OpenCV-Face-Recognition | Real-time face recognition project with OpenCV and Python | Computer Vision library

from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
import argparse
import pickle

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-e", "--embeddings",default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
    help="path to serialized db of facial embeddings")
ap.add_argument("-r", "--recognizer", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
    help="path to output model trained to recognize faces")
ap.add_argument("-l", "--le", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output",
    help="path to output label encoder")
args = vars(ap.parse_args())
# load the face embeddings
print("[INFO] loading face embeddings...")
data = pickle.loads(open(args["embeddings"], "rb").read())

# encode the labels



   print("[INFO] encoding labels...")
    le = LabelEncoder()
    labels = le.fit_transform(data["names"])

# train the model used to accept the 128-d embeddings of the face and
# then produce the actual face recognition


 print("[INFO] training model...")
    recognizer = SVC(C=1.0, kernel="linear", probability=True)
    recognizer.fit(data["embeddings"], labels)

# write the actual face recognition model to disk
f = open(args["recognizer"], "wb")
f.write(pickle.dumps(recognizer))
f.close()

#write the label encoder to disk
f = open(args["le"], "wb")
f.write(pickle.dumps(le))
f.close()