1.https://www.jb51.net/article/164348.htm
-
import cv
2
-
import numpy as np
-
img= cv
2.imread('
15_
13_
57_
06.jpg')
-
grey=cv
2.cvtColor(img,cv
2.COLOR_BGR
2GRAY)
-
retVal,greyimg=cv
2.threshold(grey,
150,
255,cv
2.THRESH_BINARY)
-
greyimg=cv
2.GaussianBlur(greyimg,(
5,
5),
0)
-
canny1 = cv
2.Canny(greyimg,
100,
150)
-
contours, hierarchy=cv
2.findContours(greyimg,cv
2.RETR_TREE,cv
2.CHAIN_APPROX_SIMPLE)
-
for c in contours:
-
if cv
2.contourArea(c)>
200:
-
rect = cv
2.minAreaRect(c)
-
# 计算最小区域的坐标
-
box = cv
2.boxPoints(rect)
-
# 坐标规范化为整数
-
box = np.int
0(box)
-
cv2.drawContours(grey,
[box], 0, (0, 0, 255), 3)
-
# cv2.drawContours(grey,contours,-1,(0,0,255),3)
-
cv2.imshow('111',greyimg)
-
cv2.imshow('222',grey)
-
cv2.imshow('3331',canny1)
-
# cv2.imshow('333',dict)
-
cv2.waitKey(0)
1.1 cv2.threshold 图像二值化
https://www.cnblogs.com/yinliang-liang/p/9293310.html
-
# 1.简单阈值
-
retVal,th
1=cv
2.threshold(gray,
100,
255,cv
2.THRESH_BINARY)
-
#Otsu 滤波
-
ret2,th
2= cv
2.threshold(gray,
0,
255,cv
2.THRESH_BINARY+cv
2.THRESH_OTSU)
-
# 2.自适应阈值
-
th3 = cv
2.adaptiveThreshold(gray,
255,cv
2.ADAPTIVE_THRESH_MEAN_C,\
-
cv2.THRESH_BINARY,
3,
2) #换行符号 \
-
th4 = cv
2.adaptiveThreshold(gray,
255,cv
2.ADAPTIVE_THRESH_GAUSSIAN_C,\
-
cv2.THRESH_BINARY,
3,
2) #换行符号 \
-
cv2.imshow('
111',gray)
-
cv2.imshow('
221',th
1)
-
cv2.imshow('
222',th
2)
-
cv2.imshow('
223',th
3)
-
cv2.imshow('
224',th
4)
2.cv2.GaussianBlur
高斯模糊，就是使用权重满足正则化的原则。越是中心的数据权重越大，使用正则化的权重去模糊数据。具体就是拿正则化的比例去卷积数据。卷积核的大小和均方差的大小。如果均方差大，说明中心的数据的权重就小一点。
-
import
numpy
-
import
numpy
as
np
-
from
scipy.stats
import
norm
-
from
math
import
floor
-
import
cv2
-
gaussian_kernel_size
=
3
# 3 * 3 #
-
gaussian_kernel_sigma
=
2
-
-
gaussian
=
norm(loc
=
0.0
,
scale
=
gaussian_kernel_sigma)
-
-
pdf
=
gaussian.pdf(
-
numpy.arange(
-
-floor(gaussian_kernel_size
*
0.5
),
-
floor(gaussian_kernel_size
*
0.5
)
+
1
,
-
dtype
=
numpy.float64
-
)
-
)
-
pdf
/=
numpy.sum(pdf)
+
numpy.finfo(numpy.float64).eps
# 别忘了这一步要归一化 #
-
-
-
data
=
numpy.array(
-
[
-
[
0,
0,
0,
0,
0],
-
[
0,
0,
0,
0,
0],
-
[
0,
0,
1,
0,
0],
-
[
0,
0,
0,
0,
0],
-
[
0,
0,
0,
0,
0]
-
]
,
-
dtype
=
numpy.float64
-
)
-
a=[]
-
-
for
i,d
in
enumerate(numpy.pad(data,
1
)):
-
for
j
in
range(5):
-
a.append(np.dot(pdf,
d[j:j
+
3
]))
-
ary=np.array(a).reshape(7,5).T
-
b=[]
-
for
i,d
in
enumerate(ary):
-
for
j
in
range(5):
-
b.append(np.dot(pdf,
d[j:j
+
3
]))
-
ary1=np.array(b).reshape(5,5)
-
print(ary1)
-
-
cv2.GaussianBlur(data,
(gaussian_kernel_size,
gaussian_kernel_size),
gaussian_kernel_sigma)
3.cv2.Sobel 先高斯然后使用X,Y两个卷积核卷积
4.cv2.Canny
-
import
cv2
-
import
numpy
as
np
-
img=
