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app.py

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+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+st.markdown("<h1 style='color:blue;'>Image Processing(comparison view)</h1>", unsafe_allow_html=True)
+
+# Upload image in the sidebar
+uploaded_file = st.sidebar.file_uploader("Upload an Image", type=["png", "jpg", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    img_array = np.array(image)
+
+    # Select an operation using a dropdown
+    option = st.selectbox("Choose an operation:", [
+        "None", "Convert to Grayscale", "Rotate Image", "Apply Text Overlay", "Blur Image", "Convert to Color Space", "Edge Detection"
+    ])
+
+    # Convert to Grayscale
+    if option == "Convert to Grayscale":
+        gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image, caption="Original Image", use_container_width=True)
+        with col2:
+            st.image(gray_image, caption="Grayscale Image", use_container_width=True)
+
+    # Rotate Image
+    elif option == "Rotate Image":
+        angle = st.slider("Select Rotation Angle", -180, 180, 0)
+        (h, w) = img_array.shape[:2]
+        center = (w // 2, h // 2)
+        matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
+        rotated_image = cv2.warpAffine(img_array, matrix, (w, h))
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image, caption="Original Image", use_container_width=True)
+        with col2:
+            st.image(rotated_image, caption=f"Rotated by {angle}°", use_container_width=True)
+
+    # Apply Text Overlay
+    elif option == "Apply Text Overlay":
+        text = st.text_input("Enter text to overlay on the image", "Hello, Streamlit!")
+        image_copy = img_array.copy()
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        cv2.putText(image_copy, text, (50, 50), font, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image, caption="Original Image", use_container_width=True)
+        with col2:
+            st.image(image_copy, caption="Image with Text Overlay", use_container_width=True)
+
+    # Blur Image
+    elif option == "Blur Image":
+        blur_level = st.slider("Select Blur Level", 1, 20, 5)
+        kernel_size = (blur_level * 2 + 1, blur_level * 2 + 1)  # Ensure it's always an odd number
+        blurred_image = cv2.GaussianBlur(img_array, kernel_size, 0)
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image, caption="Original Image", use_container_width=True)
+        with col2:
+            st.image(blurred_image, caption=f"Blurred (Level {blur_level})", use_container_width=True)
+
+    # Convert to Color Space (Fixed BGR to RGB)
+    elif option == "Convert to Color Space":
+        color_space = st.selectbox("Choose a color space:", ["RGB", "BGR2RGB", "Grayscale"])
+
+        converted_image = None  # Initialize to avoid `NoneType` errors
+
+        if color_space == "RGB":
+            converted_image = img_array  # Already in RGB format
+        elif color_space == "BGR2RGB":
+            converted_image = cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)  # Fixed BGR to RGB conversion
+        elif color_space == "Grayscale":
+            converted_image = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
+
+        if converted_image is not None:
+            col1, col2 = st.columns(2)
+            with col1:
+                st.image(image, caption="Original Image", use_container_width=True)
+            with col2:
+                st.image(converted_image, caption=f"{color_space} Image", use_container_width=True)
+
+    # Edge Detection (Canny)
+    elif option == "Edge Detection":
+        low_threshold = st.slider("Lower Threshold", 0, 255, 50)
+        high_threshold = st.slider("Upper Threshold", 0, 255, 150)
+
+        gray_image = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)  # Convert to grayscale first
+        edges = cv2.Canny(gray_image, low_threshold, high_threshold)  # Apply Canny Edge Detection
+
+        col1, col2 = st.columns(2)
+        with col1:
+            st.image(image, caption="Original Image", use_container_width=True)
+        with col2:
+            st.image(edges, caption="Edge Detection (Canny)", use_container_width=True)
+

requirements.txt

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+streamlit
+opencv-python
+numpy
+pillow