MSS&OpenCV

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pip install mss opencv-python numpy

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import cv2
import numpy as np
import mss
import time
import os

# -----------------------------
# 模板图像路径（推荐 PNG 或 JPG）
# -----------------------------
template_path = "1.tif"  # 请替换为你的图像路径

# -----------------------------
# 匹配阈值（0~1之间）
# 越接近1要求越严格，一般0.8为起点
# -----------------------------
threshold = 0.8

# -----------------------------
# 初始化变量
# -----------------------------
Result = 0
found_x, found_y = None, None

# -----------------------------
# 读取模板图像，只执行一次
# 使用灰度图加快匹配速度
# -----------------------------
template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE)
if template is None:
    raise FileNotFoundError(f"模板图像未找到：{template_path}")

# 获取模板图尺寸
template_h, template_w = template.shape[:2]

# -----------------------------
# 创建 mss 实例，进行截图
# -----------------------------
with mss.mss() as sct:
    # 可指定屏幕区域：top, left, width, height
    # monitor = {"top": 0, "left": 0, "width": 1920, "height": 1080}
    monitor = sct.monitors[1]  # 全屏（第1号显示器）

    # 延迟等待游戏加载完成（可视情况修改）
    time.sleep(1)

    # 截图一次
    screenshot = np.array(sct.grab(monitor))

    # mss 返回的是 BGRA 图像，转换为灰度图以便匹配
    screenshot_gray = cv2.cvtColor(screenshot, cv2.COLOR_BGRA2GRAY)

    # -----------------------------
    # 模板匹配（使用标准相关系数方法）
    # 返回匹配结果矩阵，每个像素点代表匹配得分
    # -----------------------------
    MSSResult = cv2.matchTemplate(screenshot_gray, template, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(MSSResult)

    # -----------------------------
    # 判断是否匹配成功
    # -----------------------------
    if max_val >= threshold:
        Result = 1
        found_x, found_y = max_loc  # 左上角坐标

# -----------------------------
# 控制台输出匹配结果
# 可用于后续逻辑判断
# -----------------------------
if Result:
    print(f"Result 1 {found_x} {found_y}")
else:
    print("Result 0")

1

pip install mss opencv-python numpy keyboard

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import cv2
import numpy as np
import mss
import time
import os
import keyboard
import threading

# -----------------------------
# 模板图像路径（推荐 PNG 或 JPG）
# -----------------------------
template_path = "1.tif"

# -----------------------------
# 匹配阈值（0~1）
# -----------------------------
threshold = 0.8

# -----------------------------
# 读取模板图像，只执行一次
# -----------------------------
template = cv2.imread(template_path, cv2.IMREAD_GRAYSCALE)
if template is None:
    raise FileNotFoundError(f"模板图像未找到：{template_path}")

template_h, template_w = template.shape[:2]

# -----------------------------
# 状态控制变量（用线程安全的方式）
# -----------------------------
paused = False
should_exit = False

# -----------------------------
# 热键回调函数
# -----------------------------
def toggle_pause():
    global paused
    paused = not paused
    print("[暂停中]" if paused else "[已恢复] 继续检测...")

def exit_program():
    global should_exit
    should_exit = True
    print("[退出指令] 脚本即将终止...")

# 注册热键（非阻塞监听）
keyboard.add_hotkey('end', toggle_pause)
keyboard.add_hotkey('ctrl+end', exit_program)

print("脚本开始运行。按 END 暂停/恢复，按 Ctrl+End 退出。")

# -----------------------------
# 创建 mss 实例（只创建一次）
# -----------------------------
with mss.mss() as sct:
    monitor = sct.monitors[1]  # 全屏截图

    while not should_exit:
        if paused:
            time.sleep(0.1)
            continue

        # 截屏并灰度处理
        screenshot = np.array(sct.grab(monitor))
        screenshot_gray = cv2.cvtColor(screenshot, cv2.COLOR_BGRA2GRAY)

        # 模板匹配
        result = cv2.matchTemplate(screenshot_gray, template, cv2.TM_CCOEFF_NORMED)
        _, max_val, _, max_loc = cv2.minMaxLoc(result)

        if max_val >= threshold:
            found_x, found_y = max_loc
            print(f"Result 1 {found_x} {found_y}")
        else:
            print("Result 0")

        time.sleep(0.3)

print("脚本已退出。")