# -*- coding: utf-8 -*-
"""
Created on Wed Oct 16 14:13:46 2019
@author: inkuml05
"""
import cv2
import numpy as np
face_classifier = cv2.CascadeClassifier(
'C:/Users/inkuml05/Downloads/opencv-master/opencv-master/data/haarcascades/haarcascade_frontalface_default.xml'
)
##creating a method to extract the face features and return the data
def face_extractor(img):
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face = face_classifier.detectMultiScale(gray, 1.3, 5)
if face is ():
return None
for (x, y, w, h) in face:
cropped_faces = img[y:y + h, x:x + w]
return cropped_faces
cap = cv2.VideoCapture(0)
count = 0
## Run the camera and capture.
while True:
break
cv2.destroyAllWindows()
cap.release()
if __name__ == "__main__":
main()
import numpy as np
import os
import cv2
face_cascade = cv2.CascadeClassifier(
'F:\\opencv\\opencv-master\\data\\haarcascades\\haarcascade_frontalface_default.xml'
)
cap = cv2.VideoCapture(0)
path = "C:\Users\Tanvee\Desktop\ForskML\Day27"
# path were u want store the data set
id = input('enter user name')
try:
# Create target Directory
os.mkdir(path + str(id))
print("Directory ", path + str(id), " Created ")
except FileExistsError:
print("Directory ", path + str(id), " already exists")
sampleN = 0
from random import randint
import cv2
import sys
import os
import traceback
import pandas as pd
import numpy as np
from natsort import natsorted
import glob
import numpy as np
import cv2 as cv
# 얼굴과 눈을 검출하기 위해 미리 학습시켜 놓은 XML 포맷으로 저장된 분류기를 로드
face_cascade = cv.CascadeClassifier('haarcascade_frontface.xml')
# 얼굴과 눈을 검출할 그레이스케일 이미지를 준비
image_path = glob.glob(
'C:/Users/mmclab1/Desktop/AI training data set/data/video_2images/*.*',
recursive=False)
image_path = natsorted(
image_path
) #'C:/Users/mmclab1/Desktop/AI training data set/data/video_2images\\id32_id35_0002_frame-2.jpg'
for path in image_path:
img = cv.imread(path)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# 이미지에서 얼굴을 검출
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
import cv2 as cv
import time
import random
import string
face_cascade = cv.CascadeClassifier(cv.data.haarcascades +
'haarcascade_frontalface_default.xml')
smile_cascade = cv.CascadeClassifier(cv.data.haarcascades +
'haarcascade_smile.xml')
cap = cv.VideoCapture(0)
if not cap.isOpened():
print("无法打开摄像头")
exit()
while (True):
ret, frame = cap.read()
if not ret:
print("无法获取画面帧")
break
faces = face_cascade.detectMultiScale(frame, 1.3, 2)
img = frame
flag = False
for (x, y, w, h) in faces:
face_area = img[y:y + h, x:x + w]
smiles = smile_cascade.detectMultiScale(face_area,
scaleFactor=1.16,
import cv2
import os
from keras.models import load_model
import numpy as np
from pygame import mixer
import time
mixer.init()
sound = mixer.Sound('alarm.wav')
face = cv2.CascadeClassifier('cascade_files\haarcascade_frontalface_alt.xml')
leye = cv2.CascadeClassifier('cascade_files\haarcascade_lefteye_2splits.xml')
reye = cv2.CascadeClassifier('cascade_files\haarcascade_righteye_2splits.xml')
lbl = ['Close', 'Open']
model = load_model('models/cnncat2.h5')
path = os.getcwd()
cap = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_COMPLEX_SMALL
count = 0
score = 0
thicc = 2
rpred = [99]
lpred = [99]
while (True):
ret, frame = cap.read()
height, width = frame.shape[:2]
