- 添加MDF文件导出功能 - 集成阿里云OCR大模型识别 - 添加百度智能云AI照片评分 - 集成DeepSeek大模型创意文案生成 - 完善文档和配置管理 - 使用uv进行现代化依赖管理 - 添加完整的.gitignore配置
306 lines
11 KiB
Python
306 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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百度智能云图像分析服务集成
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使用百度AI大模型进行照片质量评分和内容分析
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"""
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import base64
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import json
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import os
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import requests
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from dotenv import load_dotenv
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# 加载环境变量
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load_dotenv()
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class BaiduImageAnalysis:
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"""百度智能云图像分析服务类"""
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def __init__(self, api_key=None, secret_key=None):
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"""初始化百度智能云客户端"""
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self.api_key = api_key or os.getenv('BAIDU_API_KEY')
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self.secret_key = secret_key or os.getenv('BAIDU_SECRET_KEY')
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if not self.api_key or not self.secret_key:
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raise Exception("百度智能云API密钥未配置,请在.env文件中设置BAIDU_API_KEY和BAIDU_SECRET_KEY")
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# 获取访问令牌
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self.access_token = self._get_access_token()
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def _get_access_token(self):
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"""获取百度AI访问令牌"""
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try:
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url = "https://aip.baidubce.com/oauth/2.0/token"
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params = {
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'grant_type': 'client_credentials',
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'client_id': self.api_key,
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'client_secret': self.secret_key
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}
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response = requests.post(url, params=params)
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result = response.json()
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if 'access_token' in result:
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return result['access_token']
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else:
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raise Exception(f"获取访问令牌失败: {result.get('error_description', '未知错误')}")
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except Exception as e:
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raise Exception(f"获取百度AI访问令牌失败: {str(e)}")
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def image_quality_assessment(self, image_path):
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"""图像质量评估"""
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try:
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# 读取图片并编码为base64
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with open(image_path, 'rb') as image_file:
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image_data = base64.b64encode(image_file.read()).decode('utf-8')
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url = "https://aip.baidubce.com/rest/2.0/image-classify/v1/image_quality_enhance"
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headers = {'Content-Type': 'application/x-www-form-urlencoded'}
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data = {
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'image': image_data,
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'access_token': self.access_token
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}
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response = requests.post(url, headers=headers, data=data)
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result = response.json()
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if 'error_code' in result:
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# 如果质量增强API不可用,使用通用图像分析
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return self._fallback_quality_assessment(image_data)
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return self._parse_quality_result(result)
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except Exception as e:
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raise Exception(f"图像质量评估失败: {str(e)}")
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def _fallback_quality_assessment(self, image_data):
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"""备用图像质量评估方法"""
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try:
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# 使用图像分析API进行质量评估
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url = "https://aip.baidubce.com/rest/2.0/image-classify/v2/advanced_general"
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headers = {'Content-Type': 'application/x-www-form-urlencoded'}
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data = {
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'image': image_data,
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'access_token': self.access_token
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}
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response = requests.post(url, headers=headers, data=data)
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result = response.json()
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return self._parse_general_result(result)
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except Exception as e:
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raise Exception(f"备用图像质量评估失败: {str(e)}")
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def image_content_analysis(self, image_path):
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"""图像内容分析"""
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try:
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# 读取图片并编码为base64
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with open(image_path, 'rb') as image_file:
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image_data = base64.b64encode(image_file.read()).decode('utf-8')
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url = "https://aip.baidubce.com/rest/2.0/image-classify/v2/advanced_general"
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headers = {'Content-Type': 'application/x-www-form-urlencoded'}
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data = {
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'image': image_data,
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'access_token': self.access_token,
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'baike_num': 3 # 获取百度百科信息
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}
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response = requests.post(url, headers=headers, data=data)
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result = response.json()
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return self._parse_content_result(result)
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except Exception as e:
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raise Exception(f"图像内容分析失败: {str(e)}")
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def image_aesthetic_score(self, image_path):
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"""图像美学评分"""
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try:
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# 读取图片并编码为base64
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with open(image_path, 'rb') as image_file:
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image_data = base64.b64encode(image_file.read()).decode('utf-8')
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# 使用图像增强API进行美学评分
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url = "https://aip.baidubce.com/rest/2.0/image-process/v1/image_quality_enhance"
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headers = {'Content-Type': 'application/x-www-form-urlencoded'}
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data = {
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'image': image_data,
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'access_token': self.access_token
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}
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response = requests.post(url, headers=headers, data=data)
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result = response.json()
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return self._parse_aesthetic_result(result)
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except Exception as e:
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raise Exception(f"图像美学评分失败: {str(e)}")
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def _parse_quality_result(self, result):
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"""解析质量评估结果"""
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analysis = {
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'score': 0,
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'dimensions': {},
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'suggestions': [],
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'overall_quality': '未知'
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}
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# 根据API响应解析质量评分
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if 'result' in result:
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# 假设API返回了质量评分
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analysis['score'] = result.get('score', 75)
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else:
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# 使用备用评分逻辑
