Today I will share the code about Python image expansion and corrosion, image face recognition and dynamic face recognition~~~ Happy, this rookie was immersed in the cv2 library for a long time and couldn’t help it for a long time~~ I hope you can too Enjoy the little surprises that Python brings. .
0x 00 Python image expansion and corrosion
The expansion and erosion of the image is mainly to find the extremely small and extremely small areas in the image. The structural element in the code means: There are two images B and X. If X is the object to be processed and B is used to process X, then B is called a structure element, which is also called a brush vividly. Structural elements are usually relatively small images. For detailed principles and concepts, see the link at the end of the article~~
"""Picture Expansion and Corrosion"""
import cv2
#Read picture: cv2.imread(path, num)
img = cv2.imread("1.jpg",0)
#Construct a 3*3 structural element
elment = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
#Expansion image cv2.dilate (image, element structure)
dilate = cv2.dilate (img, elment)
#Erosion image cv2.erode (image, element structure)
erode = cv2.erode(img,elment)
#Subtract two images to obtain edges, the first parameter is the image after expansion, and the second parameter is the image after corrosion
result = cv2.absdiff(dilate,erode)
#阈值类型：'TERM_CRITERIA_COUNT', 'TERM_CRITERIA_EPS', 'TERM_CRITERIA_MAX_ITER',
# 'THRESH_BINARY', 'THRESH_BINARY_INV', 'THRESH_MASK', 'THRESH_OTSU',
# 'THRESH_TOZERO_INV', 'THRESH_TRIANGLE', 'THRESH_TRUNC'
retval,result = cv2.threshold(result,50,255,cv2.THRESH_BINARY);
#Reverse color, that is to reverse each pixel of the binary image
result = cv2.bitwise_not(result);
#Display image
cv2.imshow('origin',img)
#原图
cv2.imshow('result',result)
#Edge detection graph
cv2.waitKey(0)
cv2.destroyAllWindows()
0x01 picture face recognition
Image face recognition is divided into the following steps:
- Image grayscale, geometric transformation, image enhancement, normalization
- Feature point positioning, face alignment, and facial feature capture
"""Face Detection"""
import cv2
#Call face detection feature library
face = cv2.CascadeClassifier ('haarcascade_frontalface_alt2.xml')
#Read image file
sample_imag = cv2.imread('1.jpg')
#Face Detection
faces = face.detectMultiScale(sample_imag,scaleFactor=1.1,minNeig hbors=5,minSize=(10,10))
#画架处理
for (x,y,w,h) in faces:
cv2.rectangle(sample_imag,(x,y),(x+w,y+h),(0,255,0),2)
#Result write image
cv2.imwrite('face.jpg',sample_imag)
print("detect success")
#New window display image
cv2.namedWindow("Image")
cv2.imshow("Image",sample_imag)
cv2.waitKey(0)
cv2.destroyAllWindows()
Show me how to identify me Zhan Huang: handsome
There is also the time to identify the warrior: (No way, no way, I didn’t even recognize it~~ I went to study training library)
0x02 dynamic face recognition
Dynamic face recognition does not need to stop and wait, as long as you appear within a certain recognition range, whether you are walking or standing still, the system will automatically recognize it, that is to say, if people walk past in a natural form, the camera will proceed. Capture and collect information, and issue corresponding instructions for dynamic face recognition.
The first step is to obtain feature data that is helpful for face classification based on the shape description of the face organs and the spacing characteristics between them. The feature weight generally includes the Euclidean spacing, curvature, and viewpoint between feature points.
import cv2
# 1. Use OpenCV's classifier
# 2. Read photos from camera or local
# 3. Change the frame on the picture
# 4. Show pictures on a new window
# 1. Use OpenCV's classifier/feature library
detector = cv2.CascadeClassifier ('haarcascade_frontalface_alt2.xml')
# 2. Read photos from camera or local
cap = cv2.VideoCapture(0)
while True:
ret, img = cap.read ()
faces = detector.detectMultiScale(img,1.3,5)
# 3. Change the frame on the picture
for (x,y,w,h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.imshow('frame',img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# 4. Show pictures on a new window
cap.release()
cv2.destroyAllWindows()
0x03 detailed principle reference
Corrosion and expansion of pictures: https://blog.csdn.net/zqx951102/article/details/82997588
https://blog.csdn.net/weixin_39128119/article/details/84172385
Principle of face recognition (with PPT included): https://blog.csdn.net/weixin_42346564/article/details/82500454
Dynamic face recognition: https://blog.csdn.net/dedsaa/article/details/94173130
0x04 summary
I was super happy when I was learning this. It was the kind of long-sighted feeling and the kind of light in my eyes~~Learning Python is really happy. . . I hope you can also have fun from the code. Now, the following is the xml URL of the face recognition library. Download it and try it. You will be super happy to recognize it. .
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