msg

app.py

@@ -11,6 +11,10 @@ model = from_pretrained_keras("keras-io/super-resolution")
 model.summary()
 
 def infer(image):
+
+   nx=image.shape[0]
+   ny=image.shape[1]
+
    img = Image.fromarray(image)
    # img = img.resize((100,100))
    # img = img.crop((0,100,0,100))
@@ -22,6 +26,9 @@ def infer(image):
    input = np.expand_dims(y, axis=0)
    out = model.predict(input)
 
+   nxo = out.squeeze().shape[0]
+   nyo = out.squeeze().shape[1]
+
    out_img_y = out[0]
    out_img_y *= 255.0
 
@@ -34,12 +41,17 @@ def infer(image):
    out_img = Image.merge("YCbCr", (out_img_y, out_img_cb, out_img_cr)).convert(
    "RGB"
    )
-   return (img,out_img)
+
+   out = {}
+   out.update( {'input image size': (nx,ny) } )
+   out.update( {'output image size': (nxo,nyo) } )
+
+   return (pd.DataFrame(data=out.values(), index=out.keys()).transpose(), img,out_img)
 	
 article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1609.05158' target='_blank'>Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network</a></p><center> <a href='https://keras.io/examples/vision/super_resolution_sub_pixel/' target='_blank'>Image Super-Resolution using an Efficient Sub-Pixel CNN</a></p> <center>Contributors: <a href='https://twitter.com/Cr0wley_zz'>Devjyoti Chakraborty</a>|<a href='https://twitter.com/ritwik_raha'>Ritwik Raha</a>|<a href='https://twitter.com/ariG23498'>Aritra Roy Gosthipaty</a></center>"
 
-examples = [['examples/2000-04-28-18-21-24_L5_rgb.jpg'],['examples/2000-08-02-18-23-18_L5_rgb.jpg'],
-            ['examples/2000-08-18-18-23-46_L5_rgb.jpg'],['examples/2000-09-19-18-24-18_L5_rgb.jpg'],['examples/2000-10-21-18-24-43_L5_rgb.jpg']]
+# examples = [['examples/2000-04-28-18-21-24_L5_rgb.jpg'],['examples/2000-08-02-18-23-18_L5_rgb.jpg'],
+#             ['examples/2000-08-18-18-23-46_L5_rgb.jpg'],['examples/2000-09-19-18-24-18_L5_rgb.jpg'],['examples/2000-10-21-18-24-43_L5_rgb.jpg']]
 
 examples= [[l] for l in glob('examples/tiles/*.jpg')]