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cv.py
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cv.py
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import imutils,cv2,numpy as np
from util import *
class UIMatcher:
@staticmethod
def findGreenArrow(screen):
'''
检测政策界面中 绿箭头的中心位置
@return: 绿箭头坐标list
'''
# 增加判断screen，也就是截图是否成功的判断
if screen.size:
dstPoints = []
img2 = cv2.split(screen)
# 分离B 二值化
ret, dst1 = cv2.threshold(img2[0], 20, 255, cv2.THRESH_BINARY_INV)
# 分离G 二值化
ret, dst2 = cv2.threshold(img2[1], 220, 255, cv2.THRESH_BINARY)
# 分离R 二值化
ret, dst3 = cv2.threshold(img2[2], 20, 255, cv2.THRESH_BINARY_INV)
img2 = dst1&dst2&dst3 # 相与
# 模糊边界
# img2 = cv2.GaussianBlur(img2, (5, 5), 0)
# import matplotlib.pyplot as plt
# plt.imshow(img2,cmap='gray')
# plt.show()
# 找轮廓
cnts = cv2.findContours(img2, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[1] if imutils.is_cv3() else cnts[0]
if len(cnts):
for c in cnts:
# 获取中心点
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#
dstPoints.append((cX,cY))
# 画出轮廓和中点
# cv2.drawContours(img2, [c], -1, (0, 255, 0), 2)
# cv2.circle(img2, (cX, cY), 20, (255, 255, 255), 1)
# cv2.putText(img2, "center", (cX - 20, cY - 20),
# cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# plt.imshow(img2,cmap='gray')
# plt.show()
return dstPoints
else:
raise Exception('Screen process is unsuccessful')
@staticmethod
def findTaskBubble(screen):
'''
检测城市任务那块区域黄色气泡是否出现
@return: 是否出现
'''
dstPoints = []
h=len(screen)
w=len(screen[0])
# 截取气泡周围区域
img2 = cv2.split(screen[int(0.777*h):int(0.831*h),int(0.164*w):int(0.284*w)])
ret, B = cv2.threshold(img2[0], 120, 255, cv2.THRESH_BINARY_INV)
ret, G = cv2.threshold(img2[1], 210, 255, cv2.THRESH_BINARY_INV)
ret, R = cv2.threshold(img2[2], 230, 255, cv2.THRESH_BINARY)
img2 = R&B&G # 相与
# 模糊边界
img2 = cv2.GaussianBlur(img2, (5, 5), 0)
# 找轮廓
cnts = cv2.findContours(img2, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
if len(cnts[1]):
return True
else:
return False
@staticmethod
def findGreenLight(diff_screens, th=100):
screen_before, screen_after = diff_screens
# 转换成有符号数以处理相减后的负值
screen_before = screen_before.astype(np.int16)
screen_after = screen_after.astype(np.int16)
diff = screen_after - screen_before
h=len(diff)
w=len(diff[0])
B,G,R = cv2.split(diff)
# 负值取0
G[G < 0] = 0
G = G.astype(np.uint8)
# 二值化后相与, 相当于取中间范围内的值
ret, G1 = cv2.threshold(G, 140, 255, cv2.THRESH_BINARY_INV)
ret, G2 = cv2.threshold(G, 22, 255, cv2.THRESH_BINARY)
img0 = G1&G2
# 均值模糊(降噪 好像也没啥卵用)
img0 = cv2.medianBlur(img0,9)
# import matplotlib.pyplot as plt
# plt.imshow(img0,cmap='gray')
# plt.show()
buildings = []
for building_ID in range(1,10):
square = UIMatcher.getLittleSquare(img0,BUILDING_POSITIONS[building_ID],edge=0.1)
buildings.append(np.mean(square))
# 返回平均亮度最强的建筑物
return buildings.index(max(buildings))+1
@staticmethod
def detectCross(screen, th = 5):
'''
探测叉叉是否出现, 先截取叉叉所在的小方块,然后对灰度图二值化,再求平均值判断
'''
screen = cv2.cvtColor(screen,cv2.COLOR_RGB2GRAY)
good_id_list = []
for good_id in CROSS_POSITIONS.keys():
square = UIMatcher.getLittleSquare(screen,CROSS_POSITIONS[good_id])
ret, W = cv2.threshold(square, 250, 255, cv2.THRESH_BINARY)
# import matplotlib.pyplot as plt
# plt.imshow(W,cmap='gray')
# plt.show()
# 二值化后求平均值
if np.mean(W) > th:
good_id_list.append(good_id)
# print(good_id_list)
return good_id_list
@staticmethod
def getPixel(img, rx, ry):
"""
获取某一坐标的RGB值(灰度图会报错)
"""
pixel = img[int(ry*len(img)), int(rx*len(img[0]))]
return pixel[2],pixel[1],pixel[0]
@staticmethod
def getLittleSquare(img, rel_pos, edge=0.01):
'''
截取rel_pos附近一个小方块
'''
rx,ry = rel_pos
h=len(img)
w=len(img[0])
scale = h/w
x0 = int((rx-edge*scale)*w)
x1 = int((rx+edge*scale)*w)
y0 = int((ry-edge)*h)
y1 = int((ry+edge)*h)
return img[y0:y1,x0:x1]
@staticmethod
def findRedBagOpen(screen):
results = []
if screen.size:
img2 = cv2.split(screen)
ret, dst1 = cv2.threshold(img2[0], 200, 255, cv2.THRESH_BINARY_INV)
ret, dst2 = cv2.threshold(img2[1], 160, 240, cv2.THRESH_BINARY)
ret, dst3 = cv2.threshold(img2[2], 170, 255, cv2.THRESH_BINARY_INV)
img2 = dst1&dst2&dst3 # 相与
cnts = cv2.findContours(img2, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[1] if imutils.is_cv3() else cnts[0]
dstPoints = []
if len(cnts):
for c in cnts:
# 获取中心点
M = cv2.moments(c)
if not M["m00"]:
continue
area = cv2.contourArea(c)
if area < 30000:
continue
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#
dstPoints.append((cX,cY))
return dstPoints
else:
raise Exception('Screen process is unsuccessful')