人工智能AI:Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战(不定时更新)
# -*- coding: utf-8 -*-
import cv2
#读取待检测的图像
image = cv2.imread('dface3.jpg')
# 获取xml文件,加载人脸检测器
faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# 色彩转换,转换为灰度图像
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
# 调用函数detectMultiScale
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.15,
minNeighbors = 5,
minSize = (5,5)
)
print(faces)
#打印输出测试结果
print("发现{0}个人脸!".format(len(faces)))
#逐个标记人脸
for(x,y,w,h) in faces:
#矩形标注
# cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)
cv2.circle(image,(int((x+x+w)/2),int((y+y+h)/2)),int(w/2),(0,255,0),2)
#显示结果
cv2.imshow("dect",image)
#保存检测结果
cv2.imwrite("re.jpg",image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# -*- coding: utf-8 -*-
import cv2
import numpy as np
images=[]
images.append(cv2.imread("a1.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("a2.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("b1.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("b2.png",cv2.IMREAD_GRAYSCALE))
labels=[0,0,1,1]
#print(labels)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.train(images, np.array(labels))
predict_image=cv2.imread("a3.png",cv2.IMREAD_GRAYSCALE)
label,confidence= recognizer.predict(predict_image)
print("label=",label)
print("confidence=",confidence)
# -*- coding: utf-8 -*-
import cv2
import numpy as np
images=[]
images.append(cv2.imread("e01.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("e02.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("e11.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("e12.png",cv2.IMREAD_GRAYSCALE))
labels=[0,0,1,1]
#print(labels)
recognizer = cv2.face.EigenFaceRecognizer_create()
recognizer.train(images, np.array(labels))
predict_image=cv2.imread("eTest.png",cv2.IMREAD_GRAYSCALE)
label,confidence= recognizer.predict(predict_image)
print("label=",label)
print("confidence=",confidence)
# -*- coding: utf-8 -*-
import cv2
import numpy as np
images=[]
images.append(cv2.imread("f01.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("f02.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("f11.png",cv2.IMREAD_GRAYSCALE))
images.append(cv2.imread("f12.png",cv2.IMREAD_GRAYSCALE))
labels=[0,0,1,1]
#print(labels)
recognizer = cv2.face.FisherFaceRecognizer_create()
recognizer.train(images, np.array(labels))
predict_image=cv2.imread("fTest.png",cv2.IMREAD_GRAYSCALE)
label,confidence= recognizer.predict(predict_image)
print("label=",label)
print("confidence=",confidence)