# coding: utf-8
import operator
from PIL import Image
import numpy as np
import cv2
"""图片处理: 图片截取、图片相似度比对、哈希算法比对"""
def cmp_pic(pic1, pic2):
"""
比对图片相似度
@param pic1:
@param pic2:
@return:
"""
a = Image.open(pic1)
b = Image.open(pic2)
return operator.eq(a, b)
def image_interception(image):
"""
图片截取
@param image: 目标图片
@return:
"""
img = cv2.imread(image)
print('图片{}高度、宽度、通道数为：{}'.format(image, img.shape)) # (1792, 828, 3) 高度、宽度、通道数
cropped = img[170:650, 0:900] # 裁剪坐标为[y0:y1, x0:x1]
cv2.imwrite(image, cropped)
return image
def aHash(img):
"""
均值哈希算法
@param img:
@return:
"""
# 缩放为8*8
img = cv2.resize(cv2.imread(img), (8, 8))
# 转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# s为像素和初值为0，hash_str为hash值初值为''
s = 0
hash_str = ''
# 遍历累加求像素和
for i in range(8):
for j in range(8):
s = s + gray[i, j]
# 求平均灰度
avg = s / 64
# 灰度大于平均值为1相反为0生成图片的hash值
for i in range(8):
for j in range(8):
if gray[i, j] > avg:
hash_str = hash_str + '1'
else:
hash_str = hash_str + '0'
return hash_str
def dHash(img):
"""
差值感知算法
@param img:
@return:
"""
# 缩放8*8
img = cv2.resize(cv2.imread(img), (9, 8))
# 转换灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hash_str = ''
# 每行前一个像素大于后一个像素为1，相反为0，生成哈希
for i in range(8):
for j in range(8):
if gray[i, j] > gray[i, j + 1]:
hash_str = hash_str + '1'
else:
hash_str = hash_str + '0'
return hash_str
def pHash(img):
"""
感知哈希算法(pHash)
@param img:
@return:
"""
# 缩放32*32
img = cv2.resize(cv2.imread(img), (32, 32)) # , interpolation=cv2.INTER_CUBIC
# 转换为灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 将灰度图转为浮点型，再进行dct变换
dct = cv2.dct(np.float32(gray))
# opencv实现的掩码操作
dct_roi = dct[0:8, 0:8]
hash = []
avreage = np.mean(dct_roi)
for i in range(dct_roi.shape[0]):
for j in range(dct_roi.shape[1]):
if dct_roi[i, j] > avreage:
hash.append(1)
else:
hash.append(0)
return hash
def classify_hist_with_split(image1, image2, size=(256, 256)):
"""
通过得到RGB每个通道的直方图来计算相似度
@param image1:
@param image2:
@param size:
@return:
"""
# 将图像resize后，分离为RGB三个通道，再计算每个通道的相似值
image1 = cv2.resize(cv2.imread(image1), size)
image2 = cv2.resize(cv2.imread(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
# print(sub_data)
return sub_data
def calculate(image1, image2):
"""
计算单通道的直方图的相似值
@param image1:
@param image2:
@return:
"""
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
def cmpHash(hash1, hash2):
"""
Hash值对比
@param hash1:
@param hash2:
@return:
"""
n = 0
# hash长度不同则返回-1代表传参出错
if len(hash1) != len(hash2):
return -1
# 遍历判断
for i in range(len(hash1)):
# 不相等则n计数+1，n最终为相似度
if hash1[i] != hash2[i]:
n = n + 1
return n
image_interception('11.png')
image_interception('11.png')
img1 = '1.png'
img2 = '2.png'
hash1 = aHash(img1)
hash2 = aHash(img2)
n = cmpHash(hash1, hash2)
print('均值哈希算法相似度：', n)
hash1 = dHash(img1)
hash2 = dHash(img2)
n = cmpHash(hash1, hash2)
print('差值哈希算法相似度：', n)
hash1 = pHash(img1)
hash2 = pHash(img2)
n = cmpHash(hash1, hash2)
print('感知哈希算法相似度：', n)
n = classify_hist_with_split(img1, img2)
print('三直方图算法相似度：', n)
