openCV模块

# 读取图像

imgobj = cv2.imread("img.png")

# 创建窗口名字为img

cv2.namedwindow("img")

# 显示图像

cv2.imshow("img",imgobj)

# 等待事件触发，参数0表示永久等待

cv2.waitkey(0)

释放窗口

cv2.destroyAllwindows()

画出一个直方图数据图：

if __name__ == '__main__':
    img1 = cv2.imread('p1.jpg')
    hist1 = cv2.calcHist([img1],[0],None,[256],[0.0,255.0])
    pyplot.plot(range(256),hist1,'r')
    pyplot.show()
    cv2.waitKey(0)
    cv2.destroyAllWindows()

取背景相同的图的logo：先把图片转成灰度图

from PIL import Image
im = Image.open("222.png").convert('RGBA')
pixdata = im.load()

for y in range(im.size[1]):
    for x in range(im.size[0]):
        if pixdata[x,y][0] > 200 and pixdata[x,y][1] > 200 and pixdata[x,y][2] > 200 and pixdata[x,y][3]:
            pixdata[x,y]= (255, 255, 255, 0)

im.save("3333333.png")

取背景图不同的logo：

import cv2
import numpy as np

# Step1. 加载图像
img = cv2.imread('D:\untitled\aa\aa.png')

# Step2. 创建掩模、背景图和前景图
mask = np.zeros(img.shape[:2], np.uint8) # 创建大小相同的掩模
bgdModel = np.zeros((1,65), np.float64) # 创建背景图像
fgdModel = np.zeros((1,65), np.float64) # 创建前景图像

# Step3. 初始化矩形区域
# 这个矩形必须完全包含前景（相当于这里的梅西）
# rect = (50,50,450,290)
# rect = (275, 120, 170, 320)

rect = (1,1,350,300)
# Step4. GrubCut算法，迭代5次
# mask的取值为0,1,2,3
cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT) # 迭代5次

# Step5. mask中，值为2和0的统一转化为0, 1和3转化为1
mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
img = img * mask2[:,:,np.newaxis] # np.newaxis 插入一个新维度，相当于将二维矩阵扩充为三维

# cv2.imshow("dst", img)
cv2.waitKey(0)
cv2.imwrite(r"D:untitledaad.png",img)

convert函数：

值为1：为二值图像，非黑即白，0表示黑，255表示白

值为L：为灰度图

计算两张图的相似度：

import numpy as np
import cv2
from matplotlib import pyplot
# 最简单的以huidu直方图作为相似比较的实现
def classify_gray_hist(image1, image2, size=(256, 256)):
    # 先调整大小
    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])
    # 比较直方图
    pyplot.plot(range(256), hist1, 'r')
    pyplot.plot(range(256), hist2, 'y')
    pyplot.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


def classify_hist_with_split(image1, image2, size=(256, 256)):
    # 将图像resize后，分类为三个通道，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


def calculate(im1, im2):
    hist1 = cv2.calcHist([im1], [0], None, [256], [0.0, 255.0])
    hist2 = cv2.calcHist([im2], [0], None, [256], [0.0, 255.0])
    pyplot.plot(range(256), hist1, 'r')
    pyplot.plot(range(256), hist2, 'y')
    pyplot.show()
    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


# 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)  # 切换至灰度图
    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)  # 切换至灰度图
    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]
    print(dct1)
    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)  # 计算像素平均值
    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


# 差值感知算法
def dhash(image1, image2):
    image1 = cv2.resize(image1, (9, 8))
    image2 = cv2.resize(image2, (9, 8))
    gray1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)  # 切换至灰度图
    gray2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
    hash1 = dhashcaulate(gray1)
    hash2 = dhashcaulate(gray2)
    return Hamming_distance(hash1, hash2)


def dhashcaulate(gray):
    hash_str = ''
    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


if __name__ == '__main__':
    imgobj1 = cv2.imread('aa.png')
    imgobj2 = cv2.imread('bb.png')
    cv2.imshow('img1', imgobj1)
    cv2.imshow('img2', imgobj2)
    # 测试两张图片的相似度
    degree = classify_gray_hist(imgobj1,imgobj2)   #单通道直方图
    # degree = classify_hist_with_split(imgobj1,imgobj2)  #三通道直方图
    # 测试指纹的相似度
    # degree = classify_aHash(imgobj1,imgobj2)          #平均哈希法
    # degree = classify_pHash(imgobj1,imgobj2)          #感知哈希法
    # degree = dhash(imgobj1, imgobj2)  # 差值感知哈希法
    print(degree)
    cv2.waitKey(0)

使用face_recognition.face_locations(image)返回的数据（y,w,h,x）

用cv2.rectangle(img,(x,y),(w,h),(0,0,255),3)就能截出人脸

代码：

import face_recognition
import cv2
image = face_recognition.load_image_file("test.jpg")
face_locations = face_recognition.face_locations(image)
print(face_locations)
img = cv2.imread("test.jpg")
cv2.rectangle(img,(278,113),(353,188),(0,0,255), 3)
cv2.rectangle(img,(face_locations[0][3],face_locations[0][0]),(face_locations[0][1],face_locations[0][2]),(0,0,255),3)
cv2.namedWindow("sss")
cv2.imshow("sss",img)
cv2.waitKey(0)
cv2.imwrite(r"D:untitled	estaaaaaaaaaaaa.jpg",img)