# -*- 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 = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
size = width, height
print(repr(size)) # 打印分辨率
fps = 5
pre_frame = None
def is_inside(o, i):
ox, oy, ow, oh = o
ix, iy, iw, ih = i
return ox > ix and oy > iy and ox + ow < ix + iw and oy + oh < iy + ih
def draw_person(image, person):
x, y, w, h = person
cv.rectangle(image, (x, y), (x + w, y + h), (255, 0, 255), 2)
while True:
start = time.time()
ret, frame = cap.read() #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")
break
end = 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5FPS
seconds = end - start
if 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_cap
else:
# 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):
break
else:
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, datetime.datetime.now().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
cap.release()
# destroyAllWindows()关闭所有图像窗口
cv.destroyAllWindows()
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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 = cap.read()
# 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 = cap.read()
#Resize video's size to receive faster
small_frame = cv2.resize(frame,(0,0),fx=0.25,fy=0.25)
#turn BGR to RGB color
rgb_small_frame = small_frame[:, :, ::-1]
if process_this_frame:
#Find all face and faceencodings to contract
face_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 knowen
matchs = face_recognition.compare_faces(Know_face_encodings,face_encoding)
name = "Unknown"
#if we find the same face
#name =we knownface
face_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 results
for (top, right, bottom, left), name in zip(face_locations, face_names):
top*=4
right*=4
bottom*=4
left*=4
#Draw a box around face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 225), 2)
#Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom-35), (right, bottom), (0, 0, 255), cv2.FILLED)
#set zi ti
font = cv2.FONT_HERSHEY_DUPLEX
cv2.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
cap.release()
cv2.destroyAllWindows()
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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 = cap.read()
# 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 name
img_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 = cap.read()
#Resize video's size to receive faster
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#turn BGR to RGB color
rgb_small_frame = small_frame[:, :, ::-1]
if process_this_frame:
#Find all face and faceencodings to contract
face_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 knowen
matchs = face_recognition.compare_faces(Know_face_encodings,face_encoding)
name = "Unknown"
#if we find the same face
#name =we knownface
face_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 results
for (top, right, bottom, left), name in zip(face_locations, face_names):
top *= 4
right *= 4
bottom *= 4
left *= 4
#Draw a box around face
cv2.rectangle(frame, (left-10, top-100), (right+10, bottom+50), (0, 0, 225), 2)
#Draw a label with a name below the face
cv2.rectangle(frame, (left-10, bottom+15), (right+10, bottom+50), (0, 0, 255), cv2.FILLED)
#set zi ti
font = cv2.FONT_HERSHEY_DUPLEX
cv2.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
cap.release()
cv2.destroyAllWindows()
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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 = cap.read()
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 name
img_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 = cap.read()
flag = cv2.waitKey(1)
#Resize video's size to receive faster
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
#turn BGR to RGB color
rgb_small_frame = small_frame[:, :, ::-1]
if process_this_frame:
#Find all face and faceencodings to contract
face_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 knowen
matchs = face_recognition.compare_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 knownface
face_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()
break
if a == 'n':
break
face_names.append(name)
process_this_frame = not process_this_frame
#display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
top *= 4
right *= 4
bottom *= 4
left *= 4
#Draw a box around face
cv2.rectangle(frame, (left-10, top-100), (right+10, bottom+50), (0, 0, 225), 2)
#Draw a label with a name below the face
cv2.rectangle(frame, (left-10, bottom+15), (right+10, bottom+50), (0, 0, 255), cv2.FILLED)
#set zi ti
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left+6, bottom+44), font, 1.0, (255, 255, 255),1)
cv2.imshow('cap', frame)
if flag == 27:
break
cap.release()
cv2.destroyAllWindows()
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# -*-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_frontalface_default.xml')
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)
# recog.train(faces, np.array(ids))
# recog.save('trainner/trainner.yml')
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