荐 Python数字图像处理——OpenCV实例解析

程序员文章站 2023-11-11 17:32:40

目录读取图像修改像素值图像融合图像的几何变换简单阈值自适应阈值——用于解决光照问题Otsu's Binarization二值化调用摄像头读取视频保存视频OpenCV绘图设置鼠标事件轨迹栏应用图像的三通道抽取与合并展示边框的使用实现跟踪视频中的指定彩色物体图像模糊（图像平滑）与2D卷积感受光照的影响二值化处理自定义阈值处理图像模糊平滑图像边缘检测实现车牌提取形态转换结构元素内核查找图像......

读取图像

修改像素值

Otsu's Binarization二值化

使用Haar级联分类器实现人脸检测和眼睛检测

简单使用模板匹配

基于ORB的匹配器

读取图像

import cv2 as cv

# 读取彩色图像
img_cai = cv.imread("E:/1.png")
# 读取灰度图像
img_hui = cv.imread("E:/1.png",cv.IMREAD_GRAYSCALE)

print(img_cai.shape)
print(img_hui.shape)

# 注意：opencv读取的彩色图像默认的颜色是BGR matplotlib默认的彩色图像是RGB
import matplotlib.pyplot as plt
plt.imshow(img_cai)                    # 全图展示
plt.imshow(img_cai[30:160,200:320])    # 截取部分

# 保存图像
cv.imwrite("duqu.png",img_cai[30:160,200:320])

修改像素值

# 修改像素值
img[30:50,:] = [255,0,0]
plt.imshow(img)

荐
Python数字图像处理——OpenCV实例解析

图像融合

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

img1 = cv.imread('E:/4.png')
img2 = cv.imread('E:/3.png')

dst = cv.addWeighted(img1,0.7,img2[:233,:230,:],0.3,0)

cv.imshow('Original Image 1', img1)
cv.imshow('Original Image 2', img2)
cv.imshow('Fusion Image', dst)
cv.waitKey(0)
cv.destroyAllWindows()

图像的几何变换

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

lq = cv.imread('E:/6.png')
# 调整图片大小
i2 = cv.resize(lq,(200,500),interpolation=cv.INTER_CUBIC)
# 平移图片
M = np.float64([[1,0,100],[0,1,50]])
i3 = cv.warpAffine(lq,M,(300,300))
# 图片的旋转
M = cv.getRotationMatrix2D((250,250),30,1)
i4 = cv.warpAffine(lq,M,(300,300))
# 图片的倾斜
d1 = np.float32([[100,10],[300,10],[100,100]])
d2 = np.float32([[100,30],[200,30],[150,100]])
M = cv.getAffineTransform(d1,d2)
i5 = cv.warpAffine(lq,M,(300,300))
# 透视变化
d1 = np.float32([[100,100],[400,100],[100,400],[400,400]])
d2 = np.float32([[200,200],[300,200],[200,300],[300,300]])
d3 = np.float32([[0,0],[500,0],[500,0],[0,500]])
M = cv.getPerspectiveTransform(d1,d2)
i6 = cv.warpPerspective(lq,M,(300,300))
cv.imshow('Original Image', lq)
cv.imshow('Sizing Image', i2)
cv.imshow('Translation Image', i3)
cv.imshow('Rotate Image', i4)
cv.imshow('Tilt Image', i5)
cv.imshow('Perspective change Image', i6)
cv.waitKey(0)
cv.destroyAllWindows()

简单阈值

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

img = cv.imread('E:/4.png',0)
ret,thresh1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
ret,thresh2 = cv.threshold(img,127,255,cv.THRESH_BINARY_INV)
ret,thresh3 = cv.threshold(img,127,255,cv.THRESH_TRUNC)
ret,thresh4 = cv.threshold(img,127,255,cv.THRESH_TOZERO)
ret,thresh5 = cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)

titles = ['Original Image', 'BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img,thresh1,thresh2,thresh3,thresh4,thresh5]

for i in range(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

cv.imshow('Original Image',img)
cv.waitKey(0)
cv.destroyAllWindows()

自适应阈值——用于解决光照问题

cv.ADAPTIVE_THRESH_MEAN_C：该阈值是平均值的附近区域减去恒定的Ç。
cv.ADAPTIVE_THRESH_GAUSSIAN_C：阈值是邻域值减去常数C的高斯加权和。

# 用于解决光照问题
import cv2 as cv
from matplotlib import pyplot as plt

img = cv.imread('E:/3.png',0)

