OpenCV

OpenCV学习报告

读取图片和网络摄像头

1.1 图片读取

import cv2
# read image
img = cv2.imread("Resources/dnn.jpg")
cv2.imshow("Output",img)
cv2.waitKey(0)
1
2
3
4
5

在这里插入图片描述

1.2 视频读取

1.1.1 读取视频文件

import cv2
# read video
cap = cv2.VideoCapture("Resources/test_video.mp4")
while True:
   success,img = cap.read()
   cv2.imshow("Video",img)
   if cv2.waitKey(1) & 0xFF == ord('q'):
       break
1
2
3
4
5
6
7
8

在这里插入图片描述

1.1.2读取网络摄像头

import cv2
# read webcam
cap = cv2.VideoCapture(0)
cap.set(3,640) #width
cap.set(4,480) #height
cap.set(10,100)

while True:
   success,img = cap.read()
   cv2.imshow("Video",img)
   if cv2.waitKey(1) & 0xFF == ord('q'):
       break

1
2
3
4
5
6
7
8
9
10
11
12
13

在这里插入图片描述

OpenCV基础功能

import cv2
import numpy as np
# basic function
img = cv2.imread("Resources/dnn.jpg")
kernel = np.ones((5,5),np.uint8)

# 灰度转换
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# 图像模糊
imgBlur = cv2.GaussianBlur(imgGray,(7,7),0)
# 边缘检测
imgCanny = cv2.Canny(img,100,100)
# 膨胀
imgDialation = cv2.dilate(imgCanny, kernel,iterations=1)
# 腐蚀
imgEroded = cv2.erode(imgDialation,kernel,iterations=1)

# cv2.imshow("Output",img)
cv2.imshow("Gray Image",imgGray)
cv2.imshow("Blur Image",imgBlur)
cv2.imshow("Blur Image",imgCanny)
cv2.imshow("Dialation Image",imgDialation)
cv2.imshow("Eroded Image",imgEroded)

cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

在这里插入图片描述

调整、裁剪图像

3.1 调整图像大小

import cv2

# resize image

img = cv2.imread("Resources/lambo.PNG")
print(img.shape)

imgResize = cv2.resize(img,(300,200))
print(imgResize.shape)

cv2.imshow("Image",img)
cv2.imshow("Image Resize",imgResize)


cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

在这里插入图片描述

3.2 裁剪图像

import cv2

img=cv2.imread("Resources/lambo.PNG")
cv2.imshow('image',img)


print(img.shape)#height,width,dpth


crop_img=img[100:400,50:500]
cv2.imshow('crop image',crop_img)

cv2.waitKey(0)

1
2
3
4
5
6
7
8
9
10
11
12
13
14

在这里插入图片描述

图像上绘制形状和文本

4.1 图像上绘制形状

import cv2
import numpy as np
# shapes and texts
img = np.zeros((512,512,3),np.uint8)
cv2.imshow('oringin image',img)

cv2.line(img,(0,0),(img.shape[1],img.shape[0]),(0,255,0),3)
cv2.imshow('line image',img)

cv2.rectangle(img,(0,0),(250,350),(0,0,255),2)
cv2.imshow('rectangle image',img)

cv2.circle(img,(400,500),30,(255,255,0),5)
cv2.imshow('circle image',img)

cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

在这里插入图片描述

4.2图像上写文字

import cv2
import numpy as np

img = np.zeros((512,512,3),np.uint8)
cv2.imshow('oringin image',img)

cv2.putText(img,"OPENCV",(300,200),cv2.FONT_HERSHEY_COMPLEX,1,(0,150,0),1)
cv2.imshow("putText01 Image",img)
cv2.putText(img,"I LOVE XD",(100,300),cv2.FONT_HERSHEY_COMPLEX,1,(0,150,0),1)
cv2.imshow("putText02 Image",img)

cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12

在这里插入图片描述

透视变换

import cv2
import numpy as np
# warp perspective
img = cv2.imread("Resources/cards.jpg")

width,height = 250,350
pts1 = np.float32([[111,219],[287,188],[154,482],[352,440]])
pts2 = np.float32([[0,0],[width,0],[0,height],[width,height]])
matrix = cv2.getPerspectiveTransform(pts1,pts2)
imgOutput = cv2.warpPerspective(img,matrix,(width,height))

cv2.imshow("Image",img)
cv2.imshow("Output",imgOutput)

cv2.waitKey(0)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

在这里插入图片描述

图像拼接

import cv2
import numpy as np
# join images
def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver


img = cv2.imread('Resources/dnn.jpg')
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

imgStack = stackImages(0.5,([img,imgGray,img],[img,img,img]))