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
'''
github.com/razyar
khoderazyar.ir
'''
import cv2
import sys
imagePath = sys.argv[1]
cascPath = "haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascPath)
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
#flags = cv2.CV_HAAR_SCALE_IMAGE
)
print(" i found {0} faces! in this pic".format(len(faces)))
from constants import *
import cv2
import pandas as pd
import numpy as np
from PIL import Image
cascade_classifier = cv2.CascadeClassifier(CASC_PATH)
def format_image(image):
if len(image.shape) > 2 and image.shape[2] == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
else:
image = cv2.imdecode(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)
gray_border = np.zeros((150, 150), np.uint8)
gray_border[:, :] = 200
gray_border[((150 / 2) - (SIZE_FACE / 2)):((150 / 2) + (SIZE_FACE / 2)),
((150 / 2) - (SIZE_FACE / 2)):((150 / 2) +
(SIZE_FACE / 2))] = image
image = gray_border
faces = cascade_classifier.detectMultiScale(image,
scaleFactor=1.3,
minNeighbors=5)
# None is we don't found an image
if not len(faces) > 0:
#print "No hay caras"
return None
max_area_face = faces[0]
for face in faces:
if face[2] * face[3] > max_area_face[2] * max_area_face[3]:
import cv2
import dlib
import time
import threading
import math
# Haarcascade classifier to identify vehicles
carCascade = cv2.CascadeClassifier('myhaar.xml')
video = cv2.VideoCapture('4.mp4') #video
WIDTH = 1280
HEIGHT = 720
#define points to calculate speed
def estimateSpeed(location1, location2):
#calculating pixels between two points
d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2))
# ppm = location2[2] / carWidht
ppm = 8.8 #pixels per metre
d_meters = d_pixels / ppm #distance in metres
#print("d_pixels=" + str(d_pixels), "d_meters=" + str(d_meters))
fps = 18 #frames per second
speed = d_meters * fps * 3.6
return speed
def trackMultipleObjects():
rectangleColor = (0, 255, 0)
frameCounter = 0
currentCarID = 0
fps = 0
import cv2
import serial
import time
fire_cascade = cv2.CascadeClassifier('fire_detection.xml')
ser = serial.Serial(
'COM3', 9600) #change COM port number on which your arduino is connected
cap = cv2.VideoCapture(
0) #0 in case of inbuilt web cam and 1 if external cam is used
if cap.isOpened():
while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
fire = fire_cascade.detectMultiScale(img, 1.2, 5)
for (x, y, w, h) in fire:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
print('Fire is detected..!')
ser.write('p'.encode())
time.sleep(0.2)
cv2.imshow('img', img)
print('Continue Capturing Video...')
ser.write('s'.encode())
k = cv2.waitKey(30) & 0xff
if k == 27:
break
cap.release()
from os import listdir
from os.path import isfile, join
# 테스트 이미지 불러오기
mypath='./image/'
path = './data/haarcascades'
onlyfiles = [ f for f in listdir(mypath) if isfile(join(mypath,f)) ]
for i in onlyfiles:
image = cv2.imread('./image/'+i)
# RGB -> Gray로 변환
# 얼굴 찾기 위해 그레이스케일로 학습되어 있기때문에 맞춰줘야 한다.
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 정면 얼굴 인식용 cascade xml 불러오기
# 그 외에도 다양한 학습된 xml이 있으니 테스트해보면 좋을듯..
face_cascade = cv2.CascadeClassifier('./data/haarcascades/haarcascade_frontalface_default.xml')
# 이미지내에서 얼굴 검출
faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)