#常用来去除椒盐噪声
#卷积核使用奇数
blur=cv.medianBlur(img,5)

ret,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)
# cv.ADAPTIVE_THRESH_MEAN_C：该阈值是平均值的附近区域减去恒定的Ç。
th2 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)
# cv.ADAPTIVE_THRESH_GAUSSIAN_C：阈值是邻域值减去常数C的高斯加权和。
th3 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)

titles = ['Original Image', 'Global Thresholding(v=127)','Adaptive Mean Thresholding','Adaptive Gaussian Thresholding']
images = [img,th1,th2,th3]

for i in range(4):
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

cv.imshow('medianBlur',blur)
cv.waitKey(0)
cv.destroyAllWindows()

Otsu's Binarization二值化

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

img = cv.imread('E:/5.png',0)

# global thresholding
ret1,th1 = cv.threshold(img,127,255,cv.THRESH_BINARY)

# Otsu's thresholding
ret2,th2 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)

# Otsu's thresholding after Gaussian filtering
blur = cv.GaussianBlur(img,(5,5),0)
ret3,th3 = cv.threshold(blur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)

# plot all the image and their histograms
images = [img, 0, th1,
          img, 0, th2,
          blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding(v=127)',
          'Original Noisy Image','Histogram',"Otsu's Thresholding",
          'Gaussian filtered Image','Histogram',"Otsu's Thresholding" ]

for i in range(3):
    plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
    plt.title(titles[i*3]),plt.xticks([]),plt.yticks([])
    plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
    plt.title(titles[i*3+1]),plt.xticks([]), plt.yticks([])
    plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2], 'gray')
    plt.title(titles[i*3+2]),plt.xticks([]), plt.yticks([])
plt.show()

cv.imshow('Original Image',img)
cv.waitKey(0)
cv.destroyAllWindows()

调用摄像头

import numpy as np
import cv2 as cv

cap = cv.VideoCapture(0)
if not cap.isOpened():
    print("Cannot open camera")
    exit()
while True:
    # Capture frame-by-frame
    ret,frame = cap.read()

    # if frame is read correctly ret is True
    if not ret:
        print("Can't receive frame (stream end?). Exiting...")
        break
    # Our operations on the frame come here
    gray = cv.cvtColor(frame, cv.COLOR_RGB2RGBA)
    # Display the resulting frame
    cv.imshow('frame', gray)
    if cv.waitKey(1) == ord('q'):
        break

# When everything done, release the capture
cap.release()
cv.destroyAllWindows()

读取视频

import numpy as np
import cv2 as cv

cap = cv.VideoCapture('F:/video.mp4')

while cap.isOpened():
    ret,frame = cap.read()

    # if frame is read correctly ret is True
    if not ret:
        print("Can't receive frame(stream end?).Exiting...")
        break
    gray = cv.cvtColor(frame,cv.COLOR_BGR2GRAY)
    cv.imshow('frame',gray)
    if cv.waitKey(1) == ord('q'):
        break

cap.realease()
cv.destroyAllWindows()

荐
Python数字图像处理——OpenCV实例解析

保存视频

# •保存视频
cap = cv.VideoCapture("data/movbbb.mp4")

fourcc = cv.VideoWriter_fourcc(*"MJPG")
out = cv.VideoWriter("movie.mp4",-1,20,(320,176))

while True:
    # 读取每一帧的数据
    b,frame = cap.read()
    #根据返回值是b来决定是不是还有下一帧
    if b==False:
        break
    #输出保存
    out.write(frame)
    #展示一帧的数据
    cv.imshow("1.jpg",frame)
    #设置如果按键等于q键 就退出
    if cv.waitKey(1) == ord('q'):
        break
#销毁 窗口
cap.release()
out.release()
cv.destroyAllWindows()

OpenCV绘图

import numpy as np
import cv2 as cv
# Create a black image
img = np.zeros((512,512,3), np.uint8)                       # 设置黑色的背景

# Draw a diagonal blue line with thickness of 5 px
cv.line(img,(0,0),(511,511),(255,0,0),5)             # 画线
cv.rectangle(img,(384,0),(510,128),(0,255,0),3) # 画矩形
cv.circle(img,(447,63), 63, (0,0,255), -1)         # 画圆
font = cv.FONT_HERSHEY_SIMPLEX                              # 画文字图片
cv.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv.LINE_AA)

cv.imshow('Final Image', img)
cv.waitKey(0)
cv.destroyAllWindows()

