CardScoreDataServer/Test/img_score_and_insert.py

import asyncio
import aiohttp
import aiofiles
import json
import os
from typing import Dict, Any, Tuple
from datetime import datetime

# --- 配置区域 ---
# 1. 服务 URL
# INFERENCE_SERVICE_URL = "http://127.0.0.1:7744"
# STORAGE_SERVICE_URL = "http://127.0.0.1:7745"

INFERENCE_SERVICE_URL = "http://192.168.77.78:7744"
STORAGE_SERVICE_URL = "http://192.168.77.78:7745"

# 2. 要处理的卡片信息
formate_time = datetime.now().strftime("%Y-%m-%d_%H:%M")
CARD_NAME = f"卡 {formate_time}"

# 是不是反光卡
is_reflect = True
if is_reflect:
    is_reflect_card = "true"
else:
    is_reflect_card = "false"
# 3. 四张卡片图片的本地路径

front_face_img_path = r"C:\Code\ML\Image\Card\_250915_many_capture_img\_250915_1743_reflect_nature_defrct\2_front_coaxial_1_0.jpg"
front_edge_img_path = r"C:\Code\ML\Image\Card\_250915_many_capture_img\_250915_1743_reflect_nature_defrct\2_front_ring_0_1.jpg"
back_face_img_path  = r"C:\Code\ML\Image\Card\_250915_many_capture_img\_250915_1743_reflect_nature_defrct\2_back_coaxial_1_0.jpg"
back_edge_img_path  = r"C:\Code\ML\Image\Card\_250915_many_capture_img\_250915_1743_reflect_nature_defrct\2_back_ring_0_1.jpg"
IMAGE_PATHS = [
    front_edge_img_path,
    front_face_img_path,
    back_edge_img_path,
    back_face_img_path
]

# 4. 推理服务需要的 score_type 参数
SCORE_TYPES = [
    "front_corner_edge",
    "front_face",
    "back_corner_edge",
    "back_face"
]

SCORE_TO_IMAGE_TYPE_MAP = {
    "front_corner_edge": "front_edge",
    "front_face": "front_face",
    "back_corner_edge": "back_edge",
    "back_face": "back_face"
}


# --- 脚本主逻辑 ---

async def call_api_with_file(
        session: aiohttp.ClientSession,
        url: str,
        file_path: str,
        params: Dict[str, Any] = None,
        form_fields: Dict[str, Any] = None
) -> Tuple[int, bytes]:
    """通用的文件上传API调用函数 (从文件路径读取)"""
    form_data = aiohttp.FormData()

    if form_fields:
        for key, value in form_fields.items():
            form_data.add_field(key, str(value))

    async with aiofiles.open(file_path, 'rb') as f:
        content = await f.read()
        form_data.add_field(
            'file',
            content,
            filename=os.path.basename(file_path),
            content_type='image/jpeg'
        )

    try:
        async with session.post(url, data=form_data, params=params) as response:
            response_content = await response.read()
            if not response.ok:
                print(f"错误: 调用 {url} 失败, 状态码: {response.status}")
                print(f"  错误详情: {response_content.decode(errors='ignore')}")
            return response.status, response_content
    except aiohttp.ClientConnectorError as e:
        print(f"错误: 无法连接到服务 {url} - {e}")
        return 503, b"Connection Error"


async def process_single_image(
        session: aiohttp.ClientSession,
        image_path: str,
        score_type: str,
        is_reflect_card: str
) -> Dict[str, Any]:
    """处理单张图片：获取转正图和分数JSON"""
    print(f"  正在处理图片: {image_path} (类型: {score_type})")

    # 1. 获取转正后的图片
    rectify_url = f"{INFERENCE_SERVICE_URL}/api/card_inference/card_rectify_and_center"
    rectify_status, rectified_image_bytes = await call_api_with_file(
        session, url=rectify_url, file_path=image_path
    )
    if rectify_status >= 300:
        raise Exception(f"获取转正图失败: {image_path}")
    print(f"    -> 已成功获取转正图")

