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| import streamlit as st | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from io import BytesIO | |
| # Function to apply Lightroom-like filters | |
| def apply_filter(img): | |
| # Convert PIL Image to OpenCV format | |
| img = np.array(img) | |
| img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) | |
| # Exposure: Adjust brightness | |
| exposure = -0.31 | |
| img = cv2.convertScaleAbs(img, alpha=1.0, beta=exposure * 100) | |
| # Contrast | |
| contrast = -18 | |
| alpha = 1 + contrast / 100.0 # Scaling factor | |
| img = cv2.convertScaleAbs(img, alpha=alpha, beta=0) | |
| # Highlights and Shadows | |
| highlights = -100 | |
| shadows = 50 | |
| hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) | |
| v = hsv[:, :, 2].astype(np.float32) | |
| v = np.clip(v + highlights, 0, 255) | |
| v = np.clip(v + shadows, 0, 255) | |
| hsv[:, :, 2] = v.astype(np.uint8) | |
| img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) | |
| # Whites and Blacks | |
| whites = 12 | |
| blacks = -20 | |
| img = np.clip(img + whites, 0, 255).astype(np.uint8) | |
| img = np.clip(img + blacks, 0, 255).astype(np.uint8) | |
| # Temperature and Tint | |
| temp = -2 | |
| tint = 2 | |
| b, g, r = cv2.split(img) | |
| b = np.clip(b + temp * 10, 0, 255).astype(np.uint8) | |
| r = np.clip(r + tint * 10, 0, 255).astype(np.uint8) | |
| img = cv2.merge((b, g, r)) | |
| # Vibrance | |
| vibrance = 70 | |
| lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) | |
| l, a, b = cv2.split(lab) | |
| a = np.clip(a * (1 + vibrance / 100.0), 0, 255).astype(np.uint8) | |
| b = np.clip(b * (1 + vibrance / 100.0), 0, 255).astype(np.uint8) | |
| img = cv2.merge((l, a, b)) | |
| img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR) | |
| # Saturation | |
| saturation = 20 | |
| hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) | |
| s = hsv[:, :, 1].astype(np.float32) | |
| s = np.clip(s * (1 + saturation / 100.0), 0, 255).astype(np.uint8) | |
| hsv[:, :, 1] = s | |
| img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) | |
| # Texture (Clarity approximation) | |
| texture = 4 | |
| kernel = np.array([[-1, -1, -1], [-1, 8 + texture, -1], [-1, -1, -1]]) | |
| img = cv2.filter2D(img, -1, kernel) | |
| # Sharpening | |
| sharpening = 18 | |
| radius = 1.2 | |
| gaussian = cv2.GaussianBlur(img, (0, 0), sigmaX=radius) | |
| img = cv2.addWeighted(img, 1 + sharpening / 100.0, gaussian, -sharpening / 100.0, 0) | |
| return img | |
| # Streamlit app | |
| st.title("Classic Photo Editor") | |
| st.write("Upload an image to apply the Lightroom-like filter.") | |
| # Upload image | |
| uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file: | |
| # Open the uploaded image | |
| img = Image.open(uploaded_file) | |
| # Display the original image | |
| st.subheader("Original Image") | |
| st.image(img, use_column_width=True) | |
| # Apply the filter | |
| st.subheader("Filtered Image") | |
| filtered_img = apply_filter(img) | |
| # Convert back to PIL Image for display | |
| filtered_pil = Image.fromarray(cv2.cvtColor(filtered_img, cv2.COLOR_BGR2RGB)) | |
| st.image(filtered_pil, use_column_width=True) | |
| # Provide download button | |
| buf = BytesIO() | |
| filtered_pil.save(buf, format="JPEG") | |
| byte_im = buf.getvalue() | |
| st.download_button( | |
| label="Download Filtered Image", | |
| data=byte_im, | |
| file_name="filtered_image.jpg", | |
| mime="image/jpeg" | |
| ) | |