【2025年独家】GPT-4o图像API完全指南：7大功能与价格对比节省80%成本

// 图像理解API调用示例
curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "system", 
        "content": "你是一个专业的图像分析助手。"
      },
      {
        "role": "user",
        "content": [
          {"type": "text", "text": "详细分析这张图片中的内容"},
          {
            "type": "image_url",
            "image_url": {
              "url": "https://example.com/image.jpg"
            }
          }
        ]
      }
    ]
  }'
        

import requests
import base64
import json

def analyze_image(image_path, api_key, prompt="详细分析这张图片中的内容"):
    """
    使用GPT-4o分析图像内容
    
    参数:
    image_path (str): 本地图像文件路径
    api_key (str): OpenAI API密钥
    prompt (str): 分析提示词
    
    返回:
    dict: API响应
    """
    # 读取并编码图像
    with open(image_path, "rb") as image_file:
        base64_image = base64.b64encode(image_file.read()).decode('utf-8')
    
    # 准备API请求
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    
    payload = {
        "model": "gpt-4o",
        "messages": [
            {
                "role": "system",
                "content": "你是一个专业的图像分析专家，能够详细描述图像内容。"
            },
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        "max_tokens": 1000
    }
    
    # 发送请求
    response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers=headers,
        json=payload
    )
    
    return response.json()

# 使用示例
if __name__ == "__main__":
    result = analyze_image(
        "path/to/your/image.jpg",
        "your-api-key-here",
        "详细分析这张图片中的内容，特别关注主要物体和场景"
    )
    print(json.dumps(result, ensure_ascii=False, indent=2))
        

import requests
import base64
import json
import os

def generate_image(prompt, api_key, output_path="generated_image.png"):
    """
    使用GPT-4o生成图像
    
    参数:
    prompt (str): 图像生成提示词
    api_key (str): OpenAI API密钥
    output_path (str): 输出图像的保存路径
    
    返回:
    bool: 是否成功生成并保存图像
    """
    # 准备API请求
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    
    payload = {
        "model": "gpt-4o",
        "prompt": prompt,
        "n": 1,
        "size": "1024x1024",
        "response_format": "b64_json"
    }
    
    # 发送请求
    response = requests.post(
        "https://api.openai.com/v1/images/generations",
        headers=headers,
        json=payload
    )
    
    if response.status_code != 200:
        print(f"错误: {response.text}")
        return False
    
    # 解析响应并保存图像
    try:
        response_data = response.json()
        image_data = base64.b64decode(response_data["data"][0]["b64_json"])
        
        with open(output_path, "wb") as image_file:
            image_file.write(image_data)
        
        print(f"图像已成功生成并保存至: {output_path}")
        return True
    except Exception as e:
        print(f"处理响应时出错: {str(e)}")
        return False

# 使用示例
if __name__ == "__main__":
    success = generate_image(
        "创建一个未来智能城市的概念图，包含自动驾驶车辆、智能建筑和绿色能源设施，采用明亮的蓝色和绿色调，白天场景，俯视视角。",
        "your-api-key-here",
        "future_city.png"
    )
        

import requests
import base64
import json

def analyze_image_via_laozhang(image_path, api_key, prompt="详细分析这张图片中的内容"):
    """
    通过laozhang.ai中转服务使用GPT-4o分析图像
    
    参数:
    image_path (str): 本地图像文件路径
    api_key (str): laozhang.ai API密钥
    prompt (str): 分析提示词
    
    返回:
    dict: API响应
    """
    # 读取并编码图像
    with open(image_path, "rb") as image_file:
        base64_image = base64.b64encode(image_file.read()).decode('utf-8')
    
    # 准备API请求
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
    
    payload = {
        "model": "gpt-4o",
        "messages": [
            {
                "role": "system",
                "content": "你是一个专业的图像分析专家，能够详细描述图像内容。"
            },
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ],
        "max_tokens": 1000
    }
    
    # 发送请求到laozhang.ai中转API
    response = requests.post(
        "https://api.laozhang.ai/v1/chat/completions",
        headers=headers,
        json=payload
    )
    
    return response.json()

# 使用示例
if __name__ == "__main__":
    result = analyze_image_via_laozhang(
        "path/to/your/image.jpg",
        "your-laozhang-api-key-here",
        "详细分析这张图片中的内容，特别关注主要物体和场景"
    )
    print(json.dumps(result, ensure_ascii=False, indent=2))
        

import os
import hashlib
import json
import requests
import base64

class GPT4oImageCache:
    """
    GPT-4o图像生成缓存系统
    """
    def __init__(self, cache_dir="./image_cache"):
        self.cache_dir = cache_dir
        os.makedirs(cache_dir, exist_ok=True)
    
    def _get_cache_path(self, prompt):
        """生成基于提示词的缓存路径"""
        # 使用提示词的哈希作为文件名
        prompt_hash = hashlib.md5(prompt.encode('utf-8')).hexdigest()
        return os.path.join(self.cache_dir, f"{prompt_hash}.png")
    
    def get_image(self, prompt, api_key):
        """获取图像，优先从缓存中获取，没有则调用API生成"""
        cache_path = self._get_cache_path(prompt)
        
        # 检查缓存是否存在
        if os.path.exists(cache_path):
            print(f"从缓存加载图像: {cache_path}")
            return cache_path
        
        # 缓存不存在，调用API生成
        print(f"缓存未命中，正在生成新图像...")
        headers = {
            "Content-Type": "application/json",
            "Authorization": f"Bearer {api_key}"
        }
        
        payload = {
            "model": "gpt-4o",
            "prompt": prompt,
            "n": 1,
            "size": "1024x1024",
            "response_format": "b64_json"
        }
        
        # 可以在这里替换为laozhang.ai的端点以降低成本
        api_endpoint = "https://api.openai.com/v1/images/generations"
        response = requests.post(api_endpoint, headers=headers, json=payload)
        
        if response.status_code != 200:
            print(f"API调用失败: {response.text}")
            return None
        
        # 保存图像到缓存
        try:
            response_data = response.json()
            image_data = base64.b64decode(response_data["data"][0]["b64_json"])
            
            with open(cache_path, "wb") as f:
                f.write(image_data)
            
            print(f"图像已生成并缓存: {cache_path}")
            return cache_path
        except Exception as e:
            print(f"处理响应出错: {str(e)}")
            return None

# 使用示例
cache = GPT4oImageCache()
image_path = cache.get_image(
    "创建一个未来智能城市的概念图，包含自动驾驶车辆和智能建筑",
    "your-api-key-here"
)