# imgHdr = np.hstack((img,img))
# imgVer = np.vstack((img,img))
# cv2.imshow("Horizontal",imgHdr)
# cv2.imshow("Vertical",imgVer)

cv2.imshow("ImageStack",imgStack)

cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48

在这里插入图片描述

颜色检测

import cv2
import numpy as np
# color dection

def empty(a):
    pass
'''连接图片'''
def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver

# 调整滑动条的位置来改变图像的颜色阈值，从而实现对图像的颜色分割或过滤
path = 'Resources/lambo.PNG'
framWidth = 640
framHeight = 480

cap = cv2.VideoCapture(path)
cap.set(3,framWidth) #width
cap.set(4,framHeight) #height
cap.set(10,150)

cv2.namedWindow("TrackBars")
cv2.resizeWindow("TrackBars",640,240)
cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)  # hue
cv2.createTrackbar("Hue Max","TrackBars",179,179,empty)
cv2.createTrackbar("Sat Min","TrackBars",0,255,empty) # saturation
cv2.createTrackbar("Sat Max","TrackBars",255,255,empty)
cv2.createTrackbar("Val Min","TrackBars",0,255,empty)  # value
cv2.createTrackbar("Val Max","TrackBars",255,255,empty)

while True:
    img = cv2.imread(path)
    imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
    h_min = cv2.getTrackbarPos("Hue Min","TrackBars")
    h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
    s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
    s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
    v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
    v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
    # print(h_min,h_max,s_min,s_max,v_min,v_max)
    lower = np.array([h_min,s_min,v_min])
    upper = np.array([h_max,s_max,v_max])
    #用掩码对原始图像进行位运算
    mask = cv2.inRange(imgHSV,lower,upper)
    imgResult = cv2.bitwise_and(img,img,mask=mask) #二值图像

    # cv2.imshow("Original",img)
    # cv2.imshow("HSV",imgHSV)
    # cv2.imshow("Mask", mask)
    # cv2.imshow("Result", imgResult)

    imgStack = stackImages(0.6, ([img, imgHSV], [mask, imgResult]))
    cv2.imshow("Stacked Images", imgStack)

    cv2.waitKey(1)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82

在这里插入图片描述

轮廓检测

import cv2
import numpy as np

#contours / shape detection

def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver

def getContours(img):
    contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        area = cv2.contourArea(cnt)
        print(area)
        if area>500:
            cv2.drawContours(imgContour,cnt,-1,(255,0,0),3)
            #计算轮廓曲线长度
            peri = cv2.arcLength(cnt,True)
            print(peri)
            
            approx = cv2.approxPolyDP(cnt,0.02*peri,True)
            print(len(approx))
            objCor = len(approx)
            x,y,w,h = cv2.boundingRect(approx)
            
			# 图形分类
            if objCor == 3: objectType = "Tri"
            elif objCor == 4 :
                aspRatio = w / float(h)
                if aspRatio > 0.98 and aspRatio < 1.03: objectType = "Square"
                else: objectType = "Rectangle"
            elif objCor > 4: objectType = "Circles"
            else: objectType = "None"

            cv2.rectangle(imgContour,(x,y),(x+w,y+h),(0,255,0),2)
            cv2.putText(imgContour,objectType,(x+(w//2)-10,y+(h//2)-10),cv2.FONT_HERSHEY_COMPLEX,0.7,(0,0,0),2)


path = 'Resources/shapes.png'
img = cv2.imread(path)
imgContour = img.copy()

imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
imgBlur = cv2.GaussianBlur(imgGray,(7,7),1)
imgCanny = cv2.Canny(imgBlur,50,50)
getContours(imgCanny)

imgBlank = np.zeros_like(img)
imgStack = stackImages(0.8,([img,imgGray],[imgCanny,imgContour]))

cv2.imshow("Stack",imgStack)

cv2.waitKey(0)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81

在这里插入图片描述

人脸检测

9.1静态图片

import cv2
# face detection
faceCascade = cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")
img = cv2.imread("Resources/dnn.jpg")
imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

faces = faceCascade.detectMultiScale(imgGray,1.1,4)

for(x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
cv2.imshow("Result",img)

cv2.waitKey(0)
1
2
3
4
5
6
7
8
9
10
11
12
13

在这里插入图片描述

9.2 摄像头

import cv2

faceCascade = cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")

cap = cv2.VideoCapture(0)
cap.set(3,640) #width
cap.set(4,480) #height
cap.set(10,100)

while True:
   success,img = cap.read()
   cv2.imshow("Video",img)
   imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

   faces = faceCascade.detectMultiScale(imgGray, 1.1, 4)

   for (x, y, w, h) in faces:
       cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
   cv2.imshow("Result", img)
   if cv2.waitKey(1) & 0xFF == ord('q'):
       break
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

