xiaji
4178bfed06
- 新增div签名机制用于内容去重 - 实现基于最后一个div位置的智能滚动计算 - 合并开始/停止按钮为单一操作按钮 - 增加处理进度和滚动距离的详细日志 - 优化UI状态显示和提示信息
762 lines
27 KiB
Python
762 lines
27 KiB
Python
"""
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滚动截屏OCR工具
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功能：通过热键激活，手动框选区域后，自动滚动截屏并进行OCR识别
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"""
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import json
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import time
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import base64
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import io
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import tempfile
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from dataclasses import dataclass, field
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from typing import List, Tuple, Optional, Callable, Dict
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from pathlib import Path
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import cv2
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import numpy as np
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import requests
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from PIL import Image
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import pyautogui
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import keyboard
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import mouse
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from loguru import logger
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from umi_ocr_client import UmiOCRClient, check_and_wait_for_service
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@dataclass
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class DivRegion:
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"""div区域数据结构"""
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top: int
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bottom: int
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left: int
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right: int
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text: str = ""
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@property
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def height(self) -> int:
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return self.bottom - self.top
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@property
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def width(self) -> int:
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return self.right - self.left
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@dataclass
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class GapInfo:
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"""空白间隔信息"""
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start_row: int
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end_row: int
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@property
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def height(self) -> int:
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return self.end_row - self.start_row
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@dataclass
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class AnalysisResult:
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"""图像分析结果"""
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divs: List[DivRegion] = field(default_factory=list)
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gaps: List[GapInfo] = field(default_factory=list)
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class Config:
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"""配置类"""
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# 热键设置
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HOTKEY = "ctrl+f9"
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# 图像分析参数
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GRAY_THRESHOLD = 240 # 灰度阈值，接近白色的阈值
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CONSECUTIVE_LINES = 3 # 连续多少行判定为空白
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WHITE_PIXEL_RATIO = 0.9 # 一行中超过多少比例的像素为白色才认为是空白行
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# OCR设置
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OCR_ENGINE = "umi" # OCR引擎: "umi" 使用Umi-OCR, "http" 使用HTTP接口
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OCR_API_URL = "http://localhost:8000/ocr" # HTTP OCR服务地址 (OCR_ENGINE=http时使用)
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OCR_TIMEOUT = 30 # OCR请求超时时间
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# Umi-OCR设置
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UMI_OCR_HOST = "127.0.0.1"
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UMI_OCR_PORT = 1224
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# 滚动设置
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SCROLL_DELAY = 0.5 # 滚动后等待渲染的时间（秒）
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MAX_SCROLL_COUNT = 100 # 最大滚动次数，防止无限循环
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# 输出设置
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OUTPUT_DIR = "output"
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class RegionSelector:
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"""区域选择器 - 用于手动框选截图区域"""
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def __init__(self):
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self.start_pos: Optional[Tuple[int, int]] = None
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self.end_pos: Optional[Tuple[int, int]] = None
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self.is_selecting = False
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def select_region(self) -> Tuple[int, int, int, int]:
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"""
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手动选择区域，返回 (left, top, right, bottom)
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点击确定左上角，拖动释放确定右下角
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"""
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logger.info("请按住鼠标左键拖动选择区域...")