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Python数字图像处理——OpenCV实例解析

设置鼠标事件

import cv2 as cv
import numpy as np

# 查看常见事件
events = [i for i in dir(cv) if 'EVENT' in i]
print(events)

# 鼠标双击画图
# mouse callback function
def draw_circle(event,x,y,flags,param):
    if event == cv.EVENT_LBUTTONDBLCLK:
        cv.circle(img,(x,y),100,(255,0,0),-1)

# Create a black image, a window and bind the dunction to window
img = np.zeros((512,512,3), np.uint8)
cv.namedWindow('image')
cv.setMouseCallback('image', draw_circle)

while(1):
    cv.imshow('image', img)
    if cv.waitKey(20) & 0xFF == 27:
        break
cv.destroyAllWindows()

荐
Python数字图像处理——OpenCV实例解析

轨迹栏应用

import numpy as np
import cv2 as cv

def nothing(x):
    pass

# Create a black image , a window
img = np.zeros((300,512,3), np.uint8)
cv.namedWindow('image')

# create trackbars for color change
cv.createTrackbar('R', 'image', 0,255, nothing)
cv.createTrackbar('G', 'image', 0,255, nothing)
cv.createTrackbar('B', 'image', 0,255, nothing)

# create switch for ON/OFF functionality
switch = '0 : OFF \n1 : ON'
cv.createTrackbar(switch, 'image', 0,1, nothing)

while(1):
    cv.imshow('image', img)
    k = cv.waitKey(1) & 0xFF
    if k == 27:
        break

    # get current positions of fore trackbars
    r = cv.getTrackbarPos('R', 'image')
    g = cv.getTrackbarPos('G', 'image')
    b = cv.getTrackbarPos('B', 'image')
    s = cv.getTrackbarPos(switch, 'image')

    if s == 0:
        img[:] = 0
    else:
        img[:] = [b,g,r]

cv.destroyAllWindows()

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Python数字图像处理——OpenCV实例解析

图像的三通道抽取与合并

img = cv.imread("data/3.jpg")
b,g,r = cv.split(img)
img2 = cv.merge([r,g,b])
plt.imshow(img2)

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Python数字图像处理——OpenCV实例解析

展示边框的使用

src-输入图像
顶部，底部，左侧，右侧边界的宽度，以相应方向上的像素数为单位
borderType-定义要添加哪种边框的标志。它可以是以下类型：
cv.BORDER_CONSTANT-添加恒定的彩色边框。该值应作为下一个参数给出。
cv.BORDER_REFLECT-边框将是边框元素的镜像，如下所示： fedcba | abcdefgh | hgfedcb
cv.BORDER_REFLECT_101或 cv.BORDER_DEFAULT-与上述相同，但略有变化，例如： gfedcb | abcdefgh | gfedcba
cv.BORDER_REPLICATE-最后一个元素被复制，像这样： aaaaaa | abcdefgh | hhhhhhh
cv.BORDER_WRAP-无法解释，它看起来像这样： cdefgh | abcdefgh | abcdefg
价值 -边框颜色，如果边框类型为cv.BORDER_CONSTANT

import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt

BLUE = [255,0,0]

img = cv.imread("E:/9.png")

rep = cv.copyMakeBorder(img,10,10,10,10,cv.BORDER_REPLICATE)
warp = cv.copyMakeBorder(img,10,10,10,10,cv.BORDER_WRAP)
ref = cv.copyMakeBorder(img,0,100,0,0,cv.BORDER_REFLECT)
cons = cv.copyMakeBorder(img,10,10,10,10,cv.BORDER_CONSTANT,value=(255,0,0))
ref101 = cv.copyMakeBorder(img,10,10,10,10,cv.BORDER_REFLECT101)

plt.imshow(img)
plt.imshow(ref)

plt.subplot(231),plt.imshow(img,"gray"),plt.title("YUAN")
plt.subplot(232),plt.imshow(rep,"gray"),plt.title("REP")
plt.subplot(233),plt.imshow(warp,"gray"),plt.title("WARP")
plt.subplot(234),plt.imshow(ref,"gray"),plt.title("REF")
plt.subplot(235),plt.imshow(cons,"gray"),plt.title("CONS")
plt.subplot(236),plt.imshow(ref101,"gray"),plt.title("REF101")
plt.show()

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Python数字图像处理——OpenCV实例解析

实现跟踪视频中的指定彩色物体

转换色彩空间：i2 = cv.cvtColor(img,cv.COLOR_BGR2GRAY)