# 얼굴 검출되었다면 좌표 정보를 리턴받는데, 없으면 오류를 뿜을 수 있음.
for (x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2) # 원본 영상에 위치 표시
roi_gray = image_gray[y:y+h, x:x+w] # roi 생성
roi_color = image[y:y+h, x:x+w] # roi
cv2.imshow('img',image) # 이미지 띄우기
cv2.waitKey(0)
cv2.destroyAllWindows() # 윈도우 종료
import cv2
import numpy as np
# Haar Cascade
face_cascade = cv2.CascadeClassifier(
'haar_cascade_files/haarcascade_frontalface_default.xml')
if face_cascade.empty():
raise IOError('Unable to load face cascade classifier xml file')
# Video capturing and scaling
cap = cv2.VideoCapture(0)
scaling_factor = 0.5
#Capture frame
while True:
_, frame = cap.read()
#Resize frame
frame = cv2.resize(frame,
None,
fx=scaling_factor,
fy=scaling_factor,
interpolation=cv2.INTER_AREA)
#Image to grayscale conversion
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#Running face detecor on gray image
face_rects = face_cascde.detectMultiScale(gray, 1.3, 5)
def remote(url, names):
file1 = open("admin_files/logs.txt", "a+")
file2 = open("admin_files/mobile_no.txt", "r")
data = file2.read()
file2.close()
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer.yml')
cascadePath = 'haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(cascadePath)
font = cv2.FONT_HERSHEY_SIMPLEX
id = 0
#Variable to counter valid and invalid
valid = 0
invalid = 0
flag = 0
while (flag == 0):
site = requests.get(url)
img_arr = np.array(bytearray(site.content), dtype=np.uint8)
img = cv2.imdecode(img_arr, -1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray,
scaleFactor=1.2,
minNeighbors=3,
minSize=(10, 10))
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)
id, confidence = recognizer.predict(gray[y:y + h, x:x + w])
text = ""
if (confidence < 48):
valid += 1
text = names[id]
if (valid >= 60):
cv2.putText(img, str("Logged to system"), (x + 5, y - 5),
font, 1, (255, 255, 255), 2)
cv2.putText(img, str("Paused for few minutes.."),
(x + 5, y + 5 + 270), font, 1, (255, 255, 255),
2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
x = datetime.datetime.now()
x = x.strftime("%m/%d/%Y, %H:%M:%S")
msg = "\n " + text + " logged at " + x
file1.write(msg)
valid = 0
invalid = 0
time.sleep(3)
else:
cv2.putText(img, str("Detected " + text), (x + 5, y - 5),
font, 1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
else:
invalid += 1
if (invalid >= 150):
cv2.putText(
img,
str("Cannot detect the face system will be alerted.."),
(x + 5, y - 5), font, 1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
alerts.alert(data)
invalid = 0
valid = 0
else:
cv2.putText(img, str("Detecting.."), (x + 5, y - 5), font,
1, (255, 255, 255), 2)
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
flag = 1
break
cv2.imshow('camera', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.release()
cv2.destroyAllWindows()
file1.close()
from flask import Flask, render_template, Response
import cv2
face_cascade = cv2.CascadeClassifier('haarcascade.xml')
app = Flask(__name__)
camera = cv2.VideoCapture(0)
def gen_frames():
while True:
success, img = camera.read()
if not success:
break
else:
img = img
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
ret, buffer = cv2.imencode('.jpg', img)
frame = buffer.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n'
) # concat frame one by one and show result
cap = cv2.VideoCapture(0)
# Create the haar cascade
cascades = [
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
'haarcascade_frontalface_default.xml',
]
if hasattr(cv2, 'data'):
cascades.insert(0, os.path.join(cv2.data.haarcascades, 'haarcascade_frontalface_default.xml'))
for path in cascades:
if os.path.isfile(path):
print('Loading face model:', path)
faceCascade = cv2.CascadeClassifier(path)
break
else:
print('No haarcascade_frontalface_default.xml found.')
print('Try:\nsudo apt install opencv-data\nor:')
print('wget https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml')
exit()
def face_area(rect):
"Ugly hack, because Py3 can't unpack tuples in lambda."
