python实现连连看辅助（图像识别）

个人兴趣，用python实现连连看的辅助程序，总结实现过程及知识点。

总体思路

1、获取连连看程序的窗口并前置
2、游戏界面截图，将每个一小图标切图，并形成由小图标组成的二维列表
3、对图片的二维列表遍历，将二维列表转换成由数字组成的二维数组，图片相同的数值相同。
4、遍历二维数组，找到可消除的对象，实现算法：

• 两个图标相邻。（一条线连接）
• 两个图标同行，同列，且中间的图标全部为空（数值为0）（一条线连接）
• 两条线连接，转弯一次，路径上所有图标为空。（二条线连接）
• 三条线连接，转弯二次，路径上所有图标为空。（三条线连接）
• 分别点击两个图标，并将对应的二维数据值置为0

实现过程中遇到的问题

图片切割

im = image.crop((left,top,right,bottom))
//image.crop参数为一个列表或元组，顺序为(left,top,right,bottom)

找到游戏运行窗口

hdwd = win32gui.FindWindow(0,wdname)
# 设置为最前显示
win32gui.SetForegroundWindow(hdwd)

窗口不要点击最小化，点击后无法弹出来。

• 图片缩放并转为灰度

img1 = im1.resize((20, 20), Image.ANTIALIAS).convert('L')

Image.ANTIALIAS 为抗锯齿的选项，图片无毛边。

• 获取图片每个点的RGB值

pi1 = list(img1.getdata())

列表每个元素为一个三位数的值，分别代表该点的RGB值。列表pi1共400个元素。（因为图片为20*20）

• 鼠标点击消除

PyMouse.click()该方法默认双击，改为PyMouse.press() 或 PyMouse.release()

• 判断图片相似
• 汉明距离，平均哈希

def compare_img(self,im1,im2):
 img1 = im1.resize((20, 20), Image.ANTIALIAS).convert('L')
 img2 = im2.resize((20, 20), Image.ANTIALIAS).convert('L')
 pi1 = list(img1.getdata())
 pi2 = list(img2.getdata())
 avg1 = sum(pi1) / len(pi1)
 avg2 = sum(pi2) / len(pi2)
 hash1 = "".join(map(lambda p: "1" if p > avg1 else "0", pi1))
 hash2 = "".join(map(lambda p: "1" if p > avg2 else "0", pi2))
 match = 0
 for i in range(len(hash1)):
 if hash1[i] != hash2[i]:
 match += 1
 # match = sum(map(operator.ne, hash1, hash2))
 # match 值越小，相似度越高
 return match

• 计算直方图

from PIL import Image

# 将图片转化为RGB
def make_regalur_image(img, size=(8, 8)):
 gray_image = img.resize(size).convert('RGB')
 return gray_image

# 计算直方图
def hist_similar(lh, rh):
 assert len(lh) == len(rh)
 hist = sum(1 - (0 if l == r else float(abs(l - r)) / max(l, r)) for l, r in zip(lh, rh)) / len(lh)
 return hist

# 计算相似度
def calc_similar(li, ri):
 calc_sim = hist_similar(li.histogram(), ri.histogram())
 return calc_sim

if __name__ == '__main__':
 image1 = Image.open('1-10.jpg')
 image1 = make_regalur_image(image1)
 image2 = Image.open('2-11.jpg')
 image2 = make_regalur_image(image2)
 print("图片间的相似度为", calc_similar(image1, image2))
 # 值在[0,1]之间，数值越大，相似度越高

• 图片余弦相似度

from PIL import Image
from numpy import average, dot, linalg

# 对图片进行统一化处理
def get_thum(image, size=(64, 64), greyscale=False):
 # 利用image对图像大小重新设置, Image.ANTIALIAS为高质量的
 image = image.resize(size, Image.ANTIALIAS)
 if greyscale:
 # 将图片转换为L模式，其为灰度图，其每个像素用8个bit表示
 image = image.convert('L')
 return image

