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| import streamlit as st | |
| from utils import transcribe_audio_segments, create_video_segments, generate_speech | |
| from PIL import Image | |
| import os | |
| import cv2 | |
| import numpy as np | |
| from io import BytesIO | |
| import tempfile | |
| from moviepy.editor import ImageSequenceClip | |
| from rembg import remove | |
| from basicsr.archs.rrdbnet_arch import RRDBNet | |
| from realesrgan import RealESRGANer | |
| # Helper function to apply cartoon effect | |
| def cartoonize(img): | |
| gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| gray = cv2.medianBlur(gray, 5) | |
| edges = cv2.adaptiveThreshold( | |
| gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, | |
| cv2.THRESH_BINARY, 9, 9 | |
| ) | |
| color = cv2.bilateralFilter(img, 9, 300, 300) | |
| cartoon = cv2.bitwise_and(color, color, mask=edges) | |
| return cartoon | |
| # Helper function to apply edge detection | |
| def edge_detect(img): | |
| return cv2.Canny(img, 100, 200) | |
| # Helper function to enhance face (upscale image) | |
| def enhance_image(img): | |
| model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, | |
| num_block=23, num_grow_ch=32, scale=4) | |
| upsampler = RealESRGANer( | |
| scale=4, model_path='realesrgan-x4plus.pth', model=model, | |
| tile=0, tile_pad=10, pre_pad=0, half=False | |
| ) | |
| _, _, output = upsampler.enhance(np.array(img), outscale=4) | |
| return output | |
| # Save processed images | |
| def save_image(img, filename): | |
| cv2.imwrite(filename, img) | |
| # Convert images to slideshow video | |
| def create_slideshow(images, out_path="slideshow.mp4"): | |
| clip = ImageSequenceClip(images, fps=1) | |
| clip.write_videofile(out_path, codec='libx264') | |
| # Streamlit UI | |
| st.set_page_config(layout="wide") | |
| st.title("🎨 Interactive Image Effects Studio") | |
| uploaded_files = st.file_uploader("Upload images", type=["jpg", "jpeg", "png"], accept_multiple_files=True) | |
| effects = st.multiselect("Select effects to apply", ["Cartoonize", "Edge Detection", "Background Removal", "Enhance/ Upscale"]) | |
| effect_images = [] | |
| if uploaded_files: | |
| for uploaded_file in uploaded_files: | |
| image = Image.open(uploaded_file).convert("RGB") | |
| img_array = np.array(image) | |
| original_img = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR) | |
| processed_img = original_img.copy() | |
| if "Cartoonize" in effects: | |
| processed_img = cartoonize(processed_img) | |
| if "Edge Detection" in effects: | |
| processed_img = cv2.cvtColor(edge_detect(processed_img), cv2.COLOR_GRAY2BGR) | |
| if "Background Removal" in effects: | |
| processed_img = remove(Image.fromarray(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB))) | |
| processed_img = cv2.cvtColor(np.array(processed_img), cv2.COLOR_RGB2BGR) | |
| if "Enhance/ Upscale" in effects: | |
| processed_img = enhance_image(processed_img) | |
| st.image(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB), caption="Processed Image", use_column_width=True) | |
| # Save processed | |
| effect_images.append(cv2.cvtColor(processed_img, cv2.COLOR_BGR2RGB)) | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp_file: | |
| save_image(processed_img, tmp_file.name) | |
| with open(tmp_file.name, "rb") as file: | |
| btn = st.download_button( | |
| label="Download Processed Image", | |
| data=file, | |
| file_name=os.path.basename(uploaded_file.name), | |
| mime="image/jpeg" | |
| ) | |
| if st.button("Create Slideshow from Images"): | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_video: | |
| create_slideshow(effect_images, tmp_video.name) | |
| with open(tmp_video.name, "rb") as file: | |
| st.video(file) | |
| st.download_button("Download Slideshow Video", file, file_name="slideshow.mp4") | |