Related
Hey, I want to share a global NumpyArray(cameraImg) between my main
programm and the process which runs parallel. But it doesn't work. It says
"TypeError: only size-1 arrays can be converted to Python scalars"
The main Programm displa_camera() runs in a While Loop, and gets the Image of the Webcam. The img is saved in global cameraImg and used in recognize_faces() to identifiy the face depcit in the Image. It saves the name, which is added to the Image, above the Persons head. Hope you can help me, thx
import time, os, cv2 as cv2, face_recognition
from multiprocessing import Process, Array, Value
global name
name=""
window_name = 'facerecognition'
def display_camera():
cap = cv2.VideoCapture(0)
cap.set(3, 640) # set Width
cap.set(4, 480) # set Height
cascade = cv2.CascadeClassifier('cascade.xml')
global cameraImg, name
while True:
ret, img = cap.read()
cameraImg=Array('d',img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = cascade.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), (36, 255, 12), 1)
cv2.putText(img, name, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
cv2.imshow('video', img)
k = cv2.waitKey(30) & 0xff
if k == 27: # press 'ESC' to quit
break
cap.release()
cv2.destroyAllWindows()
def recognize_Faces():
global cameraImg, name
folder_dir = "../images/"
image_List=[]
for images in os.listdir(folder_dir):
# check if the image ends with png or jpg or jpeg
if (images.endswith(".png") or images.endswith(".jpg") \
or images.endswith(".jpeg")):
# display
image_List.append(images)
while True:
try:
newFaces=False
for path in image_List :
rgb_img = cv2.cvtColor(cameraImg, cv2.COLOR_BGR2RGB)
img_encoding = face_recognition.face_encodings(rgb_img)
img2 = cv2.imread("../images/"+path)
rgb_img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
img_encoding2 = face_recognition.face_encodings(rgb_img2)[0]
result = face_recognition.compare_faces(img_encoding, img_encoding2)
print("Result: ", result)
if(result[0]):
newFaces=True
name= os.path.basename("../images/"+path).split('.')[0]
except Exception as e:
print(e)
if(newFaces==False):
name=""
k = cv2.waitKey(30) & 0xff
if k == 27: # press 'ESC' to quit
break
time.sleep(3)
if __name__ == '__main__':
secondaryThread = Process(target=recognize_Faces)
secondaryThread.start()
display_camera()
I am trying to detect faces in opencv,but I'm running in some issues:
1-When I put the following syntax:gray = cv2.cvtColor(frames,cv2.COLOR_BGR2GRAY),it shows up in red and does not work.
2-The haarcascade also shows up in red:faces = face_cascade.detectMultiScale( gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30),flags = cv2.CV_HAAR_SCALE_IMAGE).I tried to do like in some tutorials but it does not work.Would you have any idea?
Here is my code:
#importing packages
import cv2
import numpy as np
#variables
webcam = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
w_size = (700,500)
#turn the webcam on
while (True):
#reading camera and turing into frames
ret,frames = webcam.read()
frames = cv2.resize(frames,w_size)
#detection
gray = cv2.cvtColor(frames,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale( gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30),flags = cv2.CV_HAAR_SCALE_IMAGE)
for (x, y, w, h) in faces:
cv2.rectangle(frames, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('face_recognition',frames)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#running script
webcam.release()
cv2.destroyAllWindows()
i simply:
added cv2.data.haarcascades as prefix of the type CascadeClassifier
deleted cv2.CV_HAAR_SCALE_IMAGE (parameter not used anymore)
Code:
import cv2
import numpy as np
import cv2.data
#variables
webcam = cv2.VideoCapture(0)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades+'haarcascade_frontalface_default.xml')
w_size = (700,500)
#turn the webcam on
while (True):
#reading camera and turing into frames
ret,frames = webcam.read()
frames = cv2.resize(frames,w_size)
#detection
gray = cv2.cvtColor(frames,cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale( gray,scaleFactor=1.1,minNeighbors=5,minSize=(30, 30))
for (x, y, w, h) in faces:
cv2.rectangle(frames, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.imshow('face_recognition',frames)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
#running script
webcam.release()
cv2.destroyAllWindows()
I am developing a facial recognition system and for that I have selected LBPH algorithm for doing the task. I have collected the sample images of user and trained it. The problem is while recognizing the face, the predict() of LBPHRecognizer always return same value for label but different value for confidence. Even if the face is unknown it returns 1.
