Update pages/Data Collection.py

pages/Data Collection.py

@@ -1820,6 +1820,12 @@ elif st.session_state.current_page == "image_transformations":
     """, language="python")
 
     
+import streamlit as st
+import numpy as np
+import cv2
+import zipfile
+import io
+
     # Function to apply affine transformations
     def apply_affine_transformation(image, transformation_type):
         transformed_images = []
@@ -1830,13 +1836,13 @@ elif st.session_state.current_page == "image_transformations":
                 angle = i * 10
                 M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angle, 1)
             elif transformation_type == "Scaling":
-                scale = 1 + (i * 0.1)
+                scale = 1 + (i * 0.05)  # Reduced scale increments
                 M = np.float32([[scale, 0, 0], [0, scale, 0]])
             elif transformation_type == "Translation":
-                tx, ty = i * 10, i * 10
+                tx, ty = i * 5, i * 5  # Reduced translation
                 M = np.float32([[1, 0, tx], [0, 1, ty]])
             elif transformation_type == "Shearing":
-                shear = 0.1 * i
+                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
@@ -1855,34 +1861,37 @@ elif st.session_state.current_page == "image_transformations":
         return transformed_images
     
     # Streamlit App
-    st.title("Affine Transformations with Multiple Buttons")
-    st.write("Upload an image and select a transformation to apply. Each button generates 10 variations.")
+    st.title("Affine Transformations with Selection First")
+    st.write("Select a transformation and then upload an image to apply it dynamically.")
     
-    # Image Uploader
-    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    # Transformation Options
+    transformation = st.selectbox(
+        "Select a transformation:",
+        ["Rotation", "Scaling", "Translation", "Shearing", "Cropping"]
+    )
     
-    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)
+    # Image Uploader (Only appears after selection)
+    if transformation:
+        uploaded_file = st.file_uploader("Now, upload an image", type=["jpg", "jpeg", "png"])
     
-        # Display the uploaded image
-        st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_column_width=True)
+        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)
     
-        # Transformation Buttons
-        transformations = ["Rotation", "Scaling", "Translation", "Shearing", "Cropping"]
+            # Display the uploaded image
+            st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image", use_container_width=True)
     
-        # Process each transformation on button click
-        for transformation in transformations:
+            # Apply Transformation
             if st.button(f"Apply {transformation}"):
                 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_column_width=True)
+                        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()
@@ -1900,7 +1909,6 @@ elif st.session_state.current_page == "image_transformations":
                         mime="application/zip"
                     )
 
-
     col1, col2 = st.columns(2)
     with col1:
         st.markdown("""