Update pages/Data Collection.py

pages/Data Collection.py

@@ -1655,7 +1655,104 @@ def Image_Augmentation_page():
         cv2.waitKey(0)
         cv2.destroyAllWindows()
         """, language="python")
-        
+
+        # Function to apply affine transformations
+        def apply_affine_transformation(image, transformation_type):
+            transformed_images = []
+            rows, cols, _ = image.shape
+        
+            for i in range(1, 11):  # Generate 10 variations
+                if transformation_type == "Rotation":
+                    angle = i * 10
+                    M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
+                elif transformation_type == "Scaling":
+                    scale = 1 + (i * 0.05)  # Reduced scale increments
+                    M = np.float32([[scale, 0, 0], [0, scale, 0]])
+                elif transformation_type == "Translation":
+                    tx, ty = i * 5, i * 5  # Reduced translation
+                    M = np.float32([[1, 0, tx], [0, 1, ty]])
+                elif transformation_type == "Shearing":
+                    shear = 0.05 * i  # Reduced shear factor
+                    M = np.float32([[1, shear, 0], [shear, 1, 0]])
+                elif transformation_type == "Cropping":
+                    # Simple cropping: reduce the size incrementally
+                    x1, y1 = i * 5, i * 5
+                    x2, y2 = cols - i * 5, rows - i * 5
+                    transformed_image = image[y1:y2, x1:x2]
+                    transformed_images.append(transformed_image)
+                    continue  # Skip warpAffine for cropping
+                else:
+                    st.error("Invalid transformation type!")
+                    return []
+        
+                transformed_image = cv2.warpAffine(image, M, (cols, rows))
+                transformed_images.append(transformed_image)
+        
+        return transformed_images
+            
+        # Streamlit App
+        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(
+            "Step 1: Select a transformation type:",
+            ["Select a Transformation", "Rotation", "Scaling", "Translation", "Shearing", "Cropping"]
+        )
+        
+        # Ensure the user selects a valid transformation
+        if transformation != "Select a Transformation":
+            # Provide guidance based on the selected 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.")
+        
+            # Image Uploader (Only appears after selection)
+            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
+                file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+                image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        
+                # Display the uploaded image
+                st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_container_width=True)
+        
+                # Automatically apply the transformation after upload
+                transformed_images = apply_affine_transformation(image, 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)
+        
+                    # 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"
+                    )
+        else:
+            st.warning("Please select a valid transformation type to proceed.")
+            
         if st.button("Back to Home"):
             st.session_state['page'] = "home"