image processing added

.gitignore

@@ -0,0 +1 @@
+venv/

app.py

@@ -0,0 +1,87 @@
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image
+
+# Function to apply filters
+def apply_filter(image, filter_name, scale):
+    if filter_name == "Grayscale":
+        return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    elif filter_name == "Blur":
+        # Ensure kernel size is a positive odd integer
+        ksize = max(1, scale)
+        ksize = ksize if ksize % 2 == 1 else ksize + 1
+        return cv2.GaussianBlur(image, (ksize, ksize), 0)
+    elif filter_name == "Sharpen":
+        kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+        return cv2.filter2D(image, -1, kernel)
+    elif filter_name == "Edge Detection":
+        # Ensure scale is within valid range for Canny
+        scale = max(1, scale)
+        return cv2.Canny(image, scale, scale * 2)
+    elif filter_name == "Brightness":
+        hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+        hsv[:, :, 2] = np.clip(hsv[:, :, 2] + scale, 0, 255)
+        return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
+    elif filter_name == "Contrast":
+        alpha = scale / 50.0
+        return cv2.convertScaleAbs(image, alpha=alpha, beta=0)
+    elif filter_name == "Threshold":
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        _, thresh = cv2.threshold(gray, scale, 255, cv2.THRESH_BINARY)
+        return thresh
+    elif filter_name == "Sepia":
+        kernel = np.array([[0.272, 0.534, 0.131],
+                           [0.349, 0.686, 0.168],
+                           [0.393, 0.769, 0.189]])
+        return cv2.transform(image, kernel)
+    else:
+        return image
+
+# Streamlit app
+st.title("🎨 Image Processing App 🖼️")
+st.write("Upload an image and apply filters to see the magic! ✨")
+
+# Upload image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read and display the original image
+    image = Image.open(uploaded_file)
+    image = np.array(image)
+    st.write("### Original Image 🖼️")
+    st.image(image, caption="Original Image", use_container_width=True)
+
+    # Select filter
+    filter_name = st.selectbox(
+        "Choose a filter 🎛️",
+        ["Grayscale", "Blur", "Sharpen", "Edge Detection", "Brightness", "Contrast", "Threshold", "Sepia"]
+    )
+
+    # Add slider for filter scale
+    scale = st.slider(f"Adjust {filter_name} intensity ⚙️", 1, 100, 50)
+
+    # Apply filter
+    processed_image = apply_filter(image, filter_name, scale)
+
+    # Display input and output images in the same row
+    col1, col2 = st.columns(2)
+    with col1:
+        st.write("### Original Image 🖼️")
+        st.image(image, caption="Original Image", use_container_width=True)
+    with col2:
+        st.write("### Processed Image 🎨")
+        st.image(processed_image, caption=f"{filter_name} Applied", use_container_width=True)
+
+    # Download button for processed image
+    processed_image_pil = Image.fromarray(processed_image)
+    st.download_button(
+        label="Download Processed Image ⬇️",
+        data=processed_image_pil.tobytes(),
+        file_name="processed_image.png",
+        mime="image/png"
+    )
+
+    st.success("✅ Image processing complete! Enjoy your masterpiece! 🎨")
+else:
+    st.info("👆 Please upload an image to get started! 🖼️")