CV检测hu矩输出txt源码-DIY初稿

import cv2
import os
import numpy as np
import functools
os.getcwd()#注意有区别，转义字符//\\
np.set_printoptions(precision=5,suppress=True)#定义输出的精度
f1 = open('W:\\BD\\Netww\\huidu001//test1.txt','w')
def sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humoment
imgh = cv2.imread('W:\\BD\\Netww\\huidu001\\cdf (1).jpg', 0)#读取灰度图格式
arr1=sys_moments(imgh)
# sd1=arr1.tolist()#数组转换为列表，不能打印输出
# print(sd1)
# print(int(functools.reduce(lambda x,y:str(x)+str(y), sd1)))set_list = []
for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)print(set_list[item])    f1.write(str(set_list[item])+"\n")
f1.close()

打印并输出，格式化

格式化输出：

import cv2
import os
import numpy as np
import functools
os.getcwd()#注意有区别，转义字符//\\
np.set_printoptions(precision=5,suppress=True)#定义输出的精度
f1 = open('W:\\BD\\Netww\\huidu001//test2.txt','w')
def sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humomentdef def_moments2(img_gray):row, col = img_gray.shape# 计算图像的0阶几何矩m00 = 0.0m10 = m01 = 0.0# 计算图像的二阶、三阶几何矩m11 = m20 = m02 = m12 = m21 = m30 = m03 = 0.0for i in range(row):for j in range(col):m00 += img_gray[i][j]m10 += i*img_gray[i][j]m20 += i**2*img_gray[i][j]m30 += i**3*img_gray[i][j]m11 += i * j * img_gray[i][j]m12 += i * j ** 2 * img_gray[i][j]m21 += i ** 2 * j * img_gray[i][j]for j in range(col):for i in range(row):m01 += (j * img_gray[i, j])m02 += (j ** 2 * img_gray[i, j])m03 += (j ** 3 * img_gray[i, j])# 由标准矩我们可以得到图像的"重心"u10 = m10 / m00u01 = m01 / m00# 计算图像的二阶中心矩、三阶中心矩y00 = y10 = y01 = y11 = y20 = y02 = y30 = y12 = y21 = y03 = 0.0for x in range(row):for y in range(col):x_ = x - u10y_ = y - u01y00 += img_gray[x][y]y10 += x_*img_gray[x][y]y01 += y_*img_gray[x][y]y11 += x_*y_*img_gray[x][y]y02 += y_**2*img_gray[x][y]y20 += x_**2*img_gray[x][y]y03 += y_**3*img_gray[x][y]y30 += x_**3*img_gray[x][y]y12 += x_*y_**2*img_gray[x][y]y21 += x_**2*y_*img_gray[x][y]# 计算图像的归一化中心矩n20 = y20 / (y00 ** 2)n02 = y02 / (y00 ** 2)n11 = y11 / (y00 ** 2)n30 = y30 / (y00 ** 2.5)n03 = y03 / (y00 ** 2.5)n12 = y12 / (y00 ** 2.5)n21 = y21 / (y00 ** 2.5)# 计算图像的七个不变矩h1 = n20 + n02h2 = (n20 - n02) ** 2 + 4 * (n11 ** 2)h3 = (n30 - 3 * n12) ** 2 + (3 * n21 - n03) ** 2h4 = (n30 + n12) ** 2 + (n21 + n03) ** 2h5 = (n30 - 3 * n12) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n21 - n03) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)h6 = (n20 - n02) * ((n30 + n12) ** 2 - (n21 + n03) ** 2) + 4 * n11 * (n30 + n12) * (n21 + n03)h7 = (3 * n21 - n03) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n12 - n30) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)inv_m7 = [h1, h2, h3, h4, h5, h6, h7]humoments = np.log(np.abs(inv_m7))return humoments
def main():input_dir = 'W:\\BD\\Netww\\huidu002\\'aa = os.listdir( input_dir )      set_list = []for i in aa:grayy=cv2.imread(input_dir+i,0)sys_humoments = sys_moments(grayy)arr1=sys_moments(grayy)   print('自带函数：\n',sys_humoments)  for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)print(set_list[item])    f1.write(str(set_list[item])+"\n")  f1.close()
#         def_humoments2 = def_moments2(grayy)
#         print('自定义函数2：\n',def_humoments2)
if __name__ == '__main__':main()    print("well done")

