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Image_Opencv
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# import streamlit as st
# import cv2
# import numpy as np
# from PIL import Image

# # Title with emojis
# st.title("🖼️ Image Processing\n(🔍 Comparison View)")

# # Sidebar for image upload and operation selection with emojis
# with st.sidebar:
#     st.write("📤 Upload & Select")
#     uploaded_file = st.file_uploader("🌄 Upload an Image", type=["png", "jpg", "jpeg"])
#     option = st.selectbox("🛠️ Choose a comparison:", [
#         "🚫 None", "⚫ Convert to Grayscale", "🔄 Rotate Image", "🌫️ Blur Image", 
#         "🌠 Convert to Color Space", "✂️ Edge Detection"
#     ])

# # Function to process the image
# def process_image(image, operation, value=None, extra_value=None):
#     if operation == "🚫 None":
#         return image
#     elif operation == "🔲 Convert to Grayscale":
#         return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
#     elif operation == "🔄 Rotate Image":
#         if value is not None:
#             (h, w) = image.shape[:2]
#             center = (w // 2, h // 2)
#             matrix = cv2.getRotationMatrix2D(center, value, 1.0)
#             return cv2.warpAffine(image, matrix, (w, h))
#     elif operation == "🌫️ Blur Image":
#         if value is not None:
#             kernel_size = (value * 2 + 1, value * 2 + 1)  # Ensure odd kernel size
#             return cv2.GaussianBlur(image, kernel_size, 0)
#     elif operation == "🌠 Convert to Color Space":
#         if value == "RGB":
#             return image  # Already in RGB
#         elif value == "BGR2RGB":
#             return cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Corrected to RGB2BGR
#         elif value == "Grayscale":
#             return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
#     elif operation == "✂️ Edge Detection":
#         if value is not None and extra_value is not None:
#             gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
#             return cv2.Canny(gray_image, value, extra_value)
#     return image

# if uploaded_file is not None:
#     # Read image
#     image = Image.open(uploaded_file)
#     img_array = np.array(image)

#     processed_img = img_array.copy()

#     # Operation-specific controls in the main area with emojis
#     if option == "🔄 Rotate Image":
#         angle = st.slider("↪️ Select Rotation Angle", -180, 180, 0)
#         processed_img = process_image(img_array, option, angle)

#     elif option == "🌫️ Blur Image":
#         blur_level = st.slider("🎨 Select Blur Level", 1, 20, 5)
#         processed_img = process_image(img_array, option, blur_level)

#     elif option == "🌠 Convert to Color Space":
#         color_space = st.selectbox("🎨 Choose a color space:", ["RGB", "BGR2RGB", "Grayscale"])
#         processed_img = process_image(img_array, option, color_space)

#     elif option == "✂️ Edge Detection":
#         low_threshold = st.slider("🔽 Lower Threshold", 0, 255, 50)
#         high_threshold = st.slider("🔼 Upper Threshold", 0, 255, 150)
#         processed_img = process_image(img_array, option, low_threshold, high_threshold)

#     else:
#         processed_img = process_image(img_array, option)

#     # Display images side by side with emojis in captions
#     col1, col2 = st.columns(2)
#     with col1:
#         st.image(image, caption="🌟 Original Image", use_container_width=True)
    
#     with col2:
#         # Convert processed image to appropriate format for display
#         if len(processed_img.shape) == 2:  # Grayscale image
#             processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2RGB)
#         else:
#             processed_img_rgb = processed_img  # Already in RGB
        
#         # Dynamic caption with emojis based on operation
#         if option == "🚫 None":
#             caption = "🚫 No Processing Applied"
#         elif option == "🔲 Convert to Grayscale":
#             caption = "🔲 Grayscale Image"
#         elif option == "🔄 Rotate Image":
#             caption = f"🔄 Rotated by {angle}°"
#         elif option == "🌫️ Blur Image":
#             caption = f"🌫️ Blurred (Level {blur_level})"
#         elif option == "🌠 Convert to Color Space":
#             caption = f"🌠 {color_space} Image"
#         elif option == "✂️ Edge Detection":
#             caption = "✂️ Edge Detection (Canny)"
        
#         st.image(processed_img_rgb, caption=caption, use_container_width=True)

#     # Download button with emoji
#     if len(processed_img.shape) == 2:
#         processed_img_download = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2BGR)
#     else:
#         processed_img_download = cv2.cvtColor(processed_img, cv2.COLOR_RGB2BGR)
    
