Update pages/7_Unstructured_data.py

pages/7_Unstructured_data.py

@@ -333,15 +333,69 @@ if file_type == "IMAGE":
         - **Splitting Channels**: Use `cv2.split()` to separate the image into individual channels (B, G, R).  
         
         ```python
+        import cv2
         blue, green, red = cv2.split(image)
         ```
         - **Merging Channels**: Combine individual channels back into a single image using `cv2.merge()`.  
               
         ```python
+        import cv2
         merged_image = cv2.merge((blue, green, red))
         ```
         """, unsafe_allow_html=True)
 
+        st.markdown("""
+        
+            **1. Read the image**
+            ```python
+            image = cv2.imread('input_image.jpg')
+            if image is None:
+                print("Error: Image not found")
+                exit()
+            ```
+            
+            **2. Convert RGB to Grayscale**
+            ```python
+            gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+            ```
+        
+            **3. Convert Grayscale back to RGB**
+            ```python
+            rgb_image = cv2.cvtColor(gray_image, cv2.COLOR_GRAY2BGR)
+            ```
+        
+            **4. Split channels (B, G, R)**
+            ```python
+            blue, green, red = cv2.split(image)
+            ```
+
+            **5. Merge channels back**
+            ```python
+            merged_image = cv2.merge((blue, green, red))
+            ```
+            
+            **6. Save the processed image**
+            ```python
+            cv2.imwrite('processed_image.jpg', merged_image)
+            ```
+        """, unsafe_allow_html=True)
+
+        st.markdown("""
+        - Explanation :
+        
+            - **Step 1**: Read the image using `cv2.imread()`. Terminates if the image file is not found.
+            
+            - **Step 2**: Convert the image to Grayscale using `cv2.cvtColor()`. This simplifies the image to a single channel.
+            
+            - **Step 3**: Convert the Grayscale image back to RGB to enable further processing.
+            
+            - **Step 4**: Use `cv2.split()` to separate the image into individual color channels.
+            
+            - **Step 5**: Combine the channels back into a single image using `cv2.merge()`.
+            
+            - **Step 6**: Save the final loaded image with `cv2.imwrite()`.
+        """, unsafe_allow_html=True)
+