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30 lines
1.1 KiB
Python
30 lines
1.1 KiB
Python
import numpy as np
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import cv2 as cv
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haar_cascade = cv.CascadeClassifier('haar_face.xml')
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people = ['Ben Afflek', 'Elton John', 'Jerry Seinfield', 'Madonna', 'Mindy Kaling'] # can manually type it
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# features = np.load('features.npy', allow_pickle = True)
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# labels = np.load('labels.npy')
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face_recognizer = cv.face.LBPHFaceRecognizer_create()
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face_recognizer.read('face_trained.yml') # trained data
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img = cv.imread(r'C:\Users\user\projects\git\opencv-course\Resources\Faces\val\ben_afflek\5.jpg') # validation
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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cv.imshow('Person', gray)
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# Detect the face in the image
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faces_rect = haar_cascade.detectMultiScale(gray, 1.1, 4)
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for (x,y,w,h) in faces_rect:
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faces_roi = gray[y:y+h, x:x+h]
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label, confidence = face_recognizer.predict(faces_roi)
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print(f'Label = {people[label]} with a confidence of {confidence}')
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cv.putText(img, str(people[label]), (20,20), cv.FONT_HERSHEY_COMPLEX, 1.0, (0, 255, 0), thickness = 2)
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cv.rectangle (img, (x,y), (x+w,y+h), (0,255,0), thickness = 2)
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cv.imshow('Detected Face', img)
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cv.waitKey(0) |