实现PDF批量模糊文本查找功能，支持多个目标文本的相似度匹配，优化文本坐标返回逻辑
This commit is contained in:
@@ -54,23 +54,23 @@ def clean_text_for_fuzzy_match(text):
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# 标准化空白字符
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cleaned = re.sub(r'\s+', ' ', cleaned.strip())
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return cleaned
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def find_fuzzy_text_positions(pdf_path, target_text, similarity_threshold=0.8):
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def find_fuzzy_text_positions_batch(pdf_path, target_texts, similarity_threshold=0.8):
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"""
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在PDF中模糊查找指定文本并返回坐标
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在PDF中批量模糊查找指定文本并返回坐标
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Args:
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pdf_path (str): PDF文件路径
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target_text (str): 要查找的文本
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target_texts (list): 要查找的文本列表
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similarity_threshold (float): 相似度阈值 (0-1)，默认0.8
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Returns:
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list: 包含匹配文本坐标信息的列表
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dict: 以target_text为键，包含匹配文本坐标信息列表为值的字典
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"""
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if not os.path.exists(pdf_path):
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raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
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# 清理目标文本
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cleaned_target = clean_text_for_fuzzy_match(target_text)
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# 初始化结果字典
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batch_results = {text: [] for text in target_texts}
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# 打开本地PDF文件
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with open(pdf_path, 'rb') as fp:
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@@ -82,366 +82,138 @@ def find_fuzzy_text_positions(pdf_path, target_text, similarity_threshold=0.8):
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device = PDFPageAggregator(rsrcmgr, laparams=laparams)
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interpreter = PDFPageInterpreter(rsrcmgr, device)
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found_positions = []
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# 处理每一页
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pages_chars = []
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for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
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interpreter.process_page(page)
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layout = device.get_result()
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char_list = parse_char_layout(layout)
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# 将页面字符组合成文本
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page_text = ''.join([char_info['char'] for char_info in char_list])
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cleaned_page_text = clean_text_for_fuzzy_match(page_text)
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# 滑动窗口查找相似文本
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pages_chars.append((page_num, char_list))
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# 为每个目标文本进行查找
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for target_text in target_texts:
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# 清理目标文本
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cleaned_target = clean_text_for_fuzzy_match(target_text)
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target_len = len(cleaned_target)
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if target_len == 0:
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continue
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found_positions = []
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# 存储所有匹配的块
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matches = []
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for i in range(len(cleaned_page_text) - target_len + 1):
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window_text = cleaned_page_text[i:i + target_len]
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similarity = SequenceMatcher(None, cleaned_target, window_text).ratio()
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# 在每一页中查找
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for page_num, char_list in pages_chars:
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# 将页面字符组合成文本
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page_text = ''.join([char_info['char'] for char_info in char_list])
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cleaned_page_text = clean_text_for_fuzzy_match(page_text)
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if similarity >= similarity_threshold:
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# 找到匹配项，记录位置和相似度
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if i < len(char_list):
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matches.append({
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'start_idx': i,
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'end_idx': min(i + target_len - 1, len(char_list) - 1),
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'similarity': similarity
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})
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# 合并相邻的匹配块
