def cal_shadow_mask(self):
'''
获取阴影部分的罩层
'''
# 根据颜色的统计分布 阴影部分的颜色取值
# 实际是背景颜色的平移, 所以我们在背景色的阈值上, 左移
self.shd_lcolor = vutils.justify_rgb_value(self.bg_lcolor - 60)
self.shd_ucolor = vutils.justify_rgb_value(self.bg_ucolor - 50)
# 获取阴影的罩层
self.shd_mask = cv2.inRange(self.img, self.shd_lcolor, self.shd_ucolor)
# 对获取的阴影罩层进行数学形态运算, 去除噪声
# 使用 9*9的核进行闭运算
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 9))
# open close
self.shd_mask = cv2.morphologyEx(self.shd_mask, cv2.MORPH_OPEN, kernel)
def getChessFootByColor(img):
'''
利用颜色检索棋子位置
'''
MIN_CHESS_WIDTH = 65
MAX_CHESS_WIDTH = 80
MIN_CHESS_HEIGHT = 200
MAX_CHESS_HEIGHT = 230
chess_mask = getChessFootMask(img)
image, contours, hier = cv2.findContours(chess_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours,
minHeight=MIN_CHESS_HEIGHT,
maxHeight=MAX_CHESS_HEIGHT,
minWidth=MIN_CHESS_WIDTH,
maxWidth=MAX_CHESS_WIDTH)
if len(contours) == 1:
# 刚好匹配到目标棋子
(x, y, w, h) = cv2.boundingRect(contours[0])
# 返回棋子坐标
return (int(x + w / 2), int(y + h))
else:
return None
def getLittleWhitePointCenter(img, offset=(0,0), debug=False):
'''
找到下一跳的中心白点
'''
lowerb = (245, 245, 245)
upperb = (245, 245, 245)
mask = cv2.inRange(img, lowerb, upperb)
image, contours, hierarchy = cv2.findContours(image=mask, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours, minWidth=35, maxWidth=45, minHeight=20, maxHeight=30)
if len(contours) != 1:
return None
(x, y, w, h) = cv2.boundingRect(contours[0])
cx = int(x+w/2+offset[0])
cy = int(y+h/2+offset[1])
if debug == False:
return (cx, cy)
else:
canvas = img.copy()
print("w: {}, h: {}".format(w, h))
cv2.rectangle(canvas, (x,y), (x+w, y+h), (0,0,255), thickness=4)
return (cx, cy), canvas
def getLittleWhitePointCenter(img, offset=(0,0), debug=False):
'''
找到下一跳的中心白点
'''
lowerb = (245, 245, 245)
upperb = (245, 245, 245)
mask = cv2.inRange(img, lowerb, upperb)
image, contours, hierarchy = cv2.findContours(image=mask, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours, minWidth=35, maxWidth=45, minHeight=20, maxHeight=30)
if len(contours) != 1:
if debug:
# 没有发现小白块 或者发现了多个
return (None, np.hstack((img, cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR))))
return None
# 确定白点的中心点
(x, y, w, h) = cv2.boundingRect(contours[0])
cx = int(x+w/2+offset[0])
cy = int(y+h/2+offset[1])
if debug == False:
return (cx, cy)
else:
canvas = img.copy()
cv2.rectangle(canvas, (x,y), (x+w, y+h), (0,0,255), thickness=4)
return (cx, cy), canvas
def cal_background_mask(self):
'''
计算图像背景罩层
'''
# 采集图像顶部 高度为100像素点的区域,对背景颜色进行统计
# 你也可以根据你的画面自行修改
bg_sample = self.img[200:400, 0:200]
# 分析背景样本图片, 得到背景颜色的阈值
(self.bg_lcolor, self.bg_ucolor) = vutils.cal_rgb_margin(bg_sample)
