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import streamlit as st
import cv2
import numpy as np
from PIL import Image
# Function to process the image
def process_image(image, operation, value=None):
if operation == "🖼 Original":
return image
elif operation == "⚫ Convert to Grayscale":
return cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
elif operation == "🌈 Convert to Color":
return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif operation == "🔄 Rotate":
if value is not None:
return rotate_image_without_cropping(image, value)
elif operation == "🌀 Blur":
if value is not None:
kernel_size = (value, value)
return cv2.GaussianBlur(image, kernel_size, 0)
elif operation == "✂ Edge Detection":
return cv2.Canny(image, 100, 200)
elif operation == "☀ Adjust Brightness":
if value is not None:
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
h, s, v = cv2.split(hsv)
v = np.clip(v.astype(np.int16) + value, 0, 255).astype(np.uint8)
final_hsv = cv2.merge((h, s, v))
return cv2.cvtColor(final_hsv, cv2.COLOR_HSV2RGB)
return image
# Fix rotation issue (avoids cropping & mirroring)
def rotate_image_without_cropping(image, angle):
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
# Compute the rotation matrix
M = cv2.getRotationMatrix2D(center, angle, 1.0)
# Compute the new bounding box size
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
new_w = int((h * sin) + (w * cos))
new_h = int((h * cos) + (w * sin))
# Adjust transformation matrix to fit in new bounds
M[0, 2] += (new_w / 2) - center[0]
M[1, 2] += (new_h / 2) - center[1]
# Apply rotation with expanded borders
rotated = cv2.warpAffine(image, M, (new_w, new_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0))
return rotated
# Streamlit app
def main():
st.title(" Image Processing App ")
st.write("📂 Upload an image and apply various transformations below! ✨")
uploaded_file = st.file_uploader("📤 Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Read image
image = Image.open(uploaded_file)
img_array = np.array(image)
img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
col1, col2 = st.columns(2)
with col1:
st.subheader("📌 Original Image")
st.image(image, use_container_width=True)
with col2:
st.subheader("🖌 Processed Image")
operation = st.selectbox(
"🎛 Select an Operation",
["🖼 Original", "⚫ Convert to Grayscale", "🌈 Convert to Color",
"🔄 Rotate", "🌀 Blur", "✂ Edge Detection", "☀ Adjust Brightness"]
)
processed_img = img_bgr.copy()
if operation == "🔄 Rotate":
angle = st.slider("↩ Rotation Angle", -180, 180, 0)
processed_img = process_image(img_bgr, operation, angle)
elif operation == "☀ Adjust Brightness":
brightness = st.slider("🔆 Brightness Level", -100, 100, 0)
processed_img = process_image(img_bgr, operation, brightness)
elif operation == "🌀 Blur":
blur_level = st.slider("🎭 Blur Intensity", 1, 25, 5, step=2) # Ensures odd kernel size
processed_img = process_image(img_bgr, operation, blur_level)
else:
processed_img = process_image(img_bgr, operation)
# Convert back to RGB
if len(processed_img.shape) == 2:
processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2RGB)
else:
processed_img_rgb = cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)
st.image(processed_img_rgb, use_container_width=True)
# Ensure grayscale images are properly handled before download
if len(processed_img.shape) == 2:
processed_img = cv2.cvtColor(processed_img, cv2.COLOR_GRAY2BGR)
is_success, buffer = cv2.imencode(".png", processed_img)
if is_success:
st.download_button(
label="📥 Download Processed Image",
data=buffer.tobytes(),
file_name="processed_image.png",
mime="image/png"
)
if __name__ == "__main__":
main()
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