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57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import os
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import cv2 as cv
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import numpy as np
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# create a list of all the people in the image
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# people = ['Ben Afflek', 'Elton John', 'Jerry Seinfield', 'Madonna', 'Mindy Kaling'] # can manually type it
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people = [] # OR you could loop over every folder in the folder below
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for i in os.listdir(r'C:\Users\user\projects\git\opencv-course\Resources\Faces\train'):
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people.append(i)
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# print(p)
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# create a variable that is equal to the base folder (the folder that contains the five folders of the people)
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DIR = r'C:\Users\user\projects\git\opencv-course\Resources\Faces\train' # change to wherever the faces folder is located
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haar_cascade = cv.CascadeClassifier('haar_face.xml')
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features = [] # the image
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labels = [] # the label that goes with the feature (image)
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def create_train():
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for person in people:
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path = os.path.join(DIR, person)
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label = people.index(person)
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for img in os.listdir(path):
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img_path = os.path.join(path, img)
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img_array = cv.imread(img_path)
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gray = cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)
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faces_rect = haar_cascade.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 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+w]
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features.append(faces_roi)
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labels.append(label)
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create_train()
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print('Training done -----------')
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features = np.array(features, dtype = object)
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labels = np.array(labels)
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# print(f'Length of the features list = {len(features)}')
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# print(f'Length of the labels list = {len(labels)}')
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face_recognizer = cv.face.LBPHFaceRecognizer_create()
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# Train the Recognizer on the features list and labels list
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face_recognizer.train(features, labels)
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face_recognizer.save('face_trained.yml')
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np.save('features.npy', features)
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np.save('labels.npy', labels) |