# 因为摄像头采集的时候, 光照分布, 导致图片不同区域的背景颜色差异.
# 背景色以顶部为准, 并适当放大一下背景色的取值范围, 我们这里取20
self.bg_lcolor = vutils.justify_rgb_value(self.bg_lcolor - 20)
self.bg_ucolor = vutils.justify_rgb_value(self.bg_ucolor + 20)
# 获取背景罩层
self.bg_mask = cv2.inRange(self.img, self.bg_lcolor, self.bg_ucolor)
# 对背景罩层进行处理 数学形态学运算, 去除噪声
# 用7*7 的核对背景图片进行闭运算
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 7))
self.bg_mask = cv2.morphologyEx(self.bg_mask, cv2.MORPH_CLOSE, kernel)
def getChessFootPosi(img):
MIN_CHESS_WIDTH = 65
MAX_CHESS_WIDTH = 80
MIN_CHESS_HEIGHT = 200
MAX_CHESS_HEIGHT = 230
chess_mask = getChessBinImg(img)
image, contours, hier = cv2.findContours(chess_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours,
minHeight=MIN_CHESS_HEIGHT,
maxHeight=MAX_CHESS_HEIGHT,
minWidth=MIN_CHESS_WIDTH,
maxWidth=MAX_CHESS_WIDTH)
rects = []
for c in contours:
(x, y, w, h) = cv2.boundingRect(c)
rects.append((x, y, w, h))
return rects, contours
def cal_top_box_center(self):
'''
获取最顶层盒子的中心
'''
canvas = np.copy(self.img)
image, contours, hier = cv2.findContours(self.box_mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours, minHeight=50, minWidth=50)
boxes = []
for c in contours:
x, y, w, h = cv2.boundingRect(c)
boxes.append((x, y, w, h))
top_box = boxes[0]
top_contour = contours[0]
for i in range(1, len(contours)):
# 对比box的y坐标的值, 获取最小 也就是最靠上方的盒子
box = boxes[i]
if top_box[1] > box[1]:
top_box = box
top_contour = contours[i]
# print("box : {}".format(top_box))
(x, y, w, h) = top_box
# 这里有个问题， 如果两个box离的很近
# 当前的盒子跟下一跳的盒子的区域会联通， 所以需要裁减
# 通过判断chess是否在box中， 来判断是否相邻的两个盒子被当成一个。
if vutils.isPointInRectangle(top_box, self.chs_fposi):
# 检测到盒子连体， 采用备用方案， 获取下一个盒子的中心。
miny = 100000
ptop = None
for points in top_contour:
(px, py) = points[0]
if py < miny:
miny = py
ptop = (px, py)
# 把最顶上的坐标中x点作为box中心点。
cx = ptop[0]
dh = int(abs(cx - self.chs_fposi[0]) / math.sqrt(3))
cy = self.chs_fposi[1] - dh
radius = abs(cy - ptop[1])
(x, y, w, h) = (cx - radius, ptop[1], 2 * radius, 2 * radius)
# 再次判断chess底部是否落在矩形区域内
# 进行对应的放缩
# 下面的这段代码纯属靠凡哥发挥 实验有效。
if vutils.isPointInRectangle((x, y, w, h), self.chs_fposi):
# 这里移动值， 我选的是chess矩形区域的宽度
delta = self.chs_rect[2]
if self.chs_fposi[0] < x:
x -= delta
else:
x += delta
w -= delta
h -= delta
self.nbox_rect = (x, y, w, h)
# 重新调整中心
self.nbox_center = (int(x + w / 2), int(y + h / 3))
else:
self.nbox_rect = (x, y, w, h)
(x, y, w, h) = self.nbox_rect
# 计算中心点, 立体盒子的高的1/3处大概就是中心点y坐标的位置
# 中心点的x左边标定为宽度的1/2处.
self.nbox_center = (int(x + w / 2), int(y + h / 3))
(x22, y22, w22, h22) = self.nbox_rect
x33, y33 = self.chs_fposi
x44, y44 = self.nbox_center
cv2.rectangle(canvas, (x22, y22), (x22 + w22, y22 + h22), (255, 0, 0),
5)
cv2.circle(canvas, (x33, y33), 5, (0, 0, 255), -1)
cv2.circle(canvas, (x44, y44), 5, (0, 0, 255), -1)
cv2.imshow('canvas', canvas)
while True:
img = ADBHelper.getScreenShotByADB()
# 获取棋子的位置
chess_posi = getChessFootPosi(img)
# 获取下一跳中心的位置
center_posi,canvas = getNextJumpPlatCenter(img)
if chess_posi is None or center_posi is None:
break
cv2.imshow('NextCenterFinder', canvas)
# 计算距离
distance = vutil.cal_distance(chess_posi, center_posi)
# 折算延迟
delay = distance2time(distance)
rc = ADBHelper.pressOnScreen((500, 500), delay=delay)
if rc:
print("成功点击 并延时 3s")
if debug == True:
# 保存日志 注意需要创建文件路径
img_name = f"{datetime.datetime.now():%Y-%m-%d-%H-%M-%S-%f.png}"
cv2.imwrite('./output/AutoJump/screenshot/'+img_name, img)
cv2.imwrite('./output/AutoJump/log/'+img_name, canvas)
key = cv2.waitKey(3000)