x, y, w, h = rect
del x, y
return w * h
import numpy as np
import cv2
faceCascade = cv2.CascadeClassifier('/usr/local/share/opencv4/haarcascades/haarcascade_frontalface_default.xml')
smileCascade = cv2.CascadeClassifier('/usr/local/share/opencv4/haarcascades/haarcascade_smile.xml')
cap = cv2.VideoCapture(0)
cap.set(3,640) # set Width
cap.set(4,480) # set Height
while (True):
# ret : frame capture (boolean)
# img : Capture frame
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("video", img)
#detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.2,
minNeighbors=5,
minSize=(20,20),
)
for(x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
roi_gray = gray[y:y+h, x:x+w]
'''
Created on 19 feb. 2018
@author: Andres
@info: https://cucopc.es/2016/06/09/raspberry-pi-reconocimiento-facial-opencv/
'''
#Ejemplo de deteccion facial con OpenCV y Python
#Por Glare
#www.robologs.net
import numpy as np
import cv2
#cargamos la plantilla e inicializamos la webcam:
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
cap = cv2.VideoCapture(0)
while (True):
#leemos un frame y lo guardamos
ret, img = cap.read()
#convertimos la imagen a blanco y negro
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#buscamos las coordenadas de los rostros (si los hay) y
#guardamos su posicion
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
#Dibujamos un rectangulo en las coordenadas de cada rostro
for (x, y, w, h) in faces:
import cv2
arquivo = 'haarcascade_frontalface_default.xml'
faceCascade = cv2.CascadeClassifier(arquivo)
captura = cv2.VideoCapture(0)
while True:
s, imagem = captura.read()
imagem = cv2.flip(imagem, 180)
faces = faceCascade.detectMultiScale(imagem,
minNeighbors=5,
minSize=(30, 30),
maxSize=(200, 200))
for (x, y, w, h) in faces:
cv2.rectangle(imagem, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow("Video", imagem)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
captura.release()
cv2.destroyAllWindows()
def _build_face_cascade(self): full_path = os.path.join(os.path.dirname(__file__), 'opencv', 'haarcascade_frontalface_default.xml') return cv2.CascadeClassifier(full_path)
import numpy as np
import cv2
face_cascade = cv2.CascadeClassifier(
'D:\\My_study\\haarcascade_frontalface_default.xml'
) # replace the path of the xml file
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
print(x, y, w, h)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
cv2.imshow('img', img)
if cv2.waitKey(1) & 0xFF == ord(' '):
break
cap.release()
cv2.destroyAllWindows()
import cv2
import math
import os
import tarfile
import numpy as np
import tensorflow as tf
IMAGES_DOWNLOAD_URL = 'http://tamaraberg.com/faceDataset/originalPics.tar.gz'
ANNOTATIONS_DOWNLOAD_URL = 'http://vis-www.cs.umass.edu/fddb/FDDB-folds.tgz'
DIRECTORY = os.path.join(os.path.dirname(__file__), 'fddb')
# CASCADES_DIR = os.path.normpath(os.path.join(cv2.__file__, '..', '..', '..', '..', 'share', 'OpenCV', 'haarcascades'))
CASCADES_DIR = "/usr/local/opt/opencv/share/OpenCV/haarcascades"
FACE_CASCADE = cv2.CascadeClassifier(
os.path.join(CASCADES_DIR, 'haarcascade_frontalface_default.xml'))
EYES_CASCADE = cv2.CascadeClassifier(
os.path.join(CASCADES_DIR, 'haarcascade_eye.xml'))
EXCLUDES = set([
'2002/07/19/big/img_445',
'2002/07/21/big/img_76',
'2002/07/22/big/img_152',
'2002/07/26/big/img_513',
'2002/07/29/big/img_136',
'2002/07/31/big/img_898',
'2002/08/05/big/img_3591',
'2002/08/07/big/img_1576',
'2002/08/16/big/img_81',
'2002/08/16/big/img_1055',
'2002/08/18/big/img_293',
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale,
scale)
else:
imgArray[x] = cv2.resize(
imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]),
None, scale, scale)
if len(imgArray[x].shape) == 2:
imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor = np.hstack(imgArray)
ver = hor
return ver
# LOAD THE CLASSIFIERS
cascade = cv2.CascadeClassifier(facecascadelocation)
faces1 = cascade.detectMultiScale(img1)
faces2 = cascade.detectMultiScale(img2)
# ADD TRACKBARS
while True:
# DISPLAY THE DETECTED OBJECTS
for (x, y, w, h) in faces1:
cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.putText(img1, "Face!", (x, y - 5), cv2.FONT_HERSHEY_COMPLEX_SMALL,
1, (0, 255, 0), 2)
roi_color = img1[y:y + h, x:x + w]
print("Image 1 has a face!")