cv2.imread('16_09_10_40.jpg')
-
-
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
-
gray=cv2.bilateralFilter(gray,d=0,sigmaColor=100,sigmaSpace=15)
-
# gray=cv2.GaussianBlur(gray,(5,5),0)
-
rows,cols=gray.shape
-
gradientX=np.zeros((rows,cols),np.uint8)
-
gradientY=np.zeros((rows,cols),np.uint8)
-
gradientXY=np.zeros((rows,cols),np.uint8)
-
pointDirection=np.zeros((rows,cols),np.uint8)
-
threshold=np.zeros((rows,cols),np.uint8)
-
highThreshold=100
-
lowThreshold=20
-
sobelx=[[-1,0,1],
-
[
-1,
0,
1]
,
-
[
-1,
0,
1]
]
-
sobely=[[1,2,1],
-
[
0,
0,
0]
,
-
[
-1,
-2,
-1]
]
-
sobelx=np.array(sobelx)
-
sobely=np.array(sobely)
-
sobel_x_h,sobel_x_w=sobelx.shape
-
sobel_y_h,sobel_y_w=sobely.shape
-
pad_x_img=np.pad(gray,((sobel_x_h//2,sobel_x_h//2),(sobel_x_w//2,sobel_x_w//2)))
-
pad_y_img=np.pad(gray,((sobel_y_h//2,sobel_y_h//2),(sobel_y_w//2,sobel_y_w//2)))
-
gradientX=cv2.filter2D(pad_x_img,-1,sobelx)
-
for
i
in
range(rows):
-
for
j
in
range(cols):
-
cur_output_x
=
pad_x_img[i:i+sobel_x_h,j:j+sobel_x_w]
*
sobelx
-
cur_output_y
=
pad_y_img[i:i
+
sobel_y_h,
j:j
+
sobel_y_w]
*
sobely
-
conv_sum_x
=
np.sum(cur_output_x)
-
conv_sum_y
=
np.sum(cur_output_y)
-
gradientY[i,
j]
=
conv_sum_y
-
gradientX[i,
j]
=
conv_sum_x
-
gradientXY[i,j]=np.sqrt(conv_sum_y**2+conv_sum_x**2)
-
pointDirection[i,j]=np.arctan(conv_sum_y/conv_sum_x)
-
-
outputImage=gradientXY.copy()
-
for
i
in
range(1,rows-1):
-
for
j
in
range(1,cols-1):
-
NE=gradientXY[i-1,j+1]
-
NW=gradientXY[i-1,j-1]
-
N=gradientXY[i-1,j]
-
W=gradientXY[i,j-1]
-
E=gradientXY[i,j+1]
-
SW=gradientXY[i+1,j-1]
-
S=gradientXY[i+1,j]
-
SE=gradientXY[i+1,j+1]
-
theta=pointDirection[i,j]
-
if
theta<=np.pi/4
and
theta>=0
:
-
gp1=(1-np.tan(theta))*E+np.tan(theta)*NE
-
gp2=(1-np.tan(theta))*W+np.tan(theta)*SW
-
elif
theta>np.pi/4:
-
gp1=(1-1/np.tan(theta))*N+1/np.tan(theta)*NE
-
gp2=(1-1/np.tan(theta))*S+1/np.tan(theta)*SW
-
elif
theta<0
and
theta>=-np.pi/4:
-
gp1
=
(1
-
np.tan(-theta))
*
E
+
np.tan(-theta)
*
SE
-
gp2
=
(1
-
np.tan(-theta))
*
W
+
np.tan(-theta)
*
NW
-
else:
-
gp1
=
(1
-
1
/np.tan(-theta))
*
S
+
1
/np.tan(-theta)
*
SE
-
gp2
=
(1
-
1
/np.tan(-theta))
*
N
+
1
/np.tan(-theta)
*
NW
-
-
if
gradientXY[i,j]<gp1
or
gradientXY[i,j]<gp2:
-
outputImage[i,j]=0
-
-
highPoints=[]
-
for
i
in
range(1,rows-1):
-
for
j
in
range(1,cols-1):
-
if
outputImage[i,j]>=highThreshold:
-
outputImage[i,j]=255
-
highPoints.append([i,j])
-
-
elif
outputImage[i,j]<lowThreshold:
-
outputImage[i,j]=0
-
-
-
def
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i,
j):
-
if
i
<=
0
or
j
<=
0
or
i
>=
rows
-
1
or
j
>=
cols - 1:
-
return
-
if
