Update app.py

app.py

@@ -97,4 +97,32 @@ if image_file is not None:
           st.write(output.shape)
           st.image(output,width=850)
 
-      st.text("DONE ! ....")
+      st.text("DONE ! ....")
+      
+if image_file is not None:
+    img=load_image(image_file)
+      
+    st.text("Making A Prediction ....")
+    st.image(img,width=850)
+      
+    img=np.asarray(img)
+  
+    img_cv=convert_one_channel(img)
+    img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
+    img_cv=np.float32(img_cv/255)
+     
+    img_cv=np.reshape(img_cv,(1,512,512,1))
+    predict_img=model.predict(img_cv)
+    predict=predict_img[1,:,:,0]
+    plt.imsave("/content/predict.png",predict)
+    
+    ## Plotting - Пример результата
+    img = cv2.imread("/content/Data/Images/107.png")  # оригинальное изображение 107.png
+
+    predict1 = cv2.resize(predict, (img.shape[1], img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
+
+    mask = np.uint8(predict1 * 255)
+    _, mask = cv2.threshold(mask, thresh=255/2, maxval=255, type=cv2.THRESH_BINARY)
+    cnts, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+    img = cv2.drawContours(img, cnts, -1, (255, 0, 0), 2)
+    cv2_imshow(img)