源码如下：

import cv2
import os
import numpy as np
import functools
os.getcwd()#注意有区别，转义字符//\\
np.set_printoptions(precision=5,suppress=True)#定义输出的精度
f1 = open('W:\\BD\\Netww\\huidu001//test2.txt','w')
def sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humomentdef def_moments2(img_gray):row, col = img_gray.shape# 计算图像的0阶几何矩m00 = 0.0m10 = m01 = 0.0# 计算图像的二阶、三阶几何矩m11 = m20 = m02 = m12 = m21 = m30 = m03 = 0.0for i in range(row):for j in range(col):m00 += img_gray[i][j]m10 += i*img_gray[i][j]m20 += i**2*img_gray[i][j]m30 += i**3*img_gray[i][j]m11 += i * j * img_gray[i][j]m12 += i * j ** 2 * img_gray[i][j]m21 += i ** 2 * j * img_gray[i][j]for j in range(col):for i in range(row):m01 += (j * img_gray[i, j])m02 += (j ** 2 * img_gray[i, j])m03 += (j ** 3 * img_gray[i, j])# 由标准矩我们可以得到图像的"重心"u10 = m10 / m00u01 = m01 / m00# 计算图像的二阶中心矩、三阶中心矩y00 = y10 = y01 = y11 = y20 = y02 = y30 = y12 = y21 = y03 = 0.0for x in range(row):for y in range(col):x_ = x - u10y_ = y - u01y00 += img_gray[x][y]y10 += x_*img_gray[x][y]y01 += y_*img_gray[x][y]y11 += x_*y_*img_gray[x][y]y02 += y_**2*img_gray[x][y]y20 += x_**2*img_gray[x][y]y03 += y_**3*img_gray[x][y]y30 += x_**3*img_gray[x][y]y12 += x_*y_**2*img_gray[x][y]y21 += x_**2*y_*img_gray[x][y]# 计算图像的归一化中心矩n20 = y20 / (y00 ** 2)n02 = y02 / (y00 ** 2)n11 = y11 / (y00 ** 2)n30 = y30 / (y00 ** 2.5)n03 = y03 / (y00 ** 2.5)n12 = y12 / (y00 ** 2.5)n21 = y21 / (y00 ** 2.5)# 计算图像的七个不变矩h1 = n20 + n02h2 = (n20 - n02) ** 2 + 4 * (n11 ** 2)h3 = (n30 - 3 * n12) ** 2 + (3 * n21 - n03) ** 2h4 = (n30 + n12) ** 2 + (n21 + n03) ** 2h5 = (n30 - 3 * n12) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n21 - n03) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)h6 = (n20 - n02) * ((n30 + n12) ** 2 - (n21 + n03) ** 2) + 4 * n11 * (n30 + n12) * (n21 + n03)h7 = (3 * n21 - n03) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n12 - n30) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)inv_m7 = [h1, h2, h3, h4, h5, h6, h7]humoments = np.log(np.abs(inv_m7))return humoments
def main():input_dir = 'W:\\BD\\Netww\\huidu002\\'aa = os.listdir( input_dir )      set_list = []for i in aa:grayy=cv2.imread(input_dir+i,0)sys_humoments = sys_moments(grayy)arr1=sys_moments(grayy)   print('自带函数：\n',sys_humoments)  for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)print(set_list[item])    f1.write("hu矩"+str(item+1)+"\t"+str(set_list[item])+"\n")  f1.close()
#         def_humoments2 = def_moments2(grayy)
#         print('自定义函数2：\n',def_humoments2)
if __name__ == '__main__':main()    print("well done")

