Python快速学习opencv
Python快速学习opencv1.安装opencv-pythonpip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple2.打开图片并在窗口显示img = cv2.imread('Resources/lena.jpg')cv2.imshow('output', img)cv2.waitKey(0)3.播放视频cap
·
Python快速学习opencv
1.安装opencv-python
pip install opencv-python -i https://pypi.tuna.tsinghua.edu.cn/simple
2.打开图片并在窗口显示
img = cv2.imread('Resources/lena.jpg')
cv2.imshow('output', img)
cv2.waitKey(0)
3.播放视频
cap = cv2.VideoCapture('Resources/video.mp4')
while True:
success, img = cap.read()
cv2.imshow('video', img)
if cv2.waitKey(1) & 0xFF == ord('q'): # 按q键关闭
break
4.显示摄像头
cap = cv2.VideoCapture(0) # 默认是用户的摄像头
cap.set(3, 640) # 设置宽高
cap.set(4, 480)
cap.set(10, 100) # 设置亮度
while True:
success, img = cap.read()
cv2.imshow('video', img)
if cv2.waitKey(1) & 0xFF == ord('q'): # 按q键关闭
break
5.图像灰度处理、高斯模糊、边缘处理、膨胀处理、腐蚀处理
kernel = np.ones((5, 5), np.uint8)
if __name__ == '__main__':
img = cv2.imread('Resources/lena.jpg')
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 灰度处理
imgBlur = cv2.GaussianBlur(imgGray, (7, 7), 0) # 高斯模糊
imgCanny = cv2.Canny(img, 150, 200) # 边缘化
imgDialation = cv2.dilate(imgCanny, kernel, iterations=1) # 膨胀处理
imgEroded = cv2.erode(imgDialation, kernel, iterations=1) # 腐蚀处理
cv2.imshow('Img Gray', imgGray)
cv2.imshow('Img Blur', imgBlur)
cv2.imshow('Img Canny', imgCanny)
cv2.imshow('Img Dialation', imgDialation)
cv2.imshow('Img imgEroded', imgEroded)
cv2.waitKey(0)
6.调整图像
img = cv2.imread('Resources/lena.jpg')
print(img.shape) # 打印的是高和宽和频道数量
imgResize = cv2.resize(img, (300, 100)) # 调整宽为300 高为100
imgCropped = img[0:200, 200:300] # 高从0到200 宽从 200到300
print(imgResize.shape)
cv2.imshow('imgCropped', imgCropped)
cv2.imshow('img', img)
cv2.waitKey(0)
7.绘制图形
img = np.zeros((512, 512, 3), np.uint8)
img[:] = 255, 0, 0 # 修改图像颜色为蓝色(BGR)
cv2.line(img, (0, 0), (200, 200), (0, 255, 0), 3) # 画一条绿线起点是(0,0) 终点是（200,200) 颜色是绿色 厚度是3
cv2.rectangle(img, (0, 0), (100, 100), (0, 0, 255), cv2.FILLED) # 填充红色矩形
cv2.circle(img, (200, 100), 30, (255, 255, 0), 5) # 填充圆形
cv2.putText(img, 'This is a Text', (250, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 150, 0), 3)
cv2.imshow('img', img)
cv2.waitKey(0)
8.截取特定位置的图片
import cv2
import numpy as np
img = cv2.imread("Resources/cards.jpg")
width, height = 250, 350
pts1 = np.float32([[111, 219], [287, 188], [154, 482], [352, 440]]) # 需要截取的区域(左上,右上,左下,右下)
pts2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]]) # 转换后的矩阵区域
matrix = cv2.getPerspectiveTransform(pts1, pts2) # 创建转换矩阵
imgOutput = cv2.warpPerspective(img, matrix, (width, height)) # 导出转换后的图片
cv2.imshow("Image", img)
cv2.imshow("Output", imgOutput)
print(imgOutput.shape)
cv2.waitKey(0)
9.堆叠图像
import cv2
import numpy as np
def stackImages(scale, imgArray): # 将图像堆叠的函数
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/lena.png')
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
imgStack = stackImages(0.5, ([img, imgGray, img], [img, img, img])) # 2*3进行堆叠
# imgHor = np.hstack((img,img)) 水平堆叠
# imgVer = np.vstack((img,img)) 竖直堆叠
#
# cv2.imshow("Horizontal",imgHor)
# cv2.imshow("Vertical",imgVer)
cv2.imshow("ImageStack", imgStack)
cv2.waitKey(0)
10.通过检测图片颜色和定义追踪栏获取指定内容
import cv2
import numpy as np
def empty(a):
pass
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
path = 'Resources/lambo.png'
cv2.namedWindow("TrackBars")
cv2.resizeWindow("TrackBars",640,240)
cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)
cv2.createTrackbar("Hue Max","TrackBars",19,179,empty)