跟踪彩色物体

拍摄视频的每一帧
从BGR转换为HSV颜色空间
我们将HSV图片的阈值范围设为蓝色
现在仅提取蓝色对象，我们就可以在该图像上执行任何操作

import cv2 as cv
import numpy as np
import time

cap = cv.VideoCapture("F:/video.mp4")

while True:
    b, frame = cap.read()

    if not b:
        break;

    if cv.waitKey(1) == ord("q"):
        break

    time.sleep(0.1)
    # 将原来的BGR数据转换 HSV数据
    hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
    # 设置紫色的最大值和最小值
    lower_hsv = np.array([125, 43, 46])
    upper_hsv = np.array([155, 255, 255])
    # 判断原图中哪些是紫色
    mask = cv.inRange(hsv, lower_hsv, upper_hsv)
    # 展示
    res = cv.bitwise_and(frame, frame, mask=mask)

    cv.imshow("1.jpg", frame)
    cv.imshow("mask.jpg", mask)
    cv.imshow("res.jpg", res)

cap.release()
cv.destroyAllWindows()

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Python数字图像处理——OpenCV实例解析

图像模糊（图像平滑）与2D卷积

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

img = cv.imread('E:/10.png')

kernel = np.ones((5,5),np.float32)/25
dst = cv.filter2D(img,-1,kernel)

plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]),plt.yticks([])
plt.subplot(122),plt.imshow(dst),plt.title('Averaging')
plt.xticks([]),plt.yticks([])
plt.show()

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Python数字图像处理——OpenCV实例解析

感受光照的影响

二值化处理

data = cv.imread("timg.jpg",cv.IMREAD_GRAYSCALE)
plt.imshow(cv.threshold(data,127,255,cv.THRESH_BINARY)[1])

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.threshold(data,127,255,cv.THRESH_TRUNC)[1])

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.threshold(data,127,255,cv.THRESH_TOZERO)[1])

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.threshold(data,127,255,cv.THRESH_OTSU)[1])

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Python数字图像处理——OpenCV实例解析

自定义阈值处理

plt.imshow(cv.adaptiveThreshold(data,127,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2))

荐
Python数字图像处理——OpenCV实例解析

plt.imshow(cv.adaptiveThreshold(data,127,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2))

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Python数字图像处理——OpenCV实例解析

图像模糊平滑图像

plt.imshow(cv.blur(data,(5,5)))

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.GaussianBlur(data,(5,5),30))

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.medianBlur(data,127))

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Python数字图像处理——OpenCV实例解析

plt.imshow(cv.bilateralFilter(data,100,100,100))

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Python数字图像处理——OpenCV实例解析

plt.imshow(data)

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Python数字图像处理——OpenCV实例解析

边缘检测

拉普拉斯

plt.imshow(cv.Laplacian(data,cv.CV_32F))

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Python数字图像处理——OpenCV实例解析

Sobel

plt.imshow(cv.Sobel(data,cv.CV_64F,1,0,ksize=5))

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Python数字图像处理——OpenCV实例解析

坎尼边缘检测

plt.imshow(cv.Canny(data,100,200))

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Python数字图像处理——OpenCV实例解析

实现车牌提取

import cv2 as cv
import matplotlib.pyplot as plt

# 读取彩色的图片
img = cv.imread("E:/chepai.png")
# 转换为灰度图
img1 = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
# 高斯模糊
img2 = cv.GaussianBlur(img1,(5,5),10)
# Laplacian进行边缘检测
img3 = cv.Sobel(img2,cv.CV_8U,1,0,ksize=1)
img4 = cv.Canny(img3,250,100)
# 进行二值化处理
i,img5 = cv.threshold(img4,0,255,cv.THRESH_BINARY)
# 可以侵蚀和扩张
kernel = cv.getStructuringElement(cv.MORPH_RECT,(43,33))
img6 = cv.dilate(img5,kernel)
# 循环找到所有的轮廓
i,j = cv.findContours(img6,cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)
result = None
for i1 in i:
    x,y,w,h = cv.boundingRect(i1)
    if w>2*h:
        print(1)
        plt.imshow(img[y:y+h,x:x+w])
        result = img[y:y+h,x:x+w]

plt.show()

