Update pages/Data Collection.py

pages/Data Collection.py

@@ -1855,18 +1855,24 @@ elif st.session_state.current_page == "image_transformations":
         return transformed_images
     
     # Streamlit App
-    st.title("Affine Transformations with Selection First")
-    st.write("Select a transformation and then upload an image to apply it dynamically.")
+    st.title("Dynamic Affine Transformation Tool")
+    st.write("Select a transformation type to proceed and learn how it works before uploading an image.")
     
-    # Transformation Options
-    transformation = st.selectbox(
-        "Select a transformation:",
-        ["Rotation", "Scaling", "Translation", "Shearing", "Cropping"]
-    )
-    
-    # Image Uploader (Only appears after selection)
+    # Provide guidance based on the selected transformation
+    if transformation:
+        if transformation == "Rotation":
+            st.info("Rotation rotates the image around a fixed point. Angles are applied in steps of 10 degrees.")
+        elif transformation == "Scaling":
+            st.info("Scaling adjusts the size of the image. The scale factor increases incrementally.")
+        elif transformation == "Translation":
+            st.info("Translation shifts the image horizontally and vertically in small steps.")
+        elif transformation == "Shearing":
+            st.info("Shearing skews the image along the x-axis or y-axis, creating a slanted effect.")
+        elif transformation == "Cropping":
+            st.info("Cropping trims the image edges step by step to focus on a smaller region.")
+
     if transformation:
-        uploaded_file = st.file_uploader("Now, upload an image", type=["jpg", "jpeg", "png"])
+        uploaded_file = st.file_uploader("Step 2: Now, upload an image", type=["jpg", "jpeg", "png"])
     
         if uploaded_file:
             # Read the uploaded file into a numpy array using OpenCV
@@ -1876,32 +1882,32 @@ elif st.session_state.current_page == "image_transformations":
             # Display the uploaded image
             st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_container_width=True)
     
-            # Apply Transformation
-            if st.button(f"Apply {transformation}"):
-                transformed_images = apply_affine_transformation(image, transformation)
+            # Automatically apply the transformation after upload
+            transformed_images = apply_affine_transformation(image, transformation)
+    
+            if transformed_images:
+                st.write(f"Generated 10 images using {transformation}:")
     
-                if transformed_images:
-                    st.write(f"Generated 10 images using {transformation}:")
+                # Display all transformed images
+                for i, img in enumerate(transformed_images):
+                    st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), caption=f"{transformation} {i+1}", use_container_width=True)
     
-                    # Display all transformed images
+                # Create ZIP file for download
+                zip_buffer = io.BytesIO()
+                with zipfile.ZipFile(zip_buffer, "w") as zip_file:
                     for i, img in enumerate(transformed_images):
-                        st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), caption=f"{transformation} {i+1}", use_container_width=True)
-    
-                    # Create ZIP file for download
-                    zip_buffer = io.BytesIO()
-                    with zipfile.ZipFile(zip_buffer, "w") as zip_file:
-                        for i, img in enumerate(transformed_images):
-                            # Save each image as bytes
-                            _, img_encoded = cv2.imencode('.jpg', img)
-                            zip_file.writestr(f"{transformation}_image_{i+1}.jpg", img_encoded.tobytes())
-    
-                    zip_buffer.seek(0)
-                    st.download_button(
-                        label=f"Download All {transformation} Images",
-                        data=zip_buffer,
-                        file_name=f"{transformation}_transformed_images.zip",
-                        mime="application/zip"
-                    )
+                        # Save each image as bytes
+                        _, img_encoded = cv2.imencode('.jpg', img)
+                        zip_file.writestr(f"{transformation}_image_{i+1}.jpg", img_encoded.tobytes())
+    
+                zip_buffer.seek(0)
+                st.download_button(
+                    label=f"Download All {transformation} Images",
+                    data=zip_buffer,
+                    file_name=f"{transformation}_transformed_images.zip",
+                    mime="application/zip"
+                )
+
 
     col1, col2 = st.columns(2)
     with col1: