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import streamlit as st
import cv2
import numpy as np
from PIL import Image
def process_image(image, option, brightness_level=0, rotation_angle=0):
if option == "🖤 Convert to Grayscale":
return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
elif option == "🌈 Convert to Color":
return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) if len(image.shape) == 2 else image
elif option == "🌪️ Blur Image":
return cv2.GaussianBlur(image, (15, 15), 0)
elif option == "☀️ Increase Brightness":
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
hsv[:, :, 2] = np.clip(hsv[:, :, 2] + brightness_level, 0, 255)
return cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
elif option == "⚡ Edge Detection":
return cv2.Canny(image, 100, 200)
elif option == "🔄 Rotate Image":
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
matrix = cv2.getRotationMatrix2D(center, rotation_angle, 1.0)
return cv2.warpAffine(image, matrix, (w, h))
return image
st.title("📷 Image Processing App")
uploaded_file = st.file_uploader("📂 Upload an image...", type=["jpg", "png", "jpeg"])
if uploaded_file is not None:
image = np.array(Image.open(uploaded_file))
col1, col2 = st.columns(2)
with col1:
st.markdown("### 🏞️ Original Image")
st.image(image, use_container_width=True)
with col2:
st.markdown("### 🎨 Processed Image")
options = [
"🖼️ Original",
"🖤 Convert to Grayscale",
"🌈 Convert to Color",
"🔄 Rotate Image",
"🌪️ Blur Image",
"⚡ Edge Detection",
"☀️ Increase Brightness"
]
selected_option = st.selectbox("🎛️ Select an Operation", options)
brightness_level = 0
rotation_angle = 0
if selected_option == "☀️ Increase Brightness":
brightness_level = st.slider("🔆 Brightness Level", -100, 100, 0)
elif selected_option == "🔄 Rotate Image":
rotation_angle = st.slider("🌀 Rotation Angle", -180, 180, 0)
processed_image = process_image(image, selected_option, brightness_level, rotation_angle)
if len(processed_image.shape) == 2: # Grayscale or Edge detection
st.image(processed_image, use_container_width=True, channels="GRAY")
else:
st.image(processed_image, use_container_width=True)
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