在这里插入图片描述

实战

10.1虚拟绘画

import cv2
import numpy as np

frameWidth = 640
frameHeight = 480
cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, 150)

# 想要检测的颜色
myColors = [[0,89,0,98,255,255], [0,47,0,97,255,255], [0,66,0,179,255,255], [0,54,0,98,255,255]]
# 想要绘制的颜色  BGR
myColorValues = [[51, 153, 255],[0, 255, 0],[255,0,0],[0,255,255]]
# 绘制的点的列表
myPoints = []  ## [x , y , colorId ]

"""获取想要绘制的，及对应的颜色"""
def findColor(img, myColors, myColorValues):
    imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    count = 0
    newPoints = []
    for color in myColors:
        lower = np.array(color[0:3])
        upper = np.array(color[3:6])
        mask = cv2.inRange(imgHSV, lower, upper) 

        x, y = getContours(mask)  # 想要绘制的点

        cv2.circle(imgResult, (x, y), 20, myColorValues[count], cv2.FILLED)  # 将点绘制在图上
        if x != 0 and y != 0:
            newPoints.append([x, y, count])  # 将点添加到 newPoints列表中，count为想要绘制颜色的编号
        count += 1
    return newPoints  


def getContours(img):
    contours, Heriachy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) 
    x, y, w, h = 0, 0, 0, 0
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > 500:
            # cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)
            peri = cv2.arcLength(cnt, True)
            approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
            x, y, w, h = cv2.boundingRect(approx)  
    return x + w // 2, y  


"""把点绘制在画布上"""
def drawOnCanvas(myPoints, myColorValues):
    for point in myPoints:
        cv2.circle(imgResult, (point[0], point[1]), 20, myColorValues[point[2]], cv2.FILLED)


while True:
    success, img = cap.read()
    imgResult = img.copy()
    newPoints = findColor(img, myColors, myColorValues)  # 想要绘制的点
    if len(newPoints) != 0:
        for newP in newPoints:
            myPoints.append(newP)
    if len(myPoints) != 0:
        drawOnCanvas(myPoints, myColorValues)  # 将点绘制在画布上

    cv2.imshow("Result", imgResult)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

        
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70

利用颜色检测滑杆来得出笔的颜色

在这里插入图片描述

10.2纸张扫描

import cv2
import numpy as np

widthImg=480
heightImg =640

img = cv2.imread("Resources/1.jpg")

"""图像预处理"""
def preProcessing(img):
    imgGray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    imgBlur = cv2.GaussianBlur(imgGray,(5,5),1)
    imgCanny = cv2.Canny(imgBlur,200,200)
    kernel = np.ones((5,5))
    imgDial = cv2.dilate(imgCanny,kernel,iterations=2)
    imgThres = cv2.erode(imgDial,kernel,iterations=1)
    return imgThres

'''获取最大轮廓角点'''
def getContours(img):
    biggest = np.array([])
    maxArea = 0
    contours,Heriachy= cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area>5000:
            peri = cv2.arcLength(cnt,True)
            approx = cv2.approxPolyDP(cnt,0.02*peri,True)
            if area >maxArea and len(approx) == 4:
                biggest = approx
                maxArea = area

    #绘制轮廓（biggest仅仅包含矩形的轮廓）
    cv2.drawContours(imgContour, biggest, -1, (255, 0, 0), 20)
    return biggest

'''矩形角点的重新处理：按照一定的顺序排列（左上，右上，左下，右下）'''
def reorder (myPoints):
    myPoints = myPoints.reshape((4,2))#四个角点
    myPointsNew = np.zeros((4,1,2),np.int32)

    #点按照一定的顺序重新排列
    add = myPoints.sum(1)#将点进行x+y计算，
    myPointsNew[0] = myPoints[np.argmin(add)] #和最小的点为左上角点
    myPointsNew[3] = myPoints[np.argmax(add)]#和最大的点为右下角点

    diff = np.diff(myPoints,axis=1)#将点进行x-y差异计算
    myPointsNew[1]= myPoints[np.argmin(diff)]#差异最小的点为右上
    myPointsNew[2] = myPoints[np.argmax(diff)]#差异最大的点为左下

    return myPointsNew

'''鸟瞰转换'''
def getWarp(img,biggest):