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print("\n>>> 请按住鼠标左键拖动选择截图区域，释放后确定 <<<")
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# 等待鼠标按下
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while not mouse.is_pressed(button='left'):
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time.sleep(0.01)
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self.start_pos = mouse.get_position()
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self.is_selecting = True
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logger.info(f"选择开始位置: {self.start_pos}")
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# 等待鼠标释放
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while mouse.is_pressed(button='left'):
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time.sleep(0.01)
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self.end_pos = mouse.get_position()
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self.is_selecting = False
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logger.info(f"选择结束位置: {self.end_pos}")
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# 计算边界
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left = min(self.start_pos[0], self.end_pos[0])
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top = min(self.start_pos[1], self.end_pos[1])
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right = max(self.start_pos[0], self.end_pos[0])
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bottom = max(self.start_pos[1], self.end_pos[1])
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logger.info(f"选定区域: ({left}, {top}, {right}, {bottom}), 尺寸: {right-left}x{bottom-top}")
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print(f"已选择区域: 左上角({left}, {top}), 右下角({right}, {bottom})")
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return left, top, right, bottom
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class ImageAnalyzer:
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"""图像分析器 - 分析div边界和空白间隔"""
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def __init__(self, config: Config):
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self.config = config
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def analyze(self, image: np.ndarray) -> AnalysisResult:
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"""
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分析图像，定位div边界
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使用灰度阈值 + 连续行判定
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"""
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result = AnalysisResult()
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# 转换为灰度图
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if len(image.shape) == 3:
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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else:
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gray = image
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height, width = gray.shape
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logger.debug(f"分析图像尺寸: {width}x{height}")
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# 逐行分析
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is_in_gap = False
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gap_start = 0
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div_start = 0
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consecutive_blank = 0
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for row in range(height):
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# 计算当前行的白色像素比例
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white_pixels = np.sum(gray[row] > self.config.GRAY_THRESHOLD)
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white_ratio = white_pixels / width
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is_blank = white_ratio > self.config.WHITE_PIXEL_RATIO
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if is_blank:
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consecutive_blank += 1
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else:
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# 如果之前是空白区域，现在进入div
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if consecutive_blank >= self.config.CONSECUTIVE_LINES and not is_in_gap:
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# 记录空白间隔
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gap_end = row - consecutive_blank
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gap = GapInfo(start_row=gap_start, end_row=gap_end)
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result.gaps.append(gap)
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logger.debug(f"发现空白间隔: 行 {gap.start_row}-{gap.end_row}, 高度 {gap.height}")
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# 记录div开始
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div_start = row
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is_in_gap = True
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consecutive_blank = 0
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gap_start = row
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# 如果连续多行都是空白，认为是间隔区域
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if consecutive_blank >= self.config.CONSECUTIVE_LINES and is_in_gap:
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# 记录div结束
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div_end = row - consecutive_blank
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if div_end > div_start:
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div = DivRegion(
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top=div_start,
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bottom=div_end,
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left=0,
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right=width
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)
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result.divs.append(div)
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logger.debug(f"发现div区域: 行 {div.top}-{div.bottom}, 高度 {div.height}")
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is_in_gap = False
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gap_start = row - consecutive_blank + 1
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# 处理最后一个div（如果图像不以空白结束）
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if not is_in_gap and div_start < height - consecutive_blank:
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div = DivRegion(
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top=div_start,
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bottom=height - consecutive_blank,
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left=0,
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right=width
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)
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result.divs.append(div)
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logger.debug(f"发现末尾div区域: 行 {div.top}-{div.bottom}, 高度 {div.height}")
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logger.info(f"分析完成: 发现 {len(result.divs)} 个div, {len(result.gaps)} 个空白间隔")
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return result
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def calculate_scroll_distance(self, result: AnalysisResult) -> int:
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"""
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根据分析结果计算滚动距离
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策略：滚动到下一个div的顶部
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"""
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if not result.divs:
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logger.warning("未检测到div，使用默认滚动距离")
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return 100
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# 获取第一个div和第一个空白间隔
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first_div = result.divs[0]
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# 如果有空白间隔，滚动距离为第一个div高度 + 其后的空白间隔
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scroll_distance = first_div.height
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# 查找第一个div之后的空白间隔