Technologies I have been using : Python 3.7.4, OpenCV 4.1.2
Code to collect sample image
import cv2
import numpy as np
import os
import requests
import time
from PIL import Image
class CollectFaceWebCam():
def __init__(self, sid):
self.studentId = sid
#capture webcam
self.LiveWebCamera = cv2.VideoCapture(0)
#pre-trained dataset (haar-cascade classifier)
self.faceDataSet = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
#sample image capture counter
self.imgCounter = 0
self.directoryName = 'sampleImgFolder'
#check path
if not os.path.exists(self.directoryName):
os.makedirs(self.directoryName)
if not os.path.exists(self.directoryName + '/' + self.studentId):
os.makedirs(self.directoryName + '/' + self.studentId)
def gen(self):
while True:
condition, frame = self.LiveWebCamera.read() #capture frame
img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#conversion to gray scale
#face detection
faces = self.faceDataSet.detectMultiScale( # Detect face sizes
img,
scaleFactor=1.3,
minNeighbors=5,
minSize=(100, 100),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
end_crd_x = x + w # face start coordinates
end_crd_y = y + h #face end coordinate
#draw rectangle
##parms image, start plot, end plot, thickness, color
cv2.rectangle(frame, (x, y), (end_crd_x, end_crd_y), (0, 255, 33), 1)
#accepts multiple face
if len(faces) >= 0:
#face must be single in frame
if len(faces) == 1:
detectedImg = img[y:y + h, x:x + w]
#checking blurness of image
blurValue = cv2.Laplacian(detectedImg, cv2.CV_64F).var()
#ignoring the blury images
if not blurValue <= 60:
newImg = img[y:y + h, x:x + w] #new img
#saving the detected faces
filename = '{}\{}\{}\{}_{}'.format(os.getcwd(), self.directoryName, self.studentId, self.studentId, self.imgCounter) + '.jpg'
cv2.imwrite(filename, newImg)
self.imgCounter += 1
else:
cv2.putText(frame,"Multiple Face not allowed", (50,150), cv2.FONT_HERSHEY_SIMPLEX, 1, (237, 20, 5), thickness=2)
cv2.putText(frame,"Collecting Sample", (50,100), cv2.FONT_HERSHEY_SIMPLEX, 1, (250, 250, 250), thickness=3)
cv2.putText(frame,"Image Count " + str(self.imgCounter), (50,200), cv2.FONT_HERSHEY_SIMPLEX, 2, (237, 20, 5), thickness=2)
cv2.imshow('Collecting Sample', frame) # display frames
k = cv2.waitKey(100) & 0xff # capture when user press 'esc'
if k == 27:
break
elif self.imgCounter == 110:
break
self.LiveWebCamera.release() #stop video capture
cv2.destroyAllWindows() #close all windows
class CleanSampleImages():
def __init__(self):
self.faceDataset = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
self.eyeDataset = cv2.CascadeClassifier('resources/haarcascade_eye.xml')
self.targetFolder = 'sampleImgFolder'
def checkFace(self):
os.chdir(self.targetFolder)
for directory in os.listdir():
os.chdir(directory)
for files in os.listdir():
imagePath = '{}/{}'.format(os.getcwd(), files)
imagePil = Image.open(imagePath).convert('L')
imageNumpy = np.array(imagePil) #conversion of normal image to numpy array
#detect face
faces = self.faceDataset.detectMultiScale(imageNumpy)
#deleting image file if face is not found
if not len(faces) == 1:
os.remove(files)
break
for (x, y, w, h) in faces:
#detect eye from selected
eyes = self.eyeDataset.detectMultiScale(imageNumpy)
if not len(eyes) > 0 and len(eyes) <=2:
#deleting image file if eye count of image is less than 0 or more than 2
os.remove(files)
os.chdir('../')
os.chdir('../')
#id must be in X-X-ID eg. a-b-342
t = CollectFaceWebCam('sa-t-1')
t.gen()
clean = CleanSampleImages
c.checkFace()
Above code consist of two class CollectFaceWebCam and CleanSampleImages. CollectFaceWebCam works for collecting the sample images. and CleanSampleImages works for cleaning the collected data. if the image does not consist of face the file is deleted.