image[i
-
1
,
j
-
1
]
>=
lowThreshold
and
image[i
-
1
,
j
-
1
]
<
255:
-
image[i
-
1
,
j
-
1
]=255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
-
1
,
j
-
1
)
-
-
if
(image[i
-
1
,
j]
>=
lowThreshold
and
image[i
-
1
,
j]
<
255
):
-
image[i
-
1
,
j]=
255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
-
1
,
j)
-
-
if
(image[i
-
1
,
j
+
1
]
>=
lowThreshold
and
image[i
-
1
,
j
+
1
]
<
255
):
-
image[i
-
1
,
j
+
1
]=255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
-
1
,
j
+
1
)
-
-
if
(image[i,
j
-
1
]
>=
lowThreshold
and
image[i,
j
-
1
]
<
255
):
-
image[i,
j
-
1
]=
255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i,
j
-
1
)
-
-
if
(image[i,
j
+
1
]
>=
lowThreshold
and
image[i,
j
+
1
]
<
255
):
-
image[i,
j
+
1
]=
255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i,
j
+
1
)
-
-
if
(image[i
+
1
,
j
-
1
]>=
lowThreshold
and
image[i
+
1
,
j
-
1
]
<
255
):
-
image[i
+
1
,
j
-
1
]=
255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
+
1
,
j
-
1
)
-
-
if
(image[i
+
1
,
j]
>=
lowThreshold
and
image[i
+
1
,
j]
<
255
):
-
image[i
+
1
,
j]=255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
+
1
,
j)
-
-
if
(image[i
+
1
,
j
+
1
]
>=
lowThreshold
and
image[i
+
1
,
j
+
1
]
<
255
):
-
image[i
+
1
,
j
+
1
]=255
-
DoubleThresholdLinkRecurrent(image,
lowThreshold,
i
+
1
,
j
+
1
)
-
for poinst in highPoints:
-
DoubleThresholdLinkRecurrent(outputImage,lowThreshold,poinst[0],poinst[1])
-
-
-
-
-
for
i
in
range(1,rows-1):
-
for
j
in
range(1,cols-1):
-
if
outputImage[i,j]<255:
-
outputImage[i,j]=0
-
-
-
# canny_car=cv2.Canny(gray,200,256)
-
-
-
higth=10
-
width=3
-
kernel1_temp
=
cv2.getStructuringElement(cv2.MORPH_RECT,(width,
higth))
-
kernel2_temp
=
cv2.getStructuringElement(cv2.MORPH_RECT,(higth,
width))
-
-
test_ONE_1
=
cv2.morphologyEx(outputImage,cv2.MORPH_CLOSE,kernel1_temp)
-
test_ONE_2
=
cv2.morphologyEx(test_ONE_1,cv2.MORPH_OPEN,kernel1_temp)
-
-
test_TWO_1
=
cv2.morphologyEx(outputImage,cv2.MORPH_CLOSE,kernel2_temp)
-
test_TWO_2
=
cv2.morphologyEx(test_TWO_1,cv2.MORPH_OPEN,kernel2_temp)
-
-
test_combine
=
test_ONE_2
+
test_TWO_2
-
-
-
-
cv2.imshow('gradientX',gradientX)
-
cv2.imshow('gradientY',gradientY)
-
cv2.imshow('gradientXY',gradientXY)
-
cv2.imshow('outputImage',outputImage)
-
cv2.imshow('canny_car',canny_car)
-
cv2.imshow('img',img)
-
cv2.imshow('test_combine',test_combine)
-
cv2.waitKey(0)
-
5.cv2.bilateralFilter 双线滤波
-
import cv
2
-
import math
-
import numpy as np
-
-
-
# 高斯核生成函数
-
def gaussian_kernel(gaussian_kernel_size, sigma=
1, k=
1):
-
if sigma ==
0:
-
sigma = ((gaussian_kernel_size -
1) *
0.
5 -
1) *
0.
3 +
0.