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for gap in result.gaps:
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if gap.start_row >= first_div.bottom:
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scroll_distance += gap.height
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break
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# 添加一些重叠，确保连续性
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overlap = min(20, first_div.height // 4)
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scroll_distance = max(scroll_distance - overlap, 50)
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logger.info(f"计算滚动距离: {scroll_distance} 像素")
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return int(scroll_distance)
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class OCREngine:
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"""OCR引擎 - 调用OCR服务识别文字"""
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def __init__(self, config: Config):
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self.config = config
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self.umi_client: Optional[UmiOCRClient] = None
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if config.OCR_ENGINE == "umi":
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self.umi_client = UmiOCRClient(
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host=config.UMI_OCR_HOST,
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port=config.UMI_OCR_PORT
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)
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def _recognize_with_http(self, image: np.ndarray) -> List[str]:
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"""使用HTTP接口进行OCR识别"""
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try:
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# 将numpy数组转换为PIL Image
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if len(image.shape) == 3:
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pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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else:
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pil_image = Image.fromarray(image)
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# 转换为base64
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buffered = io.BytesIO()
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pil_image.save(buffered, format="PNG")
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img_base64 = base64.b64encode(buffered.getvalue()).decode()
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# 调用OCR API
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response = requests.post(
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self.config.OCR_API_URL,
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json={"image": img_base64},
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timeout=self.config.OCR_TIMEOUT
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)
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response.raise_for_status()
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data = response.json()
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texts = data.get("texts", [])
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return texts
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except requests.exceptions.ConnectionError:
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logger.error(f"无法连接到OCR服务: {self.config.OCR_API_URL}")
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return []
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except Exception as e:
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logger.error(f"HTTP OCR识别失败: {e}")
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return []
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def _recognize_with_umi(self, image: np.ndarray) -> List[str]:
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"""使用Umi-OCR进行识别"""
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if not self.umi_client:
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logger.error("Umi-OCR客户端未初始化")
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return []
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try:
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# 将图像保存为临时文件
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if len(image.shape) == 3:
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pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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else:
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pil_image = Image.fromarray(image)
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# 创建临时文件
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with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_file:
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tmp_path = tmp_file.name
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pil_image.save(tmp_path, format="PNG")
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try:
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# 调用Umi-OCR识别
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text = self.umi_client.recognize_image(tmp_path, timeout=self.config.OCR_TIMEOUT)
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if text:
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# 按行分割
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lines = [line.strip() for line in text.split('\n') if line.strip()]
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return lines
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return []
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finally:
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# 删除临时文件
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try:
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Path(tmp_path).unlink()
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except Exception:
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pass
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except Exception as e:
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logger.error(f"Umi-OCR识别失败: {e}")
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return []
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def recognize(self, image: np.ndarray) -> List[str]:
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"""
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对图像进行OCR识别
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返回识别到的文字列表
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"""
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if self.config.OCR_ENGINE == "umi":
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texts = self._recognize_with_umi(image)
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else:
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texts = self._recognize_with_http(image)
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logger.info(f"OCR识别完成，识别到 {len(texts)} 段文字")
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return texts
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def recognize_divs(self, image: np.ndarray, divs: List[DivRegion]) -> List[str]:
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"""
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对每个div区域分别进行OCR识别
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"""
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all_texts = []
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for i, div in enumerate(divs):
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# 截取div区域
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div_image = image[div.top:div.bottom, div.left:div.right]
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texts = self.recognize(div_image)
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all_texts.extend(texts)
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logger.debug(f"Div {i+1} OCR结果: {texts}")
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return all_texts
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def check_service(self) -> bool:
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"""检查OCR服务是否可用"""