#     is_success, buffer = cv2.imencode(".png", processed_img_download)
#     if is_success:
#         st.download_button(
#             label="💾 Download Processed Image",
#             data=buffer.tobytes(),
#             file_name="processed_image.png",
#             mime="image/png"
#         )

import streamlit as st
import cv2
import numpy as np
from PIL import Image

# Title with emojis
st.title("🖼️ Image Processing\n(🔍 Comparison View)")

# Sidebar for image upload and operation selection with emojis
with st.sidebar:
    st.write("📤 Upload & Select")
    uploaded_file = st.file_uploader("🌄 Upload an Image", type=["png", "jpg", "jpeg"])
    option = st.selectbox("🛠️ Choose a comparison:", [
        "🚫 None", "🔲 Convert to Grayscale", "🔄 Rotate Image", "🌫️ Blur Image", 
        "🌠 Convert to Color Space", "✂️ Edge Detection"
    ])

# Function to process the image
def process_image(image, operation, value=None, extra_value=None):
    if operation == "🚫 None":
        return image
    elif operation == "🔲 Convert to Grayscale":
        return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    elif operation == "🔄 Rotate Image":
        if value is not None:
            (h, w) = image.shape[:2]
            center = (w // 2, h // 2)
            matrix = cv2.getRotationMatrix2D(center, value, 1.0)
            return cv2.warpAffine(image, matrix, (w, h))
    elif operation == "🌫️ Blur Image":
        if value is not None:
            kernel_size = (value * 2 + 1, value * 2 + 1)  # Ensure odd kernel size
            return cv2.GaussianBlur(image, kernel_size, 0)
    elif operation == "🌠 Convert to Color Space":
        if value == "RGB":
            return image  # Already in RGB
        elif value == "BGR2RGB":
            return cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        elif value == "Grayscale":
            return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    elif operation == "✂️ Edge Detection":
        if value is not None and extra_value is not None:
            gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
            return cv2.Canny(gray_image, value, extra_value)
    return image

if uploaded_file is not None:
    # Read image
    image = Image.open(uploaded_file)
    img_array = np.array(image)

    processed_img = img_array.copy()

    # Operation-specific controls in the main area with emojis
    if option == "🔄 Rotate Image":
        angle = st.slider("↪️ Select Rotation Angle", -180, 180, 0)
        processed_img = process_image(img_array, option, angle)
    elif option == "🌫️ Blur Image":
        blur_level = st.slider("🎨 Select Blur Level", 1, 20, 5)
        processed_img = process_image(img_array, option, blur_level)
    elif option == "🌠 Convert to Color Space":
        color_space = st.selectbox("🎨 Choose a color space:", ["RGB", "BGR2RGB", "Grayscale"])
        processed_img = process_image(img_array, option, color_space)
    elif option == "✂️ Edge Detection":
        low_threshold = st.slider("🔽 Lower Threshold", 0, 255, 50)
        high_threshold = st.slider("🔼 Upper Threshold", 0, 255, 150)
        processed_img = process_image(img_array, option, low_threshold, high_threshold)
    else:
        processed_img = process_image(img_array, option)

    # Display images side by side with emojis in captions
    col1, col2 = st.columns(2)
    with col1:
        st.image(image, caption="🌟 Original Image", use_container_width=True)
    
    with col2:
        # Convert processed image to appropriate format for display
        if len(processed_img.shape) == 2:  # Grayscale image
            processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2RGB)
        else:
            processed_img_rgb = processed_img  # Already in RGB
        
        # Dynamic caption with emojis based on operation
        caption = "🚫 No Processing Applied"  # Default caption
        if option == "🚫 None":
            caption = "🚫 No Processing Applied"
        elif option == "🔲 Convert to Grayscale":
            caption = "🔲 Grayscale Image"
        elif option == "🔄 Rotate Image":
            caption = f"🔄 Rotated by {angle}°"
        elif option == "🌫️ Blur Image":
            caption = f"🌫️ Blurred (Level {blur_level})"
        elif option == "🌠 Convert to Color Space":
            caption = f"🌠 {color_space} Image"
        elif option == "✂️ Edge Detection":
            caption = "✂️ Edge Detection (Canny)"
        
        st.image(processed_img_rgb, caption=caption, use_container_width=True)

    # Download button with emoji
    if len(processed_img.shape) == 2:
        processed_img_download = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2BGR)
    else:
        processed_img_download = cv2.cvtColor(processed_img, cv2.COLOR_RGB2BGR)
    
    is_success, buffer = cv2.imencode(".png", processed_img_download)
    if is_success:
        st.download_button(
            label="💾 Download Processed Image",
            data=buffer.tobytes(),
            file_name="processed_image.png",
            mime="image/png"
        )