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if matches:
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# 按起始位置排序
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matches.sort(key=lambda x: x['start_idx'])
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# 合并相邻或重叠的匹配块
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merged_matches = []
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current_match = matches[0].copy() # 创建副本
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for i in range(1, len(matches)):
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next_match = matches[i]
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# 如果下一个匹配块与当前块相邻或重叠，则合并
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# 判断条件：下一个块的起始位置 <= 当前块的结束位置 + 一些缓冲距离
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if next_match['start_idx'] <= current_match['end_idx'] + min(target_len, 10):
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# 合并索引范围
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current_match['start_idx'] = min(current_match['start_idx'], next_match['start_idx'])
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current_match['end_idx'] = max(current_match['end_idx'], next_match['end_idx'])
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# 计算加权平均相似度
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total_length = (current_match['end_idx'] - current_match['start_idx'] + 1) + \
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(next_match['end_idx'] - next_match['start_idx'] + 1)
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current_match['similarity'] = (
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current_match['similarity'] * (current_match['end_idx'] - current_match['start_idx'] + 1) +
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next_match['similarity'] * (next_match['end_idx'] - next_match['start_idx'] + 1)
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) / total_length
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else:
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# 不相邻，保存当前块，开始新的块
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merged_matches.append(current_match)
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current_match = next_match.copy() # 创建副本
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# 添加最后一个块
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merged_matches.append(current_match)
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# 为每个合并后的块生成坐标信息
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for match in merged_matches:
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start_idx = match['start_idx']
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end_idx = match['end_idx']
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# 滑动窗口查找相似文本
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matches = []
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for i in range(len(cleaned_page_text) - target_len + 1):
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window_text = cleaned_page_text[i:i + target_len]
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similarity = SequenceMatcher(None, cleaned_target, window_text).ratio()
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if start_idx < len(char_list) and end_idx < len(char_list):
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# 获取匹配区域的所有字符
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matched_chars = char_list[start_idx:end_idx+1]
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if similarity >= similarity_threshold:
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# 找到匹配项，记录位置和相似度
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if i < len(char_list):
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matches.append({
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'start_idx': i,
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'end_idx': min(i + target_len - 1, len(char_list) - 1),
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'similarity': similarity
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})
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# 合并相邻的匹配块
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if matches:
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# 按起始位置排序
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matches.sort(key=lambda x: x['start_idx'])
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# 合并相邻或重叠的匹配块
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merged_matches = []
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current_match = matches[0].copy() # 创建副本
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for i in range(1, len(matches)):
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next_match = matches[i]
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# 如果下一个匹配块与当前块相邻或重叠，则合并
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# 判断条件：下一个块的起始位置 <= 当前块的结束位置 + 一些缓冲距离
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if next_match['start_idx'] <= current_match['end_idx'] + min(target_len, 10):
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# 合并索引范围
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current_match['start_idx'] = min(current_match['start_idx'], next_match['start_idx'])
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current_match['end_idx'] = max(current_match['end_idx'], next_match['end_idx'])
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# 计算加权平均相似度
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total_length = (current_match['end_idx'] - current_match['start_idx'] + 1) + \
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(next_match['end_idx'] - next_match['start_idx'] + 1)
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current_match['similarity'] = (
