python实现运动检测

python实现运动检测

# -*- coding=GBK -*-
import cv2 as cv
import time
import datetime#打开摄像头获取图片
cap = cv.VideoCapture(0)#打开摄像头，0代表的是设备id，如果有多个摄像头，可以设置其他数值
width = (cv.CAP_PROP_FRAME_WIDTH))
height = (cv.CAP_PROP_FRAME_HEIGHT))
size = width, height
print(repr(size))  # 打印分辨率
fps = 5
pre_frame = Nonedef is_inside(o, i):ox, oy, ow, oh = oix, iy, iw, ih = ireturn ox > ix and oy > iy and ox + ow < ix + iw and oy + oh < iy + ih
def draw_person(image, person):x, y, w, h = angle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
while True:start = time.time()ret, frame = ad() #读取摄像头,它能返回两个参数，第一个参数是bool型的ret，其值为True或False，代表有没有读到图片；第二个参数是frame，是当前截取一帧的图片frame = cv.flip(frame, 1)#翻转 等于0:逆时针180度旋转， 大于0:正常 ，小于0上下颠倒if not ret:print("open carme filed")breakend = time.time()# 灰度化处理gray_cap = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)# 运动检测部分,看看是不是5FPSseconds = end - startif seconds < 1.0 / fps:time.sleep(1.0 / fps - seconds)gray_cap = cv.resize(gray_cap, (480, 480))# 用高斯滤波进行模糊处理gray_cap = cv.GaussianBlur(gray_cap, (21, 21), 0)# 如果没有背景图像就将当前帧当作背景图片if pre_frame is None:pre_frame = gray_capelse:# absdiff把两幅图的差的绝对值输出到另一幅图上面来img_delta = cv.absdiff(pre_frame, gray_cap)# threshold阈值函数(原图像应该是灰度图,对像素值进行分类的阈值,当像素值高于(有时是小于)阈值时应该被赋予的新的像素值,阈值方法)thresh = cv.threshold(img_delta, 30, 255, cv.THRESH_BINARY)[1]# 用一下腐蚀与膨胀thresh = cv.dilate(thresh, None, iterations=2)# findContours检测物体轮廓(寻找轮廓的图像,轮廓的检索模式,轮廓的近似办法)hog = cv.HOGDescriptor()hog.setSVMDetector(cv.HOGDescriptor_getDefaultPeopleDetector())found, w = hog.detectMultiScale(frame)found_filtered = []for ri, r in enumerate(found):for qi, q, in enumerate(found):if ri != qi and is_inside(r, q):breakelse:found_filtered.append(r)for person in found_filtered:draw_person(frame, person)text = "Occupied"TI = time.strftime('%Y-%m-%d', time.localtime(time.time()))cv.putText(frame, "Room Status: {}".format(text), (10, 20),cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)cv.putText(frame, w().strftime("%A %d %B %Y %I:%M:%S%p"),(10, frame.shape[0] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)# 显示当前帧并记录用户是否按下按键cv.imshow("Security Feed", frame)key = cv.waitKey(1)# 如果q键被按下，跳出循环if key == ord("q"):break# release()释放摄像头lease()# destroyAllWindows()关闭所有图像窗口cv.destroyAllWindows()

