First import opencv
1 Code to achieve the effect and display the image to be displayed under the interface
Store displayed images in the same directory
img = cv.imread('face1.jpg') function string variable fills in the name of the photo stored
In order for people to see the photos, cv.waitKey(0) is used to play the role of delay.
#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')
#显示图片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#释放内存
cv.destroyAllWindows()
2 code to achieve the effect of grayscale conversion of pictures
Grayscale conversion makes it easier for computers to recognize images
This function is used to perform grayscale conversion of images gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')
#灰度转换
gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#显示灰度图片
cv.imshow('gray',gray_img)
#保存灰度图片
cv.imwrite('gray_face1.jpg',gray_img)
#显示图片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#释放内存
cv.destroyAllWindows()
3 Code to modify image size
In order to allow all images to be displayed as required, the image size is modified.
resize_img = cv.resize(img,dsize=(200,200)) This function is used to modify the size of the image
#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')
#修改尺寸
resize_img = cv.resize(img,dsize=(200,200))
#显示原图
cv.imshow('img',img)
#显示修改后的
cv.imshow('resize_img',resize_img)
#打印原图尺寸大小
print('未修改：',img.shape)
#打印修改后的大小
print('修改后：',resize_img.shape)
#等待
while True:
if ord('q') == cv.waitKey(0):
break
#释放内存
cv.destroyAllWindows()
4Draw a rectangular circle
When performing face detection, the target to be detected will be selected with a graphic frame, so a graphic needs to be drawn
(1)cv.rectangle(img,(x,y,x +w,y+h),color=(0,0,255),thickness=1) This function is used to draw a rectangle. The first parameter is used to specify which picture to draw on, and the second parameter specifies the position where the rectangle is drawn. Length and width. The third parameter is used to select the color of the drawn rectangle, and the fourth parameter is used to select the thickness of the drawn rectangular line.
(2)cv.circle(img,center=(x+w,y+h),radius=100,color=(255,0,0),thickness=5) This function is used to draw a circle, the first The parameters specify which picture to draw on. The second parameter is the coordinates of the center of the circle. The third parameter is the radius of the circle. The fourth parameter is the thickness of the circular line.
#导入cv模块
import cv2 as cv
#读取图片
img = cv.imread('face1.jpg')
#坐标
x,y,w,h = 100,100,100,100
#绘制矩形
cv.rectangle(img,(x,y,x+w,y+h),color=(0,0,255),thickness=1)
#绘制圆形
cv.circle(img,center=(x+w,y+h),radius=100,color=(255,0,0),thickness=5)
#显示
cv.imshow('re_img',img)
while True:
if ord('q') == cv.waitKey(0):
break
#释放内存
cv.destroyAllWindows()
5 face detection
The core function in face detection is face_detect_demo()
#检测函数
def face_detect_demo():
gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
face = face_detect.detectMultiScale(gary,1.01,5,0,(100,100),(300,300))
for x,y,w,h in face:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.imshow('result',img)
1 Note that in cv.CascadeClassifier(), you must select the target file in the path where your computer installs cv.
#导入cv模块
import cv2 as cv
#检测函数
def face_detect_demo():
gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
face = face_detect.detectMultiScale(gary,1.01,5,0,(100,100),(300,300))
for x,y,w,h in face:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.imshow('result',img)
#读取图像
img = cv.imread('face1.jpg')
#检测函数
face_detect_demo()
#等待
while True:
if ord('q') == cv.waitKey(0):
break
#释放内存
cv.destroyAllWindows()
6 multiple target detections
When performing multiple target detection, only the target file in the cv.CascadeClassifier() parameter is changed.
#导入cv模块
import cv2 as cv
#检测函数
def face_detect_demo():
gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
face = face_detect.detectMultiScale(gary)
for x,y,w,h in face:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.imshow('result',img)
#读取图像
img = cv.imread('face2.jpg')
#检测函数
face_detect_demo()
#等待
while True:
if ord('q') == cv.waitKey(0):
break
#释放内存
cv.destroyAllWindows()
7Video detection
In the following function, if the parameter passed in is 0, the default camera will be opened, and video can also be passed in.
