#源码下载地址：https://files.cnblogs.com/files/cnfan/jump.rar

importosimportcv2importnumpy as npimporttimeimportrandom#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

defget_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))defjump(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) * 10cmd= ('adb shell input swipe %i %i %i %i' +str(press_time)) \% (320 + rand, 410 + rand, 320 + rand, 410 +rand)

os.system(cmd)print(cmd)defget_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] + 400x_top=int(np.mean(np.nonzero(canny_img[y_top])))

y_bottom= y_top + 50

for row inrange(y_bottom, H):if canny_img[row, x_top] !=0:

y_bottom=rowbreakx_center, y_center= x_top, (y_top + y_bottom) // 2

returnimg_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) ** 2distance= distance ** 0.5jump(distance)

time.sleep(1.3)