    #矩阵角点的处理，按照一个统一顺序排列
    biggest = reorder(biggest)
  
    pts1 = np.float32(biggest)
    pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]])

    matrix = cv2.getPerspectiveTransform(pts1, pts2)
    #鸟瞰图
    imgOutput = cv2.warpPerspective(img, matrix, (widthImg, heightImg))

    #得到的鸟瞰图，边缘有其他背景，所以裁剪边缘，并将裁剪后的图像，重新调整为原来窗口大小。
    imgCropped = imgOutput[20:imgOutput.shape[0]-20,20:imgOutput.shape[1]-20]
    imgCropped = cv2.resize(imgCropped,(widthImg,heightImg))

    return imgCropped


'''图像堆叠显示'''
def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver


while True:
    imgresize = cv2.resize(img,(widthImg,heightImg))
    imgContour = imgresize.copy()
    imgThres = preProcessing(imgresize)
    biggest = getContours(imgThres)

    if biggest.size != 0:
        # 鸟瞰转换
        imgWarped = getWarp(imgresize, biggest)
        imageArray = ([imgresize,imgThres],
                  [imgContour,imgWarped])
        cv2.imshow("ImageWarped", imgWarped)
    else:
        imageArray = ([imgContour, img])

        # 图像堆叠显示
    stackedImages = stackImages(0.5, imageArray)
    cv2.imshow("WorkFlow", stackedImages)
    cv2.waitKey(0)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125

在这里插入图片描述

10.3 车牌检测器

import cv2

frameWidth = 640
frameHeight = 480
nPlateCascade = cv2.CascadeClassifier("Resources/haarcascade_russian_plate_number.xml")
minArea = 200
color = (255,0,255)

cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10,150)
count = 0

while True:
    success, img = cap.read() 
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 

    #车牌检测
    numberPlates = nPlateCascade.detectMultiScale(imgGray, 1.1, 10)
    for (x, y, w, h) in numberPlates:
        area = w*h
        if area >minArea:
            #绘制矩形
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 2)
            #绘制文字
            cv2.putText(img,"Number Plate",(x,y-5),cv2.FONT_HERSHEY_COMPLEX_SMALL,1,color,2)
            imgRoi = img[y:y+h,x:x+w]
            cv2.imshow("ROI", imgRoi)

    cv2.imshow("Result", img)

    if cv2.waitKey(1) & 0xFF == ord('s'):
        cv2.imwrite("Resources/Scanned/NoPlate_"+str(count)+".jpg",imgRoi)
        cv2.rectangle(img,(0,200),(640,300),(0,255,0),cv2.FILLED)
        cv2.putText(img,"Scan Saved",(150,265),cv2.FONT_HERSHEY_DUPLEX,2,(0,0,255),2)
        cv2.imshow("Result",img)
        cv2.waitKey(500)
        count +=1
        break
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

在这里插入图片描述

按s键后可保存车牌

在这里插入图片描述

参考资料

ChatGPT (openai.com)

RGB Color Codes Chart 🎨 (rapidtables.com)

图像基本操作 - 【布客】OpenCV 4.0.0 中文翻译 (apachecn.org)

相关阅读:
2023国赛数学建模B题思路代码 - 多波束测线问题
 edge扩展下载出现Download interrupted
讨论面向对象及方法学的特点？为什么要用面向对象方法开发软件？
Java异常、SpringBoot中全局捕获处理异常
 HTML+CSS个人静态网页设计
 Linux -- 使用多张gpu卡进行深度学习任务（以tensorflow为例）
算法通关村第一关——链表经典问题之合并有序链表三种方法一层一层优化
 Mysql有多少种常见的日志，分别解释日志的作用
 k8s--基础--22.8--storageclass--类型--Ceph RBD
实时分布式低延迟OLAP数据库Apache Pinot探索实操
原文地址：https://blog.csdn.net/m0_56898461/article/details/132597929

OpenCV

文章目录

OpenCV学习报告

读取图片和网络摄像头

1.1 图片读取

1.2 视频读取

1.1.1 读取视频文件

1.1.2读取网络摄像头

OpenCV基础功能

调整、裁剪图像

3.1 调整图像大小

3.2 裁剪图像

图像上绘制形状和文本

4.1 图像上绘制形状

4.2图像上写文字

透视变换

图像拼接

颜色检测

轮廓检测

人脸检测

9.1静态图片

9.2 摄像头

实战

10.1虚拟绘画

10.2纸张扫描

10.3 车牌检测器

参考资料