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@@ -0,0 +1,104 @@
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+import os
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+import cv2
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+import numpy as np
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+import time
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+import random
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+
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+class Wxjump(object):
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+ '''微信跳一跳助手 '''
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+
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+ def __init__(self):
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+ pass
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+
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+def get_screenshot(id):
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+ os.system('adb shell screencap -p /sdcard/%s.png' % str(id))
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+ os.system('adb pull /sdcard/%s.png ./data/result/' % str(id))
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+
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+
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+def jump(distance):
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+ # 这个参数还需要针对屏幕分辨率进行优化
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+ press_time = int(distance * 1.35)
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+
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+ # 生成随机手机屏幕模拟触摸点
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+ # 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
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+ rand = random.randint(0, 9) * 10
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+ cmd = ('adb shell input swipe %i %i %i %i ' + str(press_time)) \
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+ % (320 + rand, 410 + rand, 320 + rand, 410 + rand)
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+ os.system(cmd)
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+ print(cmd)
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+
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+
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+def get_center(img_canny, ):
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+ # 利用边缘检测的结果寻找物块的上沿和下沿
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+ # 进而计算物块的中心点
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+ y_top = np.nonzero([max(row) for row in img_canny[400:]])[0][0] + 400
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+ x_top = int(np.mean(np.nonzero(canny_img[y_top])))
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+
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+ y_bottom = y_top + 50
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+ for row in range(y_bottom, H):
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+ if canny_img[row, x_top] != 0:
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+ y_bottom = row
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+ break
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+
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+ x_center, y_center = x_top, (y_top + y_bottom) // 2
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+ return img_canny, x_center, y_center
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+
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+
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+# 第一次跳跃的距离是固定的
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+# jump(530)
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+time.sleep(1)
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+
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+# 匹配小跳棋的模板
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+temp1 = cv2.imread('data/template/temp_player.jpg', 0)
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+w1, h1 = temp1.shape[::-1] #77*209
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+# 匹配游戏结束画面的模板
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+temp_end = cv2.imread('data/template/temp_end.jpg', 0)
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+# 匹配中心小圆点的模板
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+temp_white_circle = cv2.imread('data/template/temp_white_circle.jpg', 0)
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+w2, h2 = temp_white_circle.shape[::-1]
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+
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+# 循环直到游戏失败结束
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+for i in range(100):
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+ get_screenshot(i)
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+ img_rgb = cv2.imread('data/result/%s.png' % i, 0)
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+
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+ # 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
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+ res_end = cv2.matchTemplate(img_rgb, temp_end, cv2.TM_CCOEFF_NORMED)
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+ if cv2.minMaxLoc(res_end)[1] > 0.95:
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+ print('Game over!')
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+ break
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+
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+ # 模板匹配截图中小跳棋的位置
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+ res1 = cv2.matchTemplate(img_rgb, temp1, cv2.TM_CCOEFF_NORMED)
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+ min_val1, max_val1, min_loc1, max_loc1 = cv2.minMaxLoc(res1)
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+ center1_loc = (max_loc1[0] + 39, max_loc1[1] + 189)
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+
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+ # 先尝试匹配截图中的中心原点，
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+ # 如果匹配值没有达到0.95，则使用边缘检测匹配物块上沿
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+ res2 = cv2.matchTemplate(img_rgb, temp_white_circle, cv2.TM_CCOEFF_NORMED)
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+ min_val2, max_val2, min_loc2, max_loc2 = cv2.minMaxLoc(res2)
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+ if max_val2 > 0.95:
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+ print('found white circle!')
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+ x_center, y_center = max_loc2[0] + w2 // 2, max_loc2[1] + h2 // 2
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+ else:
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+ # 边缘检测
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+ img_rgb = cv2.GaussianBlur(img_rgb, (5, 5), 0)
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+ canny_img = cv2.Canny(img_rgb, 1, 10)
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+ H, W = canny_img.shape
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+
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+ # 消去小跳棋轮廓对边缘检测结果的干扰
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+ for k in range(max_loc1[1] - 10, max_loc1[1] + 189):
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+ for b in range(max_loc1[0] - 10, max_loc1[0] + 100):
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+ canny_img[k][b] = 0
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+ cv2.imwrite('data/result/canny_img%s.png' % i, canny_img)
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+ img_rgb, x_center, y_center = get_center(canny_img)
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+
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+ # 将图片输出以供调试
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+ img_rgb = cv2.circle(img_rgb, (x_center, y_center), 10, 255, -1)
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+ # cv2.rectangle(canny_img, max_loc1, center1_loc, 255, 2)
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+ cv2.imwrite('data/result/last.png', img_rgb)
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+
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+ distance = (center1_loc[0] - x_center) ** 2 + (center1_loc[1] - y_center) ** 2
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+ distance = distance ** 0.5
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+ jump(distance)
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+ time.sleep(random.randrange(11,20)/10.0)
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