for (x, y, w, h) in faces2:
# ----------- FUNÇÃO PARA LER O ARQUIVO E ADICIONAR OS NOMES E IDs NOS TUPLES
import webbrowser
import cv2 # Biblioteca de captura de vídeo
import math # Biblioteca de rotação de imagens
import time # Biblioteca de contagem de tempo
import os # Biblioteca de Arquivos
now_time = time.time()
face = cv2.CascadeClassifier('Haar/haarcascade_frontalcatface.xml') # Classificador "face frontal" Haar Cascade
glass_cas = cv2.CascadeClassifier('Haar/haarcascade_eye_tree_eyeglasses.xml') # Classificador "olho" Haar Cascade
WHITE = [255, 255, 255]
def FileRead():
Info = open("Names.txt", "r")# Abrir o arquivo de texto em modo read
NAME = [] # A tupla para armazenar nomes
while (True): # Leia todas as linhas no arquivo e armazene-as em duas tuplas
Line = Info.readline()
if Line == '':
break
NAME.append(Line.split(",")[1].rstrip())
print(NAME)
return NAME # Retorne as duas tuplas
def GetSite():
Info = open("Names.txt", "r")# Abrir o arquivo de texto em modo read
SITE = [] # A tupla para armazenar nomes
while (True): # Leia todas as linhas no arquivo e armazene-as em duas tuplas
Line = Info.readline()
import cv2 #importing OpenCV Python Interface
webcam = cv2.VideoCapture(0) #created VideoCapture object and passed the device index(0), since there is only one webcam connected to my computer
faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # Create the haar cascade
while True:
ret, frame = webcam.read() #capturing frame by frame
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #applying operations, converting from BGR to GRAY
faces = faceCascade.detectMultiScale( #detect faces in the image
gray,
scaleFactor=1.1,
minNeighbors=5,
minSize=(30, 30)
#flags = cv2.CV_HAAR_SCALE_IMAGE
)
print("Found {0} faces!".format(len(faces))) #print the number of faces found
for (x, y, w, h) in faces: #draw a rectangle around the faces
cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('frame', frame) #display the resulting frame
if cv2.waitKey(1) == 113: #waiting for the 'q' key to be pressed
break
def run(glassesAddr):
detector = dlib.get_frontal_face_detector()
path = os.path.join(os.path.dirname(__file__), "datas",
"shape_predictor_81_face_landmarks.dat")
predictor = dlib.shape_predictor(path)
# 打開臉部照片
path = os.path.join(os.path.dirname(__file__), "images", "saved.jpg")
faceImg = cv2.imread(path)
faceImg = imutils.resize(faceImg, width=640)
faceImg_gray = cv2.cvtColor(faceImg, cv2.COLOR_RGB2GRAY)
rects = detector(faceImg_gray, 1)
distanceOfEye = []
leftEye = []
rightEye = []
faceWidth = []
bridge = []
# 標記特徵點
for i in range(len(rects)):
landmarks = np.matrix([[p.x, p.y]
for p in predictor(faceImg, rects[i]).parts()])
