def get_color(frame, color1, color2):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
color_dict = colorList.getColorList()
mask1 = cv2.inRange(hsv, color_dict[color1][0], color_dict[color1][1])
mask2 = cv2.inRange(hsv, color_dict[color2][0], color_dict[color2][1])
mask = mask1 + mask2
cv2.imwrite(filename + color1 + color2 + '.jpg', mask)
def findcolor(self, takeHSVFrame):
night = 0
color_dict = colorList.getColorList()
# hsv = cv2.cvtColor(cutframe, cv2.COLOR_BGR2HSV)
maxsum = 0
color = 'None'
d = 'red'
# image = cutframe.copy()
mask = cv2.inRange(takeHSVFrame, color_dict[d][0], color_dict[d][1])
# cv2.imwrite(d + '.jpg', mask)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
cnts, hiera = cv2.findContours(binary, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# cv2.imshow("lunkuo",aa)
# cv2.waitKey(1000)
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
if sum < 100:
night = 1
return night
def get_color(frame):
print('go in get_color')
rate = {}
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
color = None
color_dict = colorList.getColorList()
sum = 0
for d in color_dict:
mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
# cv2.imwrite(d + '.jpg', mask)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
single_color = 0
for c in cnts:
single_color += cv2.contourArea(c)
if d == "red" or d == "white":
pass
else:
sum += single_color
rate[d] = single_color
for i in rate:
rate[i] = rate[i] / sum * 100
return rate
def find_the_maximum_color(frame):
color_dict = colorList.getColorList()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, color_dict[get_color(frame)][0], color_dict[get_color(frame)][1]) # find the maximum color
a = color_dict[get_color(frame)][0]
b = color_dict[get_color(frame)][1]
cap.release()
print('find it !')
return a,b
def get_color(frame, classFolder, imgName):
#print('go in get_color')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
color_dict = colorList.getColorList()
for color in color_dict:
mask = cv2.inRange(hsv, color_dict[color][0], color_dict[color][1])
fileName = os.path.join(classFolder, color)
cv2.imwrite(os.path.join(fileName, imgName[:-4] + '.jpg'), mask)
return
def getColor(frame):
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
maxsum = 0
color = None
color_dict = colorList.getColorList()
# 对每个颜色进行判断
for d in color_dict:
# 根据阈值构建掩膜
mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
# 腐蚀操作
mask = cv2.erode(mask, None, iterations=2)
# 膨胀操作，其实先腐蚀再膨胀的效果是开运算，去除噪点
mask = cv2.dilate(mask, None, iterations=2)
img, cnts, hiera = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# 有轮廓才进行后面的判断
if len(cnts) > 0:
# 计算识别区域的面积
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
# 找到最大面积并找到质心
if sum > maxsum:
maxsum = sum
if maxsum != 0:
color = d
else:
color = None
# 找到面积最大的轮廓
c = max(cnts, key=cv2.contourArea)
# 确定面积最大的轮廓的外接圆
((x, y), radius) = cv2.minEnclosingCircle(c)
# 计算轮廓的矩
M = cv2.moments(c)
# 计算质心
center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"]))
return color, center
def get_color(frame):
print('go in get_color')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
maxsum = -100
color = None
color_dict = colorList.getColorList()
for d in color_dict:
mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
cv2.imwrite(d + '.jpg', mask)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTsERNAL,
cv2.CHAIN_APPROX_SIMPLE)
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
if sum > maxsum:
maxsum = sum
color = d
return color
def get_color_samefolder(frame, classFolder, imgName):
#print('go in get_color')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
color_dict = colorList.getColorList()
for color in color_dict:
mask = cv2.inRange(hsv, color_dict[color][0], color_dict[color][1])
#fileName = os.path.join(classFolder, color)
fileName = classFolder
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
if sum >= 20000:
cv2.imwrite(os.path.join(fileName, imgName[:-4] + color + '.jpg'),
mask)
return
def get_color_structure(filename):
frame = cv2.imread(filename)
arr = []
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
min_count = 10000
color_dict = colorList.getColorList()
for d in color_dict:
mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
# img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
# print("%s: %s" % (d, str(sum)))
# if len(cnts) > 0:
# cv2.imwrite('./output/' + d + '.jpg', mask)
if sum > min_count:
arr.append(d)
return arr
def get_color(path):
frame = cv2.imread(path)
#area = frame.shape[0]*frame.shape[1]
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
areadic = {}
color_dict = colorList.getColorList()
for d in color_dict:
mask = cv2.inRange(hsv, color_dict[d][0], color_dict[d][1])
#cv2.imwrite(d+'.jpg',mask)
#binary = cv2.threshold(mask, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]
#binary = cv2.adaptiveThreshold(mask,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
# cv2.imwrite(d+'.png',binary)
#kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
#binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) # 开运算
binary = cv2.dilate(binary, None, iterations=0)
img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# cv2.imwrite(d+'.png',img)
sum = 0
for c in cnts:
sum += cv2.contourArea(c)
areadic[d] = sum
# areadic['Red'] += areadic['Red2']
# del areadic['Red2']
area = 0
for c in areadic.keys():
area += areadic[c]
if area == 0:
return 0
for c in areadic.keys():
areadic[c] = round(areadic[c] / area, 2)
#sorted_area = sorted(areadic.items(), key=lambda d: d[1], reverse=True)
return areadic
print('← ', count, ' times') #Move left
count = 1
if ans_lj[i] == 'R':
if i + 1 == len(ans_lj):
print('→ ', count, ' times')
break
if ans_lj[i + 1] == ans_lj[i]:
count += 1
else:
print('→ ', count, ' times') #Move right
count = 1
if __name__ == '__main__':
cost = time.time()
color_dict = colorList.getColorList()
frame = cv2.imread(filename, 1)
maze = get_rid_of_color(frame) #create maze graph
res = mazeMatrix(maze) #resize maze
ball = get_ball_color(frame) #get ball
ball_maze = mazeMatrix(ball)
bx, by = ballMatrix(ball_maze) #find ball coord
print('The coord of ball is', bx, by)
#res[6][0]=1
#res[6][1]=1
ans = bfs(res)
if ans == False: print("No way to get out of this maze")
else:
print('There are', ans, 'steps to get out')