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current_match['similarity'] * (current_match['end_idx'] - current_match['start_idx'] + 1) +
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next_match['similarity'] * (next_match['end_idx'] - next_match['start_idx'] + 1)
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) / total_length
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else:
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# 不相邻，保存当前块，开始新的块
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merged_matches.append(current_match)
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current_match = next_match.copy() # 创建副本
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# 添加最后一个块
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merged_matches.append(current_match)
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# 为每个合并后的块生成坐标信息
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for match in merged_matches:
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start_idx = match['start_idx']
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end_idx = match['end_idx']
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# 过滤掉坐标为0的字符（通常是特殊字符）
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valid_chars = [char for char in matched_chars
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if char['x'] > 0 and char['y'] > 0]
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# 如果没有有效字符，则使用所有字符
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chars_to_use = valid_chars if valid_chars else matched_chars
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# 计算边界框 (left, right, top, bottom)
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if chars_to_use:
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# 计算边界值
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left = min([char['x'] for char in chars_to_use])
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right = max([char['x'] for char in chars_to_use])
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bottom = min([char['y'] for char in chars_to_use])
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top = max([char['y'] for char in chars_to_use])
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if start_idx < len(char_list) and end_idx < len(char_list):
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# 获取匹配区域的所有字符
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matched_chars = char_list[start_idx:end_idx+1]
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# 获取匹配的文本内容
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matched_text = ''.join([char_info['char'] for char_info in chars_to_use])
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# 过滤掉坐标为0的字符（通常是特殊字符）
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valid_chars = [char for char in matched_chars
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if char['x'] > 0 and char['y'] > 0]
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# 只有当边界框有效时才添加结果
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if left >= 0 and right > left and top > bottom:
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position = [
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page_num,
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left, # left
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right, # right
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top, # top
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bottom, # bottom
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matched_text, # 添加匹配的内容
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match['similarity'] # 添加相似度信息
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]
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found_positions.append(position)
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return found_positions
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# 如果没有有效字符，则使用所有字符
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chars_to_use = valid_chars if valid_chars else matched_chars
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# 计算边界框 (left, right, top, bottom)
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if chars_to_use:
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# 计算边界值
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left = min([char['x'] for char in chars_to_use])
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right = max([char['x'] for char in chars_to_use])
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bottom = min([char['y'] for char in chars_to_use])
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top = max([char['y'] for char in chars_to_use])
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# 获取匹配的文本内容
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matched_text = ''.join([char_info['char'] for char_info in chars_to_use])
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# 只有当边界框有效时才添加结果
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if left >= 0 and right > left and top > bottom:
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position = [
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page_num,
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left, # left
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right, # right
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top, # top
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bottom, # bottom
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matched_text, # 添加匹配的内容
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match['similarity'] # 添加相似度信息
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]
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found_positions.append(position)