人脸识别

# -*- coding=UTF-8 -*-
import cv2
import os
import face_recognition
import threading,time
import numpy as np
cap = cv2.VideoCapture(0)
old = '/home/bj/PycharmProjects/facefound/drivers/god.jpg'
new = '/home/bj/PycharmProjects/facefound/newdrivers/aa.jpg'
# def add_Driver():
#     print("welcome to here ,new driver")
#     print("please input your name,and look at the camare,it will take photos after 3s:")
#     i = input(":")
#     ret, frame = ad()
#     cv2.imshow('cap', frame)
#     output_path = os.path.join(new, "%s.jpg" % i)
#     cv2.imwrite(output_path, frame)
#     return i# load oldface and read oldface_recognition
# old_path = os.path.join(old, "god.jpg")
old_image = face_recognition.load_image_file(old)
old_encoding = face_recognition.face_encodings(old_image)[0]
# load newface and read newface_recognition
#new_path = os.path.join(new, "%s.jpg" % i)
new_image = face_recognition.load_image_file(new)
new_encoding = face_recognition.face_encodings(new_image)[0]
#creat arrays of record face name
Know_face_encodings=[old_encoding,new_encoding
]
Know_face_names=["god","newdriver"
]
#Initialize some variables
face_locations=[]
face_encodings=[]
face_names=[]
process_this_frame = True
while True:ret,frame = ad()#Resize video's size to receive fastersmall_frame = size(frame,(0,0),fx=0.25,fy=0.25)
#turn BGR to RGB colorrgb_small_frame = small_frame[:, :, ::-1]if process_this_frame:#Find all face and faceencodings to contractface_locations = face_recognition.face_locations(rgb_small_frame)face_encodings = face_recognition.face_encodings(rgb_small_frame,face_locations)face_names=[]for face_encoding in face_encodings:#see if the face match the face we knowenmatchs = face_recognitionpare_faces(Know_face_encodings,face_encoding)name = "Unknown"#if we find the same face#name =we knownfaceface_distance = face_recognition.face_distance(Know_face_encodings,face_encoding)best_match_index = np.argmin(face_distance)if matchs[best_match_index]:name = Know_face_names[best_match_index]face_names.append(name)process_this_frame = not process_this_frame#display the resultsfor (top, right, bottom, left), name in zip(face_locations, face_names):top*=4right*=4bottom*=4left*=4#Draw a box angle(frame, (left, top), (right, bottom), (0, 0, 225), 2)#Draw a label with a name below angle(frame, (left, bottom-35), (right, bottom), (0, 0, 255), cv2.FILLED)#set zi tifont = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left+6, bottom-6), font, 1.0, (255, 255, 255),1)cv2.imshow('cap', frame)flag = cv2.waitKey(1)if flag == 27:break
lease()
cv2.destroyAllWindows()

人脸识别2。0

# -*- coding=UTF-8 -*-
import cv2
import os
import face_recognition
import re
import threading,time
import numpy as np
cap = cv2.VideoCapture(0)
old = 'drivers'
new = 'newdrivers'
# def add_Driver():
#     print("welcome to here ,new driver")
#     print("please input your name,and look at the camare,it will take photos after 3s:")
#     i = input(":")
#     ret, frame = ad()
#     cv2.imshow('cap', frame)
#     output_path = os.path.join(new, "%s.jpg" % i)
#     cv2.imwrite(output_path, frame)
#     return i
# add_Driver()
# load oldface and read oldface_recognition
# old_path = os.path.join(old, "god.jpg")
path='/home/bj/PycharmProjects/facefound/drivers'
filelist = os.listdir(path)
drivers_Num = len(filelist)
# old_image = face_recognition.load_image_file('god.jpg')
# old_encoding = face_recognition.face_encodings(old_image)[0]
# # load newface and read newface_recognition
# #new_path = os.path.join(new, "%s.jpg" % i)
# new_image = face_recognition.load_image_file('wangqin.jpg')
# new_encoding = face_recognition.face_encodings(new_image)[0]
Know_face_encodings = []
Know_face_names = []
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
for i in filelist:# creat arrays of record face nameimg_path = os.path.join('drivers', "%s" % i)image = face_recognition.load_image_file(img_path)img_encoding = face_recognition.face_encodings(image)[0]Know_face_encodings.append(img_encoding)Know_face_names.append(i)
#creat arrays of record face name
# Know_face_encodings = [
#     old_encoding,
#     new_encoding
# ]
# Know_face_names = [
#     "god",
#     "wangqin"
# ]
#Initialize some variables
while True:ret, frame = ad()#Resize video's size to receive fastersmall_frame = size(frame, (0, 0), fx=0.25, fy=0.25)#turn BGR to RGB colorrgb_small_frame = small_frame[:, :, ::-1]if process_this_frame:#Find all face and faceencodings to contractface_locations = face_recognition.face_locations(rgb_small_frame,model="cnn")face_encodings = face_recognition.face_encodings(rgb_small_frame,face_locations)face_names=[]for face_encoding in face_encodings:#see if the face match the face we knowenmatchs = face_recognitionpare_faces(Know_face_encodings,face_encoding)name = "Unknown"#if we find the same face#name =we knownfaceface_distances = face_recognition.face_distance(Know_face_encodings,face_encoding)best_match_index = np.argmin(face_distances)if matchs[best_match_index]:name = Know_face_names[best_match_index]face_names.append(name)process_this_frame = not process_this_frame#display the resultsfor (top, right, bottom, left), name in zip(face_locations, face_names):top *= 4right *= 4bottom *= 4left *= 4#Draw a box angle(frame, (left-10, top-100), (right+10, bottom+50), (0, 0, 225), 2)#Draw a label with a name below angle(frame, (left-10, bottom+15), (right+10, bottom+50), (0, 0, 255), cv2.FILLED)#set zi tifont = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left+6, bottom+44), font, 1.0, (255, 255, 255),1)cv2.imshow('cap', frame)flag = cv2.waitKey(1)if flag == 27:break
lease()
cv2.destroyAllWindows()

