1. 先pdf转图片

import os
from pdf2image import convert_from_path

# PDF文件路径
pdf_path = '/Users/xxx/2022.pdf'
# 输出图片的文件夹
output_folder = './output_images2022'
# 输出图片的命名格式
output_name = 'page'

# 如果输出文件夹不存在，创建它
if not os.path.exists(output_folder):
    os.makedirs(output_folder)

# 将PDF转换为图像列表，设置分辨率为300 DPI
images = convert_from_path(pdf_path, dpi=300)

# 保存每一页为PNG图片
for i, image in enumerate(images):
    image.save(f'{output_folder}/{output_name}_{i+1}.png', 'PNG')

2. OCR

from PIL import ImageEnhance
import pytesseract
from PIL import Image
from openpyxl import Workbook

# 配置 Tesseract 的路径（如果需要）
# pytesseract.pytesseract.tesseract_cmd = r'/usr/local/bin/tesseract'  # Mac 的路径
# pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'  # Windows 的路径

# 打开图片
# image_path = "/Users/xxx/page_3.png"  # 替换为你的图片路径


def enhance_image(img):
    img = img.convert('L')  # 转灰度
    img = ImageEnhance.Contrast(img).enhance(2.0)
    return img


def allimngs(image_path):

    image = Image.open(image_path)

    image = enhance_image(image)

    # 使用 pytesseract 进行 OCR
    text = pytesseract.image_to_string(image, lang="chi_sim")  # 中文

    # # 打印提取的文本
    # print("提取的文本：")
    # print(text.replace(' ', ''))

    return text.replace(' ', '')


# 统计子字符串出现次数

class TrieNode:
    def __init__(self):
        self.children = {}
        self.keywords = []


class Trie:
    def __init__(self):
        self.root = TrieNode()

    def insert(self, keyword):
        node = self.root
        for char in keyword:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.keywords.append(keyword)


def count_keywords(text, keywords):
    # 去重关键词以确保唯一性
    keywords = list(set(keywords))

    # 构建Trie树
    trie = Trie()
    for kw in keywords:
        trie.insert(kw)

    # 初始化计数器
    counters = {kw: 0 for kw in keywords}
    i = 0
    n = len(text)

    while i < n:
        current_node = trie.root
        max_len = 0
        current_len = 0
        end_pos = i

        # 寻找从当前位置开始的最长匹配关键词
        for j in range(i, n):
            char = text[j]
            if char in current_node.children:
                current_node = current_node.children[char]
                current_len += 1
                if current_node.keywords:  # 当前节点是某个关键词的结尾
                    max_len = current_len
                    end_pos = j + 1  # 更新结束位置为当前字符的下一个位置
            else:
                break  # 无后续匹配，退出循环

        if max_len > 0:
            # 更新所有匹配的关键词计数器
            for kw in current_node.keywords:
                counters[kw] += 1
            i = end_pos  # 跳跃到已匹配部分的末尾
        else:
            i += 1  # 无匹配，移动到下一个字符

    return counters


if __name__ == "__main__":
    keywords = ['矮小',
                '安于现状',
                '暗藏',
                '暗淡',
                '暗黑']
    all_text = ''
    workbook = Workbook()
    sheet = workbook.active

    for i in range(108):
        i = i+1
        image_path = f"/Users/xxx/output_images2022/page_{i}.png"
        all_text = all_text + allimngs(image_path)

    all_text = all_text.replace(' ', '').replace('\n', '')

    result = count_keywords(all_text, keywords)

    num = 1

    for k, v in result.items():
        sheet[f'A{num}'] = k
        sheet[f'B{num}'] = v
        print(k, v, num)
        num = num + 1

    workbook.save(filename='2022.xlsx')