植物图像识别python

图像识别python"/>

植物图像识别python

#-*- coding: utf-8 -*-#利用python实现多种方法来实现图像识别

importcv2importnumpy as npfrom matplotlib importpyplot as plt#最简单的以灰度直方图作为相似比较的实现

def classify_gray_hist(image1,image2,size = (256,256)):#先计算直方图

#几个参数必须用方括号括起来

#这里直接用灰度图计算直方图，所以是使用第一个通道，

#也可以进行通道分离后，得到多个通道的直方图

#bins 取为16

image1 =cv2.resize(image1,size)

image2=cv2.resize(image2,size)

hist1= cv2.calcHist([image1],[0],None,[256],[0.0,255.0])

hist2= cv2.calcHist([image2],[0],None,[256],[0.0,255.0])#可以比较下直方图

plt.plot(range(256),hist1,'r')

plt.plot(range(256),hist2,'b')

plt.show()#计算直方图的重合度

degree =0for i inrange(len(hist1)):if hist1[i] !=hist2[i]:

degree= degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))else:

degree= degree + 1degree= degree/len(hist1)returndegree#计算单通道的直方图的相似值

defcalculate(image1,image2):

hist1= cv2.calcHist([image1],[0],None,[256],[0.0,255.0])

hist2= cv2.calcHist([image2],[0],None,[256],[0.0,255.0])#计算直方图的重合度

degree =0for i inrange(len(hist1)):if hist1[i] !=hist2[i]:

degree= degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))else:

degree= degree + 1degree= degree/len(hist1)returndegree#通过得到每个通道的直方图来计算相似度

def classify_hist_with_split(image1,image2,size = (256,256)):#将图像resize后，分离为三个通道，再计算每个通道的相似值

image1 =cv2.resize(image1,size)

image2=cv2.resize(image2,size)

sub_image1=cv2.split(image1)

sub_image2=cv2.split(image2)

sub_data=0for im1,im2 inzip(sub_image1,sub_image2):

sub_data+=calculate(im1,im2)

sub_data= sub_data/3

returnsub_data#平均哈希算法计算

defclassify_aHash(image1,image2):

image1= cv2.resize(image1,(8,8))

image2= cv2.resize(image2,(8,8))

gray1=cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)

gray2=cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)

hash1=getHash(gray1)

hash2=getHash(gray2)returnHamming_distance(hash1,hash2)defclassify_pHash(image1,image2):

image1= cv2.resize(image1,(32,32))

image2= cv2.resize(image2,(32,32))

gray1=cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)

gray2=cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)#将灰度图转为浮点型，再进行dct变换

dct1 =cv2.dct(np.float32(gray1))

dct2=cv2.dct(np.float32(gray2))#取左上角的8*8，这些代表图片的最低频率

#这个操作等价于c++中利用opencv实现的掩码操作

#在python中进行掩码操作，可以直接这样取出图像矩阵的某一部分

dct1_roi = dct1[0:8,0:8]

dct2_roi= dct2[0:8,0:8]

hash1=getHash(dct1_roi)

hash2=getHash(dct2_roi)returnHamming_distance(hash1,hash2)#输入灰度图，返回hash

defgetHash(image):

avreage=np.mean(image)

hash=[]for i inrange(image.shape[0]):for j in range(image.shape[1]):if image[i,j] >avreage:

hash.append(1)else:

hash.append(0)returnhash#计算汉明距离

defHamming_distance(hash1,hash2):

num=0for index inrange(len(hash1)):if hash1[index] !=hash2[index]:

num+= 1

returnnumif __name__ == '__main__':

img1= cv2.imread('1.jpg')

cv2.imshow('img1',img1)

img2= cv2.imread('2.jpg')

cv2.imshow('img2',img2)

degree=classify_gray_hist(img1,img2)#degree = classify_hist_with_split(img1,img2)

#degree = classify_aHash(img1,img2)

#degree = classify_pHash(img1,img2)

printdegree

cv2.waitKey(0)