python + opencv 实现色弱測試游戏 自动点击游戏外挂

最近在学习python，看了几个库

今天看同事在玩一个游戏 = = 色弱测试游戏。。。。

于是。。就写了下面的东西 。

==========刚开始学习python有不对的地方欢迎指正==============

http://game.ioxapp.com/eye-test/game.html  游戏地址

http://weibo.com/tv/v/Feu5ek2IU?fid=1034:dd43566db21cf419ef3e7d631be0a89b  视频演示

python 3.6

opencv 3.2

=========思路===========

1.截取屏幕，找到游戏框

2.对框内的图像搜索边框然后取中心点

3.对比中心点颜色

4.找到不同的颜色坐标

5.点击～～～

=======问题===========

我用的macbook pro

所以我是按照我的屏幕像素写的

需要安装好自己的第三份扩展库

opencv的安装可以按照

http://blog.csdn.net/u014017080/article/details/76152650 这里

========code================

from PIL import Image,ImageGrab
from skimage.measure import compare_ssim
import imutils,numpy,time,sys
import cv2
import autopy3 as autopy

def dd(crop_img22):
    cv2.imwrite("haha2.png", crop_img22)
    img = Image.open('./haha2.png')
    img.show()
    sys.exit(0)


def diff(a,b):
    if a[1] == b[1] and a[2] == b[2] and a[0] == b[0]:
        return True
    else:
        return False

def diff2(a, b):
    if a[1] == b[1] and a[2] == b[2] and a[0] == b[0]:
        return False
    else:
        return True

# x = 7
# y = 109
# region = (x,y,x+279,y+251)
#
# x2 = 293
# y2 = 109
# region2 = (x2,y2,x2+279,y2+251)
#
#
# img = Image.open('./test.jpg')
# cropImg = img.crop(region)
# cropImg2 = img.crop(region2)

# img = cv2.imread('test.jpg',0)
# crop_img = img[300:400, 50:300]

# bbox = (22, 440, 430, 800)
# im = ImageGrab.grab(bbox)
# im.show()
time.sleep(3)



while 1:
    x = 1010
    y = 408
    # x2 = 975
    # y2 = 181

    # img = cv2.imread('./ddddd.png')
    # crop_img = img[y:y+990, x:x+1010]

    #===============
    bbox = (x, y, x+1010, y+990)
    img = ImageGrab.grab(bbox)
    #
    ## 把PIL处理的图片给cv2用
    pil_image = img
    crop_img = cv2.cvtColor(numpy.asarray(pil_image), cv2.COLOR_RGB2BGR)
    #=========================================

    crop_img2 = crop_img.copy()
    #二值化
    cv2.threshold(crop_img, 214, 255, 1, crop_img)
    crop_img = cv2.cvtColor(crop_img,cv2.COLOR_BGR2GRAY)
    # sp = crop_img.shape
    #记录轮廓数
    i = d = 1


    #获取轮廓
    crop_img, contours, hierarchy = cv2.findContours(crop_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

    #产看边界
    # cv2.drawContours(crop_img2,contours,-1,(0,0,255),3)

    # dd(crop_img2)

    #遍历轮廓
    for cc in contours:
        #求中心圆坐标
        M = cv2.moments(cc)
        if M["m10"] == 0 or M["m00"] == 0 or M["m01"] == 0 :
            continue
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])

        #绘制中心圆 跟边框
        # cv2.drawContours(crop_img2, [cc], -1, (0, 255, 0), 2)
        # cv2.circle(crop_img2, (cX, cY), 7, (255, 255, 255), -1)
        # cv2.putText(crop_img2, 'text', (cX - 20, cY - 20),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)

        if i == 1:
            a = crop_img2[cY,cX]
            d1 = (cX, cY)

        if i == 2:
            b = crop_img2[cY,cX]
            d2 = (cX, cY)

        if i == 3:
            c = crop_img2[cY,cX]
            d3 = (cX, cY)

            if diff(a,b) and diff(b,c):
                pass
            elif diff(b,c) and diff2(a,b):
                d = d1
                break
            elif diff(a,c) and diff2(b,c):
                d = d2
                break
            elif diff(a,b) and diff2(b,c):
                d = (cX, cY)
                break
        if i > 3:
            c = crop_img2[cY,cX]
            if diff2(a,c):
                d = (cX, cY)
                break
        i +=1

    # dd(crop_img)
    # dd(crop_img2)
    # cv2.circle(img2, d, 7, (255, 255, 255), -1)
    # cv2.circle(img, (d[0]+1010,d[1]+445), 7, (255, 255, 255), -1)
    # print(d)
    x = (d[0]+1010)/2
    y = (d[1]+445)/2
    # print(x,y)
    # aaa = autopy.mouse.get_pos()
    # print(aaa)
    autopy.mouse.smooth_move(int(x), int(y)) # 平滑移动鼠标（上面那个是瞬间的）
    autopy.mouse.click() # 单击

    #写入图片并且打开查看
    # dd(crop_img2)

=======总结========

把PIL处理的图片给cv2用

bbox = (x, y, x+1010, y+990)
img = ImageGrab.grab(bbox)
#
## 把PIL处理的图片给cv2用
pil_image = img
crop_img = cv2.cvtColor(numpy.asarray(pil_image), cv2.COLOR_RGB2BGR)

cv2.findContours 查找边界

cv2.moments(cc)  获取边界中心点