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from fastapi import FastAPI, File, UploadFile, Form, HTTPException |
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from fastapi.responses import StreamingResponse |
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import os |
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import cv2 |
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import numpy as np |
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import AnimeGANv3_src |
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from io import BytesIO |
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app = FastAPI() |
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os.makedirs('output', exist_ok=True) |
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def process_image(img_path, style, if_face): |
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print(img_path, style, if_face) |
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try: |
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img = cv2.imread(img_path) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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style_mapping = { |
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"AnimeGANv3_Arcane": "A", |
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"AnimeGANv3_Trump v1.0": "T", |
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"AnimeGANv3_Shinkai": "S", |
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"AnimeGANv3_PortraitSketch": "P", |
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"AnimeGANv3_Hayao": "H", |
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"AnimeGANv3_Disney v1.0": "D", |
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"AnimeGANv3_JP_face v1.0": "J", |
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"AnimeGANv3_Kpop v2.0": "K", |
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"AnimeGANv3_USA": "U" |
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} |
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f = style_mapping.get(style, "U") |
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det_face = True if if_face == "Yes" else False |
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output = AnimeGANv3_src.Convert(img, f, det_face) |
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save_path = f"output/out.{img_path.rsplit('.')[-1]}" |
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cv2.imwrite(save_path, output[:, :, ::-1]) |
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return output, save_path |
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except Exception as error: |
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print('Error', error) |
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return None, None |
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@app.post("/inference/") |
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async def inference(file: UploadFile = File(...), Style: str = Form(...), if_face: str = Form(...)): |
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try: |
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file_location = f"temp_{file.filename}" |
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with open(file_location, "wb") as f: |
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f.write(await file.read()) |
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output, save_path = process_image(file_location, Style, if_face) |
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if output is None: |
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raise HTTPException(status_code=500, detail="Processing failed") |
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with open(save_path, "rb") as result_file: |
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result_bytes = result_file.read() |
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return StreamingResponse(BytesIO(result_bytes), media_type="image/jpeg") |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=str(e)) |
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