# 计算图片的余弦距离
def image_similarity_vectors_via_numpy(image1, image2):
 image1 = get_thum(image1)
 image2 = get_thum(image2)
 images = [image1, image2]
 vectors = []
 norms = []
 for image in images:
 vector = []
 for pixel_tuple in image.getdata():
 vector.append(average(pixel_tuple))
 vectors.append(vector)
 # linalg=linear（线性）+algebra（代数），norm则表示范数
 # 求图片的范数？？
 norms.append(linalg.norm(vector, 2))
 a, b = vectors
 a_norm, b_norm = norms
 # dot返回的是点积，对二维数组（矩阵）进行计算
 res = dot(a / a_norm, b / b_norm)
 return res

if __name__ == '__main__':

 image1 = Image.open('1-9.jpg')
 image2 = Image.open('8-6.jpg')
 cosin = image_similarity_vectors_via_numpy(image1, image2)
 print('图片余弦相似度', cosin)
 # 值在[0,1]之间，数值越大，相似度越高,计算量较大，效率较低

完整代码

import win32gui
import time
from PIL import ImageGrab , Image
import numpy as np
from pymouse import PyMouse

class GameAuxiliaries(object):
 def __init__(self):
 self.wdname = r'宠物连连看经典版2,宠物连连看经典版2小游戏,4399小游戏 www.4399.com - Google Chrome'
 # self.wdname = r'main.swf - PotPlayer'
 self.image_list = {}
 self.m = PyMouse()
 def find_game_wd(self,wdname):
 # 取得窗口句柄
 hdwd = win32gui.FindWindow(0,wdname)
 # 设置为最前显示
 win32gui.SetForegroundWindow(hdwd)
 time.sleep(1)

 def get_img(self):
 image = ImageGrab.grab((417, 289, 884, 600))
 # image = ImageGrab.grab((417, 257, 885, 569))
 image.save('1.jpg','JPEG')
 for x in range(1,9):
 self.image_list[x] = {}
 for y in range(1,13):
 top = (x - 1) * 38 + (x-2)
 left =(y - 1) * 38 +(y-2)
 right = y * 38 + (y-1)
 bottom = x * 38 +(x -1)
 if top < 0:
  top = 0
 if left < 0 :
  left = 0
 im_temp = image.crop((left,top,right,bottom))
 im = im_temp.crop((1,1,37,37))
 im.save('{}-{}.jpg'.format(x,y))
 self.image_list[x][y]=im

 # 判断两个图片是否相同。汉明距离，平均哈希
 def compare_img(self,im1,im2):
 img1 = im1.resize((20, 20), Image.ANTIALIAS).convert('L')
 img2 = im2.resize((20, 20), Image.ANTIALIAS).convert('L')
 pi1 = list(img1.getdata())
 pi2 = list(img2.getdata())
 avg1 = sum(pi1) / len(pi1)
 avg2 = sum(pi2) / len(pi2)
 hash1 = "".join(map(lambda p: "1" if p > avg1 else "0", pi1))
 hash2 = "".join(map(lambda p: "1" if p > avg2 else "0", pi2))
 match = 0
 for i in range(len(hash1)):
 if hash1[i] != hash2[i]:
 match += 1
 # match = sum(map(operator.ne, hash1, hash2))
 # match 值越小，相似度越高
 return match

 # 将图片矩阵转换成数字矩阵

 def create_array(self):
 array = np.zeros((10,14),dtype=np.int32)
 img_type_list = []
 for row in range(1,len(self.image_list)+1):
 for col in range(1,len(self.image_list[1])+1):
 # im = Image.open('{}-{}.jpg'.format(row,col))
 im = self.image_list[row][col]
 for img in img_type_list:
  match = self.compare_img(im,img)
  # match = test2.image_similarity_vectors_via_numpy(im,img)
  if match <15:
  array[row][col] = img_type_list.index(img) +1

  break
 else:
  img_type_list.append(im)
  array[row][col] = len(img_type_list)

 return array

 def row_zero(self,x1,y1,x2,y2,array):
 '''相同的图片中间图标全为空'''
 if x1 == x2:
 min_y = min(y1,y2)
 max_y = max(y1,y2)
 if max_y - min_y == 1:
 return True
 for y in range(min_y+1,max_y):
 if array[x1][y] != 0 :
  return False
 return True
 else:
 return False