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
if ((idx + 1) == 39 or (idx + 1) == 44): distanceOfEye.append(pos)
if ((idx + 1) == 37 or (idx + 1) == 40): leftEye.append(pos)
if ((idx + 1) == 43 or (idx + 1) == 46): rightEye.append(pos)
if ((idx + 1) == 1 or (idx + 1) == 17): faceWidth.append(pos)
if ((idx + 1) == 28): bridge.append(pos)
# 利用cv2.circle給每個特徵點畫一個圈，共81個
cv2.circle(faceImg, pos, 7, color=(255, 255, 255))
# 利用cv2.putText輸出1-81
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(faceImg, str(idx + 1), pos, font, 0.8, (0, 255, 0), 1,
cv2.LINE_AA)
path = os.path.join(os.path.dirname(__file__), "images", "saved.jpg")
faceImg = cv2.imread(path)
# 打開眼鏡照片
path = os.path.join(os.path.dirname(__file__), glassesAddr)
glassesImg = cv2.imread(path)
glassesImg = imutils.resize(glassesImg,
width=int(Distance(distanceOfEye) * 2.5))
# 把臉部照片轉正
tempImg = rotate(faceImg, GetAngle(faceWidth))
# 開始合成臉部照片和眼鏡照片 並把照片轉回原本的樣子
src_mask = 255 * np.ones(glassesImg.shape, glassesImg.dtype)
center = (bridge[0][0], bridge[0][1] + 8)
outputImg = cv2.seamlessClone(glassesImg, tempImg, src_mask, center,
cv2.MIXED_CLONE)
outputImg = rotate(outputImg, -GetAngle(faceWidth))
# 開啟人臉辨識器
classfier = cv2.CascadeClassifier(
'D:\\opencv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt2.xml'
)
gray = cv2.cvtColor(faceImg, cv2.COLOR_BGR2GRAY)
faces = classfier.detectMultiScale(gray,
scaleFactor=1.08,
minNeighbors=5,
minSize=(32, 32))
# 抓出人臉的位置
for (x, y, w, h) in faces:
a = x + w / 2
b = y + h / 2
center = (int(a), int(b))
# 只剪取人臉 然後把臉貼回原照片 -> 目的是為了去掉旋轉後留下的黑色區塊
tempImg = outputImg[int(b - ((h / 2) * 1.2)):int(b + ((h / 2) * 1.2)),
int(a - ((w / 2) * 1.2)):int(a + ((w / 2) * 1.2))]
faceImg[int(b - ((h / 2) * 1.2)):int(b + ((h / 2) * 1.2)),
int(a - ((w / 2) * 1.2)):int(a + ((w / 2) * 1.2))] = tempImg
path = os.path.join(os.path.dirname(__file__), "images", "result.jpg")
cv2.imwrite(path, faceImg)
# OpenCV program to detect face in real time
# import libraries of python OpenCV
# where its functionality resides
import cv2
# load the required trained XML classifiers
# https://github.com/Itseez/opencv/blob/master/
# data/haarcascades/haarcascade_frontalface_default.xml
# Trained XML classifiers describes some features of some
# object we want to detect a cascade function is trained
# from a lot of positive(faces) and negative(non-faces)
# images.
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# https://github.com/Itseez/opencv/blob/master
# /data/haarcascades/haarcascade_eye.xml
# Trained XML file for detecting eyes
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')
# capture frames from a camera
cap = cv2.VideoCapture(0)
