fix: cross-domain issue (#110)

* fix:解决前后端调用时的跨域问题

* Update requirements.txt

---------

Co-authored-by: Ze-Yi LIN <58305964+Zeyi-Lin@users.noreply.github.com>

deploy_api.py

@@ -15,10 +15,20 @@ from hivision.utils import (
 import base64
 import numpy as np
 import cv2
+from starlette.middleware.cors import CORSMiddleware
 
 app = FastAPI()
 creator = IDCreator()
 
+# 添加 CORS 中间件 解决跨域问题
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # 允许的请求来源
+    allow_credentials=True,  # 允许携带 Cookie
+    allow_methods=["*"],  # 允许的请求方法，例如：GET, POST 等，也可以指定 ["GET", "POST"]
+    allow_headers=["*"],  # 允许的请求头，也可以指定具体的头部
+)
+
 
 # 将图像转换为Base64编码
 def numpy_2_base64(img: np.ndarray):
@@ -31,18 +41,17 @@ def numpy_2_base64(img: np.ndarray):
 # 证件照智能制作接口
 @app.post("/idphoto")
 async def idphoto_inference(
-    input_image: UploadFile,
-    height: int = Form(413),
-    width: int = Form(295),
-    human_matting_model: str = Form("hivision_modnet"),
-    face_detect_model: str = Form("mtcnn"),
-    hd: bool = Form(True),
-    head_measure_ratio: float = 0.2,
-    head_height_ratio: float = 0.45,
-    top_distance_max: float = 0.12,
-    top_distance_min: float = 0.10,
+        input_image: UploadFile,
+        height: int = Form(413),
+        width: int = Form(295),
+        human_matting_model: str = Form("hivision_modnet"),
+        face_detect_model: str = Form("mtcnn"),
+        hd: bool = Form(True),
+        head_measure_ratio: float = 0.2,
+        head_height_ratio: float = 0.45,
+        top_distance_max: float = 0.12,
+        top_distance_min: float = 0.10,
 ):
-
     image_bytes = await input_image.read()
     nparr = np.frombuffer(image_bytes, np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
@@ -79,8 +88,8 @@ async def idphoto_inference(
 # 人像抠图接口
 @app.post("/human_matting")
 async def human_matting_inference(
-    input_image: UploadFile,
-    human_matting_model: str = Form("hivision_modnet"),
+        input_image: UploadFile,
+        human_matting_model: str = Form("hivision_modnet"),
 ):
     image_bytes = await input_image.read()
     nparr = np.frombuffer(image_bytes, np.uint8)
@@ -108,10 +117,10 @@ async def human_matting_inference(
 # 透明图像添加纯色背景接口
 @app.post("/add_background")
 async def photo_add_background(
-    input_image: UploadFile,
-    color: str = Form("000000"),
-    kb: int = Form(50),
-    render: int = Form(0),
+        input_image: UploadFile,
+        color: str = Form("000000"),
+        kb: int = Form(50),
+        render: int = Form(0),
 ):
     render_choice = ["pure_color", "updown_gradient", "center_gradient"]
 
@@ -153,10 +162,10 @@ async def photo_add_background(
 # 六寸排版照生成接口
 @app.post("/generate_layout_photos")
 async def generate_layout_photos(
-    input_image: UploadFile,
-    height: int = Form(413),
-    width: int = Form(295),
-    kb: int = Form(50),
+        input_image: UploadFile,
+        height: int = Form(413),
+        width: int = Form(295),
+        kb: int = Form(50),
 ):
     # try:
     image_bytes = await input_image.read()
@@ -197,13 +206,13 @@ async def generate_layout_photos(
 # 透明图像添加纯色背景接口
 @app.post("/watermark")
 async def watermark(
-    input_image: UploadFile,
-    text: str = Form("Hello"),
-    size: int = 20,
-    opacity: float = 0.5,
-    angle: int = 30,
-    color: str = "#000000",
-    space: int = 25,
+        input_image: UploadFile,
+        text: str = Form("Hello"),
+        size: int = 20,
+        opacity: float = 0.5,
+        angle: int = 30,
+        color: str = "#000000",
+        space: int = 25,
 ):
     image_bytes = await input_image.read()
     nparr = np.frombuffer(image_bytes, np.uint8)
@@ -228,8 +237,8 @@ async def watermark(
 # 设置照片KB值接口(RGB图)
 @app.post("/set_kb")
 async def set_kb(
-    input_image: UploadFile,
-    kb: int = Form(50),
+        input_image: UploadFile,
+        kb: int = Form(50),
 ):
     image_bytes = await input_image.read()
     nparr = np.frombuffer(image_bytes, np.uint8)
@@ -255,17 +264,16 @@ async def set_kb(
 # 证件照智能裁剪接口
 @app.post("/idphoto_crop")
 async def idphoto_crop_inference(
-    input_image: UploadFile,
-    height: int = Form(413),
-    width: int = Form(295),
-    face_detect_model: str = Form("mtcnn"),
-    hd: bool = Form(True),
-    head_measure_ratio: float = 0.2,
-    head_height_ratio: float = 0.45,
-    top_distance_max: float = 0.12,
-    top_distance_min: float = 0.10,
+        input_image: UploadFile,
+        height: int = Form(413),
+        width: int = Form(295),
+        face_detect_model: str = Form("mtcnn"),
+        hd: bool = Form(True),
+        head_measure_ratio: float = 0.2,
+        head_height_ratio: float = 0.45,
+        top_distance_max: float = 0.12,
+        top_distance_min: float = 0.10,
 ):
-
     image_bytes = await input_image.read()
     nparr = np.frombuffer(image_bytes, np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)  # 读取图像(4通道)

requirements.txt

@@ -3,4 +3,5 @@ onnxruntime>=1.15.0
 numpy<=1.26.4
 requests
 mtcnn-runtime
-tqdm
+tqdm
+starlette