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This article mainly explains "how to realize Python face recognition". The content in the article is simple and clear, and it is easy to learn and understand. now please follow the editor's train of thought to study and learn "how to achieve Python face recognition".

Expansion and corrosion of 0x 00 Python pictures

The expansion and erosion of the image is mainly to find the maximum and small areas in the image. The structural element in the code refers to: there are two images BMagX. If X is the object being processed and B is used to deal with X, then B is called structure element, which is also vividly called brush. Structural elements are usually smaller images.

"" Image expansion and corrosion ""

Import cv2

# read pictures: cv2.imread (path, num)

Img = cv2.imread ("1.jpg", 0)

# construct a structural element of 3x3

Elment = cv2.getStructuringElement (cv2.MORPH_RECT, (3mai 3))

# inflated image cv2.dilate (image, element structure)

Dilate = cv2.dilate (img,elment)

# etching image cv2.erode (image, element structure)

Erode = cv2.erode (img,elment)

# subtract two images to get edges, the first parameter is the expanded image, and the second parameter is the corroded image

Result = cv2.absdiff (dilate,erode)

# threshold type: '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)

# invert color, that is, invert each pixel of a binary image

Result = cv2.bitwise_not (result)

# display image

Cv2.imshow ('origin',img)

# original image

Cv2.imshow ('result',result)

# Edge detection map

Cv2.waitKey (0)

Cv2.destroyAllWindows ()

Face recognition in 0x01 images

Image face recognition is divided into the following steps:

Picture grayscale, geometric transformation, image enhancement, normalization

Feature point location, face alignment, capturing facial features

"" face detection ""

Import cv2

# call the 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= (1010))

# frame processing

For (xmemy wjinh) in faces:

Cv2.rectangle (sample_imag, (xQuery), (xrew), (0pr 255), 2)

# the result is written to the image

Cv2.imwrite ('face.jpg',sample_imag)

Print ("detect success")

# create a new window to display image

Cv2.namedWindow ("Image")

Cv2.imshow ("Image", sample_imag)

Cv2.waitKey (0)

Cv2.destroyAllWindows ()

Show me James: handsome

And when it comes to identifying warriors: (no way, I didn't recognize it. I went to the training library.)

0x02 dynamic face recognition

Dynamic face recognition does not need to stop and wait, as long as you appear in a certain recognition range, whether you are walking or standing, the system will recognize automatically, that is to say, people walk past in a natural form. The camera will capture and collect information, issue corresponding instructions, and carry out dynamic face recognition.

The first thing is to obtain the feature data which is helpful to face classification according to the shape description of facial organs and the characteristics of the interval between them. The feature weight generally includes the Euclidean interval, curvature and viewpoint between feature points.

Import cv2

# 1. Classifier using OpenCV

# 2. Read photos from the camera or locally

# 3. Change the box on the picture

# 4. Show the picture on the new window

# 1. Classifier / feature library using OpenCV

Detector = cv2.CascadeClassifier ('haarcascade_frontalface_alt2.xml')

# 2. Read photos from the camera or locally

Cap = cv2.VideoCapture (0)

While True:

Ret,img = cap.read ()

Faces = detector.detectMultiScale (img,1.3,5)

# 3. Change the box on the picture

For (xmemy wjinh) 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 the picture on the new window

Cap.release ()

Cv2.destroyAllWindows ()

Thank you for your reading, the above is the content of "how to achieve Python face recognition", after the study of this article, I believe you have a deeper understanding of how to achieve Python face recognition, and the specific use needs to be verified in practice. Here is, the editor will push for you more related knowledge points of the article, welcome to follow!

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