# -*- coding: utf-8 -*-
from FGJumperMaster import FGJumperMaster
from ADBHelper import ADBHelper
from FGVisonUtil import FGVisionUtil as vutil
import cv2
import numpy as np
import time
import datetime
# 初次读入图片
img = ADBHelper.getScreenShotByADB()
vutil.printImgInfo(img)
adb = ADBHelper(1080, 1920)
cv2.namedWindow('image', flags=cv2.WINDOW_NORMAL | cv2.WINDOW_FREERATIO)
keyPressed = -1
def distance2time(distance):
ratio = 1.53
# 事件必须是整数类型
return int(distance * ratio)
def saveSampleImg(jmaster, img, tag=True):
img_name = f"{datetime.datetime.now():%Y-%m-%d-%H-%M-%S.png}"
if tag:
cv2.imwrite("./samples/right/" + img_name, img)
def getNextJumpPlatCenter(img, debug=False):
'''
获取下一跳平台的中心
'''
# 声明画布
canvas = img.copy()
edge = getCannyEdge(img)
# 获取边缘信息
image, contours, hierarchy = cv2.findContours(image=edge, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
contours = vutils.contours_filter(contours, minWidth=10, minHeight=10)
# 找到最大
next_box_cnt = min(contours, key=lambda cnt: tuple(cnt[cnt[:,:,1].argmin()][0])[1])
# 顶点序号
top_point_idx = next_box_cnt[:,:,1].argmin()
# 顶点
top_point = tuple(next_box_cnt[top_point_idx,0,:2])
# 背景色取样
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
refer_back_color = img_hsv[top_point[1], top_point[0]]
left_idx_itr = 1
right_idx_itr = -1
if next_box_cnt[top_point_idx+left_idx_itr,0, 0] > top_point[0]:
left_idx_itr, right_idx_itr = right_idx_itr, left_idx_itr
right_pt_idx = top_point_idx+right_idx_itr
left_pt_idx = top_point_idx+left_idx_itr
right_point = None
left_point = None
# 寻找右边的边缘点
while True:
# 当前right point
right_point = tuple(next_box_cnt[right_pt_idx,0,:2])
next = tuple(next_box_cnt[right_pt_idx+right_idx_itr,0,:2])
if not isNextPoint(right_point, next, x_direction=1):
break
elif isInShadow(refer_back_color, img_hsv[next[1]+5, next[0]]):
# 判断边缘下方的五个像素是不是阴影
break
right_pt_idx += right_idx_itr
# 寻找左边的边缘点
while True:
left_point = tuple(next_box_cnt[left_pt_idx,0,:2])
next = tuple(next_box_cnt[left_pt_idx+left_idx_itr,0,:2])
if not isNextPoint(left_point, next, x_direction=-1):
break
elif isInShadow(refer_back_color, img_hsv[next[1]+5, next[0]]):
break
left_pt_idx += left_idx_itr
# 调整三个点的位置
(top_point, left_point, right_point) = adjust_points(top_point, left_point, right_point)
down_point = (left_point[0]+right_point[0]-top_point[0],left_point[1]+right_point[1]-top_point[1])
# 生成下一跳平台的搜索区域
# TODO 检索椭圆形 四边形
contour = np.array([
[list(top_point)],
[list(right_point)],
[list(down_point)],
[list(left_point)]])
(x, y, w, h) = cv2.boundingRect(contour)
# 在矩形区域内检索小白点提示
res_pt = getLittleWhitePointCenter(img[y:y+h, x:x+w], offset=(x,y))
center_point = None
if res_pt is not None:
# print("find white point")
# print(res_pt)
center_point = res_pt
else:
# 取left_point与right_point 中间处作为中心点
cx = int((left_point[0]+right_point[0])/2)
cy = int((left_point[1]+right_point[1])/2)
center_point = (cx, cy)
if debug == True:
cv2.drawContours(image=canvas, contours=[next_box_cnt], contourIdx=-1, color=(125,125,125), thickness=1)
canvas[top_point[1],top_point[0]] = [0,0,255]
canvas[left_point[1],left_point[0]] = [0, 255, 0]
canvas[right_point[1], right_point[0]] = [255, 0, 0]
next_right = tuple(next_box_cnt[right_pt_idx+right_idx_itr,0,:2])
canvas[next_right[1], next_right[0]] = [0,0,0]
next_left = tuple(next_box_cnt[left_pt_idx+left_idx_itr,0,:2])
canvas[next_left[1], next_left[0]] = [0,0,0]
print('LEFT: {}, TOP: {}, RIGHT: {}'.format(left_point, top_point, right_point))
else:
# 设定圆圈半径
pt_radius = 10
