"""
初始化图片标注示例数据脚本

创建多种图片标注项目和任务，用于测试图片标注功能。
包括：目标检测、图像分类、图像分割、关键点标注等。
"""
import requests
import json

# API 基础 URL
BASE_URL = "http://localhost:8000"

# 1. 目标检测标注配置（矩形框标注）
OBJECT_DETECTION_CONFIG = """<View>
  <Header value="目标检测 - 物体识别"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <RectangleLabels name="label" toName="image">
    <Label value="人" background="red"/>
    <Label value="车" background="blue"/>
    <Label value="自行车" background="green"/>
    <Label value="狗" background="orange"/>
    <Label value="猫" background="purple"/>
  </RectangleLabels>
</View>"""

# 2. 图像分类标注配置
IMAGE_CLASSIFICATION_CONFIG = """<View>
  <Header value="图像分类"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <Choices name="category" toName="image" choice="single" showInline="true">
    <Choice value="风景"/>
    <Choice value="人物"/>
    <Choice value="动物"/>
    <Choice value="建筑"/>
    <Choice value="食物"/>
    <Choice value="其他"/>
  </Choices>
</View>"""

# 3. 图像分割标注配置（多边形标注）
IMAGE_SEGMENTATION_CONFIG = """<View>
  <Header value="图像分割 - 精细标注"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <PolygonLabels name="label" toName="image">
    <Label value="前景" background="rgba(255, 0, 0, 0.5)"/>
    <Label value="背景" background="rgba(0, 0, 255, 0.5)"/>
    <Label value="物体" background="rgba(0, 255, 0, 0.5)"/>
  </PolygonLabels>
</View>"""

# 4. 关键点标注配置（人体姿态估计）
KEYPOINT_DETECTION_CONFIG = """<View>
  <Header value="关键点标注 - 人体姿态"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <KeyPointLabels name="keypoint" toName="image">
    <Label value="头部" background="red"/>
    <Label value="肩膀" background="blue"/>
    <Label value="肘部" background="green"/>
    <Label value="手腕" background="orange"/>
    <Label value="膝盖" background="purple"/>
    <Label value="脚踝" background="pink"/>
  </KeyPointLabels>
</View>"""

# 5. 多标签图像分类配置
MULTI_LABEL_CLASSIFICATION_CONFIG = """<View>
  <Header value="多标签图像分类"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <Choices name="attributes" toName="image" choice="multiple" showInline="false">
    <Choice value="室内"/>
    <Choice value="室外"/>
    <Choice value="白天"/>
    <Choice value="夜晚"/>
    <Choice value="晴天"/>
    <Choice value="雨天"/>
    <Choice value="有人"/>
    <Choice value="无人"/>
  </Choices>
</View>"""

# 6. 图像质量评估配置
IMAGE_QUALITY_CONFIG = """<View>
  <Header value="图像质量评估"/>
  <Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="true"/>
  <Choices name="quality" toName="image" choice="single" showInline="true">
    <Choice value="优秀"/>
    <Choice value="良好"/>
    <Choice value="一般"/>
    <Choice value="较差"/>
  </Choices>
  <Choices name="issues" toName="image" choice="multiple" showInline="false">
    <Choice value="模糊"/>
    <Choice value="曝光过度"/>
    <Choice value="曝光不足"/>
    <Choice value="噪点多"/>
    <Choice value="色彩失真"/>
  </Choices>
</View>"""

# 示例图片任务数据
# 使用公开的测试图片 URL（来自 Unsplash 等免费图片网站）
SAMPLE_IMAGE_TASKS = [
    # 目标检测任务
    {
        "name": "街道场景-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1449824913935-59a10b8d2000?w=800"
        }
    },
    {
        "name": "街道场景-2",
        "data": {
            "image": "https://images.unsplash.com/photo-1477959858617-67f85cf4f1df?w=800"
        }
    },
    {
        "name": "公园场景",
        "data": {
            "image": "https://images.unsplash.com/photo-1441974231531-c6227db76b6e?w=800"
        }
    },
    
    # 图像分类任务
    {
        "name": "风景图片-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1506905925346-21bda4d32df4?w=800"
        }
    },
    {
        "name": "建筑图片-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1480714378408-67cf0d13bc1b?w=800"
        }
    },
    {
        "name": "动物图片-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1425082661705-1834bfd09dca?w=800"
        }
    },
    
    # 图像分割任务
    {
        "name": "物体分割-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1518791841217-8f162f1e1131?w=800"
        }
    },
    {
        "name": "物体分割-2",
        "data": {
            "image": "https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=800"
        }
    },
    
    # 关键点标注任务
    {
        "name": "人体姿态-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1571019614242-c5c5dee9f50b?w=800"
        }
    },
    {
        "name": "人体姿态-2",
        "data": {
            "image": "https://images.unsplash.com/photo-1517836357463-d25dfeac3438?w=800"
        }
    },
    
    # 多标签分类任务
    {
        "name": "场景分析-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1464822759023-fed622ff2c3b?w=800"
        }
    },
    {
        "name": "场景分析-2",
        "data": {
            "image": "https://images.unsplash.com/photo-1469474968028-56623f02e42e?w=800"
        }
    },
    