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batch_results[target_text] = found_positions
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return batch_results
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def find_text_positions_batch(pdf_path, target_texts):
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"""
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在PDF中模糊查找指定文本并返回坐标
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在PDF中批量查找指定文本并返回坐标
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Args:
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pdf_path (str): PDF文件路径
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target_text (str): 要查找的文本
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similarity_threshold (float): 相似度阈值 (0-1)，默认0.8
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target_texts (list): 要查找的文本列表
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Returns:
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list: 包含匹配文本坐标信息的列表
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dict: 以target_text为键，包含匹配文本坐标信息列表为值的字典
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"""
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if not os.path.exists(pdf_path):
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raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
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# 清理目标文本
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cleaned_target = clean_text_for_fuzzy_match(target_text)
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# 打开本地PDF文件
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with open(pdf_path, 'rb') as fp:
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parser = PDFParser(fp)
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doc = PDFDocument(parser)
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rsrcmgr = PDFResourceManager()
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laparams = LAParams()
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device = PDFPageAggregator(rsrcmgr, laparams=laparams)
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interpreter = PDFPageInterpreter(rsrcmgr, device)
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found_positions = []
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# 处理每一页
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for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
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interpreter.process_page(page)
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layout = device.get_result()
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char_list = parse_char_layout(layout)
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# 将页面字符组合成文本
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page_text = ''.join([char_info['char'] for char_info in char_list])
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cleaned_page_text = clean_text_for_fuzzy_match(page_text)
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# 滑动窗口查找相似文本
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target_len = len(cleaned_target)
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if target_len == 0:
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continue
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# 存储所有匹配的块
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matches = []
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for i in range(len(cleaned_page_text) - target_len + 1):
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window_text = cleaned_page_text[i:i + target_len]
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similarity = SequenceMatcher(None, cleaned_target, window_text).ratio()
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if similarity >= similarity_threshold:
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# 找到匹配项，记录位置和相似度
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if i < len(char_list):
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matches.append({
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'start_idx': i,
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'end_idx': min(i + target_len - 1, len(char_list) - 1),
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'similarity': similarity
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})
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# 合并相邻的匹配块
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if matches:
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# 按起始位置排序
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matches.sort(key=lambda x: x['start_idx'])
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# 合并相邻或重叠的匹配块
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merged_matches = []
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current_match = matches[0].copy() # 创建副本
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for i in range(1, len(matches)):
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next_match = matches[i]
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# 如果下一个匹配块与当前块相邻或重叠，则合并
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# 判断条件：下一个块的起始位置 <= 当前块的结束位置 + 一些缓冲距离
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if next_match['start_idx'] <= current_match['end_idx'] + min(target_len, 10):
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# 合并索引范围
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current_match['start_idx'] = min(current_match['start_idx'], next_match['start_idx'])
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current_match['end_idx'] = max(current_match['end_idx'], next_match['end_idx'])
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# 计算加权平均相似度
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total_length = (current_match['end_idx'] - current_match['start_idx'] + 1) + \
