import os
import cv2 as cv
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#读取文件
def read_img():
imgs = []
# walk会返回3个参数，分别是路径，目录list，文件list
for path, lists, frame in os.walk(文件名):
for f in frame:
#读取中文路径图像
img = cv.imdecode(np.fromfile(path + "/" + f, dtype=np.uint8), 1)
imgs.append(img)
return frame,imgs
#把图像分为五部分
def fif_img(img):
img= cv.cvtColor(img, cv.COLOR_BGR2HSV)
h,w = img.shape[:2]
cv.line(img,(int(w/2),0),(int(w/2),h),(255,0,0),2)
cv.line(img,(0,int(h/2)),(w,int(h/2)),(255,0,0),2)
clie = np.zeros((h,w),np.uint8)
cv.ellipse(clie,(int(w/2),int(h/2)),(int(0.375*w),int(0.375*h)),0,0,360,255,-1)
img1 = cv.bitwise_and(img,img,mask=clie)
retra,clie1 = cv.threshold(clie,0,255,cv.THRESH_BINARY_INV)
clie2 = clie1.copy()
clie3 = clie1.copy()
clie4 = clie1.copy()
cv.rectangle(clie1,(int(w/2),0),(w,h),0,-1)
cv.rectangle(clie1,(0,int(h/2)),(w,h),0,-1)
img2 = cv.bitwise_and(img,img,mask=clie1)
cv.rectangle(clie2,(0,0),(int(w/2),h),0,-1)
cv.rectangle(clie2,(int(w/2),int(h/2)),(w,h),0,-1)
img3 = cv.bitwise_and(img,img,mask=clie2)
cv.rectangle(clie3,(0,0),(w,int(h/2)),0,-1)
cv.rectangle(clie3,(int(w/2),int(h/2)),(w,h),0,-1)
img4 = cv.bitwise_and(img,img,mask=clie3)
cv.rectangle(clie4,(0,0),(w,int(h/2)),0,-1)
cv.rectangle(clie4,(0,int(h/2)),(int(w/2),h),0,-1)
img5 = cv.bitwise_and(img,img,mask=clie4)
imgs = []
imgs.extend([img1,img2,img3,img4,img5])
return imgs
def zft_img(name,img_score):
colum = dict()
for i in range(len(img_score)):
hist = []
imgs = fif_img(img_score[i])
for ig in range(len(imgs)):
hist_img = cv.calcHist([imgs[ig]], [0,1,2], None,(8,12,3), [0,180,0,256,0,256])
#归一化
hist.append(pd.Series(cv.normalize(hist_img,hist_img).flatten()))
colum[name[i]] = pd.concat(hist)
colum = pd.DataFrame(colum)
return colum
#一张图像的直方图
def first_zft(name,image):
colum = dict()
hist = []
imgs = fif_img(image)
for ig in range(len(imgs)):
hist_img = cv.calcHist([imgs[ig]], [0, 1, 2], None, (8, 12, 3), [0, 180, 0, 256, 0, 256])
# 归一化
hist.append(pd.Series(cv.normalize(hist_img, hist_img).flatten()))
colum[name] = pd.concat(hist)
colum = pd.DataFrame(colum)
return colum
#储存
def Storage_img(colum):
colum.to_csv('temp/img.csv',index=False)
#欧式距离
def o_distance(new_data,old_data):
name = old_data.columns
distances = dict()
for i in range(len(name)):
distance = 0
for j in range(len(new_data.values)):
distance = distance + ((new_data.values[j] - old_data[name[i]].values[j]) ** 2)
distances[name[i]] = np.sqrt(distance)
dist = sorted(distances.items(), key=lambda s: s[1])
print(dist)
name_img = []
for i in range(10):
print(dist[i])
name_img.append(dist[i][0])
return name_img
#卡方距离
def ka_distance(new_data,old_data):
name = old_data.columns
distances = dict()
for i in range(len(name)):
distance = 0
for j in range(len(new_data.values)):
distance = float(distance) + float(((new_data.values[j] - old_data[name[i]].values[j]) ** 2)/(new_data.values[j] + old_data[name[i]].values[j] + 0.0000001))
distances[name[i]] = distance * 0.5
dist = sorted(distances.items(), key=lambda s: s[1])
name_img = []
for i in range(10):
name_img.append(dist[i][0])
return name_img
#保存图像放入新文件夹
def write_img(img):
print("类似图像:",img)
imgs = dict()
for path, lists, frame in os.walk(文件名):
for f in frame:
# 读取中文路径图像
image = cv.imdecode(np.fromfile(path + "/" + f, dtype=np.uint8), 1)
imgs[f] = image
for ig in img:
cv.imwrite(保存路径+ig,imgs[ig])
# name,image = read_img()
# # zft_img(name,image)
# # Storage_img(zft_img(name,image))
#-*- coding: utf-8 -*-
import cv2 as cv
import numpy as np
import pandas as pd
from yitusoutu import *
img_name = input('请输入图像路径名称：')
# file = open(img_name,'r')
#读入中文
img = cv.imdecode(np.fromfile(img_name, dtype=np.uint8), 1)
cv.imshow('img',img)
name,img_score = read_img()
old_colum = zft_img(name,img_score)
new_colum = first_zft(img_name,img)
write_img(o_distance(new_colum,old_colum))
cv.waitKey(0)
cv.destroyAllWindows()