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if self.config.OCR_ENGINE == "umi":
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if not self.umi_client:
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return False
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return self.umi_client.is_service_running()
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else:
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try:
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response = requests.get(self.config.OCR_API_URL.replace('/ocr', '/health'), timeout=2)
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return response.status_code == 200
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except Exception:
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return False
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class ScrollCaptureOCR:
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"""滚动截屏OCR主类"""
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def __init__(self):
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self.config = Config()
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self.region_selector = RegionSelector()
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self.image_analyzer = ImageAnalyzer(self.config)
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self.ocr_engine = OCREngine(self.config)
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self.capture_region: Optional[Tuple[int, int, int, int]] = None
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self.previous_ocr_result: List[str] = []
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self.scroll_count = 0
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self.all_results: List[dict] = []
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# 新增：记录已处理的div信息
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self.processed_divs: List[Dict] = [] # 已处理的所有div信息
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self.last_div_signature: Optional[str] = None # 最后一个div的签名（用于去重）
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self.total_scroll_distance: int = 0 # 累计滚动距离
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self.is_first_capture: bool = True # 是否是第一次截图
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# 创建输出目录
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Path(self.config.OUTPUT_DIR).mkdir(exist_ok=True)
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def capture_screen(self) -> np.ndarray:
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"""截取指定区域的屏幕"""
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if not self.capture_region:
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raise ValueError("未设置截图区域")
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left, top, right, bottom = self.capture_region
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screenshot = pyautogui.screenshot(region=(left, top, right - left, bottom - top))
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return cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)
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def get_div_signature(self, div: DivRegion, image: np.ndarray) -> str:
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"""
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生成div的签名，用于判断是否是同一个div
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使用div的图像内容的哈希值
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"""
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import hashlib
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div_image = image[div.top:div.bottom, div.left:div.right]
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# 缩小图像以加快计算
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small = cv2.resize(div_image, (32, 32))
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# 计算平均哈希
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gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY)
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avg = gray.mean()
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hash_str = ''.join(['1' if p > avg else '0' for p in gray.flatten()])
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return hashlib.md5(hash_str.encode()).hexdigest()[:16]
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def scroll_screen(self, distance: int):
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"""在截图区域执行滚动"""
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if not self.capture_region:
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return
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# 将鼠标移动到截图区域中央
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left, top, right, bottom = self.capture_region
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center_x = (left + right) // 2
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center_y = (top + bottom) // 2
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pyautogui.moveTo(center_x, center_y)
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time.sleep(0.1)
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# 执行滚动
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pyautogui.scroll(-distance)
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logger.info(f"向下滚动 {distance} 像素")
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# 等待页面渲染
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time.sleep(self.config.SCROLL_DELAY)
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def check_duplicate(self, current_texts: List[str]) -> bool:
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"""
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检查当前OCR结果是否与上一次相同
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用于判断是否到达底部
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"""
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if not self.previous_ocr_result:
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return False
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# 简单比较：如果文字列表完全相同，认为是重复
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is_duplicate = current_texts == self.previous_ocr_result
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if is_duplicate:
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logger.info("检测到OCR结果重复，可能已到达底部")
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return is_duplicate
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def save_result(self, scroll_index: int, image: np.ndarray, texts: List[str]):
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"""保存截图和OCR结果"""
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timestamp = time.strftime("%Y%m%d_%H%M%S")
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# 保存图片
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image_path = Path(self.config.OUTPUT_DIR) / f"capture_{timestamp}_{scroll_index:03d}.png"
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cv2.imwrite(str(image_path), image)
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# 保存OCR结果
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result = {
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"index": scroll_index,
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"timestamp": timestamp,
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"image_path": str(image_path),
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"texts": texts
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}
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self.all_results.append(result)
|
||
|
||
logger.info(f"保存结果: {image_path}, 识别文字数: {len(texts)}")
|
||
|
||
def save_final_result(self):
|
||
"""保存所有结果到JSON文件"""
|
||
output_path = Path(self.config.OUTPUT_DIR) / f"all_results_{time.strftime('%Y%m%d_%H%M%S')}.json"
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(self.all_results, f, ensure_ascii=False, indent=2)
|
||
logger.info(f"所有结果已保存到: {output_path}")
|
||
print(f"\n所有结果已保存到: {output_path}")
|
||
|
||
def process_once(self) -> bool:
|
||
"""
|
||
执行一次处理循环
|
||
返回False表示应该停止
|
||
|
||
新逻辑：
|
||
1. 第一次截图：处理所有div
|
||
2. 后续截图：只处理新的div（跳过已处理的）
|
||
3. 滚动距离 = 最后一个新div的底部位置 + 空白间隔
|
||
"""
|
||
logger.info(f"=== 第 {self.scroll_count + 1} 次截屏 ===")
|
||
print(f"\n>>> 第 {self.scroll_count + 1} 次截屏处理中...")