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(next_match['end_idx'] - next_match['start_idx'] + 1)
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current_match['similarity'] = (
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current_match['similarity'] * (current_match['end_idx'] - current_match['start_idx'] + 1) +
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next_match['similarity'] * (next_match['end_idx'] - next_match['start_idx'] + 1)
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) / total_length
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else:
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# 不相邻，保存当前块，开始新的块
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merged_matches.append(current_match)
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current_match = next_match.copy() # 创建副本
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# 添加最后一个块
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merged_matches.append(current_match)
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# 为每个合并后的块生成坐标信息
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for match in merged_matches:
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start_idx = match['start_idx']
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end_idx = match['end_idx']
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if start_idx < len(char_list) and end_idx < len(char_list):
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# 获取匹配区域的所有字符
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matched_chars = char_list[start_idx:end_idx+1]
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# 计算边界框 (left, right, top, bottom)
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if matched_chars:
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# 计算边界值
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left = min([char['x'] for char in matched_chars])
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right = max([char['x'] for char in matched_chars])
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bottom = min([char['y'] for char in matched_chars])
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top = max([char['y'] for char in matched_chars])
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# 获取匹配的文本内容
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matched_text = ''.join([char_info['char'] for char_info in matched_chars])
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position = [
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page_num,
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left, # left
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right, # right
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top, # top
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bottom, # bottom
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matched_text, # 添加匹配的内容
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match['similarity'] # 添加相似度信息
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]
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found_positions.append(position)
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return found_positions
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"""
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在PDF中模糊查找指定文本并返回坐标
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Args:
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pdf_path (str): PDF文件路径
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target_text (str): 要查找的文本
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similarity_threshold (float): 相似度阈值 (0-1)，默认0.8
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Returns:
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list: 包含匹配文本坐标信息的列表
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"""
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if not os.path.exists(pdf_path):
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raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
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# 清理目标文本
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cleaned_target = clean_text_for_fuzzy_match(target_text)
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# 打开本地PDF文件
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with open(pdf_path, 'rb') as fp:
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parser = PDFParser(fp)
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doc = PDFDocument(parser)
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rsrcmgr = PDFResourceManager()
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laparams = LAParams()
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device = PDFPageAggregator(rsrcmgr, laparams=laparams)
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interpreter = PDFPageInterpreter(rsrcmgr, device)
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found_positions = []
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# 处理每一页
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for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
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interpreter.process_page(page)
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||||
layout = device.get_result()
|
||||
char_list = parse_char_layout(layout)
|
||||
|
||||
# 将页面字符组合成文本
|
||||
page_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
cleaned_page_text = clean_text_for_fuzzy_match(page_text)
|
||||
|
||||
# 滑动窗口查找相似文本
|
||||
target_len = len(cleaned_target)
|
||||
if target_len == 0:
|
||||
continue
|
||||
|
||||
# 存储所有匹配的块
|
||||
matches = []
|
||||
for i in range(len(cleaned_page_text) - target_len + 1):
|
||||
window_text = cleaned_page_text[i:i + target_len]