    # 2. 获取分数JSON
    score_url = f"{INFERENCE_SERVICE_URL}/api/card_score/score_inference"
    score_params = {
        "score_type": score_type,
        "is_reflect_card": is_reflect_card
    }
    score_status, score_json_bytes = await call_api_with_file(
        session,
        url=score_url,
        file_path=image_path,
        params=score_params
    )
    if score_status >= 300:
        raise Exception(f"获取分数JSON失败: {image_path}")

    score_json = json.loads(score_json_bytes)
    print(f"    -> 已成功获取分数JSON")

    return {
        "score_type": score_type,
        "rectified_image": rectified_image_bytes,
        "score_json": score_json
    }


async def create_card_set(session: aiohttp.ClientSession, card_name: str) -> int:
    """创建一个新的卡组并返回其ID"""
    url = f"{STORAGE_SERVICE_URL}/api/cards/created"
    params = {'card_name': card_name}
    print(f"\n[步骤 2] 正在创建卡组，名称: '{card_name}'...")
    try:
        async with session.post(url, params=params) as response:
            if response.ok:
                data = await response.json()
                card_id = data.get('id')
                if card_id is not None:
                    print(f"  -> 成功创建卡组, ID: {card_id}")
                    return card_id
                else:
                    raise Exception("创建卡组API的响应中未找到'id'字段")
            else:
                error_text = await response.text()
                raise Exception(f"创建卡组失败, 状态码: {response.status}, 详情: {error_text}")
    except aiohttp.ClientConnectorError as e:
        raise Exception(f"无法连接到存储服务 {url} - {e}")


# 【修改点】: 修正此函数
async def upload_processed_data(
        session: aiohttp.ClientSession,
        card_id: int,
        processed_data: Dict[str, Any]
):
    """上传单张转正图和对应的JSON到存储服务"""
    score_type = processed_data['score_type']
    image_type_for_storage = SCORE_TO_IMAGE_TYPE_MAP[score_type]

    print(f"  正在上传图片, 类型: {image_type_for_storage}...")

    url = f"{STORAGE_SERVICE_URL}/api/images/insert/{card_id}"

    # 直接构建FormData，因为图片数据已经在内存中 (processed_data['rectified_image'])
    form_data = aiohttp.FormData()
    form_data.add_field('image_type', image_type_for_storage)
    form_data.add_field('json_data_str', json.dumps(processed_data['score_json'], ensure_ascii=False))
    form_data.add_field(
        'image',
        processed_data['rectified_image'],
        filename='rectified.jpg',
        content_type='image/jpeg'
    )

    try:
        async with session.post(url, data=form_data) as response:
            if response.status == 201:
                print(f"    -> 成功上传并关联图片: {image_type_for_storage}")
            else:
                error_text = await response.text()
                print(
                    f"    -> 错误: 上传失败! 类型: {image_type_for_storage}, 状态码: {response.status}, 详情: {error_text}")
    except aiohttp.ClientConnectorError as e:
        print(f"    -> 错误: 无法连接到存储服务 {url} - {e}")


async def main():
    """主执行函数"""
    async with aiohttp.ClientSession() as session:
        # 步骤 1: 并发处理所有图片, 获取转正图和分数
        print("[步骤 1] 开始并发处理所有图片...")
        process_tasks = []
        for path, s_type in zip(IMAGE_PATHS, SCORE_TYPES):
            if not os.path.exists(path):
                print(f"错误：文件不存在，请检查路径配置: {path}")
                return
            task = asyncio.create_task(process_single_image(session, path, s_type, is_reflect_card))
            process_tasks.append(task)

        try:
            processed_results = await asyncio.gather(*process_tasks)
            print("  -> 所有图片处理完成！")
        except Exception as e:
            print(f"\n在处理图片过程中发生错误: {e}")
            return

        # 步骤 2: 创建卡组
        try:
            card_id = await create_card_set(session, CARD_NAME)
        except Exception as e:
            print(f"\n创建卡组时发生严重错误: {e}")
            return

        # 步骤 3: 并发上传所有处理好的数据
        print(f"\n[步骤 3] 开始为卡组ID {card_id} 并发上传图片和数据...")
        upload_tasks = []
        for result in processed_results:
            task = asyncio.create_task(upload_processed_data(session, card_id, result))
            upload_tasks.append(task)

        await asyncio.gather(*upload_tasks)
        print("  -> 所有数据上传完成！")

    print("\n====================")
    print("所有流程执行完毕！")
    print("====================")


if __name__ == "__main__":
    if len(IMAGE_PATHS) != 4 or len(SCORE_TYPES) != 4:
        print("错误: IMAGE_PATHS 和 SCORE_TYPES 列表的长度必须为4，请检查配置。")
    else:
        # 在 Windows 上使用 ProactorEventLoop 可能会更稳定
        if os.name == 'nt':
            asyncio.set_event_loop_policy(asyncio.WindowsProactorEventLoopPolicy())
        asyncio.run(main())