|
||
|
||
# 1. 截取当前屏幕
|
||
image = self.capture_screen()
|
||
height, width = image.shape[:2]
|
||
logger.info(f"截图完成，尺寸: {width}x{height}")
|
||
|
||
# 2. 分析图像，定位div边界
|
||
analysis = self.image_analyzer.analyze(image)
|
||
|
||
if not analysis.divs:
|
||
logger.warning("未检测到任何div区域，可能已到达底部或区域选择有误")
|
||
print("警告: 未检测到内容区域")
|
||
return False
|
||
|
||
logger.info(f"检测到 {len(analysis.divs)} 个div区域")
|
||
print(f"检测到 {len(analysis.divs)} 个内容区域")
|
||
|
||
# 3. 识别新的div（跳过已处理的）
|
||
new_divs = []
|
||
current_texts = []
|
||
last_processed_signature = None
|
||
|
||
for i, div in enumerate(analysis.divs):
|
||
# 生成div签名
|
||
div_signature = self.get_div_signature(div, image)
|
||
|
||
# 检查是否是已处理的div
|
||
is_processed = False
|
||
if not self.is_first_capture and self.processed_divs:
|
||
# 与已处理的div比较
|
||
for processed in self.processed_divs:
|
||
if processed.get('signature') == div_signature:
|
||
is_processed = True
|
||
logger.info(f"跳过已处理的div {i+1}")
|
||
break
|
||
|
||
if is_processed:
|
||
continue
|
||
|
||
# 新的div，进行处理
|
||
new_divs.append({
|
||
'div': div,
|
||
'signature': div_signature,
|
||
'index': i
|
||
})
|
||
|
||
logger.info(f"处理新的div {i+1}，位置: {div.top}-{div.bottom}")
|
||
print(f" 处理新区域 {i+1}/{len(analysis.divs)}...")
|
||
|
||
# 截取单个div区域
|
||
div_image = image[div.top:div.bottom, div.left:div.right]
|
||
|
||
# OCR识别
|
||
texts = self.ocr_engine.recognize(div_image)
|
||
div.text = "\n".join(texts)
|
||
current_texts.extend(texts)
|
||
|
||
# 保存单个div的结果
|
||
self.save_div_result(self.scroll_count, i, div_image, texts, div)
|
||
|
||
# 记录处理的div
|
||
self.processed_divs.append({
|
||
'signature': div_signature,
|
||
'text': div.text,
|
||
'scroll_count': self.scroll_count,
|
||
'div_index': i
|
||
})
|
||
|
||
last_processed_signature = div_signature
|
||
logger.info(f" 识别到 {len(texts)} 段文字")
|
||
|
||
# 如果没有新的div，说明已经到底
|
||
if not new_divs:
|
||
logger.info("没有新的div需要处理，可能已到达底部")
|
||
print("✓ 没有新的内容需要处理")
|
||
return False
|
||
|
||
print(f"✓ 本次处理 {len(new_divs)} 个新区域，共识别 {len(current_texts)} 段文字")
|
||
for i, text in enumerate(current_texts[:3], 1):
|
||
preview = text[:50] + "..." if len(text) > 50 else text
|
||
print(f" [{i}] {preview}")
|
||
if len(current_texts) > 3:
|
||
print(f" ... 还有 {len(current_texts) - 3} 段文字")
|
||
|
||
# 4. 保存完整截图结果
|
||
self.save_result(self.scroll_count, image, current_texts)
|
||
|
||
# 5. 判断是否到达底部（没有新内容或OCR结果重复）
|
||
if self.check_duplicate(current_texts):
|
||
print("\n>>> 检测到内容重复，已到达底部，处理完成 <<<")
|
||
return False
|
||
|
||
self.previous_ocr_result = current_texts
|
||
|
||
# 6. 计算滚动距离 - 基于最后一个新div的位置
|
||
if new_divs:
|
||
last_new_div = new_divs[-1]['div']
|
||
scroll_distance = self.calculate_scroll_based_on_last_div(
|
||
last_new_div, analysis.gaps, height
|
||
)
|
||
else:
|
||
