Delete deploy_api.py

deploy_api.py

@@ -1,370 +0,0 @@
-from fastapi import FastAPI, UploadFile, Form, File
-from hivision import IDCreator
-from hivision.error import FaceError
-from hivision.creator.layout_calculator import (
-    generate_layout_array,
-    generate_layout_image,
-)
-from hivision.creator.choose_handler import choose_handler
-from hivision.utils import (
-    add_background,
-    resize_image_to_kb,
-    bytes_2_base64,
-    base64_2_numpy,
-    hex_to_rgb,
-    add_watermark,
-    save_image_dpi_to_bytes,
-)
-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
-)
-
-# 证件照智能制作接口
-@app.get("/")
-async def welcome():
-    result_message = {
-        "status": True
-    }
-    return result_message
-
-# 证件照智能制作接口
-@app.post("/idphoto")
-async def idphoto_inference(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    height: int = Form(413),
-    width: int = Form(295),
-    human_matting_model: str = Form("modnet_photographic_portrait_matting"),
-    face_detect_model: str = Form("mtcnn"),
-    hd: bool = Form(True),
-    dpi: int = Form(300),
-    face_align: bool = Form(False),
-    head_measure_ratio: float = Form(0.2),
-    head_height_ratio: float = Form(0.45),
-    top_distance_max: float = Form(0.12),
-    top_distance_min: float = Form(0.10),
-    brightness_strength: float = Form(0),
-    contrast_strength: float = Form(0),
-    sharpen_strength: float = Form(0),
-    saturation_strength: float = Form(0),
-):  
-    # 如果传入了base64，则直接使用base64解码
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    # 否则使用上传的图片
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    # ------------------- 选择抠图与人脸检测模型 -------------------
-    choose_handler(creator, human_matting_model, face_detect_model)
-
-    # 将字符串转为元组
-    size = (int(height), int(width))
-    try:
-        result = creator(
-            img,
-            size=size,
-            head_measure_ratio=head_measure_ratio,
-            head_height_ratio=head_height_ratio,
-            head_top_range=(top_distance_max, top_distance_min),
-            face_alignment=face_align,
-            brightness_strength=brightness_strength,
-            contrast_strength=contrast_strength,
-            sharpen_strength=sharpen_strength,
-            saturation_strength=saturation_strength,
-        )
-    except FaceError:
-        result_message = {"status": False}
-    # 如果检测到人脸数量等于1, 则返回标准证和高清照结果（png 4通道图像）
-    else:
-        result_image_standard_bytes = save_image_dpi_to_bytes(cv2.cvtColor(result.standard, cv2.COLOR_RGBA2BGRA), None, dpi)
-        
-        result_message = {
-            "status": True,
-            "image_base64_standard": bytes_2_base64(result_image_standard_bytes),
-        }
-
-        # 如果hd为True, 则增加高清照结果（png 4通道图像）
-        if hd:
-            result_image_hd_bytes = save_image_dpi_to_bytes(cv2.cvtColor(result.hd, cv2.COLOR_RGBA2BGRA), None, dpi)
-            result_message["image_base64_hd"] = bytes_2_base64(result_image_hd_bytes)
-
-    return result_message
-
-
-# 人像抠图接口
-@app.post("/human_matting")
-async def human_matting_inference(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    human_matting_model: str = Form("hivision_modnet"),
-    dpi: int = Form(300),
-):
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    # ------------------- 选择抠图与人脸检测模型 -------------------
-    choose_handler(creator, human_matting_model, None)
-
-    try:
-        result = creator(
-            img,
-            change_bg_only=True,
-        )
-    except FaceError:
-        result_message = {"status": False}
-
-    else:
-        result_image_standard_bytes = save_image_dpi_to_bytes(cv2.cvtColor(result.standard, cv2.COLOR_RGBA2BGRA), None, dpi)
-        result_message = {
-            "status": True,
-            "image_base64": bytes_2_base64(result_image_standard_bytes),
-        }
-    return result_message
-
-
-# 透明图像添加纯色背景接口
-@app.post("/add_background")
-async def photo_add_background(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    color: str = Form("000000"),
-    kb: int = Form(None),
-    dpi: int = Form(300),
-    render: int = Form(0),
-):
-    render_choice = ["pure_color", "updown_gradient", "center_gradient"]
-
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
-
-    color = hex_to_rgb(color)
-    color = (color[2], color[1], color[0])
-
-    result_image = add_background(
-        img,
-        bgr=color,
-        mode=render_choice[render],
-    ).astype(np.uint8)
-
-    result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
-    if kb:
-        result_image_bytes = resize_image_to_kb(result_image, None, int(kb), dpi=dpi)
-    else:
-        result_image_bytes = save_image_dpi_to_bytes(result_image, None, dpi=dpi)
-
-    result_messgae = {
-        "status": True,
-        "image_base64": bytes_2_base64(result_image_bytes),
-    }
-
-    return result_messgae
-
-
-# 六寸排版照生成接口
-@app.post("/generate_layout_photos")
-async def generate_layout_photos(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    height: int = Form(413),