|
||||
similarity = SequenceMatcher(None, cleaned_target, window_text).ratio()
|
||||
|
||||
if similarity >= similarity_threshold:
|
||||
# 找到匹配项，记录位置和相似度
|
||||
if i < len(char_list):
|
||||
matches.append({
|
||||
'start_idx': i,
|
||||
'end_idx': min(i + target_len - 1, len(char_list) - 1),
|
||||
'similarity': similarity
|
||||
})
|
||||
|
||||
# 合并相邻的匹配块
|
||||
if matches:
|
||||
# 按起始位置排序
|
||||
matches.sort(key=lambda x: x['start_idx'])
|
||||
|
||||
# 合并相邻或重叠的匹配块
|
||||
merged_matches = []
|
||||
current_match = matches[0]
|
||||
|
||||
for i in range(1, len(matches)):
|
||||
next_match = matches[i]
|
||||
# 如果下一个匹配块与当前块相邻或重叠，则合并
|
||||
if next_match['start_idx'] <= current_match['end_idx'] + target_len:
|
||||
# 合并索引范围
|
||||
current_match['end_idx'] = max(current_match['end_idx'], next_match['end_idx'])
|
||||
# 平均相似度
|
||||
current_match['similarity'] = (current_match['similarity'] + next_match['similarity']) / 2
|
||||
else:
|
||||
# 不相邻，保存当前块，开始新的块
|
||||
merged_matches.append(current_match)
|
||||
current_match = next_match
|
||||
|
||||
# 添加最后一个块
|
||||
merged_matches.append(current_match)
|
||||
|
||||
# 为每个合并后的块生成坐标信息
|
||||
for match in merged_matches:
|
||||
start_idx = match['start_idx']
|
||||
end_idx = match['end_idx']
|
||||
|
||||
if start_idx < len(char_list) and end_idx < len(char_list):
|
||||
# 获取匹配区域的所有字符
|
||||
matched_chars = char_list[start_idx:end_idx+1]
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
if matched_chars:
|
||||
# 计算边界值
|
||||
left = min([char['x'] for char in matched_chars])
|
||||
right = max([char['x'] for char in matched_chars])
|
||||
bottom = min([char['y'] for char in matched_chars])
|
||||
top = max([char['y'] for char in matched_chars])
|
||||
|
||||
# 获取匹配的文本内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in matched_chars])
|
||||
|
||||
position = [
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
matched_text, # 添加匹配的内容
|
||||
match['similarity'] # 添加相似度信息
|
||||
]
|
||||
found_positions.append(position)
|
||||
|
||||
return found_positions
|
||||
def find_text_positions(pdf_path, target_text):
|
||||
"""
|
||||
在PDF中查找指定文本并返回坐标
|
||||
|
||||
Args:
|
||||
pdf_path (str): PDF文件路径
|
||||
target_text (str): 要查找的文本
|
||||
|
||||
Returns:
|
||||
list: 包含匹配文本坐标信息的列表
|
||||
"""
|
||||
if not os.path.exists(pdf_path):
|
||||
raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
|
||||
|
||||
# 标准化目标文本
|
||||
normalized_target = normalize_text(target_text)
|
||||
# 初始化结果字典
|
||||
batch_results = {text: [] for text in target_texts}
|
||||
|
||||
# 打开本地PDF文件
|
||||
with open(pdf_path, 'rb') as fp:
|
||||
@@ -468,101 +240,32 @@ def find_text_positions(pdf_path, target_text):
|
||||
full_text = ''.join([char_info['char'] for char_info in all_chars])
|
||||
normalized_full_text = normalize_text(full_text)
|
||||
|
||||
# 在标准化文本中查找目标文本
|
||||
found_positions = []
|
||||
start = 0
|
||||
while True:
|
||||
pos = normalized_full_text.find(normalized_target, start)
|
||||
if pos == -1:
|
||||
break
|
||||
# 为每个目标文本查找位置
|
||||
for target_text in target_texts:
|
||||
# 标准化目标文本
|
||||
normalized_target = normalize_text(target_text)
|
||||
|
||||
# 找到匹配项，获取对应的坐标信息
|
||||
if pos < len(all_chars):
|
||||
start_char = all_chars[pos]
|
||||
end_pos = pos + len(normalized_target) - 1
|
||||
if end_pos < len(all_chars):
|
||||
end_char = all_chars[end_pos]
|
||||
# 确定在哪一页
|
||||
page_num = 1
|
||||
for i, page_start in enumerate(page_start_indices):
|
||||
if pos >= page_start:
|
||||
page_num = i + 1
|
||||
|
||||
# 获取匹配的文本内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in all_chars[pos:pos+len(normalized_target)]])
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
left = min(start_char['x'], end_char['x'])
|
||||
right = max(start_char['x'], end_char['x'])
|
||||
bottom = min(start_char['y'], end_char['y'])
|
||||
top = max(start_char['y'], end_char['y'])
|
||||
|
||||
position=[
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
matched_text, # 添加匹配的内容
|
||||
1.0 # 添加相似度信息（精确匹配为1.0）
|
||||
]
|
||||
found_positions.append(position)
|
||||
|
||||
start = pos + 1
|
||||
|
||||
return found_positions
|
||||
|
||||
def find_text_in_pdf_per_page(pdf_path, target_text):
|
||||
"""
|
||||
在PDF中逐页查找指定文本并返回坐标
|
||||
|
||||
Args:
|
||||
pdf_path (str): PDF文件路径
|
||||
target_text (str): 要查找的文本
|
||||
|
||||
Returns:
|
||||
list: 包含匹配文本坐标信息的列表
|
||||
"""
|
||||
if not os.path.exists(pdf_path):
|
||||
raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
|
||||
|
||||
# 标准化目标文本
|
||||
normalized_target = normalize_text(target_text)
|
||||
|
||||
# 打开本地PDF文件
|
||||
with open(pdf_path, 'rb') as fp:
|
||||
parser = PDFParser(fp)
|
||||
doc = PDFDocument(parser)
|
||||
|
||||
rsrcmgr = PDFResourceManager()
|
||||
laparams = LAParams()
|
||||
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
|
||||
interpreter = PDFPageInterpreter(rsrcmgr, device)
|
||||
|
||||
found_positions = []
|
||||
|
||||
# 处理每一页
|
||||
for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
|
||||
interpreter.process_page(page)
|
||||
layout = device.get_result()
|
||||
char_list = parse_char_layout(layout)
|
||||
|
||||
# 将页面字符组合成文本并标准化
|
||||
page_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
normalized_page_text = normalize_text(page_text)
|
||||
|
||||
# 在页面文本中查找目标文本
|
||||
pos = normalized_page_text.find(normalized_target)
|
||||
if pos != -1:
|
||||
found_positions = []
|
||||
start = 0
|
||||
while True:
|
||||
pos = normalized_full_text.find(normalized_target, start)
|
||||
if pos == -1:
|
||||
break
|
||||
|
||||
# 找到匹配项，获取对应的坐标信息
|
||||
if pos < len(char_list):
|
||||
start_char = char_list[pos]
|
||||
if pos < len(all_chars):
|
||||
start_char = all_chars[pos]
|
||||
end_pos = pos + len(normalized_target) - 1
|
||||
if end_pos < len(char_list):
|
||||
