598 lines
24 KiB
Python
598 lines
24 KiB
Python
# coding=utf-8
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import uiautomator2 as u2
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import time
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import logging
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import sys
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import os
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import cv2
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import numpy as np
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import re
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# 添加项目根目录到 sys.path 以便导入 Util
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project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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if project_root not in sys.path:
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sys.path.append(project_root)
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import json
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from Util.VLMKit import VLMKit
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from Util.EasyOcrKit import EasyOcrKit
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# 初始化 VLMKit 和 EasyOcrKit
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vlm_kit = VLMKit()
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ocr_kit = EasyOcrKit()
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger("WxUtil")
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async def get_vlm_json(image_path, prompt):
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"""
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通用 VLM 分析函数，返回 JSON 数据 (自动处理归一化坐标的反归一化)
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"""
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try:
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# 调用 VLM
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response = await vlm_kit.analyze_image(image_path, prompt)
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json_str = vlm_kit.extract_json(response)
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result_data = json.loads(json_str)
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# 获取图片尺寸进行坐标反归一化
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try:
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from PIL import Image
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with Image.open(image_path) as img:
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width, height = img.size
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# 定义反归一化函数
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def denormalize(point):
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if not point or len(point) != 2:
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return point
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return [int(point[0] / 1000 * width), int(point[1] / 1000 * height)]
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# 递归遍历字典进行反归一化 (仅针对常见坐标字段 center, input_box)
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def recursive_denormalize(data):
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if isinstance(data, dict):
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for key, value in data.items():
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if key in ["center", "input_box", "coordinates"] and isinstance(value, list) and len(value) == 2:
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data[key] = denormalize(value)
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elif isinstance(value, (dict, list)):
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recursive_denormalize(value)
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elif isinstance(data, list):
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for item in data:
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recursive_denormalize(item)
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recursive_denormalize(result_data)
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except Exception as e:
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logger.warning(f"坐标反归一化失败: {e}，将使用原始坐标")
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return result_data
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except Exception as e:
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logger.error(f"VLM Analysis Failed: {e}", exc_info=True)
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return None
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async def get_vlm_analysis(image_path):
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"""
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仅调用 VLM 分析图片，返回原始 JSON 数据 (dict)
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"""
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logger.info(f"正在使用 VLM 分析图片: {image_path}")
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# 构造 Prompt
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prompt = """
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请分析这张微信聊天截图。
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【核心任务】
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识别图中的【语音消息气泡】和【文本消息气泡】，并区分【发送者】。
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⚠️ **特别注意**：必须识别屏幕上**所有**的消息，特别是位于**屏幕最底部**的消息，哪怕只有一部分，也要识别！
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【重要判别规则】
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1. 👤 **发送者 (Sender)**：
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- **对方 (Other)**：气泡在屏幕**左侧**，通常为白色或灰色，头像在左边。
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- **我 (Me)**：气泡在屏幕**右侧**，通常为绿色，头像在右边。
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2. 🔊 **语音消息 (Voice)**：
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- **视觉特征**：
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- **高度**：固定（单行）。
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- **宽度**：随时长（1"~60"）变化。
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- **极短 (1"-2")**：气泡非常短，形状接近一个小正方形。
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- **极长 (60")**：气泡很长，宽度接近屏幕的一半。
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- **内容**：气泡内**只有一个**表示时长的数字（如 `8"`）和一个声波图标。
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- **辅助文字**：语音气泡右侧可能会有灰色的“转文字”或“取消转文字”字样，**请忽略这些文字**，依然将该气泡识别为语音消息！
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- **绝对排除**：凡是包含汉字、长句子的气泡，**统统不是**语音消息。
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3. 📝 **文本消息 (Text)**：
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- **视觉特征**：气泡内包含汉字、标点符号、表情等文本内容。
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4. 🔴 **未读状态 (Unread) - 极度重要！**：
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- **特征**：语音气泡的右上角（或紧邻右侧）有一个明显的**红色圆形小点**。
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- **判别**：
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- 只要看到红色小点，`is_unread` 必须为 **true**。
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- 如果没有红色小点，`is_unread` 为 false。
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- **注意**：红点可能很小，请仔细观察！这是判断是否处理的关键依据。即使红点旁边有灰色的“转文字”字样，只要有红点，就是未读！
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【坐标系统】
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**必须使用 [0-1000] 的归一化坐标系。**
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- 左上角为 [0, 0]，右下角为 [1000, 1000]。
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- 请返回气泡的**几何中心点**的归一化坐标。
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【输出格式】
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请返回纯 JSON 格式：
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{
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"is_chat_interface": true,
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"input_box": [x, y],
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"messages": [
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{
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"type": "voice",
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"sender": "对方" | "我",
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"status": "converted" | "unconverted",
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"is_unread": true | false,
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"center": [x, y],
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"content": "8\""
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},
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{
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"type": "text",
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"sender": "对方" | "我",
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"center": [x, y],
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"content": "这里是文本内容"
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}
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]
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}
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注意：
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1. 坐标 `center` 和 `input_box` 必须是 [0-1000] 的归一化坐标。
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2. `status` 判断：如果语音气泡的正下方紧挨着一条文本消息（通常是转换出的文字），则为 `converted`，否则为 `unconverted`。
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3. `is_unread` 判断：务必准确识别红点！如果有红点则为 true。
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4. 请按从上到下的顺序输出所有消息。
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"""
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try:
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# 调用 VLM
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response = await vlm_kit.analyze_image(image_path, prompt)
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json_str = vlm_kit.extract_json(response)
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result_data = json.loads(json_str)
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# 获取图片尺寸进行坐标反归一化
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try:
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from PIL import Image
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with Image.open(image_path) as img:
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width, height = img.size
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# 定义反归一化函数
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def denormalize(point):
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if not point or len(point) != 2:
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return point
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return [int(point[0] / 1000 * width), int(point[1] / 1000 * height)]
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# 反归一化 input_box
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if result_data.get("input_box"):
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result_data["input_box"] = denormalize(result_data["input_box"])
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# 反归一化 messages
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if result_data.get("messages"):
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for msg in result_data["messages"]:
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if msg.get("center"):
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msg["center"] = denormalize(msg["center"])
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if msg.get("coordinates"): # 兼容旧字段
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msg["coordinates"] = denormalize(msg["coordinates"])
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except Exception as e:
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logger.warning(f"坐标反归一化失败: {e}，将使用原始坐标")
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return result_data
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except Exception as e:
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logger.error(f"VLM Analysis Failed: {e}", exc_info=True)
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return None
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async def analyze_chat_image(image_path, output_path, device=None, target_name="对方"):
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"""
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使用 VLM 识别微信聊天截图中的对话内容、语音消息状态以及输入框位置
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替代原本的 CV/OCR 方案
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"""
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# 语音识别标志
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should_trigger_convert = False
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try:
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result_data = await get_vlm_analysis(image_path)
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if not result_data:
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return [], None
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try:
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# 检查是否为聊天界面
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is_chat = result_data.get("is_chat_interface", False)
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if not is_chat:
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logger.warning("VLM 判断当前不是微信聊天界面")
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return None, None
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if isinstance(result_data, list):
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# 兼容旧格式
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messages = result_data
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input_field_coordinates = None
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else:
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messages = result_data.get("messages", [])
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input_field_coordinates = result_data.get("input_box") # input_box
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except Exception as e:
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logger.error(f"解析 VLM 结果失败: {e}")
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return [], None
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dialogue_log = []
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unconverted_voices = []
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# 处理识别结果
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for msg in messages:
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sender = msg.get('sender', '未知')
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msg_type = msg.get('type', 'other')
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content = msg.get('content', '')
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coords = msg.get('center', [0, 0]) # center
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status = msg.get('status', 'unconverted')
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is_converted = (status == "converted")
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# 记录对话日志
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if msg_type == 'voice':
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if is_converted:
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dialogue_log.append(f"{sender}: [语音] {content} (已转换)")
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else:
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dialogue_log.append(f"{sender}: [语音] (待转换)")
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# 将 center 转换为 coordinates 供后续使用
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msg['coordinates'] = coords
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unconverted_voices.append(msg)
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elif msg_type == 'text':
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dialogue_log.append(f"{sender}: {content}")
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logger.info(f"VLM 识别: {sender} [{msg_type}] {content} (Converted: {is_converted})")
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# 处理未转换的语音消息
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if unconverted_voices:
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logger.info(f"发现 {len(unconverted_voices)} 条未转换的语音消息，将仅处理最后一条...")
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# 仅保留最后一条语音消息进行处理
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unconverted_voices = [unconverted_voices[-1]]
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# 使用传入的 device 或创建新连接
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d = device if device else u2.connect()
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for voice in unconverted_voices:
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vx, vy = voice['coordinates']
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logger.info(f"长按语音消息: ({vx}, {vy})")
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d.long_click(vx, vy, 1.5)
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time.sleep(1.0)
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# 查找“转文字” (使用 OCR)
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menu_shot_path = os.path.join(os.path.dirname(image_path), "temp_menu_shot.jpg")
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d.screenshot(menu_shot_path)
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# OCR 识别
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ocr_results = ocr_kit.read_text(menu_shot_path)
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convert_btn_center = None
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for bbox, text, conf in ocr_results:
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if "转文字" in text or "转换为文字" in text:
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# bbox is [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
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# Calculate center
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c_x = int((bbox[0][0] + bbox[2][0]) / 2)
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c_y = int((bbox[0][1] + bbox[2][1]) / 2)
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convert_btn_center = (c_x, c_y)
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break
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if convert_btn_center:
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logger.info(f"OCR 找到 '转文字' 按钮: {convert_btn_center}")
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d.click(convert_btn_center[0], convert_btn_center[1])
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should_trigger_convert = True
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# 动态等待: 60s语音约需10s转换，比例约 1/6
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duration_str = voice.get('content', '0').replace('"', '').strip()
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try:
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duration = int(duration_str)
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except:
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duration = 10 # 默认值
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wait_seconds = max(2, duration / 5.0) # 稍微多等一点，用 /5.0
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logger.info(f"语音时长 {duration}s，预计等待转换 {wait_seconds:.1f}s...")
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time.sleep(wait_seconds)
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else:
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logger.warning("OCR 未找到 '转文字' 菜单项")
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# 点击空白处关闭菜单，避免遮挡
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d.click(vx + 200, vy)
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if should_trigger_convert:
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# 转换完成后稍微多等一下，确保 UI 刷新
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time.sleep(1.0)
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return "VOICE_CONVERTING", input_field_coordinates
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return dialogue_log, input_field_coordinates
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except Exception as e:
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logger.error(f"VLM 分析失败: {e}", exc_info=True)
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return [], None
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def clean_screenshots_dir():
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"""清理截图目录"""
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screenshot_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Screenshots")
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if not os.path.exists(screenshot_dir):
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os.makedirs(screenshot_dir)
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return
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for f in os.listdir(screenshot_dir):
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if f.lower().endswith(('.jpg', '.png', '.jpeg')):
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try:
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os.remove(os.path.join(screenshot_dir, f))
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except Exception as e:
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logger.warning(f"Failed to delete {f}: {e}")
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def is_in_chat_interface(d):
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"""
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检查是否在微信聊天界面
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"""
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try:
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# 1. 底部语音/键盘切换按钮
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if d(description="切换到语音").exists or d(description="切换到键盘").exists:
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return True
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# 2. 底部输入框
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if d(className="android.widget.EditText").exists:
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return True
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# 3. 底部“按住说话”按钮
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if d(text="按住说话").exists:
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return True
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# 4. 右上角更多按钮
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if d(description="聊天信息").exists:
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return True
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except Exception as e:
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logger.warning(f"is_in_chat_interface check failed: {e}")
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return False
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def find_input_box_center(image_path):
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"""
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寻找输入框中心坐标 (兜底策略)
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优先使用几何特征 (底部 88% 处)
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"""
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try:
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if not os.path.exists(image_path):
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return (540, 2100), None
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img = cv2.imread(image_path)
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if img is None:
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return (540, 2100), None
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h, w = img.shape[:2]
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# 策略：直接返回屏幕底部 88% 处的中心点
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center_x = int(w * 0.5)
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center_y = int(h * 0.88)
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logger.info(f"find_input_box_center fallback: ({center_x}, {center_y})")
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return (center_x, center_y), None
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except Exception as e:
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logger.error(f"find_input_box_center error: {e}")
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return (540, 2100), None
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def find_template_match(screen_path, template_path, threshold=0.8):
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"""
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使用 OpenCV 模板匹配寻找按钮中心坐标
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"""
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try:
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if not os.path.exists(template_path):
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logger.error(f"Template file not found: {template_path}")
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return None
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img = cv2.imread(screen_path)
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template = cv2.imread(template_path)
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if img is None or template is None:
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return None
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h, w = template.shape[:2]
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res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
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min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
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if max_val >= threshold:
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center_x = max_loc[0] + w // 2
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center_y = max_loc[1] + h // 2
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logger.info(f"Template matched! Score: {max_val:.2f}, Center: ({center_x}, {center_y})")
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return (center_x, center_y)
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logger.info(f"Template not matched. Max score: {max_val:.2f}")
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return None
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except Exception as e:
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logger.error(f"Template matching failed: {e}")
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return None
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def find_all_template_matches(screen_path, template_path, threshold=0.8):
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"""
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使用 OpenCV 模板匹配寻找**所有**符合条件的坐标
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"""
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try:
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if not os.path.exists(template_path):
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logger.error(f"Template file not found: {template_path}")
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return []
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img = cv2.imread(screen_path)
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template = cv2.imread(template_path)
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if img is None or template is None:
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return []
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h, w = template.shape[:2]
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res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
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# 找到所有大于阈值的点
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loc = np.where(res >= threshold)
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points = []
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for pt in zip(*loc[::-1]): # Switch collumns and rows
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center_x = pt[0] + w // 2
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center_y = pt[1] + h // 2
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points.append((center_x, center_y))
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# 简单的去重（非极大值抑制的简化版，合并相近的点）
|
||
# 这里假设红点不会重叠，暂时直接返回，或者做一个简单的聚类
|
||
# 实际应用中，matchTemplate 对同一个目标周围可能会有多个连续的匹配点
|
||
# 我们需要合并它们
|
||
|
||
unique_points = []
|
||
for p in points:
|
||
is_close = False
|
||
for up in unique_points:
|
||
if abs(p[0] - up[0]) < 10 and abs(p[1] - up[1]) < 10:
|
||
is_close = True
|
||
break
|
||
if not is_close:
|
||
unique_points.append(p)
|
||
|
||
if unique_points:
|
||
logger.info(f"Found {len(unique_points)} matches for {os.path.basename(template_path)}")
|
||
|
||
return unique_points
|
||
|
||
except Exception as e:
|
||
logger.error(f"find_all_template_matches failed: {e}")
|
||
return []
|
||
|
||
def perform_input_action(d, center_point, text, auto_send=True):
|
||
"""
|
||
执行输入操作
|
||
"""
|
||
try:
|
||
# --- 新增逻辑：确保处于文字输入模式 ---
|
||
logger.info("正在检查输入模式...")
|
||
tmp_check_shot = os.path.join(os.path.dirname(os.path.abspath(__file__)), "temp_input_check.jpg")
|
||
d.screenshot(tmp_check_shot)
|
||
|
||
wen_zi_template = r"d:\dsWork\aiData\WeiXin\Templates\wen_zi_input.jpg"
|
||
input_text_template = r"d:\dsWork\aiData\WeiXin\Templates\input_text.jpg"
|
||
|
||
# 1. 检查是否存在 '切换到文字' 图标 (表示当前是语音模式)
|
||
# 注意：这里假设 wen_zi_input.jpg 是那个“键盘”图标
|
||
wen_zi_pos = find_template_match(tmp_check_shot, wen_zi_template, threshold=0.8)
|
||
|
||
if wen_zi_pos:
|
||
logger.info(f"检测到语音模式 (找到切换文字图标: {wen_zi_pos})，点击切换...")
|
||
d.click(wen_zi_pos[0], wen_zi_pos[1])
|
||
time.sleep(1.0) # 等待 UI 切换
|
||
else:
|
||
# 2. 如果没找到切换图标，假设是文字模式，尝试点击输入区域标识
|
||
logger.info("未检测到语音模式切换图标，尝试寻找文字输入区域...")
|
||
input_text_pos = find_template_match(tmp_check_shot, input_text_template, threshold=0.8)
|
||
if input_text_pos:
|
||
logger.info(f"找到文字输入区域标识 (input_text.jpg): {input_text_pos}，点击激活...")
|
||
d.click(input_text_pos[0], input_text_pos[1])
|
||
time.sleep(0.5)
|
||
else:
|
||
logger.info("未找到特定的输入区域标识，将使用默认坐标或控件查找。")
|
||
|
||
# 清理临时文件
|
||
if os.path.exists(tmp_check_shot):
|
||
try:
|
||
os.remove(tmp_check_shot)
|
||
except:
|
||
pass
|
||
# --- 新增逻辑结束 ---
|
||
|
||
# 1. 尝试找到原生输入框并输入
|
||
edit_text = d(className="android.widget.EditText")
|
||
input_success = False
|
||
|
||
if edit_text.exists:
|
||
logger.info("Found native EditText, using set_text")
|
||
try:
|
||
edit_text.click()
|
||
time.sleep(0.5)
|
||
edit_text.set_text(text)
|
||
input_success = True
|
||
except Exception as e:
|
||
logger.warning(f"Native input failed: {e}")
|
||
|
||
# 2. 如果原生输入失败，使用坐标点击 + 粘贴/输入
|
||
if not input_success:
|
||
cx, cy = center_point
|
||
logger.info(f"Using coordinate input: {center_point}")
|
||
d.click(cx, cy)
|
||
time.sleep(1.0)
|
||
|
||
try:
|
||
d.send_keys(text)
|
||
except Exception:
|
||
logger.warning("send_keys failed, trying set_clipboard")
|
||
d.set_clipboard(text)
|
||
d.click(cx, cy)
|
||
time.sleep(0.5)
|
||
# 尝试粘贴
|
||
d.press("paste")
|
||
|
||
time.sleep(1.0)
|
||
|
||
# 3. 发送
|
||
if auto_send:
|
||
# 优先使用模板匹配寻找“发送”按钮
|
||
logger.info("尝试使用模板匹配寻找'发送'按钮...")
|
||
tmp_screen = os.path.join(os.path.dirname(os.path.abspath(__file__)), "temp_send_check.jpg")
|
||
d.screenshot(tmp_screen)
|
||
|
||
# 使用绝对路径
|
||
template_path = r"d:\dsWork\aiData\WeiXin\Templates\send.jpg"
|
||
send_btn_pos = find_template_match(tmp_screen, template_path, threshold=0.7) # 稍微降低阈值以提高召回
|
||
|
||
if send_btn_pos:
|
||
logger.info(f"通过模板匹配找到发送按钮: {send_btn_pos}, 点击...")
|
||
d.click(send_btn_pos[0], send_btn_pos[1])
|
||
else:
|
||
logger.warning("模板匹配未找到发送按钮，尝试原生控件查找...")
|
||
if d(text="发送").exists:
|
||
d(text="发送").click()
|
||
logger.info("Clicked '发送'")
|
||
else:
|
||
d.press("enter")
|
||
logger.info("Pressed Enter")
|
||
|
||
# 清理临时文件
|
||
if os.path.exists(tmp_screen):
|
||
try:
|
||
os.remove(tmp_screen)
|
||
except:
|
||
pass
|
||
|
||
except Exception as e:
|
||
logger.error(f"perform_input_action error: {e}")
|
||
|
||
def match_template_center(image_path, template_path, threshold=0.8):
|
||
"""
|
||
使用 OpenCV 模板匹配寻找目标图片中心坐标
|
||
"""
|
||
try:
|
||
if not os.path.exists(image_path) or not os.path.exists(template_path):
|
||
logger.error(f"Image or template not found: {image_path}, {template_path}")
|
||
return None
|
||
|
||
img = cv2.imread(image_path)
|
||
template = cv2.imread(template_path)
|
||
|
||
if img is None or template is None:
|
||
logger.error("Failed to read image or template")
|
||
return None
|
||
|
||
# 转换为灰度图进行匹配
|
||
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
|
||
template_gray = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
|
||
|
||
# 模板匹配
|
||
result = cv2.matchTemplate(img_gray, template_gray, cv2.TM_CCOEFF_NORMED)
|
||
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
|
||
|
||
if max_val >= threshold:
|
||
h, w = template_gray.shape
|
||
top_left = max_loc
|
||
center_x = int(top_left[0] + w / 2)
|
||
center_y = int(top_left[1] + h / 2)
|
||
logger.info(f"Template matched with confidence {max_val:.2f} at ({center_x}, {center_y})")
|
||
return (center_x, center_y)
|
||
else:
|
||
logger.warning(f"Template match failed. Max confidence: {max_val:.2f} < Threshold: {threshold}")
|
||
return None
|
||
|
||
except Exception as e:
|
||
logger.error(f"match_template_center error: {e}")
|
||
return None
|
||
|
||
|