35. Face detection and recognition of OpenCV - Example 2 (face recognition training local data)
preface
face detection refers to the process of locating faces in images. Face recognition is to further judge people's identity on the basis of face detection. This example is a summary of the previous codes related to face detection and recognition.
1, Collect face samples
put the face image to be recognized in the face folder:
photos placed in each folder are as follows:
import os
import cv2
import imghdr
import numpy as np
from imutils import *
# Face detection and saving
def facedetect(image, output):
# Get file name
name = os.path.basename(image)
# Read in picture
image = cv2.imread(image)
# Change to grayscale
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Cascade classifier, face detection
detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
rects = detector.detectMultiScale(image, scaleFactor=1.1, minNeighbors=3, minSize=(20, 20), flags=cv2.CASCADE_SCALE_IMAGE)
# Loop each face
for (x,y,w,h) in rects:
# The face is intercepted and transformed into a fixed size of 200 * 200
f = cv2.resize(image[y:y+h, x:x+w], (200,200))
# Write to specified path
cv2.imwrite(os.path.join(output, name), f)
# Detect and intercept face
def predict_face(path, output):
# If the folder does not exist, create a folder
if not os.path.exists(output):
os.makedirs(output)
# Cycle the pictures under each character's folder
for files in os.listdir(path):
# Detect whether it is a folder
if os.path.isdir(os.path.join(path, files)):
# Define the output path of the intercepted face
output2 = os.path.join(output, files)
# If the folder does not exist, create a folder
if not os.path.exists(output2):
os.makedirs(output2)
# Full path to people folder
files = os.path.join(path, files)
# Cycle each photo of each person
for file in os.listdir(files):
# Photo full path
file = os.path.join(files, file)
# Detect face and save
facedetect(file, output2)
# Sample path and acquisition path
predict_face('faces', 'predict_faces')
get predict after running the program_ Faces folder, which contains the detection pictures of predicted faces. After opening, you need to delete the pictures with recognition errors (the red box is the recognition error image):
2, Generate Label
the program will be based on predict_ According to the number of people in the faces subfolder, labels starting from 0 are automatically generated.
# Generate label file
def get_label(path):
fh = open("label.txt", 'w')
# Represent face label
label = 0
for root, dirs, files in os.walk(path):
# Cycle through each folder
for subdir in dirs:
# Folder full path
subdir_path = os.path.join(root,subdir)
# Cycle through each photo below each people folder
for file in os.listdir(subdir_path):
# Photo full path
filepath = os.path.join(subdir_path, file)
# Determine whether the file type is a picture type
imgType = imghdr.what(filepath)
if imgType == 'jpeg' or imgType == 'png':
# Save picture path
fh.write(filepath);
fh.write(";")
# label
fh.write(str(label))
fh.write("\n")
# Everyone's label is different. Count from 0
label = label + 1
fh.close()
# Path to collect face Tags
get_label('predict_faces')
generated label Txt will generate corresponding labels according to the classified paths in the folder, as shown below:
3, Train your own data model
when calling the face recognizer, the program will read in the known image and the corresponding label corresponding to the label file for training, and save the training file at the end.
# Save picture data
images = []
# Save label
labels = []
# Open file
fh = open("label.txt")
# Loop each line
for line in fh:
# To; Split string
arr = line.split(";")
# Part 0 is the picture path to read the file
img = cv2.imread(arr[0],0)
# Save picture data
images.append(img)
# Save the corresponding label data
labels.append(int(arr[1]))
# model = cv2.face.EigenFaceRecognizer_create()
# model = cv2.face.FisherFaceRecognizer_create()
model = cv2.face.LBPHFaceRecognizer_create()
# Training model
model.train(np.array(images), np.array(labels))
# Save model
model.save("predict_face_NBAstar.xml")
generated label file and saved model:
4, Face recognition (picture file)
prepare unknown face images to be predicted in advance and put them into the test folder.
after detecting the face, pass the model Predict () method.
# Define character names
name= ['KobeBryant','LeBronJames','TracyMcGrady']
# Load the trained model
model.read('predict_face_NBAstar.xml')
i=0
# Read in the test picture to do the test
for file in os.listdir('test'):
file = os.path.join('test', file)
# Determine file type
imgType = imghdr.what(file)
if imgType == 'jpeg' or imgType == 'png'or 'jpg':
# Read in picture
image = cv2.imread(file)
# Change to grayscale
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# cascade classifier
detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
rects = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3, minSize=(20, 20), flags=cv2.CASCADE_SCALE_IMAGE)
# Loop each face
for (x,y,w,h) in rects:
# Draw a rectangular box
cv2.rectangle(image, (x,y), (x+w,y+h), (255,255,255), 2)
# Face recognition
face = cv2.resize(gray[y:y+h,x:x+w], (200,200))
# Predict people
params = model.predict(face)
# Write the character's name
cv2.putText(image,name[params[0]],(x,y-5),cv2.FONT_HERSHEY_PLAIN,1,(255,255,255),1)
i += 1
cv2.imshow("test"+str(i),image)
cv2.waitKey(0)
cv2.destroyAllWindows()
the recognition results are as follows:
5, Face recognition using video
the model generated by the picture file can be recognized by the camera.
import cv2
capture = cv2.VideoCapture(0)
frame_width = capture.get(cv2.CAP_PROP_FRAME_WIDTH)
frame_height = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
fps = capture.get(cv2.CAP_PROP_FPS)
if capture.isOpened() is False:
print('CAMERA ERROR !')
exit(0)
model = cv2.face.LBPHFFaceRecognizer_create()
name= ['Kobe Bryant','LeBron James','Tracy McGrady']
model.read('predict_face_NBAstar.xml')
detector = cv2.CascadeClassifier("haarcascade_frontalface_alt.xml")
while capture.isOpened():
ret, frame = capture.read()
if ret is True:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=3, minSize=(20, 20), flags=cv2.CASCADE_SCALE_IMAGE)
for (x,y,w,h) in rects:
cv2.rectangle(frame, (x,y), (x+w,y+h), (255,255,255), 2)
face = cv2.resize(gray[y:y+h,x:x+w], (200,200))
params = model.predict(face)
cv2.putText(frame,name[params[0]],(x,y-5),cv2.FONT_HERSHEY_PLAIN,1,(255,255,255),1)
cv2.imshow('FACE', frame)
k = cv2.waitKey(100)
if k == ord('q'):
break
else:
break
capture.release()
cv2.destroyAllWindows()
the recognition results are as follows:
6, Opencv Python resource download
Opencv Python test pictures, Chinese official documents, opencv-4.5.4 source code
summary
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