Update app.py

app.py

@@ -512,6 +512,50 @@ def result(Input,Choice):
         Psnr = 20 * math.log10(MAX / math.sqrt(mse))
       return Input,Psnr
       
+    if Choice=="Indoor-Coloring":
+      pre_trained = tf.keras.models.load_model("gradio_pix2pix.h5")
+      size0 = Input.shape[0]
+      size1 = Input.shape[1]
+      start = Input
+      Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
+      Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
+      Input = np.array(Input).reshape(1,256,256,1)
+      prediction = pre_trained(Input,training=True)
+      Input = prediction[0]
+      Input = (Input+1)*127.5
+      Input = np.uint8(Input)
+      Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
+      finish = Input
+      mse = np.mean((start - finish) ** 2)
+      MAX = np.iinfo(start.dtype).max
+      if mse == 0:
+        Psnr = 100
+      else:
+        Psnr = 20 * math.log10(MAX / math.sqrt(mse))
+      return Input,Psnr
+      
+    if Choice=="Outdoor-Coloring":
+      pre_trained = tf.keras.models.load_model("gradio_pix2pix.h5")
+      size0 = Input.shape[0]
+      size1 = Input.shape[1]
+      start = Input
+      Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
+      Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
+      Input = np.array(Input).reshape(1,256,256,1)
+      prediction = pre_trained(Input,training=True)
+      Input = prediction[0]
+      Input = (Input+1)*127.5
+      Input = np.uint8(Input)
+      Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
+      finish = Input
+      mse = np.mean((start - finish) ** 2)
+      MAX = np.iinfo(start.dtype).max
+      if mse == 0:
+        Psnr = 100
+      else:
+        Psnr = 20 * math.log10(MAX / math.sqrt(mse))
+      return Input,Psnr
+      
     if Choice=="Enhancement":
       pre_trained2 = tf.keras.models.load_model("gradio_pix2pix.h5")
       size0 = Input.shape[0]
@@ -533,6 +577,6 @@ def result(Input,Choice):
 
 import gradio as gr
 
-iface = gr.Interface(fn=result, inputs=[gr.inputs.Image(type="numpy",image_mode="RGB"),gr.inputs.Radio(["Coloring","Enhancement","Repair","Repair and Color"])], outputs=[gr.outputs.Image( type="auto", label="Output"),gr.outputs.Textbox(type="number",label="Psnr")],theme="grass",live=True
+iface = gr.Interface(fn=result, inputs=[gr.inputs.Image(type="numpy",image_mode="RGB"),gr.inputs.Radio(["Indoor-Coloring","Outdoor-Coloring","Enhancement","Repair","Repair and Color"])], outputs=[gr.outputs.Image( type="auto", label="Output"),gr.outputs.Textbox(type="number",label="Psnr")],theme="grass",live=True
 ,css=""" body {background-color: rgba(127,191,63,0.48)} """,title="Image Enhancement",article=""" <a href="https://docs.google.com/document/d/19k6dyR5x_hd1M0yoU8i49dlDWvFmtnBT/edit?usp=sharing&ouid=115743073712072785012&rtpof=true&sd=true" download="example.docx"><img src="https://img.icons8.com/external-itim2101-lineal-color-itim2101/64/000000/external-article-blogger-and-influencer-itim2101-lineal-color-itim2101-1.png" alt="Article"></a>""",examples=[["dog.jpg","Coloring"],["woman.png","Coloring"]]) 
 iface.launch(debug="True",show_tips="True",inbrowser=True)