Add file.txt

app.py

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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"
+        )

requirements.txt

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+streamlit
+opencv-python
+numpy
+pillow