Celebrity doppelganger inference

celebdp.py

import os
import cv2
import numpy as np
import dlib

# Load the descriptors
descriptors = np.load(r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger\descriptors.npy",
                      allow_pickle=True)

# Rearrange data, each 128 items corresponds to one image descriptor
faceDescriptorsEnrolled = np.reshape(descriptors, (13110, 128))

# Load test image
im = cv2.imread(r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger\TestImages\Amanda.png")

# Path to landmarks and face recognition model files
PREDICTOR_PATH = r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger\DlibFacialLandmarkDetector" \
                 r"\shape_predictor_68_face_landmarks.dat "

FACE_RECOGNITION_MODEL_PATH = r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger" \
                              r"\DlibFaceRecognitionModel\dlib_face_recognition_resnet_model_v1.dat "

# Initialize face detector, facial landmarks detector and face recognizer
faceDetector = dlib.get_frontal_face_detector()
shapePredictor = dlib.shape_predictor(PREDICTOR_PATH)
faceRecognizer = dlib.face_recognition_model_v1(FACE_RECOGNITION_MODEL_PATH)

# detect faces in image
faces = faceDetector(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))

# Now process each face we found
for face in faces:
    # Find facial landmarks for each detected face
    shape = shapePredictor(cv2.cvtColor(im, cv2.COLOR_BGR2RGB), face)

    # find coordinates of face rectangle
    x1 = face.left()
    y1 = face.top()
    x2 = face.right()
    y2 = face.bottom()

    # Compute face descriptor using neural network defined in Dlib using facial landmark shape
    faceDescriptor = faceRecognizer.compute_face_descriptor(im, shape)

    # Convert face descriptor from Dlib's format to list, then a NumPy array
    faceDescriptorList = [m for m in faceDescriptor]
    faceDescriptorNdarray = np.asarray(faceDescriptorList, dtype=np.float64)
    faceDescriptorNdarray = faceDescriptorNdarray[np.newaxis, :]

distances = np.linalg.norm(faceDescriptorsEnrolled - faceDescriptorNdarray, axis=1)

# Calculate minimum distance and index of this face
argmin = np.argmin(distances)  # index
minDistance = distances[argmin]  # minimum distance
print("The distance between the input and the celebrity images is: {}".format(minDistance))

# Opening celebrity names file
txtFile = open(r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger\ListOfCelebNames\celebNames.txt", "r")
celebNames = []
for line in txtFile:
    stripped_line = line.strip()
    line_list = stripped_line.split()
    celebNames.append(line_list)
txtFile.close()

# Your celeb doppelganger
celebName = celebNames[argmin][0]
print("Your celebrity doppelganger is {}".format(celebName))

celebrityImFilePath = os.path.join(r"C:\Users\felipe.cunha\Documents\venv\cv2\CelebrityDoppelganger\vgg_face_dataset"
                                   r"\files",
                                   str(celebName + ".txt"))

cv2.imshow("Input image", im)
cv2.waitKey(0)