end_char = char_list[end_pos]
|
||||
if end_pos < len(all_chars):
|
||||
end_char = all_chars[end_pos]
|
||||
# 确定在哪一页
|
||||
page_num = 1
|
||||
for i, page_start in enumerate(page_start_indices):
|
||||
if pos >= page_start:
|
||||
page_num = i + 1
|
||||
|
||||
# 获取匹配的文本内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in char_list[pos:pos+len(normalized_target)]])
|
||||
matched_text = ''.join([char_info['char'] for char_info in all_chars[pos:pos+len(normalized_target)]])
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
left = min(start_char['x'], end_char['x'])
|
||||
@@ -570,41 +273,37 @@ def find_text_in_pdf_per_page(pdf_path, target_text):
|
||||
bottom = min(start_char['y'], end_char['y'])
|
||||
top = max(start_char['y'], end_char['y'])
|
||||
|
||||
position=[
|
||||
position = [
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
matched_text, # 添加匹配的内容
|
||||
1.0 # 添加相似度信息（精确匹配为1.0）
|
||||
]
|
||||
found_positions.append(position)
|
||||
|
||||
return found_positions
|
||||
|
||||
start = pos + 1
|
||||
|
||||
batch_results[target_text] = found_positions
|
||||
|
||||
return batch_results
|
||||
|
||||
def find_partial_text_positions(pdf_path, target_text, min_match_ratio=0.7):
|
||||
def find_text_in_pdf_per_page_batch(pdf_path, target_texts):
|
||||
"""
|
||||
查找部分匹配的文本（适用于较长的文本）
|
||||
在PDF中逐页批量查找指定文本并返回坐标
|
||||
|
||||
Args:
|
||||
pdf_path (str): PDF文件路径
|
||||
target_text (str): 要查找的文本
|
||||
min_match_ratio (float): 最小匹配比例 (0-1)
|
||||
target_texts (list): 要查找的文本列表
|
||||
|
||||
Returns:
|
||||
list: 包含匹配文本坐标信息的列表
|
||||
dict: 以target_text为键，包含匹配文本坐标信息列表为值的字典
|
||||
"""
|
||||
if not os.path.exists(pdf_path):
|
||||
raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
|
||||
|
||||
# 将目标文本分割成关键词或短语
|
||||
normalized_target = normalize_text(target_text)
|
||||
# 提取关键词（移除常见停用词后的词）
|
||||
keywords = [word for word in normalized_target.split() if len(word) > 2]
|
||||
|
||||
if not keywords:
|
||||
keywords = normalized_target.split() # 如果没有长词，则使用所有词
|
||||
# 初始化结果字典
|
||||
batch_results = {text: [] for text in target_texts}
|
||||
|
||||
# 打开本地PDF文件
|
||||
with open(pdf_path, 'rb') as fp:
|
||||
@@ -616,7 +315,77 @@ def find_partial_text_positions(pdf_path, target_text, min_match_ratio=0.7):
|
||||
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
|
||||
interpreter = PDFPageInterpreter(rsrcmgr, device)
|
||||
|
||||
found_positions = []
|
||||
# 处理每一页
|
||||
for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
|
||||
interpreter.process_page(page)
|
||||
layout = device.get_result()
|
||||
char_list = parse_char_layout(layout)
|
||||
|
||||
# 将页面字符组合成文本并标准化
|
||||
page_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
normalized_page_text = normalize_text(page_text)
|
||||
|
||||
# 为每个目标文本在当前页查找
|
||||
for target_text in target_texts:
|
||||
normalized_target = normalize_text(target_text)
|
||||
|
||||
# 在页面文本中查找目标文本
|
||||
pos = normalized_page_text.find(normalized_target)
|
||||
if pos != -1:
|
||||
# 找到匹配项，获取对应的坐标信息
|
||||
if pos < len(char_list):
|
||||
start_char = char_list[pos]
|
||||
end_pos = pos + len(normalized_target) - 1
|
||||
if end_pos < len(char_list):
|
||||
end_char = char_list[end_pos]
|
||||
|
||||
# 获取匹配的文本内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in char_list[pos:pos+len(normalized_target)]])
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
left = min(start_char['x'], end_char['x'])
|
||||
right = max(start_char['x'], end_char['x'])
|
||||
bottom = min(start_char['y'], end_char['y'])
|
||||
top = max(start_char['y'], end_char['y'])
|
||||
|
||||
position = [
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
]
|
||||
batch_results[target_text].append(position)
|
||||
|
||||
return batch_results
|
||||
|
||||
def find_partial_text_positions_batch(pdf_path, target_texts, min_match_ratio=0.7):
|
||||
"""
|
||||
批量查找部分匹配的文本（适用于较长的文本）
|
||||
|
||||
Args:
|
||||
pdf_path (str): PDF文件路径
|
||||
target_texts (list): 要查找的文本列表
|
||||
min_match_ratio (float): 最小匹配比例 (0-1)
|
||||
|
||||
Returns:
|
||||
dict: 以target_text为键，包含匹配文本坐标信息列表为值的字典
|
||||
"""
|
||||
if not os.path.exists(pdf_path):
|
||||
raise FileNotFoundError(f"PDF文件不存在: {pdf_path}")
|
||||
|
||||
# 初始化结果字典
|
||||
batch_results = {text: [] for text in target_texts}
|
||||
|
||||
# 打开本地PDF文件
|
||||
with open(pdf_path, 'rb') as fp:
|
||||
parser = PDFParser(fp)
|
||||
doc = PDFDocument(parser)
|
||||
|
||||
rsrcmgr = PDFResourceManager()
|
||||
laparams = LAParams()
|
||||
device = PDFPageAggregator(rsrcmgr, laparams=laparams)
|
||||
interpreter = PDFPageInterpreter(rsrcmgr, device)
|
||||
|
||||
# 处理每一页
|
||||
for page_num, page in enumerate(PDFPage.create_pages(doc), 1):
|
||||
@@ -628,90 +397,136 @@ def find_partial_text_positions(pdf_path, target_text, min_match_ratio=0.7):
|
||||
page_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
normalized_page_text = normalize_text(page_text)
|
||||
|
||||
# 计算匹配的关键词数量
|
||||
matched_keywords = 0
|
||||
for keyword in keywords:
|
||||
if keyword in normalized_page_text:
|
||||
matched_keywords += 1
|
||||
|
||||
# 如果匹配的关键词比例超过阈值，则认为找到匹配
|
||||
if len(keywords) > 0 and (matched_keywords / len(keywords)) >= min_match_ratio:
|
||||
# 简单起见，返回页面第一个字符和最后一个字符的坐标
|
||||
if char_list:
|
||||
start_char = char_list[0]
|
||||
end_char = char_list[-1]
|
||||
match_ratio = matched_keywords / len(keywords)
|
||||
# 为每个目标文本计算匹配
|
||||
for target_text in target_texts:
|
||||
# 将目标文本分割成关键词或短语
|
||||
normalized_target = normalize_text(target_text)
|
||||
# 提取关键词（移除常见停用词后的词）
|
||||
keywords = [word for word in normalized_target.split() if len(word) > 2]
|
||||
|
||||
if not keywords:
|
||||
keywords = normalized_target.split() # 如果没有长词，则使用所有词
|
||||
|
||||
if not keywords:
|
||||
continue
|
||||
|
||||