8
-
-
X = np.linspace(-k, k, gaussian_kernel_size)
-
Y = np.linspace(-k, k, gaussian_kernel_size)
-
x, y = np.meshgrid(X, Y)
-
gauss = np.exp(-(x **
2 + y **
2) / (
2 * sigma **
2))
-
-
return gauss * (
1 / np.sum(gauss))
-
-
-
def pixel_kernel(pix, box, pg):
-
box1 = np.zeros(box.shape)
-
for i in range(len(box)):
-
t = int(math.fabs(int(pix) - int(box[i])))
-
box1[i] = pg[t]
-
return box
1
-
-
-
def pixel_gaussion(sigma=
30):
-
box = np.zeros(
256)
-
for i in range(
0,
255):
-
box[i] = np.exp(-(i **
2) / (
2 * sigma **
2))
-
-
return box
-
-
-
def spilt(a):
-
if a /
2 ==
0:
-
x1 = x
2 = a /
2
-
else:
-
x1 = math.floor(a /
2)
-
x2 = a - x
1
-
return -x
1, x
2
-
-
-
def get_pixel(i, j, gaussian_kernel_size, gray):
-
temp = np.zeros(gaussian_kernel_size * gaussian_kernel_size)
-
count =
0
-
x1, x
2 = spilt(gaussian_kernel_size)
-
for m in range(x
1, x
2):
-
for n in range(x
1, x
2):
-
if i + m <
0 or i + m > gray.shape[
0] -
1 or j + n <
0 or j + n > gray.shape[
1] -
1:
-
temp[count] = gray[i, j]
-
else:
-
temp[count] = gray[i + m, j + n]
-
count +=
1
-
return temp
-
-
-
def main():
-
# 高斯kernel_size=5
-
gaussian_kernel_size =
5
-
# 空间高斯sigma
-
s_sigma =
10
-
# 灰度高斯sigma
-
g_sigma =
30
-
gk = gaussian_kernel(gaussian_kernel_size, s_sigma) # 空间高斯核
-
img = cv
2.imread('
16_
09_
11_
55.jpg')
-
gray = cv
2.cvtColor(img, cv
2.COLOR_BGR
2GRAY)
-
rows, cols = gray.shape
-
mybilateralFilter = np.zeros(gray.shape,np.uint
8)
-
pg = pixel_gaussion(g_sigma)
-
print(np.sum(pg)) # list
-
for i in range(rows):
-
for j in range(cols):
-
box = get_pixel(i, j, gaussian_kernel_size, gray) # 获取相邻像素
-
-
pk = pixel_kernel(gray[i, j], box, pg)
-
box = np.array(box).reshape(gaussian_kernel_size, gaussian_kernel_size)
-
pk = np.array(pk).reshape(gaussian_kernel_size, gaussian_kernel_size)
-
evalue = np.multiply(gk, pk)
-
-
mybilateralFilter[i, j] = int(np.sum(box * evalue))
-
-
blur = cv
2.bilateralFilter(gray,
9,
10,
30)
-
cv2.imshow('myblur', mybilateralFilter)
-
cv2.imshow('blur',blur)
-
cv2.waitKey(
0)
-
-
-
if __name__ ==
"__main__":
-
main()
6.cv2.kmeans 聚类
-
import cv
2
-
import numpy as np
-
import math
-
import os
-
-
-
srcImage=cv
2.imread(
"15_13_57_06.jpg")
-
-
# 均值聚类提取前景:二维转一维
-
def kmean_img(srcImage):
-
imgVec = np.float
32(srcImage.reshape((-
1,
3)))
-
criteria = (cv
2.TERM_CRITERIA_EPS + cv
2.TERM_CRITERIA_MAX_ITER,
10,
1.
0)
-
flags = cv
2.KMEANS_RANDOM_CENTERS
-
ret,label,clusCenter = cv
2.kmeans(imgVec,
2,None,criteria,
10,flags)
-
clusCenter = np.uint
8(clusCenter)
-
clusResult = clusCenter[label.flatten()]
-
imgres = clusResult.reshape((srcImage.shape))
-
imgres = cv
2.cvtColor(imgres,cv
2.COLOR_BGR
2GRAY)
-
max=int(np.max(imgres))
-
min=int(np.min(imgres))
-
bwThresh = (max+min)/
2
-
_,thresh = cv
2.threshold(imgres,bwThresh,
255,cv
2.THRESH_BINARY_INV)
-
thresh1=np.zeros(thresh.shape,np.uint
