微信跳一跳python程序，信跳python程序,#源码下载地址：ht

微信跳一跳python程序，信跳python程序,#源码下载地址：ht

#源码下载地址：https://files.cnblogs.com/files/cnfan/jump.rar
import osimport cv2import numpy as npimport timeimport random# 使用的Python库及对应版本：# python 3.6# opencv-python 3.3.0# numpy 1.13.3# 用到了opencv库中的模板匹配和边缘检测功能def get_screenshot(id):    #os.system(‘adb shell /system/bin/screencap -p /sdcard/screenshot.png‘)#获取当前界面的手机截图    #os.system(‘adb pull /sdcard/screenshot.png d:/fan/screenshot.png‘)#下载当前这个截图到当前电脑当前文件夹下    os.system(‘adb shell screencap -p /sdcard/%s.png‘ % str(id))    os.system(‘adb pull /sdcard/%s.png .‘ % str(id))def jump(distance):    # 这个参数还需要针对屏幕分辨率进行优化    press_time = int(distance * 1.35)    # 生成随机手机屏幕模拟触摸点    # 模拟触摸点如果每次都是同一位置，成绩上传可能无法通过验证    rand = random.randint(0, 9) * 10    cmd = (‘adb shell input swipe %i %i %i %i ‘ + str(press_time))           % (320 + rand, 410 + rand, 320 + rand, 410 + rand)    os.system(cmd)    print(cmd)def get_center(img_canny, ):    # 利用边缘检测的结果寻找物块的上沿和下沿    # 进而计算物块的中心点    y_top = np.nonzero([max(row) for row in img_canny[400:]])[0][0] + 400    x_top = int(np.mean(np.nonzero(canny_img[y_top])))    y_bottom = y_top + 50    for row in range(y_bottom, H):        if canny_img[row, x_top] != 0:            y_bottom = row            break    x_center, y_center = x_top, (y_top + y_bottom) // 2    return img_canny, x_center, y_center# 第一次跳跃的距离是固定的jump(530)time.sleep(1)# 匹配小跳棋的模板temp1 = cv2.imread(‘temp_player.jpg‘, 0)w1, h1 = temp1.shape[::-1]# 匹配游戏结束画面的模板temp_end = cv2.imread(‘temp_end.jpg‘, 0)# 匹配中心小圆点的模板temp_white_circle = cv2.imread(‘temp_white_circle.jpg‘, 0)w2, h2 = temp_white_circle.shape[::-1]# 循环直到游戏失败结束for i in range(1000):    get_screenshot(0)    img_rgb = cv2.imread(‘%s.png‘ % 0, 0)    # 如果在游戏截图中匹配到带"再玩一局"字样的模板，则循环中止    res_end = cv2.matchTemplate(img_rgb, temp_end, cv2.TM_CCOEFF_NORMED)    if cv2.minMaxLoc(res_end)[1] > 0.95:        print(‘Game over!‘)        break    # 模板匹配截图中小跳棋的位置    res1 = cv2.matchTemplate(img_rgb, temp1, cv2.TM_CCOEFF_NORMED)    min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1)    center1_loc = (max_loc1[0] + 39, max_loc1[1] + 189)    # 先尝试匹配截图中的中心原点，    # 如果匹配值没有达到0.95，则使用边缘检测匹配物块上沿    res2 = cv2.matchTemplate(img_rgb, temp_white_circle, cv2.TM_CCOEFF_NORMED)    min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2)    if max_val2 > 0.95:        print(‘found white circle!‘)        x_center, y_center = max_loc2[0] + w2 // 2, max_loc2[1] + h2 // 2    else:        # 边缘检测        img_rgb = cv2.GaussianBlur(img_rgb, (5, 5), 0)        canny_img = cv2.Canny(img_rgb, 1, 10)        H, W = canny_img.shape        # 消去小跳棋轮廓对边缘检测结果的干扰        for k in range(max_loc1[1] - 10, max_loc1[1] + 189):            for b in range(max_loc1[0] - 10, max_loc1[0] + 100):                canny_img[k][b] = 0        img_rgb, x_center, y_center = get_center(canny_img)    # 将图片输出以供调试    img_rgb = cv2.circle(img_rgb, (x_center, y_center), 10, 255, -1)    # cv2.rectangle(canny_img, max_loc1, center1_loc, 255, 2)    cv2.imwrite(‘last.png‘, img_rgb)    distance = (center1_loc[0] - x_center) ** 2 + (center1_loc[1] - y_center) ** 2    distance = distance ** 0.5    jump(distance)    time.sleep(1.3)

微信跳一跳python程序