# 绘制轮廓
cv2.drawContours(image=canvas, contours=[next_box_cnt], contourIdx=-1, color=(0,0,255), thickness=3)
cv2.circle(canvas, top_point, pt_radius, (0, 255, 0), thickness=-1)
cv2.circle(canvas, left_point, pt_radius, (0, 255, 255), thickness=-1)
cv2.circle(canvas, right_point, pt_radius, (255, 0, 0), thickness=-1)
cv2.circle(canvas, down_point, pt_radius, (255,255,0), thickness=-1)
# 绘制检索框
# x,y,w,h = next_plat_rect
# cv2.rectangle(canvas, (x,y), (x+w, y+h), (255,255,255), thickness=2)
cv2.circle(canvas, center_point, pt_radius, (45,100,255), thickness=-1)
return center_point,canvas
def getNextJumpPlatCenter(img, debug=False):
'''
获取下一跳平台的中心
'''
# 声明画布
canvas = img.copy()
# 获取边缘图像
edge = getCannyEdge(img)
# 获取边缘信息
image, contours, hierarchy = cv2.findContours(image=edge, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
# 对轮廓点集进行过滤
contours = vutils.contours_filter(contours, minWidth=50, minHeight=50)
# 找到最高顶点所在轮廓
next_box_cnt = min(contours, key=lambda cnt: tuple(cnt[cnt[:,:,1].argmin()][0])[1])
# 获取顶点序号
top_point_idx = next_box_cnt[:,:,1].argmin()
# 顶点
top_point = tuple(next_box_cnt[top_point_idx,0,:2])
# 背景色取样
img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
refer_back_color = img_hsv[top_point[1], top_point[0]]
# 分别向左向右找到边界点
left_idx_itr = 1
right_idx_itr = -1
# 校正方向对应的序号叠加
if next_box_cnt[top_point_idx+left_idx_itr,0, 0] > top_point[0]:
left_idx_itr, right_idx_itr = right_idx_itr, left_idx_itr
right_pt_idx = top_point_idx+right_idx_itr
left_pt_idx = top_point_idx+left_idx_itr
right_point = None
left_point = None
# 寻找右边的边缘点
while True:
# 当前right point
right_point = tuple(next_box_cnt[right_pt_idx,0,:2])
# 获取下一个右边的点
next = tuple(next_box_cnt[right_pt_idx+right_idx_itr,0,:2])
# 判断这个点是否可以延伸
if not isNextPoint(right_point, next, x_direction=1):
break
elif isInShadow(refer_back_color, img_hsv[next[1]+5, next[0]]):
# 判断边缘下方的五个像素是不是阴影
break
right_pt_idx += right_idx_itr
# 寻找左边的边缘点
while True:
left_point = tuple(next_box_cnt[left_pt_idx,0,:2])
next = tuple(next_box_cnt[left_pt_idx+left_idx_itr,0,:2])
if not isNextPoint(left_point, next, x_direction=-1):
break
elif isInShadow(refer_back_color, img_hsv[next[1]+5, next[0]]):
break
left_pt_idx += left_idx_itr
# 调整三个点的位置
(top_point, left_point, right_point) = adjust_points(top_point, left_point, right_point)
# 通过平行四边形的定理 获取下方的点
down_point = (left_point[0]+right_point[0]-top_point[0],left_point[1]+right_point[1]-top_point[1])
# 生成下一跳平台的搜索区域
contour = np.array([
[list(top_point)],
[list(right_point)],
[list(down_point)],
[list(left_point)]])
(x, y, w, h) = cv2.boundingRect(contour)
# 在矩形区域内检索小白点提示
res_pt = getLittleWhitePointCenter(img[y:y+h, x:x+w], offset=(x,y))
center_point = None
if res_pt is not None:
# 如果存在小白点 就直接作为中心点
center_point = res_pt
else:
# 取left_point与right_point 中间处作为中心点
cx = int((left_point[0]+right_point[0])/2)
cy = int((left_point[1]+right_point[1])/2)
center_point = (cx, cy)
# 设定圆圈半径
pt_radius = 10
# 绘制下一跳中心点
cv2.circle(canvas, center_point, pt_radius, (45,100,255), thickness=-1)
if debug == True:
# 绘制轮廓
cv2.drawContours(image=canvas, contours=[next_box_cnt], contourIdx=-1, color=(0,0,255), thickness=3)
# 绘制平行四边形的四个点
cv2.circle(canvas, top_point, pt_radius, (0, 255, 0), thickness=-1)
cv2.circle(canvas, left_point, pt_radius, (0, 255, 255), thickness=-1)
cv2.circle(canvas, right_point, pt_radius, (255, 0, 0), thickness=-1)
cv2.circle(canvas, down_point, pt_radius, (255,255,0), thickness=-1)
return center_point,canvas