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Python数字图像处理——OpenCV实例解析

形态转换

erosion = cv.erode(img,kernel,iterations = 1) 1.侵蚀
dilation = cv.dilate(img,kernel,iterations = 1) 2.扩张
opening = cv.morphologyEx(img, cv.MORPH_OPEN, kernel) 3.开场
closing = cv.morphologyEx(img, cv.MORPH_CLOSE, kernel) 4.闭幕
gradient = cv.morphologyEx(img, cv.MORPH_GRADIENT, kernel) 5.形态梯度
tophat = cv.morphologyEx(img, cv.MORPH_TOPHAT, kernel) 6.高顶礼帽
blackhat = cv.morphologyEx(img, cv.MORPH_BLACKHAT, kernel) 7.黑帽

结构元素内核

矩形内核 cv.getStructuringElement（cv.MORPH_RECT，（5,5））
椭圆形内核 cv.getStructuringElement（cv.MORPH_ELLIPSE，（5,5））
十字形内核 cv.getStructuringElement（cv.MORPH_CROSS，（5,5））

查找图像渐变、边缘

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

img = cv.imread('E:/3.PNG', 0)

laplacian = cv.Laplacian(img, cv.CV_64F)
sobelx = cv.Sobel(img, cv.CV_64F, 1,0, ksize=5)
sobely = cv.Sobel(img, cv.CV_64F, 0,1, ksize=5)

plt.subplot(2,2,1), plt.imshow(img, cmap='gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,2), plt.imshow(laplacian, cmap='gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,3), plt.imshow(sobelx, cmap='gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,4), plt.imshow(sobely, cmap='gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])

plt.show()

荐
Python数字图像处理——OpenCV实例解析

使用Haar级联分类器实现人脸检测和眼睛检测

import cv2 as cv
from matplotlib import pyplot as plt

m = cv.imread(r'E:\together.png')

face = cv.CascadeClassifier()
eye = cv.CascadeClassifier()

face.load(r"E:\software\python3.8.2\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml")
eye.load(r"E:\software\python3.8.2\Lib\site-packages\cv2\data\haarcascade_eye.xml")

maray = cv.cvtColor(m, cv.COLOR_BGR2GRAY)
faces = face.detectMultiScale(maray)

for (x,y,w,h) in faces:
    center = (x + w//2, y + h//2)
    m = cv.ellipse(m, center, (w//2, h//2), 0, 0, 360, (255, 0, 255), 4)
    faceROI = maray[y:y+h,x:x+w]

plt.imshow(m)
plt.show()

荐
Python数字图像处理——OpenCV实例解析

简单使用模板匹配

import numpy as np
import cv2 as cv

# ROI值先写  目标对象值先写
h = cv.imread("E:/h.png")
# 被搜索图片
hua = cv.imread("E:/hua.png")
huagray = cv.cvtColor(hua, cv.COLOR_BGR2GRAY)
hgray = cv.cvtColor(h, cv.COLOR_BGR2GRAY)

res = cv.matchTemplate(huagray, hgray, cv.TM_CCOEFF_NORMED)

threshold = 0.6
loc = np.where(res >= threshold)
for i in zip(*loc[::-1]):
    cv.rectangle(hua, i, (i[0]+158, i[1]+131), (0,0,255), 2)

cv.imwrite('E:/res.png', hua)

荐
Python数字图像处理——OpenCV实例解析

基于ORB的匹配器

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

# ROI值先写  目标对象值先写
h = cv.imread("E:/h.png")
# 被搜索图片
hua = cv.imread("E:/hua.png")

orb = cv.ORB_create()

huakp,huades = orb.detectAndCompute(hua, None)
hkp,hdes = orb.detectAndCompute(h, None)

bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)

matchers = bf.match(huades,hdes)

matches = sorted(matchers, key=lambda x:x.distance)

im = cv.drawMatches(hua, huakp, h, hkp, matches, None, flags=cv.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)

plt.imshow(im)
plt.show()

荐
Python数字图像处理——OpenCV实例解析

本文地址：https://blog.csdn.net/IT_charge/article/details/107356700

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荐 Python数字图像处理——OpenCV实例解析

读取图像

修改像素值

图像融合

图像的几何变换

简单阈值

自适应阈值——用于解决光照问题

Otsu's Binarization二值化

调用摄像头

读取视频

保存视频

OpenCV绘图

设置鼠标事件

轨迹栏应用

图像的三通道抽取与合并

展示边框的使用

实现跟踪视频中的指定彩色物体

图像模糊（图像平滑）与2D卷积

感受光照的影响

二值化处理

自定义阈值处理

图像模糊平滑图像

边缘检测

实现车牌提取

形态转换

结构元素内核

查找图像渐变、边缘

使用Haar级联分类器实现人脸检测和眼睛检测

简单使用模板匹配

基于ORB的匹配器