Claude CodeandClaude
5e5020a38a
主要改进： 🎨 UI优化 - 实现学而思风格的题目卡片设计 - 优化数学公式和坐标点显示格式 - 添加专业的选项和答案展示 - 改进响应式设计和交互体验 🔧 技术优化 - 修复图片关联算法，避免重复分配 - 智能图片与题目匹配算法 - 添加admin用户便捷登录功能 - 优化题目文本格式化 📊 功能增强 - 可折叠的答案解析区域 - 动态分值提取和显示 - 专业的数学符号高亮 - 子题清晰分隔显示 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
319 lines
12 KiB
Python
319 lines
12 KiB
Python
"""
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PDF图片提取器 - 从PDF中提取嵌入的图片并与题目关联
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专门处理数学试卷中的几何图形、函数图像、表格等
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"""
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import os
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import time
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import hashlib
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import tempfile
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from typing import List, Dict, Tuple, Optional
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import fitz # PyMuPDF
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from PIL import Image
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import uuid
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class PDFImageExtractor:
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def __init__(self, output_dir: str = "uploads/images"):
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self.output_dir = output_dir
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# 确保输出目录存在
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os.makedirs(output_dir, exist_ok=True)
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# 图片提取配置
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self.min_image_size = (50, 50) # 最小图片尺寸，过滤掉太小的图片
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self.max_image_size = (2000, 2000) # 最大图片尺寸
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self.supported_formats = ['png', 'jpg', 'jpeg', 'gif', 'bmp']
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def extract_images_from_pdf(self, pdf_path: str) -> List[Dict]:
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"""
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从PDF中提取所有图片
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返回图片信息列表，包含图片路径、位置、尺寸等
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"""
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print(f"🖼️ 开始从PDF提取图片: {pdf_path}")
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start_time = time.time()
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try:
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doc = fitz.open(pdf_path)
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extracted_images = []
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for page_num in range(len(doc)):
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page = doc[page_num]
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print(f"📄 处理第{page_num + 1}页...")
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# 获取页面中的图片
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image_list = page.get_images(full=True)
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for img_index, img in enumerate(image_list):
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# 提取图片信息
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xref = img[0] # 图片引用
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# 获取图片数据
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base_image = doc.extract_image(xref)
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image_bytes = base_image["image"]
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image_ext = base_image["ext"]
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# 验证图片格式
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if image_ext.lower() not in self.supported_formats:
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continue
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# 生成唯一文件名
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image_hash = hashlib.md5(image_bytes).hexdigest()
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filename = f"page_{page_num + 1}_img_{img_index + 1}_{image_hash[:8]}.{image_ext}"
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image_path = os.path.join(self.output_dir, filename)
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# 保存图片
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with open(image_path, "wb") as img_file:
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img_file.write(image_bytes)
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# 获取图片在页面中的位置
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image_rects = page.get_image_rects(xref)
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# 验证图片尺寸
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try:
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with Image.open(image_path) as pil_img:
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width, height = pil_img.size
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# 过滤太小或太大的图片
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if (width < self.min_image_size[0] or
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height < self.min_image_size[1] or
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width > self.max_image_size[0] or
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height > self.max_image_size[1]):
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os.remove(image_path) # 删除不符合条件的图片
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continue
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except Exception as e:
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print(f"⚠️ 图片验证失败: {e}")
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if os.path.exists(image_path):
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os.remove(image_path)
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continue
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# 记录图片信息
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image_info = {
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"filename": filename,
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"path": image_path,
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"relative_path": f"images/{filename}", # 用于前端访问
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"page": page_num + 1,
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"width": width,
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"height": height,
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"format": image_ext.upper(),
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"size_bytes": len(image_bytes),
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"hash": image_hash,
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"rects": [] # 图片在页面中的位置
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}
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# 添加位置信息
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for rect in image_rects:
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image_info["rects"].append({
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"x0": rect.x0,
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"y0": rect.y0,
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"x1": rect.x1,
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"y1": rect.y1,
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"width": rect.width,
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"height": rect.height
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})
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extracted_images.append(image_info)
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print(f"✅ 提取图片: {filename} ({width}x{height})")
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doc.close()
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total_time = time.time() - start_time
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print(f"🎉 图片提取完成: {len(extracted_images)}张图片, 耗时{total_time:.2f}秒")
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return extracted_images
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except Exception as e:
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print(f"❌ PDF图片提取失败: {str(e)}")
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return []
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def associate_images_with_questions(self, images: List[Dict], questions: List[Dict]) -> List[Dict]:
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"""
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将图片与题目关联
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基于页面位置和题目边界框进行智能匹配
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"""
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print(f"🔗 开始关联图片与题目: {len(images)}张图片, {len(questions)}道题目")
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# 用于跟踪已分配的图片
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used_images = set()
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# 按页码组织题目
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questions_by_page = {}
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for i, question in enumerate(questions):
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page = question.get("page", 1)
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if page not in questions_by_page:
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questions_by_page[page] = []
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questions_by_page[page].append((i, question))
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# 为每页进行图片关联
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for page_num, page_questions in questions_by_page.items():
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# 获取当前页的图片
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page_images = [img for img in images if img["page"] == page_num]
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if not page_images:
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continue
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# 按图片在页面中的垂直位置排序（从上到下）
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page_images_sorted = sorted(page_images, key=lambda x: min(rect["y0"] for rect in x["rects"]) if x["rects"] else 0)
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# 为每个题目进行图片分配
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for question_idx, question in page_questions:
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question_images = []
