?首先導入opencv

1代碼實現(xiàn)效果，在界面下顯示所要顯示的圖片

在同一目錄下存放顯示的圖片

img = cv.imread('face1.jpg')函數(shù)字符串變量填寫存放照片的名字

為了讓人眼看到照片所以使用cv.waitKey(0),起到delay的作用

#導入cv模塊
import cv2 as cv
#讀取圖片
img = cv.imread('face1.jpg')
#顯示圖片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#釋放內(nèi)存
cv.destroyAllWindows()

2代碼實現(xiàn)效果對圖片進行灰度轉(zhuǎn)換

灰度轉(zhuǎn)換可以讓計算機更輕易對圖片進行識別

此函數(shù)用來進行圖片的灰度轉(zhuǎn)換 gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)

#導入cv模塊
import cv2 as cv
#讀取圖片
img = cv.imread('face1.jpg')
#灰度轉(zhuǎn)換
gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#顯示灰度圖片
cv.imshow('gray',gray_img)
#保存灰度圖片
cv.imwrite('gray_face1.jpg',gray_img)
#顯示圖片
cv.imshow('read_img',img)
#等待
cv.waitKey(0)
#釋放內(nèi)存
cv.destroyAllWindows()

3代碼修改圖片尺寸

為了讓所有圖片都能按要求顯示所以修改圖片尺寸

resize_img = cv.resize(img,dsize=(200,200))此函數(shù)用來修改圖片的尺寸

#導入cv模塊
import cv2 as cv
#讀取圖片
img = cv.imread('face1.jpg')
#修改尺寸
resize_img = cv.resize(img,dsize=(200,200))
#顯示原圖
cv.imshow('img',img)
#顯示修改后的
cv.imshow('resize_img',resize_img)
#打印原圖尺寸大小
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
#釋放內(nèi)存
cv.destroyAllWindows()

4繪制矩形圓形

在進行人臉檢測時會將所要檢測的目標用圖形框選出來所以需要繪制圖形
(1)cv.rectangle(img,(x,y,x+w,y+h),color=(0,0,255),thickness=1)此函數(shù)用來繪制矩形第一個參數(shù)用來指定在哪一個圖片上繪制,第二個參數(shù)指定繪制矩形的位置,長和寬.第三個參數(shù)用來選定繪制矩形的顏色,第四個參數(shù)用來選擇繪制矩形線條的粗細.

(2)cv.circle(img,center=(x+w,y+h),radius=100,color=(255,0,0),thickness=5)此函數(shù)用來繪制圓形,第一個參數(shù)指定在哪個圖片上繪制,第二個參數(shù)是圓心的坐標,第三個參數(shù)是圓的半徑,第四個參數(shù)是繪制圓形線條的粗細

#導入cv模塊
import cv2 as cv
#讀取圖片
img = cv.imread('face1.jpg')
#坐標
x,y,w,h = 100,100,100,100
#繪制矩形
cv.rectangle(img,(x,y,x+w,y+h),color=(0,0,255),thickness=1)
#繪制圓形
cv.circle(img,center=(x+w,y+h),radius=100,color=(255,0,0),thickness=5)
#顯示
cv.imshow('re_img',img)
while True:
    if ord('q') == cv.waitKey(0):
        break
#釋放內(nèi)存
cv.destroyAllWindows()

5人臉檢測

人臉檢測中核心函數(shù)為face_detect_demo()

#檢測函數(shù)
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注意在cv.CascadeClassifier()中位置一定要選擇此時你計算機安裝cv的路徑中的目標文件

#導入cv模塊
import cv2 as cv
#檢測函數(shù)
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)

#讀取圖像
img = cv.imread('face1.jpg')
#檢測函數(shù)
face_detect_demo()
#等待
while True:
    if ord('q') == cv.waitKey(0):
        break
#釋放內(nèi)存
cv.destroyAllWindows()

6多個目標檢測

在進行多個目標檢測時只是改變了cv.CascadeClassifier()參數(shù)中的目標文件

#導入cv模塊
import cv2 as cv
#檢測函數(shù)
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)

