python 实时调取摄像头的示例代码

调取摄像头的实现

import numpy as npimport cv2cap = cv2.VideoCapture(0)#参数为0时调用本地摄像头；url连接调取网络摄像头；文件地址获取本地视频while(True):ret,frame=cap.read()#灰度化gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)cv2.imshow(’frame’,gray)#普通图片cv2.imshow(’frame’,frame)if cv2.waitKey(1)&0xFF==ord(’q’):breakcap.release()cv2.destroyAllWindows()

opencv代码

# -*- coding: utf-8 -*-'''Spyder EditorThis is a temporary script file.'''#设置工作路径import osos.chdir(’E:0yflSH-spyder-workspace’)os.path.abspath(’.’)import numpy as npimport cv2#1.1读取图片imread；展示图片imshow；导出图片imwrite#只是灰度图片img=cv2.imread(’Myhero.jpg’,cv2.IMREAD_GRAYSCALE)#彩色图片img=cv2.imread(’Myhero.jpg’,cv2.IMREAD_COLOR)#彩色以及带有透明度img=cv2.imread(’Myhero.jpg’,cv2.IMREAD_UNCHANGED)print(img)#设置窗口可自动调节大小cv2.namedWindow(’image’,cv2.WINDOW_NORMAL)cv2.imshow(’image’,img)k=cv2.waitKey(0)#如果输入escif k==27: #exit cv2.destroyAllWindows#如果输入selif k==ord(’s’): #save picture and exit cv2.imwrite(’Myhero_out.png’,img) cv2.destroyAllWindows()#1.2视频读取#打开内置摄像头cap=cv2.VideoCapture(0)#打开视频cap=cv2.VideoCapture(’why.mp4’)#或者视频每秒多少帧的数据fps=cap.get(5)i=0while(True): #读取一帧 ret,frame=cap.read() #转化为灰图 gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) #设置导出文件名编号 i = i + 1 #每1s导出一张 if i/fps==int(i/fps): #导出文件名为why+编号+.png #若想要导出灰图，则将下面frame改为gray即可 cv2.imwrite('why'+str(int(i/fps))+'.png',frame) #读完之后结束退出 if cv2.waitKey(1)==ord(’q’): breakcap.release()cv2.destoryAllWindows()#1.3图像像素修改rangexmin=100rangexmax=120rangeymin=90rangeymax=100img=cv2.imread(’Myhero.jpg’,0)img[rangexmin:rangexmax,rangeymin:rangeymax]=[[255]*(rangeymax-rangeymin)]*(rangexmax-rangexmin)cv2.imwrite(’Myhero_out2.png’,img)#拆分以及合并图像通道1b,g,r=cv2.split(img)img=cv2.merge(b,g,r)#png转eps，不过非常模糊from matplotlib import pyplot as pltimg=cv2.imread(’wechat1.png’,cv2.IMREAD_COLOR)plt.imsave(’wechat_out.eps’,img)#图像按比例混合img1=cv2.imread(’Myhero.jpg’,cv2.IMREAD_COLOR)img2=cv2.imread(’Myhero_out.png’,cv2.IMREAD_COLOR)dst=cv2.addWeighted(img1,0.5,img2,0.5,0)cv2.imwrite('Myhero_combi.jpg',dst)#1.4按位运算#加载图像img1=cv2.imread('Myhero.jpg')img2=cv2.imread('why1.png')#后面那张图更大rows,cols,channels=img1.shapeROI=img2[0:rows,0:cols]#做一个ROI为图像的大小img2gray=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)#小于175的改为0，大于175的赋值为255ret,mask=cv2.threshold(img2gray,175,255,cv2.THRESH_BINARY)cv2.imwrite('Myhero_mask.jpg',mask)#255-mask=mask_invmask_inv=cv2.bitwise_not(mask)cv2.imwrite('Myhero_mask_inv.jpg',mask_inv)#在mask白色区域显示成ROI，背景图片img2_bg=cv2.bitwise_and(ROI,ROI,mask=mask)cv2.imwrite('Myhero_pic2_backgroud.jpg',img2_bg)#除了mask以外的区域都显示成img1，前景图片img1_fg=cv2.bitwise_and(img1,img1,mask=mask_inv)cv2.imwrite('Myhero_pic2_frontgroud.jpg',img1_fg)#前景图片加上背景图片dst = cv2.add(img2_bg,img1_fg)img2[0:rows, 0:cols ] = dstcv2.imwrite('Myhero_pic2_addgroud.jpg',dst)#finished#构建淹膜方法2#截取帧ret,frame=cap.read()#转换到HSVhsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)#设定蓝色的阈值lower_blue=np.array([110,50,50])upper_blue=np.array([130,255,255])#根据阈值构建掩模mask=cv2.inRange(hsv,lower_blue,upper_blue)#对原图像和掩模进行位运算res=cv2.bitwise_and(frame,frame,mask=mask)#图片放缩，用的插值方法，所以不会损害清晰度res=cv2.resize(img1,None,fx=2,fy=2,interpolation=cv2.INTER_CUBIC)cv2.imwrite('Myhero_bigger.jpg',res)#第二种插值方法height,width=img.shape[:2]res=cv2.resize(img,(2*width,2*height),interpolation=cv2.INTER_CUBIC)#edge现实图片中不好用，人工画的图片还可以img = cv2.imread(’why3.png’,0)edges = cv2.Canny(img,50,100)cv2.imwrite('why3_edge.png',edges)#识别轮廓，并保存轮廓点contoursimg=cv2.imread(’why129.png’)imgray=cv2.imread(’why129.png’,cv2.IMREAD_GRAYSCALE)ret,thresh = cv2.threshold(imgray,127,255,0)cv2.imwrite('2.jpg',thresh)image, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)img = cv2.drawContours(img, contours, -1, (0,255,0), 3)cv2.imwrite('3.jpg',img)#轮廓img = cv2.imread(’why3.png’,0)ret,thresh = cv2.threshold(img,127,255,0)contours,hierarchy = cv2.findContours(thresh, 1, 2)cnt = contours[0] #近似轮廓epsilon = 0.1*cv2.arcLength(cnt,True)approx = cv2.approxPolyDP(cnt,epsilon,True)img = cv2.drawContours(img, approx, -1, (0,255,0), 3)cv2.imwrite('4.jpg',img)from matplotlib import pyplot as plt#图像识别/匹配img_rgb = cv2.imread(’why174.png’)img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)img2=img_gray.copy()template = cv2.imread(’0temp.png’,0)w, h = template.shape[::-1]#共有六种识别方法methods = [’cv2.TM_CCOEFF’, ’cv2.TM_CCOEFF_NORMED’, ’cv2.TM_CCORR’, ’cv2.TM_CCORR_NORMED’, ’cv2.TM_SQDIFF’, ’cv2.TM_SQDIFF_NORMED’]for meth in methods: img = img2.copy() #eval返回某个式子的计算结果 method = eval(meth) #下面使用匹配方法 res = cv2.matchTemplate(img,template,method) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]: top_left = min_loc else: top_left = max_loc bottom_right = (top_left[0] + w, top_left[1] + h) #画矩形把他框出来 cv2.rectangle(img,top_left, bottom_right, 255, 2) plt.subplot(121),plt.imshow(res,cmap = ’gray’) plt.title(’Matching Result’), plt.xticks([]), plt.yticks([]) plt.subplot(122),plt.imshow(img,cmap = ’gray’) plt.title(’Detected Point’), plt.xticks([]), plt.yticks([]) plt.suptitle(meth) plt.show() #这个匹配结果太差#选取3，5，6的匹配方式会稍微好点：cv2.TM_CCORR；cv2.TM_SQDIFF，cv2.TM_SQDIFF_NORMED#视频人脸识别#https://blog.csdn.net/wsywb111/article/details/79152425import cv2from PIL import Imagecap=cv2.VideoCapture('why.mp4')#告诉Opencv使用人脸识别分类器classfier=cv2.CascadeClassifier('E:0yflopencv-masterdatahaarcascadeshaarcascade_frontalface_alt2.xml')count=0while cap.isOpened(): ret,frame=cap.read() if not ret: break grey=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faceRect=classfier.detectMultiScale(grey,scaleFactor=1.2, minNeighbors=3, minSize=(32, 32)) if len(faceRect)>0: count=count+1print(count)#137这种程度可以识别，111没有成功识别，大概是侧脸的缘故#截出人脸image_name='why111.png'frame=cv2.imread(image_name,0)if not (frame is None): #导入测试集 classfier=cv2.CascadeClassifier('E:0yflopencv-masterdatahaarcascadeshaarcascade_frontalface_alt2.xml') #使用测试集导出人脸的位置，存在faceRect中，可以检测多张人脸 faceRect=classfier.detectMultiScale(frame,scaleFactor=3.0, minNeighbors=3, minSize=(32, 32)) count=0 for (x1,y1,w,h) in faceRect: count=count+1 #截取上述图片的人脸部分并保存每一张识别出的人脸 Image.open(image_name).crop((x1,y1,x1+w,y1+h)).save(image_name.split('.')[0]+'_face_'+str(count)+'.png') if count==0: print ('No face detected!')else: print ('Picture '+ image_name +' is not exist in '+os.path.abspath('.'))#人脸上画出矩形from PIL import Image,ImageDrawimage_name='why111.png'frame=cv2.imread(image_name,0)if not (frame is None): classfier=cv2.CascadeClassifier('E:0yflopencv-masterdatahaarcascadeshaarcascade_frontalface_alt2.xml') faceRect=classfier.detectMultiScale(frame,scaleFactor=3.0, minNeighbors=3, minSize=(32, 32)) #画框框 img = Image.open(image_name) draw_instance = ImageDraw.Draw(img) count=0 for (x1,y1,w,h) in faceRect: draw_instance.rectangle((x1,y1,x1+w,y1+h), outline=(255, 0,0)) img.save(’drawfaces_’+image_name) count=count+1 if count==0: print ('No face detected!')else: print ('Picture '+ image_name +' is not exist in '+os.path.abspath('.'))#detectFaces()返回图像中所有人脸的矩形坐标（矩形左上、右下顶点）#使用haar特征的级联分类器haarcascade_frontalface_default.xml，在haarcascades目录下还有其他的训练好的xml文件可供选择。#注：haarcascades目录下训练好的分类器必须以灰度图作为输入。from PIL import Image,ImageDrawimage_name='why63.png'frame=cv2.imread(image_name,0)if not (frame is None): classfier=cv2.CascadeClassifier('E:0yflopencv-masterdatahaarcascadeshaarcascade_fullbody.xml') faceRect=classfier.detectMultiScale(frame,scaleFactor=3.0, minNeighbors=3, minSize=(32, 32)) #画框框 img = Image.open(image_name) draw_instance = ImageDraw.Draw(img) count=0 for (x1,y1,w,h) in faceRect: draw_instance.rectangle((x1,y1,x1+w,y1+h), outline=(255, 0,0)) img.save(’drawfaces_’+image_name) count=count+1 if count==0: print ('No face detected!')else: print ('Picture '+ image_name +' is not exist in '+os.path.abspath('.'))

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