_loaded_affine = create_model(dim = 128)
+vxm_model_loaded_affine.load_weights('./modelbest/')
+
+
+
+def imgnt_reg(img1,img2, model_selected):
+    fixed_images = np.empty((1, 128, 128, 3))
+    moving_images = np.empty((1, 128, 128, 3))
+    # prepare inputs:
+    fixed_images[0] = preprocess_image(img1, dim = 128)
+    moving_images[0] = preprocess_image(img2, dim = 128)
+    
+    model_inputs = [moving_images/255, fixed_images/255]
+    if model_selected == "Imagenet-wild":
+        Y_predict = vxm_model_loaded(model_inputs)
+    else:
+        Y_predict = vxm_model_loaded_affine(model_inputs)
+    img_out = Y_predict[0][0].numpy()*255
+    registered_img = Image.fromarray(img_out.astype(np.uint8))
+    return registered_img
+
+
+
+with gr.Blocks() as demo:
+    #gr.Markdown("<h1><center> Message Encryption</center></h1>")
+    #gr.Markdown("<center> Encrypt your message and let your friends decrypt it on the same day.</center>")
+    
+    image_1 = gr.Image(
+            label = "Fixed Image",
+            source = "upload", 
+            type = "filepath",
+            elem_id = "image-in",
+        )
+    image_2 = gr.Image(
+            label = "Moving Image",
+            source = "upload", 
+            type = "filepath",
+            elem_id = "image-in",
+        )
+    
+    model_list = gr.Dropdown(
+            ["Imagenet-wild", "Imagenet-affine"], label="Model", info="select a model"
+        )
+    
+    out_image = gr.Image(placeholder='Output', label = "ٌRegistered image",
+            #source = "upload", 
+            #type = "filepath",
+            elem_id = "image-out"
+        )
+    inputs = [image_1, image_2, model_list]
+    iface = gr.Interface(fn=imgnt_reg, inputs=inputs,outputs=out_image,
+    title="Imagenet registration V1",
+    description="Upload 2 images to generate a registered one:",
+    examples=[["./examples/ex1.jpg","./examples/ex2.jpg"]],
+                         )
+
+demo.queue(default_enabled = True).launch(debug = True)