人脸识别+加入人脸库

# -*- coding=UTF-8 -*-
import cv2
import os
import face_recognition
import numpy as np
import sys
cap = cv2.VideoCapture(0)
new = '/home/bj/PycharmProjects/facefound/drivers'
Know_face_encodings = []
Know_face_names = []
face_locations = []
face_encodings = []
face_names = []
filelist = os.listdir(new)
process_this_frame = True
def add_Driver():print("welcome to here ,new driver")print("please input your name,and look at the camare,it will take photos after 3s:")i = input(":")ret, frame = ad()cv2.imshow('cap', frame)output_path = os.path.join(new, "%s.jpg" % i)cv2.imwrite(output_path, frame)out = face_recognition.load_image_file(output_path)out_encoding = face_recognition.face_encodings(out)[0]Know_face_encodings.append(out_encoding)Know_face_names.append(i)for i in filelist:# creat arrays of record face nameimg_path = os.path.join('drivers', "%s" % i)image = face_recognition.load_image_file(img_path)img_encoding = face_recognition.face_encodings(image)[0]Know_face_encodings.append(img_encoding)Know_face_names.append(i)
#Initialize some variables
while True:ret, frame = ad()flag = cv2.waitKey(1)#Resize video's size to receive fastersmall_frame = size(frame, (0, 0), fx=0.25, fy=0.25)#turn BGR to RGB colorrgb_small_frame = small_frame[:, :, ::-1]if process_this_frame:#Find all face and faceencodings to contractface_locations = face_recognition.face_locations(rgb_small_frame,model="cnn")face_encodings = face_recognition.face_encodings(rgb_small_frame,face_locations)face_names=[]for face_encoding in face_encodings:#see if the face match the face we knowenmatchs = face_recognitionpare_faces(Know_face_encodings,face_encoding)name = "Unknown"# print("if you want to join us,push 'o':")# if flag==13 :#     add_Driver()# else:#if we find the same face#name =we knownfaceface_distances = face_recognition.face_distance(Know_face_encodings,face_encoding)best_match_index = np.argmin(face_distances)print(best_match_index)if matchs[best_match_index]:name = Know_face_names[best_match_index]if best_match_index == 0:while(1):print("do you want to join in?")a=input()if a == 'y':add_Driver()breakif a == 'n':breakface_names.append(name)process_this_frame = not process_this_frame#display the resultsfor (top, right, bottom, left), name in zip(face_locations, face_names):top *= 4right *= 4bottom *= 4left *= 4#Draw a box angle(frame, (left-10, top-100), (right+10, bottom+50), (0, 0, 225), 2)#Draw a label with a name below angle(frame, (left-10, bottom+15), (right+10, bottom+50), (0, 0, 255), cv2.FILLED)#set zi tifont = cv2.FONT_HERSHEY_DUPLEXcv2.putText(frame, name, (left+6, bottom+44), font, 1.0, (255, 255, 255),1)cv2.imshow('cap', frame)if flag == 27:break
lease()
cv2.destroyAllWindows()

# -*-coding:utf-8-*-
import cv2
import os
import numpy as np
from PIL import Image
path = 'drivers'recog = cv2.face.LBPHFaceRecognizer_create(1, 8, 3, 3, 50)face_detector = cv2.CascadeClassifier(r'/home/bj/PycharmProjects/FACE/haarcascade_l')
def get_images(path):image_paths = [os.path.join(path, f) for f in os.listdir(path)]face_samples = []ids = []for image_path in image_paths:img = Image.open(image_path).convert('L')img_np = np.array(img, 'uint8')face = face_detector.detectMultiScale(img_np)print(img_np)print(face)for(x, y, w, h) in face:face_samples.append(img_np[y:y+h, x:x+w])ids.append(id)print("ID=id")return face_samples, ids
print('thhhhh')
faces, ids = get_images(path)
# print(faces,ids)
# ain(faces, np.array(ids))
# recog.save('l')