331 lines
12 KiB
Python
331 lines
12 KiB
Python
# coding=utf-8
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import cv2
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import numpy as np
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import sys
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import os
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import logging
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import re
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import time
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try:
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import uiautomator2 as u2
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except ImportError:
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u2 = None
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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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from Util.EasyOcrKit import get_easyocr_reader
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(message)s')
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logger = logging.getLogger(__name__)
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def find_input_box_center(image_path):
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"""
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识别底部输入框的中心坐标
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"""
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try:
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img_data = np.fromfile(image_path, dtype=np.uint8)
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img = cv2.imdecode(img_data, cv2.IMREAD_COLOR)
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if img is None:
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return None
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height, width = img.shape[:2]
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# 截取底部 15% 区域
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bottom_h = int(height * 0.15)
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crop_y_start = height - bottom_h
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crop = img[crop_y_start:height, 0:width]
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# 预处理
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gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
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# 简单二值化，输入框通常是浅色或深色背景上的反色
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# 这里假设深色模式下，输入框可能较亮，或者有边框
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# 尝试自适应阈值
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, 11, 2)
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contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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best_cnt = None
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max_area = 0
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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# 筛选条件：宽度较大 (比如 > 50% 屏幕宽)，高度适中
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if w > width * 0.5 and h > 20:
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if w * h > max_area:
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max_area = w * h
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best_cnt = (x, y, w, h)
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if best_cnt:
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x, y, w, h = best_cnt
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center_x = x + w // 2
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center_y = crop_y_start + y + h // 2
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logger.info(f"找到输入框: ({center_x}, {center_y}), 尺寸: {w}x{h}")
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return center_x, center_y
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else:
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# 兜底：返回底部区域中心
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logger.warning("未找到明显输入框轮廓，使用默认坐标")
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return width // 2, int(height * 0.95)
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except Exception as e:
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logger.error(f"查找输入框失败: {e}")
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return None
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def perform_input_action(coords, text):
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"""
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点击坐标并输入文本
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"""
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if u2 is None:
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logger.error("未安装 uiautomator2 库，无法执行自动化操作")
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return
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if not coords:
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logger.error("坐标无效，无法执行点击输入")
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return
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x, y = coords
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try:
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# 连接设备 (默认连接第一个 USB 设备)
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d = u2.connect()
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logger.info(f"设备连接成功: {d.info.get('serial')}")
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# 点击输入框
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logger.info(f"点击坐标: ({x}, {y})")
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d.click(x, y)
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# 等待键盘弹出或输入框激活
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time.sleep(1)
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# 输入文本
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logger.info(f"输入文本: {text}")
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d.send_keys(text)
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# 尝试发送回车键以确认输入 (视具体情况而定)
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# d.press("enter")
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logger.info("输入完成")
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except Exception as e:
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logger.error(f"自动化操作失败: {e}")
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print(f"自动化操作失败: {e}")
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def analyze_chat_image(image_path, output_path, target_name="对方"):
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# 读取图片（支持中文路径）
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img_data = np.fromfile(image_path, dtype=np.uint8)
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img = cv2.imdecode(img_data, cv2.IMREAD_COLOR)
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# 备份一份干净的图片用于 OCR (避免识别到画上去的框)
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img_clean = img.copy()
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height, width = img.shape[:2]
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print(f"图片尺寸: {width}x{height}")
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# 1. 预处理
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# 转为灰度图
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 使用自适应阈值二值化
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, 11, 2)
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# 形态学操作：闭运算，填充内部空洞
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
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# 2. 轮廓查找
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contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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print(f"检测到轮廓数量: {len(contours)}")
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# 收集所有符合条件的头像
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avatars = []
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# 3. 筛选轮廓
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for contour in contours:
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# 获取外接矩形
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x, y, w, h = cv2.boundingRect(contour)
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# 筛选条件优化：
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# 1. 形状接近正方形 (放宽宽高比限制: 0.8 ~ 1.2)
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aspect_ratio = float(w) / h
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# 2. 尺寸适中
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# 假设头像宽度在屏幕宽度的 6% 到 15% 之间
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min_w = width * 0.06
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max_w = width * 0.15
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# 3. 位置筛选
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# 排除底部输入框区域 (假设底部 10% 为输入区域)
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if y > height * 0.9:
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continue
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# 左侧头像：靠左边 (x < width * 0.18)
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# 右侧头像：靠右边 (x > width * 0.82)
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is_left = x < width * 0.18
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is_right = x > width * 0.82
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if 0.8 <= aspect_ratio <= 1.2 and min_w < w < max_w:
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if is_left or is_right:
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side = "Left" if is_left else "Right"
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avatars.append({
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'x': x, 'y': y, 'w': w, 'h': h,
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'side': side
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})
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# 按 y 坐标排序
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avatars.sort(key=lambda a: a['y'])
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print(f"找到有效头像数量: {len(avatars)}")
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# 初始化 OCR
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try:
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reader = get_easyocr_reader(gpu=True)
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print("OCR 初始化成功")
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except Exception as e:
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print(f"OCR 初始化失败: {e}")
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reader = None
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dialogue_log = []
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# 4. 绘制对话内容框 (Green/Red Boxes)
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# 策略：按顺序遍历头像，如果发现同侧连续，则视为一组。
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# 从当前组的第一个头像上方开始，直到下一个不同侧的头像上方（或底部）。
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if avatars:
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i = 0
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while i < len(avatars):
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current_group_start = i
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current_side = avatars[i]['side']
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# 找到当前组的结束位置 (即下一个不同侧头像的索引)
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j = i + 1
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while j < len(avatars) and avatars[j]['side'] == current_side:
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j += 1
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# 当前组范围: avatars[i] ... avatars[j-1]
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# 确定绘制区域的 Y 轴范围
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# Start Y: 当前组第一个头像的上方 (例如 -10px)
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start_y = max(0, avatars[i]['y'] - 10)
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# End Y: 下一组第一个头像的上方 (减去较大间距，例如 -30px)，或者当前组最后一个头像的底部加上边距
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# 为了让框之间有明显间隔，我们采取策略：
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# 如果有下一组，End Y = 下一组第一个头像的 y - 30 (留出间隙)
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# 如果没有下一组，End Y = 屏幕底部区域上方
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if j < len(avatars):
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end_y = max(start_y + 10, avatars[j]['y'] - 30)
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else:
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end_y = int(height * 0.9) # 到底部输入框上方
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# 绘制大框
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# 左侧 (Left) -> 对方 -> 绿色 (0, 255, 0)
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# 右侧 (Right) -> 我 -> 红色 (0, 0, 255)
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# 注意 OpenCV 颜色是 BGR
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box_color = (0, 255, 0) if current_side == "Left" else (0, 0, 255)
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# 绘制矩形 (空心，线宽 5)
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# X 轴范围：0 到 width
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cv2.rectangle(img, (0, start_y), (width, end_y), box_color, 5)
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print(f"绘制内容框: 侧别={current_side}, 范围 Y={start_y} to {end_y}")
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# OCR 识别区域文本
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if reader:
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try:
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# 从原图(img_clean)裁剪区域
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# 注意边界检查
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safe_start_y = max(0, start_y)
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safe_end_y = min(height, end_y)
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if safe_end_y > safe_start_y:
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roi_img = img_clean[safe_start_y:safe_end_y, 0:width]
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# 识别
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results = reader.read_text(roi_img)
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# 过滤关键词
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ignore_keywords = ["点击查看对话内容", "以上是打招呼的消息", "和 Kimi 的对话", "Kim智能助手"]
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# 过滤并拼接文本 (置信度 > 0.3)
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texts = []
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for res in results:
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text = res[1]
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prob = res[2]
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if prob > 0.3:
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# 1. 检查是否包含屏蔽词
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if any(kw in text for kw in ignore_keywords):
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continue
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# 2. 检查是否为单行时间 (如 11:35, 09:00)
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# 正则匹配: 只有时间格式，没有其他文字
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if re.match(r'^\s*\d{1,2}:\d{2}\s*$', text):
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continue
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texts.append(text)
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combined_text = " ".join(texts)
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if combined_text.strip():
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role = target_name if current_side == "Left" else "我"
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dialogue_log.append(f"{role}: {combined_text}")
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print(f" -> OCR结果: {combined_text}")
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except Exception as e:
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print(f" -> OCR出错: {e}")
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# 移动到下一组
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i = j
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# 5. 绘制头像框 (Blue/Yellow Boxes) - 画在内容框之上
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for av in avatars:
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x, y, w, h = av['x'], av['y'], av['w'], av['h']
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# 左侧：蓝色 (BGR: 255, 0, 0)
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# 右侧：黄色 (BGR: 0, 255, 255)
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color = (255, 0, 0) if av['side'] == "Left" else (0, 255, 255)
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cv2.rectangle(img, (x, y), (x + w, y + h), color, 10)
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print(f"绘制头像: 位置=({x},{y}), 侧别={av['side']}")
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# 6. 保存结果
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try:
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# cv2.imwrite 不支持中文路径，使用 imencode + tofile
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ext = os.path.splitext(output_path)[1]
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cv2.imencode(ext, img)[1].tofile(output_path)
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print(f"结果已保存至: {output_path}")
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except Exception as e:
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print(f"保存图片失败: {e}")
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print("\n" + "="*30)
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print("对话内容汇总:")
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for line in dialogue_log:
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print(line)
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print("="*30 + "\n")
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if __name__ == "__main__":
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# 输入文件路径
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input_file = r"d:\dsWork\aiData\Test\Screenshots\chat_result_20260121_113553.jpg"
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# 输出文件路径
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output_file = r"d:\dsWork\aiData\Test\Screenshots\chat_result_analyzed.jpg"
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# 目标联系人名称 (对应搜索关键字)
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target_name = "糖豆爸爸"
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analyze_chat_image(input_file, output_file, target_name=target_name)
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# 2. 查找输入框并执行自动化操作
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print("\n" + "="*30)
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print("开始执行自动化输入...")
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# 注意: 这里使用 input_file (截图) 来定位坐标
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# 前提是截图时的界面布局与当前设备界面一致
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coords = find_input_box_center(input_file)
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if coords:
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print(f"输入框坐标: {coords}")
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perform_input_action(coords, "AI助手我现在可以开始和你聊天了！")
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else:
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print("未找到输入框坐标")
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