升级版效果

import cv2
import os
import numpy as np
import functoolsdef sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humomentdef def_moments2(img_gray):row, col = img_gray.shape# 计算图像的0阶几何矩m00 = 0.0m10 = m01 = 0.0# 计算图像的二阶、三阶几何矩m11 = m20 = m02 = m12 = m21 = m30 = m03 = 0.0for i in range(row):for j in range(col):m00 += img_gray[i][j]m10 += i*img_gray[i][j]m20 += i**2*img_gray[i][j]m30 += i**3*img_gray[i][j]m11 += i * j * img_gray[i][j]m12 += i * j ** 2 * img_gray[i][j]m21 += i ** 2 * j * img_gray[i][j]for j in range(col):for i in range(row):m01 += (j * img_gray[i, j])m02 += (j ** 2 * img_gray[i, j])m03 += (j ** 3 * img_gray[i, j])# 由标准矩我们可以得到图像的"重心"u10 = m10 / m00u01 = m01 / m00# 计算图像的二阶中心矩、三阶中心矩y00 = y10 = y01 = y11 = y20 = y02 = y30 = y12 = y21 = y03 = 0.0for x in range(row):for y in range(col):x_ = x - u10y_ = y - u01y00 += img_gray[x][y]y10 += x_*img_gray[x][y]y01 += y_*img_gray[x][y]y11 += x_*y_*img_gray[x][y]y02 += y_**2*img_gray[x][y]y20 += x_**2*img_gray[x][y]y03 += y_**3*img_gray[x][y]y30 += x_**3*img_gray[x][y]y12 += x_*y_**2*img_gray[x][y]y21 += x_**2*y_*img_gray[x][y]# 计算图像的归一化中心矩n20 = y20 / (y00 ** 2)n02 = y02 / (y00 ** 2)n11 = y11 / (y00 ** 2)n30 = y30 / (y00 ** 2.5)n03 = y03 / (y00 ** 2.5)n12 = y12 / (y00 ** 2.5)n21 = y21 / (y00 ** 2.5)# 计算图像的七个不变矩h1 = n20 + n02h2 = (n20 - n02) ** 2 + 4 * (n11 ** 2)h3 = (n30 - 3 * n12) ** 2 + (3 * n21 - n03) ** 2h4 = (n30 + n12) ** 2 + (n21 + n03) ** 2h5 = (n30 - 3 * n12) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n21 - n03) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)h6 = (n20 - n02) * ((n30 + n12) ** 2 - (n21 + n03) ** 2) + 4 * n11 * (n30 + n12) * (n21 + n03)h7 = (3 * n21 - n03) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n12 - n30) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)inv_m7 = [h1, h2, h3, h4, h5, h6, h7]humoments = np.log(np.abs(inv_m7))return humomentsdef main2():input_dir = 'W:\\BD\\Netww\\huidu002\\'aa = os.listdir( input_dir )for i in aa:grayy=cv2.imread(input_dir+i,0)sys_humoments = sys_moments(grayy)
#         def_humoments = def_moments(grayy)def_humoments2 = def_moments2(grayy)print('自带函数：\n',sys_humoments)
#         print('自定义函数：\n',def_humoments) print('自定义函数2：\n',def_humoments2)
def main():f1 = open("S:\\AdobeppPS\\ceshi01.txt",'w+')input_dir = "S:\\AdobeppPS\\SKOO\\"aa = os.listdir( input_dir) abc=0set_list = []list00=[]for ia in aa:grayy=cv2.imread(input_dir+ia,0)sys_humoments0 = sys_moments(grayy)arr1=sys_moments(grayy)  abc=abc+1print("第 "+str(abc)+"张图片自带函数测试数据：")
#         print(sys_humoments0)for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)list00.append(s)
#             print("\t",format(set_list[item],".3f"))             f1.write("第 "+str(abc)+"张图片："+str(format(set_list[item],".4f"))+"\n")
#         f1.write("第 "+str(abc)+"张图片："+str(format(set_list[item],".4f"))+"\n") for cs in range(len(aa)):print("\n",format(list00[cs],".3f")) f1.close()
if __name__ == '__main__':main()   #         print("第 "+str(abc)+"张图片自带函数测试数据：")
#         print(sys_humoments0)
#         print(arr1[2,0])#输出二阶矩，一维向量print("well done")

