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Create app.py
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app.py
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import gradio as gr
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import torch
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from torchvision.transforms.functional import to_tensor, to_pil_image
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from PIL import Image
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import cv2
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import numpy as np
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# Load AnimeGANv2 pretrained generator (e.g., "hayao")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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anime_model = torch.hub.load(
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"bryandlee/animegan2-pytorch:main",
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"generator",
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pretrained="hayao", # options: "hayao", "paprika", "face_paint_v1", "face_paint_v2"
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).to(device).eval()
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def apply_animegan(img: np.ndarray) -> np.ndarray:
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pil = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
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tensor = to_tensor(pil).unsqueeze(0).mul(2).sub(1).to(device) # [-1,1]
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with torch.no_grad():
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out = anime_model(tensor)[0]
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out = out.clamp(-1, 1).add(1).div(2)
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pil_out = to_pil_image(out.cpu())
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return cv2.cvtColor(np.array(pil_out), cv2.COLOR_RGB2BGR)
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def apply_cartoon_filter(img: np.ndarray) -> np.ndarray:
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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gray = cv2.medianBlur(gray, 5)
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edges = cv2.adaptiveThreshold(gray, 255,
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cv2.ADAPTIVE_THRESH_MEAN_C,
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cv2.THRESH_BINARY, 9, 9)
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color = cv2.bilateralFilter(img, 9, 300, 300)
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return cv2.bitwise_and(color, color, mask=edges)
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def cartoonizer(input_img, style):
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img = cv2.cvtColor(np.array(input_img), cv2.COLOR_RGB2BGR)
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if style == "Cartoon Filter":
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out = apply_cartoon_filter(img)
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elif style == "AnimeGANv2 (Hayao)":
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out = apply_animegan(img)
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else:
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out = img
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return Image.fromarray(cv2.cvtColor(out, cv2.COLOR_BGR2RGB))
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gr.Interface(
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fn=cartoonizer,
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inputs=[
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gr.Image(type="pil", label="Upload Image"),
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gr.Radio(["Cartoon Filter", "AnimeGANv2 (Hayao)"], label="Style")
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],
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outputs=gr.Image(type="pil", label="Cartoonized Output"),
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title="🎨 Cartoonizer with AnimeGANv2",
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description="Upload any image and convert to general cartoon or Hayao‑style anime."
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).launch()
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