# 获取页面文本作为匹配内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
left = min(start_char['x'], end_char['x'])
|
||||
right = max(start_char['x'], end_char['x'])
|
||||
bottom = min(start_char['y'], end_char['y'])
|
||||
top = max(start_char['y'], end_char['y'])
|
||||
|
||||
position = [
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
matched_text[:100] + "..." if len(matched_text) > 100 else matched_text, # 添加匹配的内容（限制长度）
|
||||
match_ratio # 添加匹配比例信息
|
||||
]
|
||||
found_positions.append(position)
|
||||
|
||||
return found_positions
|
||||
def smart_fuzzy_find_text(pdf_path, target_text, similarity_threshold=0.8):
|
||||
# 计算匹配的关键词数量
|
||||
matched_keywords = 0
|
||||
for keyword in keywords:
|
||||
if keyword in normalized_page_text:
|
||||
matched_keywords += 1
|
||||
|
||||
# 如果匹配的关键词比例超过阈值，则认为找到匹配
|
||||
if len(keywords) > 0 and (matched_keywords / len(keywords)) >= min_match_ratio:
|
||||
# 简单起见，返回页面第一个字符和最后一个字符的坐标
|
||||
if char_list:
|
||||
start_char = char_list[0]
|
||||
end_char = char_list[-1]
|
||||
match_ratio = matched_keywords / len(keywords)
|
||||
|
||||
# 获取页面文本作为匹配内容
|
||||
matched_text = ''.join([char_info['char'] for char_info in char_list])
|
||||
|
||||
# 计算边界框 (left, right, top, bottom)
|
||||
left = min(start_char['x'], end_char['x'])
|
||||
right = max(start_char['x'], end_char['x'])
|
||||
bottom = min(start_char['y'], end_char['y'])
|
||||
top = max(start_char['y'], end_char['y'])
|
||||
|
||||
position = [
|
||||
page_num,
|
||||
left, # left
|
||||
right, # right
|
||||
top, # top
|
||||
bottom, # bottom
|
||||
]
|
||||
batch_results[target_text].append(position)
|
||||
|
||||
return batch_results
|
||||
|
||||
def smart_fuzzy_find_text_batch(pdf_path, target_texts, similarity_threshold=0.8):
|
||||
"""
|
||||
智能模糊文本查找，结合多种方法
|
||||
智能批量模糊文本查找，结合多种方法
|
||||
|
||||
Args:
|
||||
pdf_path (str): PDF文件路径
|
||||
target_text (str): 要查找的文本
|
||||
target_texts (list): 要查找的文本列表
|
||||
similarity_threshold (float): 相似度阈值
|
||||
|
||||
Returns:
|
||||
list: 包含匹配文本坐标信息的列表
|
||||
dict: 以target_text为键，包含匹配文本坐标信息列表为值的字典
|
||||
"""
|
||||
# 初始化结果字典
|
||||
batch_results = {text: [] for text in target_texts}
|
||||
|
||||
# 方法1: 精确匹配
|
||||
exact_results = find_text_in_pdf_per_page(pdf_path, target_text)
|
||||
if exact_results:
|
||||
return exact_results
|
||||
exact_results = find_text_in_pdf_per_page_batch(pdf_path, target_texts)
|
||||
|
||||
# 方法2: 模糊匹配
|
||||
fuzzy_results = find_fuzzy_text_positions(pdf_path, target_text, similarity_threshold)
|
||||
if fuzzy_results:
|
||||
return fuzzy_results
|
||||
# 对于已经找到精确匹配的文本，直接使用结果
|
||||
remaining_texts = []
|
||||
for text in target_texts:
|
||||
if exact_results.get(text):
|
||||
batch_results[text] = exact_results[text]
|
||||
else:
|
||||
remaining_texts.append(text)
|
||||
|
||||
# 方法3: 部分匹配（关键词匹配）
|
||||
partial_results = find_partial_text_positions(pdf_path, target_text, 0.5)
|
||||
return partial_results
|
||||
if not remaining_texts:
|
||||
return batch_results
|
||||
|
||||
# 方法2: 模糊匹配（仅对未找到精确匹配的文本）
|
||||
fuzzy_results = find_fuzzy_text_positions_batch(pdf_path, remaining_texts, similarity_threshold)
|
||||
|
||||
# 更新结果
|
||||
for text in remaining_texts:
|
||||
if fuzzy_results.get(text):
|
||||
batch_results[text] = fuzzy_results[text]
|
||||
remaining_texts = [t for t in remaining_texts if t != text] # 从剩余文本中移除
|
||||
|
||||
if not remaining_texts:
|
||||
return batch_results
|
||||
|
||||
# 方法3: 部分匹配（关键词匹配，仅对仍未找到匹配的文本）
|
||||
partial_results = find_partial_text_positions_batch(pdf_path, remaining_texts, 0.5)
|
||||
|
||||
# 更新最终结果
|
||||
for text in remaining_texts:
|
||||
if partial_results.get(text):
|
||||
batch_results[text] = partial_results[text]
|
||||
|
||||
return batch_results
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 使用本地PDF文件
|
||||
pdf_file_path = 'F:\\gitea\\ragflow_api_test\\ai协作方式.pdf' # 修改为你的PDF文件路径
|
||||
target_text = '''创建 `plan` 文件: 固化和锁定最终的"怎么做"
|
||||
• 基于 `plan` 执行: 精准驱动 AI 完成任务'''
|
||||
target_texts = [
|
||||
'''创建 `plan` 文件: 固化和锁定最终的"怎么做"
|
||||
• 基于 `plan` 执行: 精准驱动 AI 完成任务''',
|
||||
"其他要查找的文本1",
|
||||
"其他要查找的文本2"
|
||||
]
|
||||
|
||||
try:
|
||||
print("智能模糊查找:")
|
||||
positions = smart_fuzzy_find_text(pdf_file_path, target_text, similarity_threshold=0.7)
|
||||
print("批量智能模糊查找:")
|
||||
batch_positions = smart_fuzzy_find_text_batch(pdf_file_path, target_texts, similarity_threshold=0.7)
|
||||
|
||||
if positions:
|
||||
print(f"找到文本在以下位置:")
|
||||
for pos in positions:
|
||||
if len(pos) >= 7: # 包含匹配内容和相似度信息
|
||||
print(f"页面: {pos[0]}, 边界框: Left({pos[1]:.2f}), Right({pos[2]:.2f}), Top({pos[3]:.2f}), Bottom({pos[4]:.2f}), 相似度: {pos[6]:.2f}")
|
||||
print(f"匹配内容: {pos[5][:50]}{'...' if len(pos[5]) > 50 else ''}")
|
||||
print("-" * 50)
|
||||
else:
|
||||
print(f"页面: {pos[0]}, 边界框: Left({pos[1]:.2f}), Right({pos[2]:.2f}), Top({pos[3]:.2f}), Bottom({pos[4]:.2f})")
|
||||
else:
|
||||
print("未找到文本")
|
||||
|
||||
for target_text, positions in batch_positions.items():
|
||||
print(f"\n查找文本: {target_text[:50]}{'...' if len(target_text) > 50 else ''}")
|
||||
if positions:
|
||||
print(f"找到文本在以下位置:")
|
||||
for pos in positions:
|
||||
if len(pos) >= 6: # 包含匹配内容和相似度信息
|
||||
print(f"页面: {pos[0]}, 边界框: Left({pos[1]:.2f}), Right({pos[2]:.2f}), Top({pos[3]:.2f}), Bottom({pos[4]:.2f})")
|
||||
if len(pos) >= 7: # 包含相似度信息
|
||||
print(f"相似度: {pos[6]:.2f}")
|
||||
if len(pos) >= 6: # 包含匹配内容
|
||||
print(f"匹配内容: {pos[5][:50]}{'...' if len(pos[5]) > 50 else ''}")
|
||||
print("-" * 50)
|
||||
else:
|
||||
print(f"页面: {pos[0]}, 边界框: Left({pos[1]:.2f}), Right({pos[2]:.2f}), Top({pos[3]:.2f}), Bottom({pos[4]:.2f})")
|
||||
else:
|
||||
print("未找到文本")
|
||||
|
||||
except FileNotFoundError as e:
|
||||
print(e)
|
||||
except Exception as e:
|
||||
print(f"处理PDF时出错: {e}")
|
||||
print(f"处理PDF时出错: {e}")
|
||||
|
||||
Reference in New Issue
Block a user