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import streamlit as st |
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import os |
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import cv2 |
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import numpy as np |
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from PIL import Image |
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from rembg import remove |
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from realesrgan import RealESRGAN |
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from moviepy.editor import ImageSequenceClip |
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st.title("🖼️ AI Image & Video Effects Studio") |
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st.markdown("Upload multiple images, apply cool effects, and export a video!") |
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uploaded_files = st.file_uploader("Upload images", type=["png", "jpg", "jpeg"], accept_multiple_files=True) |
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effect = st.selectbox("Choose an effect to apply", ["None", "Cartoon", "Edge Detection", "Remove Background", "Face Enhancement"]) |
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output_dir = "outputs" |
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os.makedirs(output_dir, exist_ok=True) |
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processed_images = [] |
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def cartoonize(image): |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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gray = cv2.medianBlur(gray, 5) |
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edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, |
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cv2.THRESH_BINARY, 9, 9) |
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color = cv2.bilateralFilter(image, 9, 300, 300) |
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cartoon = cv2.bitwise_and(color, color, mask=edges) |
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return cartoon |
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def edge_detect(image): |
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) |
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edges = cv2.Canny(gray, 100, 200) |
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return cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR) |
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def enhance_face(image_pil): |
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from realesrgan import RealESRGAN |
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model = RealESRGAN("cpu", scale=2) |
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model.load_weights("RealESRGAN_x2.pth") |
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return model.predict(image_pil) |
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def save_video(images, filename="output_video.mp4"): |
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clip = ImageSequenceClip(images, fps=1) |
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clip.write_videofile(filename, codec="libx264") |
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return filename |
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if uploaded_files: |
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for file in uploaded_files: |
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img = Image.open(file).convert("RGB") |
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np_img = np.array(img) |
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np_img_bgr = cv2.cvtColor(np_img, cv2.COLOR_RGB2BGR) |
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if effect == "Cartoon": |
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result = cartoonize(np_img_bgr) |
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elif effect == "Edge Detection": |
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result = edge_detect(np_img_bgr) |
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elif effect == "Remove Background": |
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result = remove(np_img) |
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elif effect == "Face Enhancement": |
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result = enhance_face(img) |
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else: |
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result = np_img_bgr |
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result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB) if isinstance(result, np.ndarray) else result |
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final_img = Image.fromarray(result_rgb) |
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st.image(final_img, caption="Processed Image", use_column_width=True) |
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img_path = os.path.join(output_dir, f"{file.name}") |
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final_img.save(img_path) |
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processed_images.append(img_path) |
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if st.button("🎞️ Convert to Video Slideshow"): |
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video_path = save_video(processed_images) |
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st.video(video_path) |
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for img_path in processed_images: |
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with open(img_path, "rb") as f: |
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st.download_button("Download Image", f, file_name=os.path.basename(img_path)) |
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