# loop runs if capturing has been initialized.
while 1:
# reads frames from a camera
ret, img = cap.read()
# convert to gray scale of each frames
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
import cv2
import pickle
faces_cascade = cv2.CascadeClassifier(
'cascades/data/haarcascade_frontalface_alt2.xml')
# cascade for the eyes and the smile
eye_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_eye.xml')
smile_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_smile.xml')
flag = False
flag2 = True
recognizer = cv2.face.LBPHFaceRecognizer_create(
) # adding face recognizer from faces_train
recognizer.read("trainner.yml") # bring in the trained data file
# to label the recognized image we are loading the pickle file we created
labels = {"person_name": 1}
with open("labels.pickle", 'rb') as f:
og_labels = pickle.load(f)
labels = {v: k for k, v in og_labels.items()}
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faces_cascade.detectMultiScale(gray,
scaleFactor=1.5,
minNeighbors=3)
for (x, y, w, h) in faces:
# print(x,y,w,h)
# commented upper line to just see the clear output if it recognize me or not
# 捕获指定摄像头的实时视频流
cap = cv2.VideoCapture(int(sys.argv[1]))
# 人脸识别分类器本地存储路径
cascade_path = "C:/Programs/Anaconda3/Lib/site-packages/cv2/data/haarcascade_frontalface_alt.xml"
# 循环检测识别人脸
while True:
_, frame = cap.read() # 读取一帧视频
# 图像灰化，降低计算复杂度
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 使用人脸识别分类器，读入分类器
cascade = cv2.CascadeClassifier(cascade_path)
# 利用分类器识别出哪个区域为人脸
faceRects = cascade.detectMultiScale(frame_gray, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))
if len(faceRects) > 0:
for faceRect in faceRects:
x, y, w, h = faceRect
# 截取脸部图像提交给模型识别这是谁
image = frame[y - 10: y + h + 10, x - 10: x + w + 10]
faceID = model.face_predict(image)
print(faceID)
# 判断识别出的是谁
who = ''
if faceID == 0:
cursor = conn.execute(cmd)
isRecordExist = 0
# Neu gia tri ID da co thi update nguoc lai insert
for row in cursor:
isRecordExist = 1
if (isRecordExist == 1):
cmd = "UPDATE facedata SET Name='" + str(Name) + "'WHERE ID=" + str(Id)
else:
cmd = "INSERT INTO facedata(ID,Name) Values(" + str(Id) + ",'" + str(
Name) + "')"
conn.execute(cmd)
conn.commit()
conn.close()
faces_cascade = cv.CascadeClassifier('haarcascade_frontalface_default.xml')
cam = cv.VideoCapture(0)
ident = input('Enter your ID: ')
name = input('Enter your name: ')
insertOrUpdate(ident, name)
number_sample = 0
while True:
ret, img = cam.read()
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# phat hien khuon mat
faces = faces_cascade.detectMultiScale(img_gray, 1.3, 5)
for (x, y, w, h) in faces:
number_sample = number_sample + 1
# luu khuon mat dung dia chi file dataset
cv.imwrite(
import cv2
import os
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video width
cam.set(4, 480) # set video height
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# enter unique number id for face
face_id = input('\n enter face id : ')
print("\n Now camera is opening ...")
count = 0
while(True):
ret, img = cam.read()
img=cv2.flip(img,+1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_detector.detectMultiScale(gray, 1.3, 1)
for (x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (255,0,0), 2)
count += 1
# directory for DataSet where will be store all face sample
cv2.imwrite("DataSet/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])
cv2.imshow('dataSetCreater', img)
import cv2
import os
import imutils
personName = 'Vlady'
dataPath = 'C:/Users/Nils Choque/Desktop/Reconocimiento Facial/Capturas'
personPath = dataPath + '/' + personName
if not os.path.exists(personPath):
print('Carpeta creada: ', personPath)
os.makedirs(personPath)
cap = cv2.VideoCapture('Vlady.mp4')
faceClassif = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_default.xml')
count = 0
while True:
ret, frame = cap.read()
if ret == False: break
frame = imutils.resize(frame, width=640)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
auxFrame = frame.copy()
faces = faceClassif.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
rostro = auxFrame[y:y + h, x:x + w]
rostro = cv2.resize(rostro, (150, 150), interpolation=cv2.INTER_CUBIC)
cv2.imwrite(personPath + '/rostro_{}.jpg'.format(count), rostro)
count = count + 1
cv2.imshow('frame', frame)
k = cv2.waitKey(1)
if k == 27 or count >= 300:
