import skimage,math,numpy
from sklearn.decomposition import PCA
from skimage import io,transform,measure
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu, threshold_sauvola
path='F:\数据挖掘原理\dataset\题目三\圆叶\*.jpg'
coll1 = io.ImageCollection(path)
path='F:\数据挖掘原理\dataset\题目三\尖叶\*.jpg'
coll2 = io.ImageCollection(path)
feature1=[]
#提取圆叶的特征值
for num in range(0,len(coll1)):
#转换为二值图
origin_img=coll1[num]
origin_img = transform.rescale(origin_img, 0.1)
gray_img=rgb2gray(origin_img)
otsu_threshold = threshold_otsu(gray_img)
gray_img = skimage.img_as_ubyte(gray_img)
bin_image = gray_img > otsu_threshold
labels=measure.label(bin_image)#连通区域标记
repr=measure.regionprops(labels)#获取连通域的特征属性
max_area=0
yezi=repr[0]
for i in range(0,len(repr)):
if(repr[i].area>=max_area):
max_area=repr[i].area
yezi=repr[i]
Cir_ex=(4*math.pi*max_area)/(yezi.perimeter*yezi.perimeter)
Rec_ex=yezi.extent
#得到特征圆形度和矩形度
a=numpy.zeros([bin_image.shape[1]])
bin_image1=numpy.row_stack((a,bin_image,a))
a1=numpy.zeros([bin_image1.shape[0]])
a1=a1.T
bin_image1=numpy.c_[a1,bin_image1,a1]
max_height=0
outline=[]
for y in range(1,bin_image1.shape[0]-2):
for x in range(1,bin_image1.shape[1]-1):
if((bin_image1[y][x-1]==0 and bin_image1[y][x]==1)or(bin_image1[y][x]==1 and bin_image1[y][x+1]==0) or( bin_image1[y-1][x]==0 and bin_image1[y][x]==1 )or(bin_image1[y+1][x]==0 and bin_image1[y][x]==1)):
outline.append([x,y]) # 提取边缘坐标
for i in range(0,len(outline)):
for j in range(i+1,len(outline)):
length=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(length>max_height):
max_height=length # 提取叶子的最大长度
x1=outline[i][0]
x2=outline[j][0]
y1=outline[i][1]
y2=outline[j][1]
max_width=0
min=math.fabs((outline[1][0]-outline[0][0])*(x2-x1)+(outline[1][1]-outline[0][1])*(y2-y1))
for i in range(0,len(outline)-1):
for j in range(i+1,len(outline)):
if(math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))<min):
min=math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))
width=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(width>max_width):
max_width=width # 提取叶子的最大宽度
x3=outline[i][0]
x4=outline[j][0]
y3=outline[i][1]
y4=outline[j][1]
wh_cp=max_width/max_height
feature1.append([Cir_ex,Rec_ex,wh_cp])
feature2=[]
#提取尖叶的特征值
for num in range(0,len(coll2)):
#转换为二值图
origin_img=coll2[num]
origin_img = transform.rescale(origin_img, 0.1)
gray_img=rgb2gray(origin_img)
otsu_threshold = threshold_otsu(gray_img)
gray_img = skimage.img_as_ubyte(gray_img)
bin_image = gray_img > otsu_threshold
labels=measure.label(bin_image)#连通区域标记
repr=measure.regionprops(labels)#获取连通域的特征属性
max_area=0
yezi=repr[0]
for i in range(0,len(repr)):
if(repr[i].area>=max_area):
max_area=repr[i].area
yezi=repr[i]
Cir_ex=(4*math.pi*max_area)/(yezi.perimeter*yezi.perimeter)
Rec_ex=yezi.extent
#得到特征圆形度和矩形度
a=numpy.zeros([bin_image.shape[1]])
bin_image1=numpy.row_stack((a,bin_image,a))
a1=numpy.zeros([bin_image1.shape[0]])
a1=a1.T
bin_image1=numpy.c_[a1,bin_image1,a1]
max_height=0
outline=[]
for y in range(1,bin_image1.shape[0]-2):
for x in range(1,bin_image1.shape[1]-1):
if((bin_image1[y][x-1]==0 and bin_image1[y][x]==1)or(bin_image1[y][x]==1 and bin_image1[y][x+1]==0) or( bin_image1[y-1][x]==0 and bin_image1[y][x]==1 )or(bin_image1[y+1][x]==0 and bin_image1[y][x]==1)):
outline.append([x,y]) # 提取边缘坐标
for i in range(0,len(outline)):
for j in range(i+1,len(outline)):
length=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(length>max_height):
max_height=length # 提取叶子的最大长度
x1=outline[i][0]
x2=outline[j][0]
y1=outline[i][1]
y2=outline[j][1]
max_width=0
min=math.fabs((outline[1][0]-outline[0][0])*(x2-x1)+(outline[1][1]-outline[0][1])*(y2-y1))
for i in range(0,len(outline)-1):
for j in range(i+1,len(outline)):
if(math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))<min):
min=math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))
width=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(width>max_width):
max_width=width # 提取叶子的最大宽度
x3=outline[i][0]
x4=outline[j][0]
y3=outline[i][1]
y4=outline[j][1]
wh_cp=max_width/max_height
feature2.append([Cir_ex,Rec_ex,wh_cp])
#特征主成分分析
feature=feature1+feature2
pca=PCA(n_components=3)
pca.fit(feature)
weight=pca.explained_variance_ratio_
print(pca.explained_variance_ratio_)
#得到圆叶判别公式
#Fx=特征1*权重+特征2*权重+特征3*权重
Fx1=[]
for fe in feature1:
Fx=fe[0]*weight[0]+fe[1]*weight[1]+fe[2]*weight[2]
Fx1.append(Fx)
m1=numpy.max(Fx1)
m2=numpy.min(Fx1)
print(Fx1)
print(m1)
print(m2)
#尖叶叶特征主成分分析
pca=PCA(n_components=3)
pca.fit(feature2)
#得到尖叶判别公式
#Fx=特征1*权重+特征2*权重+特征3*权重
Fx2=[]
for fe in feature2:
Fx=fe[0]*weight[0]+fe[1]*weight[1]+fe[2]*weight[2]
Fx2.append(Fx)
print(Fx2)
l1=numpy.max(Fx2)
l2=numpy.min(Fx2)
print(l1)
print(l2)
path='F:\数据挖掘原理\dataset\题目三\叶子\*.jpg'
coll3 = io.ImageCollection(path)
#提取叶子的特征值
i=1
j=1
for num in range(0,len(coll3)):
#转换为二值图
origin_img=coll3[num]
origin_img = transform.rescale(origin_img, 0.1)
gray_img=rgb2gray(origin_img)
otsu_threshold = threshold_otsu(gray_img)
gray_img = skimage.img_as_ubyte(gray_img)
bin_image = gray_img > otsu_threshold
labels=measure.label(bin_image)#连通区域标记
repr=measure.regionprops(labels)#获取连通域的特征属性
max_area=0
yezi=repr[0]
for i in range(0,len(repr)):
if(repr[i].area>=max_area):
max_area=repr[i].area
yezi=repr[i]
Cir_ex=(4*math.pi*max_area)/(yezi.perimeter*yezi.perimeter)
Rec_ex=yezi.extent
#得到特征圆形度和矩形度
a=numpy.zeros([bin_image.shape[1]])
bin_image1=numpy.row_stack((a,bin_image,a))
a1=numpy.zeros([bin_image1.shape[0]])
a1=a1.T
bin_image1=numpy.c_[a1,bin_image1,a1]
max_height=0
outline=[]
for y in range(1,bin_image1.shape[0]-2):
for x in range(1,bin_image1.shape[1]-1):
if((bin_image1[y][x-1]==0 and bin_image1[y][x]==1)or(bin_image1[y][x]==1 and bin_image1[y][x+1]==0) or( bin_image1[y-1][x]==0 and bin_image1[y][x]==1 )or(bin_image1[y+1][x]==0 and bin_image1[y][x]==1)):
outline.append([x,y]) # 提取边缘坐标
for i in range(0,len(outline)):
for j in range(i+1,len(outline)):
length=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(length>max_height):
max_height=length # 提取叶子的最大长度
x1=outline[i][0]
x2=outline[j][0]
y1=outline[i][1]
y2=outline[j][1]
max_width=0
min_=math.fabs((outline[1][0]-outline[0][0])*(x2-x1)+(outline[1][1]-outline[0][1])*(y2-y1))
for i in range(0,len(outline)-1):
for j in range(i+1,len(outline)):
if(math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))<min_):
min_=math.fabs((outline[j][0]-outline[i][0])*(x2-x1)+(outline[j][1]-outline[i][1])*(y2-y1))
width=math.sqrt((outline[j][0]-outline[i][0])*(outline[j][0]-outline[i][0])+(outline[j][1]-outline[i][1])*(outline[j][1]-outline[i][1]))
if(width>max_width):
max_width=width # 提取叶子的最大宽度
x3=outline[i][0]
x4=outline[j][0]
y3=outline[i][1]
y4=outline[j][1]
wh_cp=max_width/max_height
if((Cir_ex*weight[0]+Rec_ex*weight[1]+wh_cp*weight[2])>=m2 and (Cir_ex*weight[0]+Rec_ex*weight[1]+wh_cp*weight[2])<=m1):
print("圆叶",end='')
str1="yuan"+str(i)+".jpg"
io.imsave("F:\数据挖掘原理\image_classification_demo-master\叶子\圆叶\{0}".format(str1),coll3[num])
i=i+1
elif((Cir_ex*weight[0]+Rec_ex*weight[1]+wh_cp*weight[2])>=l2 and (Cir_ex*weight[0]+Rec_ex*weight[1]+wh_cp*weight[2])<=l1):
print("尖叶",end=' ')
str2="jian"+str(j)+".jpg"
io.imsave("F:\数据挖掘原理\image_classification_demo-master\叶子\尖叶\{0}".format(str2),coll3[num])
j=j+1
else:
print("无法识别")
叶片形状分类
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