#讀取圖像
img = cv.imread('face2.jpg')
#檢測函數(shù)
face_detect_demo()
#等待
while True:
    if ord('q') == cv.waitKey(0):
        break
#釋放內(nèi)存
cv.destroyAllWindows()

7視頻檢測

在下面函數(shù)中如果參數(shù)傳入的是0則將打開默認的攝像頭,也可以傳入視頻

#讀取攝像頭
cap = cv.VideoCapture(0)

#導入cv模塊
import cv2 as cv
#檢測函數(shù)
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)

#讀取攝像頭
cap = cv.VideoCapture(0)
#循環(huán)
while True:
    flag,frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord('q') == cv.waitKey(1):
        break
#釋放內(nèi)存
cv.destroyAllWindows()
#釋放攝像頭
cap.release()

8人臉信息錄入

#導入模塊
import cv2
#攝像頭
cap=cv2.VideoCapture(0)

falg = 1
num = 1

while(cap.isOpened()):#檢測是否在開啟狀態(tài)
    ret_flag,Vshow = cap.read()#得到每幀圖像
    cv2.imshow("Capture_Test",Vshow)#顯示圖像
    k = cv2.waitKey(1) & 0xFF#按鍵判斷
    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
#釋放攝像頭
cap.release()
#釋放內(nèi)存
cv2.destroyAllWindows()

9模型訓練

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)]
    # 檢測人臉
    face_detector = cv2.CascadeClassifier('D:\OpenCV\OPENCV(WIN)\opencv\sources\data\haarcascades\haarcascade_frontalface_alt2.xml')
    # 打印數(shù)組imagePaths
    print('數(shù)據(jù)排列：',imagePaths)
    # 遍歷列表中的圖片
    for imagePath in imagePaths:
        #打開圖片,黑白化
        PIL_img=Image.open(imagePath).convert('L')
        #將圖像轉(zhuǎn)換為數(shù)組，以黑白深淺
       # PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
        img_numpy=np.array(PIL_img,'uint8')
        #獲取圖片人臉特征
        faces = face_detector.detectMultiScale(img_numpy)
        #獲取每張圖片的id和姓名
        id = int(os.path.split(imagePath)[1].split('.')[0])
        #預防無面容照片
        for x,y,w,h in faces:
            ids.append(id)
            facesSamples.append(img_numpy[y:y+h,x:x+w])
        #打印臉部特征和id
        #print('fs:', facesSamples)
        print('id:', id)
        #print('fs:', facesSamples[id])
    print('fs:', facesSamples)
    #print('臉部例子：',facesSamples[0])
    #print('身份信息：',ids[0])
    return facesSamples,ids

if __name__ == '__main__':
    #圖片路徑
    path='D:\opencv_date'
    #獲取圖像數(shù)組和id標簽數(shù)組和姓名
    faces,ids=getImageAndLabels(path)
    #獲取訓練對象
    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)

10人臉識別

import cv2
import numpy as np
import os
# coding=utf-8
import urllib
import urllib.request
import hashlib

#加載訓練數(shù)據(jù)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': '短信發(fā)送成功',
    '-1': '參數(shù)不全',
    '-2': '服務器空間不支持,請確認支持curl或者fsocket,聯(lián)系您的空間商解決或者更換空間',
    '30': '密碼錯誤',
    '40': '賬號不存在',
    '41': '余額不足',
    '42': '賬戶已過期',
    '43': 'IP地址限制',
    '50': '內(nèi)容含有敏感詞'
}


def warning():
    smsapi = "http://api.smsbao.com/"
    # 短信平臺賬號
    user = '13******10'
    # 短信平臺密碼
    password = md5('*******')
    # 要發(fā)送的短信內(nèi)容
    content = '【報警】\n原因：檢測到未知人員\n地點：xxx'
    # 要發(fā)送短信的手機號碼
    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])

#準備識別的圖片
def face_detect_demo(img):
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#轉(zhuǎn)換為灰度
    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)
        # 人臉識別
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
        #print('標簽id:',ids,'置信評分：', 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網(wǎng)頁視頻文章來源地址http://www.zghlxwxcb.cn/news/detail-574534.html

import cv2

class CaptureVideo(object):
	def net_video(self):
		# 獲取網(wǎng)絡視頻流
		#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()
	

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