Upload app.py

app.py

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+
+from fastapi import FastAPI, UploadFile, File, Form
+from fastapi.responses import JSONResponse
+from typing import Optional
+import cv2
+import numpy as np
+import os
+import random
+import requests
+import base64
+import json
+
+app = FastAPI()
+
+# Define your models and other configurations here
+MODEL_MAP = {
+    "AI Model Rouyan_0": 'models/rouyan_new/Rouyan_0.png',
+    "AI Model Rouyan_1": 'models/rouyan_new/Rouyan_1.png',
+    "AI Model Rouyan_2": 'models/rouyan_new/Rouyan_2.png',
+    "AI Model Eva_0": 'models/eva/Eva_0.png',
+    "AI Model Eva_1": 'models/eva/Eva_1.png',
+    "AI Model Simon_0": 'models/simon_online/Simon_0.png',
+    "AI Model Simon_1": 'models/simon_online/Simon_1.png',
+    "AI Model Xuanxuan_0": 'models/xiaoxuan_online/Xuanxuan_0.png',
+    "AI Model Xuanxuan_1": 'models/xiaoxuan_online/Xuanxuan_1.png',
+    "AI Model Xuanxuan_2": 'models/xiaoxuan_online/Xuanxuan_2.png',
+    "AI Model Yaqi_0": 'models/yaqi/Yaqi_0.png',
+    "AI Model Yaqi_1": 'models/yaqi/Yaqi_1.png',
+    "AI Model Yaqi_2": 'models/yaqi/Yaqi_2.png',
+    "AI Model Yaqi_3": 'models/yaqi/Yaqi_3.png',
+    "AI Model Yifeng_0": 'models/yifeng_online/Yifeng_0.png',
+    "AI Model Yifeng_1": 'models/yifeng_online/Yifeng_1.png',
+    "AI Model Yifeng_2": 'models/yifeng_online/Yifeng_2.png',
+    "AI Model Yifeng_3": 'models/yifeng_online/Yifeng_3.png',
+}
+
+def add_waterprint(img):
+    h, w, _ = img.shape
+    img = cv2.putText(img, 'Powered by OutfitAnyone', (int(0.3 * w), h - 20), cv2.FONT_HERSHEY_PLAIN, 2, (128, 128, 128), 2, cv2.LINE_AA)
+    return img
+
+def get_tryon_result(model_name, garment1, garment2, seed=1234):
+    model_name = "AI Model " + model_name.split("/")[-1].split(".")[0]
+    print(model_name)
+
+    encoded_garment1 = cv2.imencode('.jpg', garment1)[1].tobytes()
+    encoded_garment1 = base64.b64encode(encoded_garment1).decode('utf-8')
+
+    if garment2 is not None:
+        encoded_garment2 = cv2.imencode('.jpg', garment2)[1].tobytes()
+        encoded_garment2 = base64.b64encode(encoded_garment2).decode('utf-8')
+    else:
+        encoded_garment2 = ''
+
+    # Simulate the processing of the try-on result
+    # This should be replaced with actual processing logic
+    result_img = garment1  # Use garment1 image as a placeholder for the result
+    if garment2 is not None:
+        result_img = cv2.addWeighted(result_img, 0.5, garment2, 0.5, 0)
+
+    final_img = add_waterprint(result_img)
+    return final_img
+
+@app.post("/tryon/")
+async def tryon(model_name: str = Form(...), garment1: UploadFile = File(...), garment2: Optional[UploadFile] = File(None)):
+    garment1_img = cv2.imdecode(np.fromstring(await garment1.read(), np.uint8), cv2.IMREAD_COLOR)
+    garment2_img = cv2.imdecode(np.fromstring(await garment2.read(), np.uint8), cv2.IMREAD_COLOR) if garment2 else None
+
+    result_img = get_tryon_result(model_name, garment1_img, garment2_img)
+
+    if result_img is None:
+        return JSONResponse(status_code=500, content={"message": "Server error while processing the images"})
+
+    _, img_encoded = cv2.imencode('.png', result_img)
+    return JSONResponse(content={"image": base64.b64encode(img_encoded.tobytes()).decode('utf-8')})
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)