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import streamlit as st |
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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 io import BytesIO |
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from openvino.runtime import Core |
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import random |
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st.set_page_config(page_title="Anime & Cartoon Stylizer", layout="wide") |
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st.title("🖼️ Anime & Cartoon Stylizer with OpenVINO") |
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persona = st.radio("Choose your vibe:", ["Classic", "ChatBoy 💘", "Poetic 🌸"], horizontal=True) |
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style = st.selectbox("Choose your style:", ["AnimeGAN", "CartoonGAN"]) |
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@st.cache_resource |
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def load_model(style_name): |
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ie = Core() |
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model_path = "animegan.xml" if style_name == "AnimeGAN" else "cartoongan.xml" |
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model = ie.read_model(model=model_path) |
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compiled_model = ie.compile_model(model=model, device_name="CPU") |
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return compiled_model, compiled_model.input(0), compiled_model.output(0) |
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def preprocess(image: Image.Image): |
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img = np.array(image.resize((256, 256))).astype(np.float32) |
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img = img / 127.5 - 1.0 |
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img = np.transpose(img, (2, 0, 1)) |
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return np.expand_dims(img, axis=0) |
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def postprocess(output): |
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result = output.squeeze().transpose(1, 2, 0) |
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result = (result + 1.0) * 127.5 |
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return np.clip(result, 0, 255).astype(np.uint8) |
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def get_compliment(): |
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return random.choice([ |
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"✨ You look like the protagonist of a dreamy anime romance.", |
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"💘 That transformation? Utterly magical.", |
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"🌸 Your photo just bloomed into a masterpiece.", |
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"🎀 If Studio Ghibli saw this, they'd cast you instantly.", |
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"🫶 This anime version of you? It's giving main character energy." |
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]) |
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uploaded_file = st.file_uploader("Upload a photo", type=["jpg", "jpeg", "png"]) |
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if uploaded_file: |
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image = Image.open(uploaded_file).convert("RGB") |
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st.subheader("📸 Original vs Stylized") |
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col1, col2 = st.columns(2) |
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col1.image(image, caption="Original", use_column_width=True) |
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if st.button("Generate Style"): |
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compiled_model, input_layer, output_layer = load_model(style) |
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input_tensor = preprocess(image) |
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input_name = input_layer.get_any_name() |
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output_name = output_layer.get_any_name() |
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result_dict = compiled_model.infer({input_name: input_tensor}) |
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output = result_dict[output_name] |
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result = postprocess(output) |
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result_pil = Image.fromarray(result) |
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col2.image(result_pil, caption=f"{style} Style", use_column_width=True) |
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buf = BytesIO() |
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result_pil.save(buf, format="PNG") |
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st.download_button( |
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"Download Stylized Image", |
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data=buf.getvalue(), |
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file_name=f"{style.lower()}_style.png", |
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mime="image/png" |
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) |
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if persona == "ChatBoy 💘": |
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st.markdown(f"**{get_compliment()}**") |
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elif persona == "Poetic 🌸": |
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st.markdown("🌙 *Your image now dances in the moonlight of a painted dream.*") |
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