Update pages/Data Collection.py

pages/Data Collection.py

@@ -3,6 +3,8 @@ import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 import cv2
+import zipfile
+import io
 
 st.set_page_config(
     page_title="HomePage",
@@ -1817,6 +1819,88 @@ elif st.session_state.current_page == "image_transformations":
     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 * 0.1)
+                M = np.float32([[scale, 0, 0], [0, scale, 0]])
+            elif transformation_type == "Translation":
+                tx, ty = i * 10, i * 10
+                M = np.float32([[1, 0, tx], [0, 1, ty]])
+            elif transformation_type == "Shearing":
+                shear = 0.1 * i
+                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("Affine Transformations with Multiple Buttons")
+    st.write("Upload an image and select a transformation to apply. Each button generates 10 variations.")
+    
+    # Image Uploader
+    uploaded_file = st.file_uploader("Choose 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_column_width=True)
+    
+        # Transformation Buttons
+        transformations = ["Rotation", "Scaling", "Translation", "Shearing", "Cropping"]
+    
+        # Process each transformation on button click
+        for transformation in transformations:
+            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)
+    
+                    # 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"
+                    )
+
+
     col1, col2 = st.columns(2)
     with col1:
         st.markdown("""