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Update app.py
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app.py
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# 啟動 ONNX 模型 Session
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if ort.get_device() == 'GPU' else ['CPUExecutionProvider']
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session = ort.InferenceSession('AnimeGANv3_Hayao_STYLE_36.onnx', providers=providers)
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#
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def process_image(img):
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h, w = img.shape[:2]
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def to_8s(x): return 256 if x < 256 else x - x % 8
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 127.5 - 1.0
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return img
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def cartoonize_image(input_image):
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# PIL 轉 numpy array
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img = np.array(input_image)
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h, w = img.shape[:2]
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processed = process_image(img)
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processed = np.expand_dims(processed, axis=0)
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# 模型推論
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input_name = session.get_inputs()[0].name
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output = session.run(None, {input_name: processed})[0]
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result = (np.squeeze(output) + 1.) / 2 * 255
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result = cv2.resize(result, (w, h))
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result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
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return Image.fromarray(result)
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def cartoonize_image(input_image):
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# PIL 轉 numpy array
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img = np.array(input_image)
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h, w = img.shape[:2]
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processed = process_image(img)
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processed = np.expand_dims(processed, axis=0)
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# 模型推論
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input_name = session.get_inputs()[0].name
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output = session.run(None, {input_name: processed})[0]
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result = (np.squeeze(output) + 1.) / 2 * 255
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result = np.clip(result, 0, 255).astype(np.uint8)
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result = cv2.resize(result, (w, h))
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result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
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# 轉換結果為 PIL
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result_image = Image.fromarray(result)
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#
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os.makedirs("outputs", exist_ok=True)
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filename = f"outputs/{uuid.uuid4().hex}.png"
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result_image.save(filename)
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return result_image, filename
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#
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interface = gr.Interface(
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fn=cartoonize_image,
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inputs=gr.Image(type="pil", label="上傳圖片"),
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outputs=[
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gr.Image(type="pil", label="宮崎駿風格轉換結果"),
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gr.File(label="⬇️
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],
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title="🎨 AnimeGANv3 - 宮崎駿風格轉換器",
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description="上傳圖片,將自動轉換為宮崎駿動畫風格!",
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allow_flagging="never"
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)
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interface.launch()
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import gradio as gr
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import onnxruntime as ort
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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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import os
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import uuid
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# 啟動 ONNX 模型 Session
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if ort.get_device() == 'GPU' else ['CPUExecutionProvider']
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session = ort.InferenceSession('AnimeGANv3_Hayao_STYLE_36.onnx', providers=providers)
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# 圖片處理
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def process_image(img):
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h, w = img.shape[:2]
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def to_8s(x): return 256 if x < 256 else x - x % 8
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype(np.float32) / 127.5 - 1.0
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return img
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# 圖片轉換 + 下載功能
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def cartoonize_image(input_image):
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img = np.array(input_image)
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h, w = img.shape[:2]
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processed = process_image(img)
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processed = np.expand_dims(processed, axis=0)
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input_name = session.get_inputs()[0].name
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output = session.run(None, {input_name: processed})[0]
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result = (np.squeeze(output) + 1.) / 2 * 255
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result = cv2.resize(result, (w, h))
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result = cv2.cvtColor(result, cv2.COLOR_RGB2BGR)
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result_image = Image.fromarray(result)
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# 保存下載檔案
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os.makedirs("outputs", exist_ok=True)
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filename = f"outputs/{uuid.uuid4().hex}.png"
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result_image.save(filename)
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return result_image, filename
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# Gradio UI
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interface = gr.Interface(
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fn=cartoonize_image,
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inputs=gr.Image(type="pil", label="上傳圖片"),
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outputs=[
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gr.Image(type="pil", label="宮崎駿風格轉換結果"),
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gr.File(label="⬇️ 下載圖片")
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],
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title="🎨 AnimeGANv3 - 宮崎駿風格轉換器",
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description="上傳圖片,將自動轉換為宮崎駿動畫風格!",
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allow_flagging="never"
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)
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interface.launch()
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