注意这里的hu矩是进行了转换后的数据结果

最终版，自定义函数是一维数组，有bug，而且运行迟钝，借助强悍的数据分析，缺乏很大的改进机制。

import cv2
import os
import numpy as np
import functoolsdef sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humomentdef def_moments2(img_gray):row, col = img_gray.shape# 计算图像的0阶几何矩m00 = 0.0m10 = m01 = 0.0# 计算图像的二阶、三阶几何矩m11 = m20 = m02 = m12 = m21 = m30 = m03 = 0.0for i in range(row):for j in range(col):m00 += img_gray[i][j]m10 += i*img_gray[i][j]m20 += i**2*img_gray[i][j]m30 += i**3*img_gray[i][j]m11 += i * j * img_gray[i][j]m12 += i * j ** 2 * img_gray[i][j]m21 += i ** 2 * j * img_gray[i][j]for j in range(col):for i in range(row):m01 += (j * img_gray[i, j])m02 += (j ** 2 * img_gray[i, j])m03 += (j ** 3 * img_gray[i, j])# 由标准矩我们可以得到图像的"重心"u10 = m10 / m00u01 = m01 / m00# 计算图像的二阶中心矩、三阶中心矩y00 = y10 = y01 = y11 = y20 = y02 = y30 = y12 = y21 = y03 = 0.0for x in range(row):for y in range(col):x_ = x - u10y_ = y - u01y00 += img_gray[x][y]y10 += x_*img_gray[x][y]y01 += y_*img_gray[x][y]y11 += x_*y_*img_gray[x][y]y02 += y_**2*img_gray[x][y]y20 += x_**2*img_gray[x][y]y03 += y_**3*img_gray[x][y]y30 += x_**3*img_gray[x][y]y12 += x_*y_**2*img_gray[x][y]y21 += x_**2*y_*img_gray[x][y]# 计算图像的归一化中心矩n20 = y20 / (y00 ** 2)n02 = y02 / (y00 ** 2)n11 = y11 / (y00 ** 2)n30 = y30 / (y00 ** 2.5)n03 = y03 / (y00 ** 2.5)n12 = y12 / (y00 ** 2.5)n21 = y21 / (y00 ** 2.5)# 计算图像的七个不变矩h1 = n20 + n02h2 = (n20 - n02) ** 2 + 4 * (n11 ** 2)h3 = (n30 - 3 * n12) ** 2 + (3 * n21 - n03) ** 2h4 = (n30 + n12) ** 2 + (n21 + n03) ** 2h5 = (n30 - 3 * n12) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n21 - n03) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)h6 = (n20 - n02) * ((n30 + n12) ** 2 - (n21 + n03) ** 2) + 4 * n11 * (n30 + n12) * (n21 + n03)h7 = (3 * n21 - n03) * (n30 + n12) * ((n30 + n12) ** 2 - 3 * (n21 + n03) ** 2) + (3 * n12 - n30) * (n21 + n03) \* (3 * (n30 + n12) ** 2 - (n21 + n03) ** 2)inv_m7 = [h1, h2, h3, h4, h5, h6, h7]humoments = np.log(np.abs(inv_m7))return humomentsdef main2():input_dir = 'W:\\BD\\Netww\\huidu002\\'aa = os.listdir( input_dir )for i in aa:grayy=cv2.imread(input_dir+i,0)sys_humoments = sys_moments(grayy)
#         def_humoments = def_moments(grayy)
#         def_humoments2 = def_moments2(grayy)print('自带函数：\n',sys_humoments)print('自定义函数2：\n',def_humoments2)
def main():f1 = open("S:\\AdobeppPS\\ceshi01.txt",'w+')input_dir = "S:\\AdobeppPS\\SKOO\\"aa = os.listdir( input_dir) abc=0set_list = []list00=[]zs=len(aa)*7for ia in aa:grayy=cv2.imread(input_dir+ia,0)
#         arr1=def_moments2(grayy)arr1=sys_moments(grayy)
#         print(type(arr1))for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)list00.append(s)for cs0 in range(zs):if cs0%7==0:abc=abc+1
#             f1.write("第 "+str(abc)+"张图片:")f1.write("第 "+str(abc)+"张图片:\t"+str(format(list00[cs0],".3f"))+"\n")f1.close()
if __name__ == '__main__':main()   print("well done")

精简版：

import cv2
import os
import numpy as np
import functoolsdef sys_moments(img):moments = cv2.moments(img)#返回的是一个字典，三阶及以下的几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)humoments = cv2.HuMoments(moments)#根据几何矩（mpq）、中心矩(mupq)和归一化的矩(nupq)计算出hu不变矩humoment = -(np.log(np.abs(humoments)))/np.log(10)return humomentdef main():f1 = open("S:\\AdobeppPS\\ceshi01.txt",'w+')input_dir = "S:\\AdobeppPS\\SKOO\\"aa = os.listdir( input_dir) abc=0set_list = []list00=[]zs=len(aa)*7for ia in aa:grayy=cv2.imread(input_dir+ia,0)
#         arr1=def_moments2(grayy)arr1=sys_moments(grayy)
#         print(type(arr1))for item in range(len(arr1)):s=arr1[item,0]set_list.append(s)list00.append(s)for cs0 in range(zs):if cs0%7==0:abc=abc+1f1.write("第 "+str(abc)+"张图片:\t"+str(format(list00[cs0],".3f"))+"\n")f1.close()
if __name__ == '__main__':main()   print("well done")

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