微信跳一跳python程序

2018/02/01 19:10
阅读数 9
#源码下载地址:https://files.cnblogs.com/files/cnfan/jump.rar
import
os import cv2 import numpy as np import time import 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)

 

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