 def col_zero(self,x1,y1,x2,y2,array):
 '''相同的图片同列'''
 if y1 == y2:
 min_x = min(x1,x2)
 max_x = max(x1,x2)
 if max_x - min_x == 1:
 return True
 for x in range(min_x+1,max_x):
 if array[x][y1] != 0 :
  return False
 return True
 else:
 return False

 def two_line(self,x1,y1,x2,y2,array):
 '''两条线相连，转弯一次'''
 for row in range(1,9):
 for col in range(1,13):
 if row == x1 and col == y2 and array[row][col]==0 and self.row_zero(x1,y1,row,col,array) and self.col_zero(x2,y2,row,col,array):
  return True
 if row == x2 and col == y1 and array[row][col]==0 and self.row_zero(x2,y2,row,col,array) and self.col_zero(x1,y1,row,col,array):
  return True
 return False

 def three_line(self,x1,y1,x2,y2,array):
 '''三条线相连，转弯两次'''
 for row1 in range(10):
 for col1 in range(14):
 for row2 in range(10):
  for col2 in range(14):
  if array[row1][col1] == array[row2][col2] == 0 and self.row_zero(x1,y1,row1,col1,array) and self.row_zero(x2,y2,row2,col2,array) and self.col_zero(row1,col1,row2,col2,array):
  return True
  if array[row1][col1] == array[row2][col2] == 0 and self.col_zero(x1,y1,row1,col1,array) and self.col_zero(x2,y2,row2,col2,array) and self.row_zero(row1,col1,row2,col2,array):
  return True
  if array[row1][col1] == array[row2][col2] == 0 and self.row_zero(x2,y2,row1,col1,array) and self.row_zero(x1,y1,row2,col2,array) and self.col_zero(row1,col1,row2,col2,array):
  return True
  if array[row1][col1] == array[row2][col2] == 0 and self.col_zero(x2,y2,row1,col1,array) and self.col_zero(x1,y1,row2,col2,array) and self.row_zero(row1,col1,row2,col2,array):
  return True
 return False

 def mouse_click(self,x,y):

 top = (x - 1) * 38 + (x - 2)
 left = (y - 1) * 38 + (y - 2)
 right = y * 38 + (y - 1)
 bottom = x * 38 + (x - 1)
 if top < 0:
 top = 0
 if left < 0:
 left = 0

 self.m.press(int(417+(left+right)/2) ,int(289+(top+bottom)/2) )

 def find_same_img(self,array):

 for x1 in range(1,9):
 for y1 in range(1,13):
 if array[x1][y1] == 0:
  continue
 for x2 in range(1,9):
  for y2 in range(1,13):
  if x1==x2 and y1 == y2:
  continue
  if array[x2][y2] == 0 :
  continue
  if array[x1][y1] != array[x2][y2] :
  continue
  if array[x1][y1] ==array[x2][y2] and (self.row_zero(x1,y1,x2,y2,array) or self.col_zero(x1,y1,x2,y2,array) or self.two_line(x1,y1,x2,y2,array) or self.three_line(x1,y1,x2,y2,array)):
  print("可消除！x{}y{} 和 x{}y{}".format(x1,y1,x2,y2))
  self.mouse_click(x1,y1)
  time.sleep(0.1)
  self.mouse_click(x2,y2)
  time.sleep(0.1)
  array[x1][y1]=array[x2][y2]=0

 def run(self):
 #找到游戏运行窗口
 self.find_game_wd(self.wdname)
 # 截图，切割成小图标
 self.get_img()
 # 将图片矩阵转换成数字矩阵
 array = self.create_array()
 print(array)
 # 遍历矩阵，找到可消除项，点击消除
 for i in range(10):
 self.find_same_img(array)
 print(array)

if __name__ == '__main__':
 ga = GameAuxiliaries()
 ga.run()

总结

该程序其实未能完全实现辅助功能，主要是因为图片切割时未找到更好的规则，造成图片识别困难，缩放比例和判断阀值未找到一个平衡点，阀值太大，则将不同的图标识别为相同，阀值太小，相同的图标又判断为不一样。

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