8)
-
thresh1[thresh==
0]=
255
-
kernel=cv
2.getStructuringElement(cv
2.MORPH_RECT,(
9,
9))
-
test_ONE_2 = cv
2.dilate(thresh
1, kernel)
-
# threshRotate = cv2.merge([thresh,thresh,thresh])
-
# cv2.imshow("thresh",thresh)
-
# cv2.waitKey(0)
-
# 确定前景外接矩形
-
# find contours
-
contours, hierarchy = cv
2.findContours(test_ONE_
2, cv
2.RETR_TREE , cv
2.CHAIN_APPROX_NONE )
-
maxconArea =
0
-
maxAreaPos = -
1
-
for i in range(len(contours)):
-
if maxconArea < cv
2.contourArea(contours[i]):
-
maxconArea = cv
2.contourArea(contours[i])
-
maxAreaPos = i
-
objCont = contours[maxAreaPos]
-
print(maxconArea)
-
rect = cv
2.minAreaRect(objCont)
-
box = cv
2.boxPoints(rect)
-
box = np.int
0(box)
-
srcImage=cv
2.drawContours(srcImage,[box],-
1,(
255,
255,
0),
3)
-
# for c in contours:
-
# # if cv2.contourArea(c) > 2000:
-
# rect = cv2.minAreaRect(c)
-
# # 计算最小区域的坐标
-
# box = cv2.boxPoints(rect)
-
# # 坐标规范化为整数
-
# box = np.int0(box)
-
# cv2.drawContours(srcImage, [box], 0, (255, 0, 0), 3)
-
cv2.imshow(
"srcImage",srcImage)
-
# cv2.imshow("thresh1",thresh1)
-
cv2.imshow(
"imgres",imgres)
-
# cv2.imshow("test_ONE_2",test_ONE_2)
-
cv2.waitKey(
0)
-
-
-
def panelAbstract(srcImage):
-
# read pic shape
-
imgHeight,imgWidth = srcImage.shape[:
2]
-
imgHeight = int(imgHeight);imgWidth = int(imgWidth)
-
# 均值聚类提取前景:二维转一维
-
imgVec = np.float
32(srcImage.reshape((-
1,
3)))
-
criteria = (cv
2.TERM_CRITERIA_EPS + cv
2.TERM_CRITERIA_MAX_ITER,
10,
1.
0)
-
flags = cv
2.KMEANS_RANDOM_CENTERS
-
ret,label,clusCenter = cv
2.kmeans(imgVec,
2,None,criteria,
10,flags)
-
clusCenter = np.uint
8(clusCenter)
-
clusResult = clusCenter[label.flatten()]
-
imgres = clusResult.reshape((srcImage.shape))
-
imgres = cv
2.cvtColor(imgres,cv
2.COLOR_BGR
2GRAY)
-
bwThresh = int((int(np.max(imgres))+int(np.min(imgres)))/
2)
-
_,thresh = cv
2.threshold(imgres,bwThresh,
255,cv
2.THRESH_BINARY_INV)
-
threshRotate = cv
2.merge([thresh,thresh,thresh])
-
# 确定前景外接矩形
-
#find contours
-
kernel=cv
2.getStructuringElement(cv
2.MORPH_RECT,(
9,
9))
-
thresh1=np.zeros(thresh.shape,np.uint
8)
-
thresh1[thresh==
0]=
255
-
test_ONE_2 = cv
2.dilate(thresh
1, kernel)
-
contours, hierarchy = cv
2.findContours(test_ONE_
2,cv
2.RETR_EXTERNAL,cv
2.CHAIN_APPROX_SIMPLE)
-
minvalx = np.max([imgHeight,imgWidth]);maxvalx =
0
-
minvaly = np.max([imgHeight,imgWidth]);maxvaly =
0
-
maxconArea =
0;maxAreaPos = -
1
-
for i in range(len(contours)):
-
if maxconArea < cv
2.contourArea(contours[i]):
-
maxconArea = cv
2.contourArea(contours[i])
-
maxAreaPos = i
-
objCont = contours[maxAreaPos]
-
rect = cv
2.minAreaRect(objCont)
-
# box = cv2.boxPoints(rect)
-
# box = np.int0(box)
-
# srcImage=cv2.drawContours(srcImage,[box],-1,(255,255,0),3)
-
# # for c in contours:
-
# # # if cv2.contourArea(c) > 2000:
-
# # rect = cv2.minAreaRect(c)
-
# # # 计算最小区域的坐标
-
# # box = cv2.boxPoints(rect)
-
# # # 坐标规范化为整数
-
# # box = np.int0(box)
-
# # cv2.drawContours(srcImage, [box], 0, (255, 0, 0), 3)
-
# cv2.imshow("srcImage",srcImage)
-
# # cv2.imshow("thresh1",thresh1)
-
# cv2.imshow("imgres",imgres)
-
# # cv2.imshow("test_ONE_2",test_ONE_2)
-
# cv2.waitKey(0)
-
# # 旋转校正前景
-
# rect = cv2.minAreaRect(objCont)
-
for j in range(len(objCont)):
-
minvaly = np.min([minvaly,objCont[j][
0][
0]])
-
maxvaly = np.max([maxvaly,objCont[j][
0][
0]])
-
minvalx = np.min([minvalx,objCont[j][
0][
1]])
-
maxvalx = np.max([maxvalx,objCont[j][
0][
1]])
-
if rect[
2] <=-
45:
-
rotAgl =
90 +rect[
2]
-
else:
-
rotAgl = rect[
2]
-
if rotAgl ==
0:
-
panelImg = srcImage[minvalx:maxvalx,minvaly:maxvaly,:]
-
else:
-
rotCtr = rect[
0]
-
rotCtr = (int(rotCtr[
0]),int(rotCtr[
1]))
-
rotMdl = cv
2.getRotationMatrix
2D(rotCtr,rotAgl,
1)
-
imgHeight,imgWidth = srcImage.shape[:
2]
-
#图像的旋转
-
dstHeight = math.sqrt(imgWidth *imgWidth + imgHeight*imgHeight)
-
dstRotimg = cv
2.warpAffine(threshRotate,rotMdl,(int(dstHeight),int(dstHeight)))
-
dstImage = cv
2.warpAffine(srcImage,rotMdl,(int(dstHeight),int(dstHeight)))
-
dstRotimg = cv
2.cvtColor(dstRotimg,cv
2.COLOR_BGR
2GRAY)
-
cv2.imshow('dstRotimg',dstRotimg)
-
cv2.waitKey(
0)
-
_,dstRotBW = cv
2.threshold(dstRotimg,
127,
255,
0)
-
contours, hierarchy = cv
2.findContours(dstRotBW,cv
2.RETR_EXTERNAL,cv
2.CHAIN_APPROX_SIMPLE)
-
maxcntArea =
0;maxAreaPos = -
1
-
for i in range(len(contours)):
-
if maxcntArea < cv
2.contourArea(contours[i]):
-
maxcntArea = cv
2.contourArea(contours[i])
-
maxAreaPos = i
-
x,y,w,h = cv
2.boundingRect(contours[maxAreaPos])
-
#提取前景：panel
-
panelImg = dstImage[int(y):int(y+h),int(x):int(x+w),:]
-
-
return panelImg
-
-
-
# 使用grabCut算法
-
def grabcut_img(img):
-
mask = np.zeros(img.shape[:
2], np.uint
8)
-
# 背景模型
-
bgdModel = np.zeros((
1,
65), np.float
64)
-
# 前景模型
-
fgdModel = np.zeros((
1,
65), np.float
64)
-
-
rect = (
0,
0,
512,
640)
-
# 使用grabCut算法
-
cv2.grabCut(img, mask, rect, bgdModel, fgdModel,
5, cv
2.GC_INIT_WITH_RECT)
-
-
mask2 = np.where((mask ==
2) | (mask ==
0),
0,
1).astype('uint
8')
-
img = img * mask
2[:, :, np.newaxis]
-
-
cv2.imshow(
"thresh",img)
-
cv2.imshow(
"mask2",mask
2)
-
cv2.waitKey(
0)
-
-
path=r
"C:\Users\39314\Desktop\qingdao"
-
for file in os.listdir(path):
-
if file.endswith('jpg'):
-
file_path=os.path.join(path,file)
-
srcImage=cv
2.imread(file_path)
-
kmean_img(srcImage)
-
# kmean_img(srcImage)
-
# a = panelAbstract(srcImage)
-
# cv2.imshow('figa', a)
-
# cv2.waitKey(0)
-
# cv2.destroyAllWindows()
-
-
7.方法柔和
-
import cv
2
-
import numpy as np
-
import math
-
import os
-
#首先背景分割，切出需要检测的范围
-
#然后使用双线滤波的canny边缘检测算子
-
-
def kmean_img(srcImage):
-
dstimg=np.zeros(srcImage.shape,np.uint
8)
-
imgVec = np.float
32(srcImage.reshape((-
1,
3)))
-
criteria = (cv
2.TERM_CRITERIA_EPS + cv
2.TERM_CRITERIA_MAX_ITER,
10,
1.