Code to Train images
import os
import cv2
import numpy as np
from PIL import Image
class Trainer():
def __init__(self):
self.recognizer = cv2.face.LBPHFaceRecognizer_create()
self.targetImagesDirectory="sampleImgFolder"
self.dataset = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
def getImgwithId(self):
sampleImage, sampleImageId = [], []
filename = '{}\\{}'.format(os.getcwd(), self.targetImagesDirectory)
if os.path.exists(filename):
os.chdir(filename)
print('current path is ' + os.getcwd())
for f in os.listdir():
imgPath = os.path.join(filename, f)
os.chdir(imgPath)
for file in os.listdir():
#reteving id from filename (filename format : ta-s-ID_Filename.jpg)
id = file.split('_')
id = id[0].split('-')
id = id[2]
imageFilePath = imgPath + '\\' + file
imagePil = Image.open(imageFilePath).convert('L')
#conversion to numpy array
imageNp = np.array(imagePil, 'uint8')
faces = self.dataset.detectMultiScale(imageNp)
for (x, y, w, h) in faces:
sampleImage.append(imageNp)
sampleImageId.append(id)
os.chdir('../')
os.chdir('../')
return sampleImage, np.array(sampleImageId, dtype = int)
def train(self, data, label):
try:
self.recognizer.train(data, label)
self.msg = 'Training Successful'
print('writting')
self.recognizer.write('date.yml')
print('writing finished')
except:
self.msg = 'Core: Training Error'
print('except')
tr = Trainer()
sampleFaces, sampleFaceId = (tr.getImgwithId())
tr.train(sampleFaces, sampleFaceId)
Code to recognize face
import os
import cv2
import numpy as np
from PIL import Image
class Recognizer():
def __init__(self):
self.recognizer = cv2.face.LBPHFaceRecognizer_create()
self.recognizer.read('date.yml')
self.targetImagesDirectory="sampleImgFolder"
self.dataset = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
self.captureVideo = cv2.VideoCapture(0)
self.font = cv2.FONT_HERSHEY_SIMPLEX = 2 # Font
self.predictedUser = []
def gen(self):
while True:
condition, frame = self.captureVideo.read() #capture frame
img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)#conversion to gray scale
#face detection
faces = self.dataset.detectMultiScale( # Detect face sizes
img,
scaleFactor=1.3,
minNeighbors=5,
minSize=(100, 100),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
end_crd_x = x + w # face start coordinates
end_crd_y = y + h #face end coordinate
#draw rectangle
##parms image, start plot, end plot, thickness, color
cv2.rectangle(frame, (x, y), (end_crd_x, end_crd_y), (0, 255, 33), 1)
predictUser, confidence = self.recognizer.predict(img[y:y+h,x:x+w])
self.predictedUser.append(predictUser)
cv2.imshow('test', frame)
k = cv2.waitKey(100) & 0xff # capture when user press 'esc'
if k == 27:
break
self.captureVideo.release()
cv2.destroyAllWindows()
r = Recognizer()
r.gen()
print(r.predictedUser)
"predictUser, confidence = self.recognizer.predict(img[y:y+h,x:x+w])" line of code in Recognizer class always return same value for label. The output of code to recognize face is attached below:
I would love to know why and where the problem is, as My skills and research could not lead me to identification of problem.
It might because of the data-collection process. I see you using a cascade classifier multiple times, you could limit it. While checking for a face on webcam, you could use classifiers at the time and store only the extracted/cropped faces. Also during prediction, use confidence as a threshold to limit the false prediction.
I'm trying to make a simple haar cascade program to detect a face.
faceCascade = cv2.CascadeClassifier('D:\\Python\\Python37\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalcatface.xml')
body_cascade = cv2.CascadeClassifier('haarcascade_upperbody.xml')
video_capture = cv2.VideoCapture(0)
img_counter = 0
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.5,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('FaceDetection', frame)
k = input()
# ESC Pressed
if k % 256 == 27:
break
video_capture.release()
cv2.destroyAllWindows()
But every time I launch it, my webcam window just froze and crash :(
My PC is powerful enough for sure, why could it happen?
I have made few changes to your code. For human face use haarcascade_frontalface_default.xml and for the cat face use haarcascade_frontalcatface.xml. Try the code below and it will work like a charm :)
import cv2
#faceCascade = cv2.CascadeClassifier('D:\\Python\\Python37\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalcatface.xml')
faceCascade = cv2.CascadeClassifier('D:\\Python\\Python37\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_default.xml')
body_cascade = cv2.CascadeClassifier('haarcascade_upperbody.xml')
video_capture = cv2.VideoCapture(0)
img_counter = 0
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.5,
minNeighbors=5,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE
)
for (x, y, w, h) in faces:
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('FaceDetection', frame)
#k = input()
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
# ESC Pressed
# if k % 256 == 27:
# break
video_capture.release()
cv2.destroyAllWindows()
Hi everyone I'm working on OpenCV(Python)on a face recognition program. I have two files, one which captures a new user's face and stores it by the name supplied by user. The second file recognizes the user using webcam. Now, my concern is that the user is getting recognised correctly but the name is only shown and not saved. How could I save the name of the recognised person so that it can be transfered or done some operations upon?
