【PYTHON OPENCV】dlib library to calculate the 128D descriptor to be used for face

 """

This script makes used of dlib library to calculate the 128-dimensional (128D) descriptor to be used for face recognition. Face recognition model can be downloaded from: https://github.com/davisking/dlib-models/blob/master/dlib_face_recognition_resnet_model_v1.dat.bz2 """ # Import required packages: import cv2 import dlib import numpy as np # Load shape predictor, face enconder and face detector using dlib library: pose_predictor_5_point = dlib.shape_predictor("shape_predictor_5_face_landmarks.dat") face_encoder = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat") detector = dlib.get_frontal_face_detector() def face_encodings(face_image, number_of_times_to_upsample=1, num_jitters=1): """Returns the 128D descriptor for each face in the image""" # Detect faces: face_locations = detector(face_image, number_of_times_to_upsample) # Detected landmarks: raw_landmarks = [pose_predictor_5_point(face_image, face_location) for face_location in face_locations] # Calculate the face encoding for every detected face using the detected landmarks for each one: return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks] # Load image: image = cv2.imread("jared_1.jpg") # Convert image from BGR (OpenCV format) to RGB (dlib format): rgb = image[:, :, ::-1] # Calculate the encodings for every face of the image: encodings = face_encodings(rgb) # Show the first encoding: print(encodings[0])

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