scroll_distance = int(height * 0.8) # 默认滚动80%高度
|
||
|
||
# 7. 执行滚动
|
||
self.scroll_screen(scroll_distance)
|
||
self.total_scroll_distance += scroll_distance
|
||
|
||
self.scroll_count += 1
|
||
self.is_first_capture = False # 标记不再是第一次
|
||
|
||
# 检查最大滚动次数
|
||
if self.scroll_count >= self.config.MAX_SCROLL_COUNT:
|
||
logger.warning(f"达到最大滚动次数限制 ({self.config.MAX_SCROLL_COUNT})")
|
||
print(f"\n>>> 达到最大滚动次数限制，处理完成 <<<")
|
||
return False
|
||
|
||
return True
|
||
|
||
def calculate_scroll_based_on_last_div(self, last_div: DivRegion, gaps: List[GapInfo], image_height: int) -> int:
|
||
"""
|
||
基于最后一个div计算滚动距离
|
||
|
||
策略：滚动到最后一个div的底部 + 其后空白间隔
|
||
这样可以让下一个新div出现在截图区域的顶部
|
||
"""
|
||
last_div_bottom = last_div.bottom
|
||
|
||
# 查找最后一个div之后的空白间隔
|
||
last_gap_height = 0
|
||
for gap in gaps:
|
||
if gap.start_row >= last_div.bottom:
|
||
last_gap_height = gap.height
|
||
break
|
||
|
||
# 计算滚动距离
|
||
scroll_distance = last_div_bottom + last_gap_height
|
||
|
||
# 确保至少滚动一定距离（避免 stuck）
|
||
min_scroll = 50
|
||
scroll_distance = max(scroll_distance, min_scroll)
|
||
|
||
# 限制最大滚动距离（不超过图片高度的90%，保留一些重叠）
|
||
max_scroll = int(image_height * 0.9)
|
||
scroll_distance = min(scroll_distance, max_scroll)
|
||
|
||
logger.info(f"滚动距离计算: 最后div底部={last_div_bottom}, "
|
||
f"空白间隔={last_gap_height}, 滚动距离={scroll_distance}")
|
||
print(f" 滚动距离: {scroll_distance} 像素 (基于最后一个内容区域)")
|
||
|
||
return int(scroll_distance)
|
||
|
||
def save_div_result(self, scroll_index: int, div_index: int, image: np.ndarray, texts: List[str], div: DivRegion):
|
||
"""保存单个div的结果"""
|
||
timestamp = time.strftime("%Y%m%d_%H%M%S")
|
||
|
||
# 保存div图片
|
||
div_dir = Path(self.config.OUTPUT_DIR) / f"scroll_{scroll_index:03d}"
|
||
div_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
image_path = div_dir / f"div_{div_index:02d}_{timestamp}.png"
|
||
cv2.imwrite(str(image_path), image)
|
||
|
||
# 保存div OCR结果
|
||
result = {
|
||
"scroll_index": scroll_index,
|
||
"div_index": div_index,
|
||
"timestamp": timestamp,
|
||
"div_position": {"top": div.top, "bottom": div.bottom, "left": div.left, "right": div.right},
|
||
"image_path": str(image_path),
|
||
"texts": texts
|
||
}
|
||
|
||
# 也添加到总结果中
|
||
if not hasattr(self, '_div_results'):
|
||
self._div_results = []
|
||
self._div_results.append(result)
|
||
|
||
logger.debug(f"保存div结果: scroll={scroll_index}, div={div_index}, 文字数={len(texts)}")
|
||
|
||
def run(self):
|
||
"""主运行流程"""
|
||
print("=" * 60)
|
||
print("滚动截屏OCR工具")
|
||
print("=" * 60)
|
||
print(f"\n使用说明:")
|
||
print(f"1. 按下热键 {self.config.HOTKEY} 启动")
|
||
print(f"2. 按住鼠标左键拖动选择截图区域")
|
||
print(f"3. 程序将自动滚动截屏并进行OCR识别")
|
||
print(f"4. 当检测到重复内容时自动停止")
|
||
print(f"5. 结果将保存在 '{self.config.OUTPUT_DIR}' 目录")
|
||
print("\n" + "=" * 60)
|
||
|
||
logger.info("程序启动，等待热键触发...")