#__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
fn_name = "aditya"
path = os.path.join(fn_dir, fn_name)
if not os.path.isdir(path):
os.mkdir(path)
(im_width, im_height) = (112, 92)
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
# The program loops until it has 20 images of the face.
count = 0
while count < 20:
(rval, im) = webcam.read()
im = cv2.flip(im, 1, 0)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
faces = haar_cascade.detectMultiScale(mini)
faces = sorted(faces, key=lambda x: x[3])
if faces:
face_i = faces[0]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
pin=sorted([int(n[:n.find('.')]) for n in os.listdir(path)
if n[0]!='.' ]+[0])[-1] + 1
cv2.imwrite('%s/%s.png' % (path, pin), face_resize)
cv2.rectangle(im, (x, y), (x + w, y + h), (0, 255, 0), 3)
cv2.putText(im, fn_name, (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN,
1,(0, 255, 0))
count += 1
cv2.imshow('OpenCV', im)
key = cv2.waitKey(10)
if key == 27:
break
Code for face recognition from the dataset
__author__ = 'ADMIN'
import cv2, sys, numpy, os
size = 4
fn_haar = 'haarcascade_frontalface_default.xml'
fn_dir = 'att_faces'
# Part 1: Create fisherRecognizer
print('Training...')
# Create a list of images and a list of corresponding names
(images, lables, names, id) = ([], [], {}, 0)
for (subdirs, dirs, files) in os.walk(fn_dir):
for subdir in dirs:
names[id] = subdir
subjectpath = os.path.join(fn_dir, subdir)
for filename in os.listdir(subjectpath):
path = subjectpath + '/' + filename
lable = id
images.append(cv2.imread(path, 0))
lables.append(int(lable))
id += 1
(im_width, im_height) = (112, 92)
# Create a Numpy array from the two lists above
(images, lables) = [numpy.array(lis) for lis in [images, lables]]
# OpenCV trains a model from the images
# NOTE FOR OpenCV2: remove '.face'
model = cv2.createFisherFaceRecognizer()
model.train(images, lables)
# Part 2: Use fisherRecognizer on camera stream
haar_cascade = cv2.CascadeClassifier(fn_haar)
webcam = cv2.VideoCapture(0)
while True:
(rval, frame) = webcam.read()
frame=cv2.flip(frame,1,0)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mini = cv2.resize(gray, (gray.shape[1] / size, gray.shape[0] / size))
faces = haar_cascade.detectMultiScale(mini)
for i in range(len(faces)):
face_i = faces[i]
(x, y, w, h) = [v * size for v in face_i]
face = gray[y:y + h, x:x + w]
face_resize = cv2.resize(face, (im_width, im_height))
# Try to recognize the face
prediction = model.predict(face_resize)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
# Write the name of recognized face
# [1]
cv2.putText(frame,
'%s - %.0f' % (names[prediction[0]],prediction[1]),
(x-10, y-10), cv2.FONT_HERSHEY_PLAIN,1,(0, 255, 0))
cv2.imshow('OpenCV', frame)
key = cv2.waitKey(10)
if key == 27:
break
This is my code. where i am not using any sql-server.
I am encoding images from the folder and it will show the recognized face with the name of the image saved. if the image is saved as .. abc.jpg. then it will detect the face during live streaming and show abc.jpg
here is my code :
from PIL import Image
import face_recognition
import cv2
import os
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
known_face_encodings=[]
known_face_names = []
user_appeared = []
root = "/home/erp-next/open cv/dataset/"
for filename in os.listdir(root):
if filename.endswith('.jpg' or '.png'):
try:
print(filename)
path = os.path.join(root, filename)
filter_image = face_recognition.load_image_file(path)
filter_face_encoding = face_recognition.face_encodings(filter_image)
known_face_encodings.append(filter_face_encoding[0])
known_face_names.append(filename)
except:
print("An exception occurred : " + filename )
#print(known_face_encodings)
print(known_face_names)
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
# process_this_frame = True
def face():
while True:
process_this_frame = True
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
k = cv2.waitKey(1)
if k%256 == 27:
# ESC pressed
print("Escape hit, closing...")
break
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
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 is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
print(name)
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):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
face()
i am also using face_recognition library to encode and detect face.
Thanks.