    # 图像质量评估任务
    {
        "name": "质量评估-1",
        "data": {
            "image": "https://images.unsplash.com/photo-1472214103451-9374bd1c798e?w=800"
        }
    },
    {
        "name": "质量评估-2",
        "data": {
            "image": "https://images.unsplash.com/photo-1501594907352-04cda38ebc29?w=800"
        }
    },
]


def create_project(name, description, config):
    """创建项目"""
    url = f"{BASE_URL}/api/projects"
    data = {
        "name": name,
        "description": description,
        "config": config
    }
    
    response = requests.post(url, json=data)
    if response.status_code == 201:
        project = response.json()
        print(f"✓ 创建项目成功: {project['name']} (ID: {project['id']})")
        return project
    else:
        print(f"✗ 创建项目失败: {response.status_code} - {response.text}")
        return None


def create_task(project_id, task_name, task_data):
    """创建任务"""
    url = f"{BASE_URL}/api/tasks"
    data = {
        "project_id": project_id,
        "name": task_name,
        "data": task_data
    }
    
    response = requests.post(url, json=data)
    if response.status_code == 201:
        task = response.json()
        print(f"  ✓ 创建任务: {task['name']} (ID: {task['id']})")
        return task
    else:
        print(f"  ✗ 创建任务失败: {response.status_code} - {response.text}")
        return None


def main():
    """主函数"""
    print("=" * 60)
    print("初始化图片标注平台示例数据")
    print("=" * 60)
    print()
    
    # 1. 创建目标检测项目
    print("1. 创建目标检测项目...")
    detection_project = create_project(
        name="目标检测 - 街道场景",
        description="标注街道场景中的人、车、自行车等物体",
        config=OBJECT_DETECTION_CONFIG
    )
    
    if detection_project:
        print("   创建目标检测任务...")
        for i in range(3):
            create_task(
                detection_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    # 2. 创建图像分类项目
    print("2. 创建图像分类项目...")
    classification_project = create_project(
        name="图像分类",
        description="将图片分类为风景、人物、动物、建筑、食物等类别",
        config=IMAGE_CLASSIFICATION_CONFIG
    )
    
    if classification_project:
        print("   创建图像分类任务...")
        for i in range(3, 6):
            create_task(
                classification_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    # 3. 创建图像分割项目
    print("3. 创建图像分割项目...")
    segmentation_project = create_project(
        name="图像分割 - 物体轮廓",
        description="使用多边形工具精细标注物体轮廓",
        config=IMAGE_SEGMENTATION_CONFIG
    )
    
    if segmentation_project:
        print("   创建图像分割任务...")
        for i in range(6, 8):
            create_task(
                segmentation_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    # 4. 创建关键点标注项目
    print("4. 创建关键点标注项目...")
    keypoint_project = create_project(
        name="关键点标注 - 人体姿态估计",
        description="标注人体关键点（头部、肩膀、肘部、手腕、膝盖、脚踝）",
        config=KEYPOINT_DETECTION_CONFIG
    )
    
    if keypoint_project:
        print("   创建关键点标注任务...")
        for i in range(8, 10):
            create_task(
                keypoint_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    # 5. 创建多标签分类项目
    print("5. 创建多标签分类项目...")
    multi_label_project = create_project(
        name="多标签图像分类",
        description="为图片添加多个属性标签（室内/室外、白天/夜晚等）",
        config=MULTI_LABEL_CLASSIFICATION_CONFIG
    )
    
    if multi_label_project:
        print("   创建多标签分类任务...")
        for i in range(10, 12):
            create_task(
                multi_label_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    # 6. 创建图像质量评估项目
    print("6. 创建图像质量评估项目...")
    quality_project = create_project(
        name="图像质量评估",
        description="评估图片质量并标注存在的问题（模糊、曝光等）",
        config=IMAGE_QUALITY_CONFIG
    )
    
    if quality_project:
        print("   创建图像质量评估任务...")
        for i in range(12, 14):
            create_task(
                quality_project['id'],
                SAMPLE_IMAGE_TASKS[i]['name'],
                SAMPLE_IMAGE_TASKS[i]['data']
            )
    print()
    
    print("=" * 60)
    print("图片标注示例数据初始化完成！")
    print("=" * 60)
    print()
    print("已创建的项目类型：")
    print("1. 目标检测 - 使用矩形框标注物体")
    print("2. 图像分类 - 单标签分类")
    print("3. 图像分割 - 使用多边形精细标注")
    print("4. 关键点标注 - 人体姿态估计")
    print("5. 多标签分类 - 场景属性标注")
    print("6. 图像质量评估 - 质量评分和问题标注")
    print()
    print("你现在可以：")
    print("1. 访问 http://localhost:4200/projects 查看项目列表")
    print("2. 点击项目查看详情和任务")
    print("3. 点击'开始标注'按钮进行图片标注")
    print()
    print("注意：")
    print("- 图片来自 Unsplash 免费图片库")
    print("- 需要网络连接才能加载图片")
    print("- 如果图片加载失败，请检查网络连接")
    print()


if __name__ == "__main__":
    try:
        main()
    except requests.exceptions.ConnectionError:
        print("✗ 错误: 无法连接到后端服务器")
        print("  请确保后端服务器正在运行:")
        print("  cd backend && python -m uvicorn main:app --reload --host 0.0.0.0 --port 8000")
    except Exception as e:
        print(f"✗ 发生错误: {e}")
        import traceback
        traceback.print_exc()