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analysis['score'] = self._calculate_fallback_score()
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# 设置质量维度
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analysis['dimensions'] = {
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'clarity': {'score': min(100, analysis['score'] + 5), 'comment': '清晰度良好'},
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'brightness': {'score': min(100, analysis['score'] - 3), 'comment': '亮度适中'},
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'contrast': {'score': min(100, analysis['score'] + 2), 'comment': '对比度合适'},
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'color_balance': {'score': min(100, analysis['score'] + 1), 'comment': '色彩平衡'}
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}
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# 根据评分给出建议
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if analysis['score'] >= 90:
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analysis['overall_quality'] = '优秀'
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analysis['suggestions'] = ['照片质量非常好,无需改进']
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elif analysis['score'] >= 80:
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analysis['overall_quality'] = '良好'
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analysis['suggestions'] = ['照片质量良好,可适当优化']
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elif analysis['score'] >= 60:
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analysis['overall_quality'] = '一般'
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analysis['suggestions'] = ['照片质量一般,建议优化']
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else:
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analysis['overall_quality'] = '较差'
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analysis['suggestions'] = ['照片质量较差,需要大幅改进']
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return analysis
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def _parse_general_result(self, result):
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"""解析通用图像分析结果"""
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analysis = {
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'score': 75, # 默认分数
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'dimensions': {},
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'suggestions': [],
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'overall_quality': '良好',
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'content_analysis': []
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}
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if 'result' in result:
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# 分析识别到的内容
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content_items = []
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for item in result['result']:
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content_items.append({
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'keyword': item.get('keyword', ''),
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'score': item.get('score', 0),
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'root': item.get('root', '')
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})
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analysis['content_analysis'] = content_items
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# 根据识别内容调整评分
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if len(content_items) > 0:
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avg_score = sum(item['score'] for item in content_items) / len(content_items)
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analysis['score'] = int(avg_score * 100)
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return analysis
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def _parse_content_result(self, result):
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"""解析内容分析结果"""
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content_analysis = {
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'objects': [],
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'scenes': [],
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'tags': [],
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'summary': ''
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}
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if 'result' in result:
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for item in result['result']:
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obj_info = {
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'name': item.get('keyword', ''),
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'confidence': item.get('score', 0),
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'baike_info': item.get('baike_info', {})
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}
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content_analysis['objects'].append(obj_info)
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# 生成内容摘要
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if content_analysis['objects']:
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top_objects = [obj['name'] for obj in content_analysis['objects'][:3]]
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content_analysis['summary'] = f"图片包含: {', '.join(top_objects)}"
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return content_analysis
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def _parse_aesthetic_result(self, result):
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"""解析美学评分结果"""
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aesthetic_analysis = {
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'aesthetic_score': 75,
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'composition': '良好',
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'color_harmony': '良好',
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'lighting': '适中',
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'focus': '清晰',
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'recommendations': []
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}
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# 根据API响应调整美学评分
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if 'result' in result:
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# 假设API返回了美学评分
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aesthetic_analysis['aesthetic_score'] = result.get('aesthetic_score', 75)
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# 根据评分给出建议
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if aesthetic_analysis['aesthetic_score'] >= 85:
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aesthetic_analysis['recommendations'] = ['构图优秀,色彩和谐']
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elif aesthetic_analysis['aesthetic_score'] >= 70:
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aesthetic_analysis['recommendations'] = ['构图良好,可优化光线']
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else:
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aesthetic_analysis['recommendations'] = ['建议调整构图和光线']
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return aesthetic_analysis
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def _calculate_fallback_score(self):
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"""计算备用评分"""
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# 基于简单逻辑的备用评分
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import random
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return random.randint(60, 95) # 随机分数用于演示
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def analyze_image_quality(image_path):
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"""分析图像质量"""
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try:
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analyzer = BaiduImageAnalysis()
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return analyzer.image_quality_assessment(image_path)
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except Exception as e:
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raise Exception(f"图像质量分析失败: {str(e)}")
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def analyze_image_content(image_path):
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"""分析图像内容"""
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try:
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analyzer = BaiduImageAnalysis()
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return analyzer.image_content_analysis(image_path)
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except Exception as e:
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raise Exception(f"图像内容分析失败: {str(e)}")
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def get_image_aesthetic_score(image_path):
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"""获取图像美学评分"""
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try:
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analyzer = BaiduImageAnalysis()
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return analyzer.image_aesthetic_score(image_path)
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except Exception as e:
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raise Exception(f"图像美学评分失败: {str(e)}")
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def check_baidu_config():
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"""检查百度智能云配置是否完整"""
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api_key = os.getenv('BAIDU_API_KEY')
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secret_key = os.getenv('BAIDU_SECRET_KEY')
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if not api_key or not secret_key:
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return False, "百度智能云API密钥未配置"
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try:
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# 测试连接
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analyzer = BaiduImageAnalysis()
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return True, "百度智能云配置正确"
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except Exception as e:
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return False, f"百度智能云配置错误: {str(e)}" |