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# 获取题目的边界框信息
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question_bbox = self._get_question_bbox(question)
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# 遍历页面中的图片，寻找合适的匹配
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for img_idx, image in enumerate(page_images_sorted):
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img_filename = image["filename"]
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# 跳过已分配的图片
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if img_filename in used_images:
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continue
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# 检查图片是否适合当前题目
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if self._should_associate_image(question, image, question_bbox):
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question_images.append({
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"filename": image["filename"],
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"path": image["relative_path"],
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"width": image["width"],
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"height": image["height"],
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"format": image["format"]
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})
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used_images.add(img_filename)
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print(f"📝 题目{question.get('number', '?')}关联图片: {img_filename}")
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break # 每个题目最多关联一张图片，除非明确需要多张
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question["images"] = question_images
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if not question_images:
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print(f"📝 题目{question.get('number', '?')}未关联图片")
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return questions
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def _get_question_bbox(self, question: Dict) -> Dict:
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"""
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获取题目的边界框信息
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"""
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# 尝试从不同字段获取边界框
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bbox = question.get("bbox")
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if bbox and isinstance(bbox, dict):
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return bbox
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# 尝试从page_spans获取
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page_spans = question.get("page_spans")
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if page_spans and len(page_spans) > 0:
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span = page_spans[0]
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if isinstance(span, dict) and "bbox" in span:
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return span["bbox"]
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# 默认边界框（整个页面）
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return {"x0": 0, "y0": 0, "x1": 1000, "y1": 1000}
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def _should_associate_image(self, question: Dict, image: Dict, question_bbox: Dict) -> bool:
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"""
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判断图片是否应该关联到当前题目
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"""
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# 策略1: 基于几何位置匹配
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if image.get("rects"):
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for rect in image["rects"]:
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# 检查图片是否在题目的合理范围内
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# 考虑页面布局和图片通常的放置位置
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img_center_y = (rect["y0"] + rect["y1"]) / 2
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question_center_y = (question_bbox.get("y0", 0) + question_bbox.get("y1", 1000)) / 2
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# 如果图片中心在题目中心的合理范围内（上下200点）
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if abs(img_center_y - question_center_y) < 200:
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return True
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# 如果图片在题目下方（通常图片紧跟题目）
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if rect["y0"] > question_bbox.get("y1", 0) and rect["y0"] - question_bbox.get("y1", 0) < 300:
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return True
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# 策略2: 基于题目内容判断
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question_text = question.get("text", "").lower()
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question_type = question.get("type", "")
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# 选择题通常较少有图片
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if question_type in ["single_choice", "multiple_choice"]:
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# 只有明确提到图形时才关联图片
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if any(keyword in question_text for keyword in ["如图", "下图", "图形", "图像", "坐标", "函数图像"]):
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return True
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# 解答题、证明题等更可能有图片
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elif question_type in ["essay", "calculation", "proof"]:
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return True
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# 默认不关联（更保守的策略）
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return False
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def extract_and_associate(self, pdf_path: str, questions: List[Dict]) -> Tuple[List[Dict], List[Dict]]:
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"""
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提取PDF图片并与题目关联
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返回 (更新后的题目列表, 图片列表)
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"""
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# 1. 提取图片
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images = self.extract_images_from_pdf(pdf_path)
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# 2. 关联图片与题目
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updated_questions = self.associate_images_with_questions(images, questions)
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return updated_questions, images
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def clean_unused_images(self, used_images: List[str]):
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"""
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清理未使用的图片文件
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"""
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try:
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if not os.path.exists(self.output_dir):
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return
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# 获取所有图片文件
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all_files = os.listdir(self.output_dir)
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# 删除未使用的文件
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for filename in all_files:
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if filename not in used_images:
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file_path = os.path.join(self.output_dir, filename)
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try:
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os.remove(file_path)
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print(f"🗑️ 清理未使用图片: {filename}")
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except Exception as e:
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print(f"⚠️ 清理文件失败: {filename}, {e}")
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except Exception as e:
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print(f"⚠️ 清理图片失败: {e}")
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# 测试函数
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def test_image_extractor():
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"""
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测试图片提取器
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"""
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pdf_path = "/Users/brunogao/Downloads/深圳市八年级（上）期中数学试卷1.pdf"
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if not os.path.exists(pdf_path):
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print(f"❌ 测试文件不存在: {pdf_path}")
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return
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extractor = PDFImageExtractor()
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# 模拟一些题目数据
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sample_questions = [
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{"number": "1", "page": 1, "text": "题目1内容"},
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{"number": "2", "page": 1, "text": "题目2内容"},
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{"number": "3", "page": 2, "text": "题目3内容"},
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]
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# 提取并关联图片
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updated_questions, images = extractor.extract_and_associate(pdf_path, sample_questions)
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print(f"\n📊 测试结果:")
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print(f"- 提取图片数: {len(images)}")
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print(f"- 处理题目数: {len(updated_questions)}")
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for question in updated_questions:
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if question.get("images"):
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print(f"题目{question['number']}: {len(question['images'])}张图片")
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for img in question["images"]:
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print(f" - {img['filename']} ({img['width']}x{img['height']})")
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if __name__ == "__main__":
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test_image_extractor() |