picture2.py

# -*- coding: utf-8 -*-  
#feimengjuan  
# 利用python实现多种方法来实现图像识别  
from skimage import io,transform
from tensorflow.python.framework import graph_util
import glob
import os
import tensorflow as tf
import numpy as np
import time
from PIL import Image
import matplotlib.image as mpimg
import collections
import cv2  
from matplotlib import pyplot 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 = 0  
	for i in range(len(hist1)):  
		if hist1[i] != hist2[i]:  
			degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))  
		else:  
			degree = degree + 1  
	degree = degree/len(hist1)  
	return degree  
  
# 计算单通道的直方图的相似值  
def calculate(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 = 0  
	for i in range(len(hist1)):  
		if hist1[i] != hist2[i]:  
			degree = degree + (1 - abs(hist1[i]-hist2[i])/max(hist1[i],hist2[i]))  
		else:  
			degree = degree + 1  
	degree = degree/len(hist1)  
	return degree  
  
# 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 = 0  
	for im1,im2 in zip(sub_image1,sub_image2):  
		sub_data += calculate(im1,im2)  
	sub_data = sub_data/3  
	return sub_data  
  
# 平均哈希算法计算  
def classify_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)  
	return Hamming_distance(hash1,hash2)  
  
def classify_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)  
	return Hamming_distance(hash1,hash2)  
  
# 输入灰度图，返回hash  
def getHash(image):  
	avreage = np.mean(image)  
	hash = []  
	for i in range(image.shape[0]):  
		for j in range(image.shape[1]):  
			if image[i,j] > avreage:  
				hash.append(1)  
			else:  
				hash.append(0)  
	return hash  
  
  
# 计算汉明距离  
def Hamming_distance(hash1,hash2):  
	num = 0  
	for index in range(len(hash1)):  
		if hash1[index] != hash2[index]:  
			num += 1  
	return num  
  
  
if __name__ == '__main__':  
	path='/home/quheng/图片相似度比较/move4/'
	img1 = cv2.imread('/home/quheng/图片相似度比较/1.jpg')  
	cv2.imshow('img1',img1)
	cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]
	sum = 0
	for im in glob.glob(path +'/*.jpg'):#获取当前文件夹下的文件
		print('reading the images:%s'%(im))
		img2=cv2.imread(im)
		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)
		sum = sum + degree 
		average = sum / 701
	print(average)
	cv2.waitKey(0)  
"""
opencv连续读取文件夹下的图片（python）：网上有许多顺序读取图片的程序，但python下的很少，需要用到其他模块的函数，今天介绍一个最简单的：

cv2.imread()

使用方法：

for i in range(20) #循环次数

image=cv2.imread("D:\\picture\\%d.jpg"%(i))#路径自己选择。
"""