0)
-
flags = cv
2.KMEANS_RANDOM_CENTERS
-
ret,label,clusCenter = cv
2.kmeans(imgVec,
2,None,criteria,
10,flags)
-
clusCenter = np.uint
8(clusCenter)
-
clusResult = clusCenter[label.flatten()]
-
imgres = clusResult.reshape((srcImage.shape))
-
imgres = cv
2.cvtColor(imgres,cv
2.COLOR_BGR
2GRAY)
-
max=int(np.max(imgres))
-
min=int(np.min(imgres))
-
bwThresh = (max+min)/
2
-
_,thresh = cv
2.threshold(imgres,bwThresh,
255,cv
2.THRESH_BINARY_INV)
-
thresh1=np.zeros(thresh.shape,np.uint
8)
-
thresh1[thresh==
0]=
255
-
kernel=cv
2.getStructuringElement(cv
2.MORPH_RECT,(
9,
9))
-
test_ONE_2 = cv
2.dilate(thresh
1, kernel)
-
contours, hierarchy = cv
2.findContours(test_ONE_
2, cv
2.RETR_TREE , cv
2.CHAIN_APPROX_NONE )
-
maxconArea =
0
-
maxAreaPos = -
1
-
for i in range(len(contours)):
-
if maxconArea < cv
2.contourArea(contours[i]):
-
maxconArea = cv
2.contourArea(contours[i])
-
maxAreaPos = i
-
objCont = contours[maxAreaPos]
-
rect = cv
2.minAreaRect(objCont)
-
box = cv
2.boxPoints(rect)
-
box = np.int
0(box)
-
minx=np.min(box[:,
1])
-
miny=np.min(box[:,
0])
-
maxx=np.max(box[:,
1])
-
maxy=np.max(box[:,
0])
-
dstimg[minx:maxx,miny:maxy]=srcImage[minx:maxx,miny:maxy]
-
return dstimg
-
# # srcImage=cv2.drawContours(srcImage,[box],-1,(255,255,0),3)
-
# srcImage = cv2.drawContours(srcImage, [box], -1, (255, 255, 0), 3)
-
# cv2.imshow("srcImage", srcImage)
-
# cv2.imshow("dstimg",dstimg)
-
# # cv2.imshow("thresh1",thresh1)
-
# cv2.imshow("imgres",imgres)
-
# # cv2.imshow("test_ONE_2",test_ONE_2)
-
# cv2.waitKey(0)
-
-
-
def canny(img):
-
gray = cv
2.cvtColor(img, cv
2.COLOR_BGR
2GRAY)
-
blur=cv
2.GaussianBlur(gray,(
5,
5),
0)
-
canny_car = cv
2.Canny(blur,
200,
256)
-
return canny_car
-
-
def bilateralFilter_canny(img):
-
gray = cv
2.cvtColor(img, cv
2.COLOR_BGR
2GRAY)
-
blur = cv
2.bilateralFilter(gray,
9,
10,
30)
-
canny_car=my_canny(blur)
-
higth =
11
-
width =
3
-
kernel1_temp = cv
2.getStructuringElement(cv
2.MORPH_RECT, (width, higth))
-
kernel2_temp = cv
2.getStructuringElement(cv
2.MORPH_RECT, (higth, width))
-
-
test_ONE_1 = cv
2.morphologyEx(canny_car, cv
2.MORPH_CLOSE, kernel
1_temp)
-
test_ONE_2 = cv
2.morphologyEx(test_ONE_
1, cv
2.MORPH_OPEN, kernel
1_temp)
-
-
test_TWO_1 = cv
2.morphologyEx(canny_car, cv
2.MORPH_CLOSE, kernel
2_temp)
-
test_TWO_2 = cv
2.morphologyEx(test_TWO_
1, cv
2.MORPH_OPEN, kernel
2_temp)
-
-
test_combine = cv
2.bitwise_and(test_ONE_
2 ,test_TWO_
2)
-
# canny_car = cv2.Canny(blur, 200, 256)
-
return test_combine
-
-
def my_canny(gray):
-
rows, cols = gray.shape
-
gradientY = np.zeros((rows, cols), np.uint
8)
-
gradientXY = np.zeros((rows, cols), np.uint
8)
-
pointDirection = np.zeros((rows, cols), np.uint
8)
-
highThreshold =
100
-
lowThreshold =
20
-
sobelx =
[[-1, 0, 1],
-
[-1, 0, 1],
-
[-1, 0, 1]]
-
sobely =
[[1, 2, 1],
-
[0, 0, 0],
-
[-1, -2, -1]]
-
sobelx = np.array(sobelx)
-
sobely = np.array(sobely)
-
sobel_x_h, sobel_x_w = sobelx.shape
-
sobel_y_h, sobel_y_w = sobely.shape
-
pad_x_img = np.pad(gray, ((sobel_x_h //
2, sobel_x_h //
2), (sobel_x_w //
2, sobel_x_w //
2)))
-
pad_y_img = np.pad(gray, ((sobel_y_h //
2, sobel_y_h //
2), (sobel_y_w //
2, sobel_y_w //
2)))
-
gradientX = cv
2.filter
2D(pad_x_img, -
1, sobelx)
-
for i in range(rows):
-
for j in range(cols):
-
cur_output_x = pad_x_img[i:i + sobel_x_h, j:j + sobel_x_w] * sobelx
-
cur_output_y = pad_y_img[i:i + sobel_y_h, j:j + sobel_y_w] * sobely
-