cv2.createTrackbar("Sat Min","TrackBars",110,255,empty)
cv2.createTrackbar("Sat Max","TrackBars",240,255,empty)
cv2.createTrackbar("Val Min","TrackBars",153,255,empty)
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])
mask = cv2.inRange(imgHSV,lower,upper)
imgResult = cv2.bitwise_and(img,img,mask=mask)
# 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)
11.图形形状识别
import cv2
import numpy as np
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):
# 获取所有边缘化后的图形
contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
area = cv2.contourArea(cnt) # 图像面积
print(area)
if area > 500:
# 绘图
cv2.drawContours(imgContour, cnt, -1, (255, 0, 0), 3)
# 图像轮廓周长
peri = cv2.arcLength(cnt, True)
# print(peri)
# 获取折点的列表
approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
print(len(approx))
# 点的个数
objCor = len(approx)
# 获取图形的外界矩形.
x, y, w, h = cv2.boundingRect(approx)
# 判断图形形状
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"
# 绘制外界矩形
cv2.rectangle(imgContour, (x, y), (x + w, y + h), (0, 255, 0), 2)
# 判断图形形状并用文字说明
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(0.6, ([img, imgGray, imgBlur],
[imgCanny, imgContour, imgBlank]))
cv2.imshow("Stack", imgStack)
cv2.waitKey(0)
12.人脸识别
import cv2
faceCascade= cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")
cap = cv2.VideoCapture(0) # 默认是用户的摄像头
cap.set(3, 640) # 设置宽高
cap.set(4, 480)
cap.set(10, 100) # 设置亮度
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'): # 按q键关闭
break
13.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)
myColors = [[5,107,0,19,255,255],
[133,56,0,159,156,255],
[57,76,0,100,255,255],
[90,48,0,118,255,255]]
myColorValues = [[51,153,255], ## BGR
[255,0,255],
[0,255,0],
[255,0,0]]
myPoints = [] ## [x , y , colorId ]
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]) #这里的color用追踪器获取颜色
upper = np.array(color[3:6])
mask = cv2.inRange(imgHSV,lower,upper) # 通过hsv得到蒙版
x,y=getContours(mask)
cv2.circle(imgResult,(x,y),15,myColorValues[count],cv2.FILLED)
if x!=0 and y!=0:
newPoints.append([x,y,count])
count +=1
#cv2.imshow(str(color[0]),mask)
return newPoints
def getContours(img): #找到图形的中心位置
contours,hierarchy = 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
def drawOnCanvas(myPoints,myColorValues):
for point in myPoints:
cv2.circle(imgResult, (point[0], point[1]), 10, myColorValues[point[2]], cv2.FILLED)
while True:
success, img = cap.read()
imgResult = img.copy()
newPoints = findColor(img, myColors,myColorValues)
if len(newPoints)!=0:
for newP in newPoints:
myPoints.append(newP)
if len(myPoints)!=0:
drawOnCanvas(myPoints,myColorValues)
cv2.imshow("Result", imgResult)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
14.捕捉车牌号码
import cv2
#############################################
frameWidth = 640
frameHeight = 480
nPlateCascade = cv2.CascadeClassifier("Resources/haarcascade_russian_plate_number.xml")
minArea = 200
color = (255,0,255)
###############################################
cap = cv2.VideoCapture("Resources/video12.mp4")
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)
numberPlates = nPlateCascade.detectMultiScale(imgGray, 1.1, 10)
for (x, y, w, h) in numberPlates:
area = w*h
if area >minArea:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 255), 2)
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)
# 如果按下s 键则会保存截取的图片
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
CSDN联合极客时间，共同打造面向开发者的精品内容学习社区，助力成长！
更多推荐
2
0- 0
已为社区贡献7条内容
所有评论(0)