|
||
print(f"\n>>> 等待热键 {self.config.HOTKEY} 启动... <<<")
|
||
|
||
# 注册热键
|
||
keyboard.add_hotkey(self.config.HOTKEY, self._on_hotkey)
|
||
|
||
# 保持程序运行
|
||
try:
|
||
while True:
|
||
time.sleep(0.1)
|
||
except KeyboardInterrupt:
|
||
logger.info("程序被用户中断")
|
||
print("\n>>> 程序已停止 <<<")
|
||
|
||
def _on_hotkey(self):
|
||
"""热键回调函数"""
|
||
logger.info("热键触发，开始处理")
|
||
print(f"\n{'='*60}")
|
||
print("热键已触发！")
|
||
|
||
# 检查OCR服务
|
||
print("\n>>> 检查OCR服务... <<<")
|
||
if not self.ocr_engine.check_service():
|
||
if self.config.OCR_ENGINE == "umi":
|
||
print("✗ Umi-OCR服务未运行")
|
||
print("请先启动Umi-OCR软件并开启HTTP服务：")
|
||
print(" 1. 打开Umi-OCR")
|
||
print(" 2. 进入 设置 -> HTTP接口")
|
||
print(" 3. 勾选 '启用HTTP服务'")
|
||
print(f" 4. 确保端口为 {self.config.UMI_OCR_PORT}")
|
||
else:
|
||
print(f"✗ OCR服务未运行: {self.config.OCR_API_URL}")
|
||
return
|
||
|
||
print("✓ OCR服务运行中")
|
||
|
||
# 选择区域
|
||
try:
|
||
self.capture_region = self.region_selector.select_region()
|
||
except Exception as e:
|
||
logger.error(f"区域选择失败: {e}")
|
||
print(f"区域选择失败: {e}")
|
||
return
|
||
|
||
# 重置状态
|
||
self.previous_ocr_result = []
|
||
self.scroll_count = 0
|
||
self.all_results = []
|
||
|
||
print(f"\n>>> 开始自动滚动截屏和OCR识别... <<<")
|
||
|
||
# 循环处理
|
||
try:
|
||
while self.process_once():
|
||
pass
|
||
except Exception as e:
|
||
logger.error(f"处理过程中出错: {e}", exc_info=True)
|
||
print(f"\n错误: {e}")
|
||
|
||
# 保存最终结果
|
||
if self.all_results:
|
||
self.save_final_result()
|
||
print(f"\n共处理 {len(self.all_results)} 次截屏")
|
||
print(f"结果保存在: {Path(self.config.OUTPUT_DIR).absolute()}")
|
||
|
||
print(f"\n{'='*60}")
|
||
print(">>> 等待下一次热键触发... <<<")
|
||
logger.info("处理完成，等待下一次热键触发")
|
||
|
||
|
||
def main():
|
||
"""入口函数"""
|
||
app = ScrollCaptureOCR()
|
||
app.run()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|