conv_sum_x = np.sum(cur_output_x)
-
conv_sum_y = np.sum(cur_output_y)
-
gradientY[i, j] = conv_sum_y
-
gradientX[i, j] = conv_sum_x
-
gradientXY[i, j] = np.sqrt(conv_sum_y **
2 + conv_sum_x **
2)
-
pointDirection[i, j] = np.arctan(conv_sum_y / conv_sum_x)
-
-
outputImage = gradientXY.copy()
-
for i in range(
1, rows -
1):
-
for j in range(
1, cols -
1):
-
NE = gradientXY[i -
1, j +
1]
-
NW = gradientXY[i -
1, j -
1]
-
N = gradientXY[i -
1, j]
-
W = gradientXY[i, j -
1]
-
E = gradientXY[i, j +
1]
-
SW = gradientXY[i +
1, j -
1]
-
S = gradientXY[i +
1, j]
-
SE = gradientXY[i +
1, j +
1]
-
theta = pointDirection[i, j]
-
if theta <= np.pi /
4 and theta >=
0:
-
gp1 = (
1 - np.tan(theta)) * E + np.tan(theta) * NE
-
gp2 = (
1 - np.tan(theta)) * W + np.tan(theta) * SW
-
elif theta > np.pi /
4:
-
gp1 = (
1 -
1 / np.tan(theta)) * N +
1 / np.tan(theta) * NE
-
gp2 = (
1 -
1 / np.tan(theta)) * S +
1 / np.tan(theta) * SW
-
elif theta <
0 and theta >= -np.pi /
4:
-
gp1 = (
1 - np.tan(-theta)) * E + np.tan(-theta) * SE
-
gp2 = (
1 - np.tan(-theta)) * W + np.tan(-theta) * NW
-
else:
-
gp1 = (
1 -
1 / np.tan(-theta)) * S +
1 / np.tan(-theta) * SE
-
gp2 = (
1 -
1 / np.tan(-theta)) * N +
1 / np.tan(-theta) * NW
-
-
if gradientXY[i, j] < gp
1 or gradientXY[i, j] < gp
2:
-
outputImage[i, j] =
0
-
-
highPoints =
[]
-
for i in range(
1, rows -
1):
-
for j in range(
1, cols -
1):
-
if outputImage[i, j] >= highThreshold:
-
outputImage[i, j] =
255
-
highPoints.append([i, j])
-
-
elif outputImage[i, j] < lowThreshold:
-
outputImage[i, j] =
0
-
-
def DoubleThresholdLinkRecurrent(image, lowThreshold, i, j):
-
if i <=
0 or j <=
0 or i >= rows -
1 or j >= cols -
1:
-
return
-
if image[i -
1, j -
1] >= lowThreshold and image[i -
1, j -
1] <
255:
-
image[i -
1, j -
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i -
1, j -
1)
-
-
if (image[i -
1, j] >= lowThreshold and image[i -
1, j] <
255):
-
image[i -
1, j] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i -
1, j)
-
-
if (image[i -
1, j +
1] >= lowThreshold and image[i -
1, j +
1] <
255):
-
image[i -
1, j +
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i -
1, j +
1)
-
-
if (image[i, j -
1] >= lowThreshold and image[i, j -
1] <
255):
-
image[i, j -
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i, j -
1)
-
-
if (image[i, j +
1] >= lowThreshold and image[i, j +
1] <
255):
-
image[i, j +
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i, j +
1)
-
-
if (image[i +
1, j -
1] >= lowThreshold and image[i +
1, j -
1] <
255):
-
image[i +
1, j -
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i +
1, j -
1)
-
-
if (image[i +
1, j] >= lowThreshold and image[i +
1, j] <
255):
-
image[i +
1, j] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i +
1, j)
-
-
if (image[i +
1, j +
1] >= lowThreshold and image[i +
1, j +
1] <
255):
-
image[i +
1, j +
1] =
255
-
DoubleThresholdLinkRecurrent(image, lowThreshold, i +
1, j +
1)
-
-
for poinst in highPoints:
-
DoubleThresholdLinkRecurrent(outputImage, lowThreshold, poinst[
0], poinst[
1])
-
-
for i in range(
1, rows -
1):
-
for j in range(
1, cols -
1):
-
if outputImage[i, j] <
255:
-
outputImage[i, j] =
0
-
-
return outputImage
-
-
srcImage=cv
2.imread(
"15_13_57_06.jpg")
-
dstimg=kmean_img(srcImage)
-
canny_car=bilateralFilter_canny(dstimg)
-
cv2.imshow(
"canny_car",canny_car)
-
cv2.waitKey(
0)
转载：https://blog.csdn.net/qq_33228039/article/details/115913414
查看评论