#Read camera
cap = cv.VideoCapture(0)
#导入cv模块
import cv2 as cv
#检测函数
def face_detect_demo(img):
gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
face = face_detect.detectMultiScale(gary)
for x,y,w,h in face:
cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv.imshow('result',img)
#读取摄像头
cap = cv.VideoCapture(0)
#循环
while True:
flag,frame = cap.read()
if not flag:
break
face_detect_demo(frame)
if ord('q') == cv.waitKey(1):
break
#释放内存
cv.destroyAllWindows()
#释放摄像头
cap.release()
8 Face information entry
#导入模块
import cv2
#摄像头
cap=cv2.VideoCapture(0)
falg = 1
num = 1
while(cap.isOpened()):#检测是否在开启状态
ret_flag,Vshow = cap.read()#得到每帧图像
cv2.imshow("Capture_Test",Vshow)#显示图像
k = cv2.waitKey(1) & 0xFF#按键判断
if k == ord('s'):#保存
cv2.imwrite("D:/opencv_date/"+str(num)+".cjc"+".jpg",Vshow)
print("success to save"+str(num)+".jpg")
print("-------------------")
num += 1
elif k == ord(' '):#退出
break
#释放摄像头
cap.release()
#释放内存
cv2.destroyAllWindows()
9 model training
import os
import cv2
from PIL import Image
import numpy as np
def getImageAndLabels(path):
facesSamples = []
ids = []
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
# 检测人脸
face_detector = cv2.CascadeClassifier('D:\OpenCV\OPENCV(WIN)\opencv\sources\data\haarcascades\haarcascade_frontalface_alt2.xml')
# 打印数组imagePaths
print('数据排列：',imagePaths)
# 遍历列表中的图片
for imagePath in imagePaths:
#打开图片,黑白化
PIL_img=Image.open(imagePath).convert('L')
#将图像转换为数组，以黑白深浅
# PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
img_numpy=np.array(PIL_img,'uint8')
#获取图片人脸特征
faces = face_detector.detectMultiScale(img_numpy)
#获取每张图片的id和姓名
id = int(os.path.split(imagePath)[1].split('.')[0])
#预防无面容照片
for x,y,w,h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y+h,x:x+w])
#打印脸部特征和id
#print('fs:', facesSamples)
print('id:', id)
#print('fs:', facesSamples[id])
print('fs:', facesSamples)
#print('脸部例子：',facesSamples[0])
#print('身份信息：',ids[0])
return facesSamples,ids
if __name__ == '__main__':
#图片路径
path='D:\opencv_date'
#获取图像数组和id标签数组和姓名
faces,ids=getImageAndLabels(path)
#获取训练对象
recognizer=cv2.face.LBPHFaceRecognizer_create()
#recognizer.train(faces,names)#np.array(ids)
recognizer.train(faces,np.array(ids))
#保存文件
recognizer.write('trainer/trainer.yml')
#save_to_file('names.txt',names)
10Face recognition
import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib
#加载训练数据集文件
recogizer=cv2.face.LBPHFaceRecognizer_create()
recogizer.read('trainer/trainer.yml')
names=[]
warningtime = 0
def md5(str):
import hashlib
m = hashlib.md5()
m.update(str.encode("utf8"))
return m.hexdigest()
statusStr = {
'0': '短信发送成功',
'-1': '参数不全',
'-2': '服务器空间不支持,请确认支持curl或者fsocket,联系您的空间商解决或者更换空间',
'30': '密码错误',
'40': '账号不存在',
'41': '余额不足',
'42': '账户已过期',
'43': 'IP地址限制',
'50': '内容含有敏感词'
}
def warning():
smsapi = "http://api.smsbao.com/"
# 短信平台账号
user = '13******10'
# 短信平台密码
password = md5('*******')
# 要发送的短信内容
content = '【报警】\n原因：检测到未知人员\n地点：xxx'
# 要发送短信的手机号码
phone = '*******'
data = urllib.parse.urlencode({'u': user, 'p': password, 'm': phone, 'c': content})
send_url = smsapi + 'sms?' + data
response = urllib.request.urlopen(send_url)
the_page = response.read().decode('utf-8')
print(statusStr[the_page])
#准备识别的图片
def face_detect_demo(img):
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换为灰度
face_detector=cv2.CascadeClassifier('D:\OpenCV\OPENCV(WIN)\opencv\sources\data\haarcascades\haarcascade_frontalface_alt2.xml')
face=face_detector.detectMultiScale(gray,1.1,5,cv2.CASCADE_SCALE_IMAGE,(100,100),(300,300))
#face=face_detector.detectMultiScale(gray)
for x,y,w,h in face:
cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1)
# 人脸识别
ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
#print('标签id:',ids,'置信评分：', confidence)
if confidence > 80:
global warningtime
warningtime += 1
if warningtime > 100:
warning()
warningtime = 0
cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
else:
cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
cv2.imshow('result',img)
#print('bug:',ids)
def name():
path = 'D:\opencv_date'
#names = []
imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
for imagePath in imagePaths:
name = str(os.path.split(imagePath)[1].split('.',2)[1])
names.append(name)
cap=cv2.VideoCapture('1.mp4')
name()
while True:
flag,frame=cap.read()
if not flag:
break
face_detect_demo(frame)
if ord(' ') == cv2.waitKey(10):
break
cv2.destroyAllWindows()
cap.release()11 web videos
import cv2
class CaptureVideo(object):
def net_video(self):
# 获取网络视频流
#cam = cv2.VideoCapture("rtmp://192.168.0.10/live/test")
cam = cv2.VideoCapture("rtmp://58.200.131.2:1935/livetv/hunantv")
while cam.isOpened():
sucess, frame = cam.read()
cv2.imshow("Network", frame)
cv2.waitKey(1)
if __name__ == "__main__":
capture_video = CaptureVideo()