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：
   1. 两个图标相邻。（一条线连接）
   2. 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）
   3. 两条线连接，转弯一次，路径上所有图标为空。（二条线连接）
   4. 三条线连接，转弯二次，路径上所有图标为空。（三条线连接）
   5. 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。