File size: 2,944 Bytes
93bcd22 58a3156 93bcd22 f79b705 93bcd22 f79b705 93bcd22 b6d6a51 93bcd22 58a3156 f79b705 a018a8e ea4fd35 a018a8e f79b705 2afc16d f79b705 ea4fd35 f79b705 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import streamlit as st
import cv2
import numpy as np
from PIL import Image
from io import BytesIO
from openvino.runtime import Core
import random
st.set_page_config(page_title="Anime & Cartoon Stylizer", layout="wide")
st.title("🖼️ Anime & Cartoon Stylizer with OpenVINO")
persona = st.radio("Choose your vibe:", ["Classic", "ChatBoy 💘", "Poetic 🌸"], horizontal=True)
style = st.selectbox("Choose your style:", ["AnimeGAN", "CartoonGAN"])
@st.cache_resource
def load_model(style_name):
ie = Core()
model_path = "animegan.xml" if style_name == "AnimeGAN" else "cartoongan.xml"
model = ie.read_model(model=model_path)
compiled_model = ie.compile_model(model=model, device_name="CPU")
return compiled_model, compiled_model.input(0), compiled_model.output(0)
def preprocess(image: Image.Image):
img = np.array(image.resize((256, 256))).astype(np.float32)
img = img / 127.5 - 1.0
img = np.transpose(img, (2, 0, 1))
return np.expand_dims(img, axis=0)
def postprocess(output):
result = output.squeeze().transpose(1, 2, 0)
result = (result + 1.0) * 127.5
return np.clip(result, 0, 255).astype(np.uint8)
def get_compliment():
return random.choice([
"✨ You look like the protagonist of a dreamy anime romance.",
"💘 That transformation? Utterly magical.",
"🌸 Your photo just bloomed into a masterpiece.",
"🎀 If Studio Ghibli saw this, they'd cast you instantly.",
"🫶 This anime version of you? It's giving main character energy."
])
uploaded_file = st.file_uploader("Upload a photo", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file).convert("RGB")
st.subheader("📸 Original vs Stylized")
col1, col2 = st.columns(2)
col1.image(image, caption="Original", use_column_width=True)
if st.button("Generate Style"):
compiled_model, input_layer, output_layer = load_model(style)
input_tensor = preprocess(image)
# ✅ Use blob names for inference:
input_name = input_layer.get_any_name()
output_name = output_layer.get_any_name()
# Run inference
result_dict = compiled_model.infer({input_name: input_tensor})
output = result_dict[output_name]
result = postprocess(output)
result_pil = Image.fromarray(result)
col2.image(result_pil, caption=f"{style} Style", use_column_width=True)
buf = BytesIO()
result_pil.save(buf, format="PNG")
st.download_button(
"Download Stylized Image",
data=buf.getvalue(),
file_name=f"{style.lower()}_style.png",
mime="image/png"
)
# Persona reaction
if persona == "ChatBoy 💘":
st.markdown(f"**{get_compliment()}**")
elif persona == "Poetic 🌸":
st.markdown("🌙 *Your image now dances in the moonlight of a painted dream.*")
|