-    width: int = Form(295),
-    kb: int = Form(None),
-    dpi: int = Form(300),
-):
-    # try:
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    size = (int(height), int(width))
-
-    typography_arr, typography_rotate = generate_layout_array(
-        input_height=size[0], input_width=size[1]
-    )
-
-    result_layout_image = generate_layout_image(
-        img, typography_arr, typography_rotate, height=size[0], width=size[1]
-    ).astype(np.uint8)
-
-    result_layout_image = cv2.cvtColor(result_layout_image, cv2.COLOR_RGB2BGR)
-    if kb:
-        result_layout_image_bytes = resize_image_to_kb(
-            result_layout_image, None, int(kb), dpi=dpi
-        )
-    else:
-        result_layout_image_bytes = save_image_dpi_to_bytes(result_layout_image, None, dpi=dpi)
-        
-    result_layout_image_base64 = bytes_2_base64(result_layout_image_bytes)
-
-    result_messgae = {
-        "status": True,
-        "image_base64": result_layout_image_base64,
-    }
-
-    return result_messgae
-
-
-# 透明图像添加水印接口
-@app.post("/watermark")
-async def watermark(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    text: str = Form("Hello"),
-    size: int = 20,
-    opacity: float = 0.5,
-    angle: int = 30,
-    color: str = "#000000",
-    space: int = 25,
-    kb: int = Form(None),
-    dpi: int = Form(300),
-):
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    try:
-        result_image = add_watermark(img, text, size, opacity, angle, color, space)
-
-        result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
-        if kb:
-            result_image_bytes = resize_image_to_kb(result_image, None, int(kb), dpi=dpi)
-        else:
-            result_image_bytes = save_image_dpi_to_bytes(result_image, None, dpi=dpi)
-        result_image_base64 = bytes_2_base64(result_image_bytes)
-
-        result_messgae = {
-            "status": True,
-            "image_base64": result_image_base64,
-        }
-    except Exception as e:
-        result_messgae = {
-            "status": False,
-            "error": str(e),
-        }
-
-    return result_messgae
-
-
-# 设置照片KB值接口(RGB图)
-@app.post("/set_kb")
-async def set_kb(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    dpi: int = Form(300),
-    kb: int = Form(50),
-):
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        image_bytes = await input_image.read()
-        nparr = np.frombuffer(image_bytes, np.uint8)
-        img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
-
-    try:
-        result_image = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
-        result_image_bytes = resize_image_to_kb(result_image, None, int(kb), dpi=dpi)
-        result_image_base64 = bytes_2_base64(result_image_bytes)
-
-        result_messgae = {
-            "status": True,
-            "image_base64": result_image_base64,
-        }
-    except Exception as e:
-        result_messgae = {
-            "status": False,
-            "error": e,
-        }
-
-    return result_messgae
-
-
-# 证件照智能裁剪接口
-@app.post("/idphoto_crop")
-async def idphoto_crop_inference(
-    input_image: UploadFile = File(None),
-    input_image_base64: str = Form(None),
-    height: int = Form(413),
-    width: int = Form(295),
-    face_detect_model: str = Form("mtcnn"),
-    hd: bool = Form(True),
-    dpi: int = Form(300),
-    head_measure_ratio: float = Form(0.2),
-    head_height_ratio: float = Form(0.45),
-    top_distance_max: float = Form(0.12),
-    top_distance_min: float = Form(0.10),
-):
-    if input_image_base64:
-        img = base64_2_numpy(input_image_base64)
-    else:
-        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)
-
-    # ------------------- 选择抠图与人脸检测模型 -------------------
-    choose_handler(creator, face_detect_option=face_detect_model)
-
-    # 将字符串转为元组
-    size = (int(height), int(width))
-    try:
-        result = creator(
-            img,
-            size=size,
-            head_measure_ratio=head_measure_ratio,
-            head_height_ratio=head_height_ratio,
-            head_top_range=(top_distance_max, top_distance_min),
-            crop_only=True,
-        )
-    except FaceError:
-        result_message = {"status": False}
-    # 如果检测到人脸数量等于1, 则返回标准证和高清照结果（png 4通道图像）
-    else:
-        result_image_standard_bytes = save_image_dpi_to_bytes(cv2.cvtColor(result.standard, cv2.COLOR_RGBA2BGRA), None, dpi)
-        
-        result_message = {
-            "status": True,
-            "image_base64_standard": bytes_2_base64(result_image_standard_bytes),
-        }
-
-        # 如果hd为True, 则增加高清照结果（png 4通道图像）
-        if hd:
-            result_image_hd_bytes = save_image_dpi_to_bytes(cv2.cvtColor(result.hd, cv2.COLOR_RGBA2BGRA), None, dpi)
-            result_message["image_base64_hd"] = bytes_2_base64(result_image_hd_bytes)
-
-    return result_message
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    # 在8080端口运行推理服务
-    uvicorn.run(app, host="0.0.0.0", port=8080)