Measure Distance Between Two Images Blog

calculate-distance-between-faces.py

# importing required libraries
import cv2
import  face_recognition
import os
import numpy as np

# initialize the empty list of name label
known_face_names = []

# initialize the empty list for storing the encoding of each face
known_face_encodings = []

# initialize the folder path consisting of training dataset
training_dataset_path = 'Training data/'

# read all the image file with extension .JPG present under the training_dataset_path
training_dataset = filter(lambda x: x.endswith('.jpg'), os.listdir(training_dataset_path))

# Sort in alphabetical order
training_dataset = sorted(training_dataset)

# define function for generating known face encodings
def generate_known_face_encodings():

  # Get full paths to training images
  paths_to_training_images = [training_dataset_path + x for x in training_dataset]

  for index,image_file in enumerate(paths_to_training_images):

    # Get list of names of people by eliminating the .JPG extension from image filenames
    known_face_names.append(image_file[image_file.rindex('/')+1:][:-4])

    # read image file
    image = cv2.imread(image_file)
    
    # detect face from image and get the face encoding
    face_encoding = face_recognition.face_encodings(image)[0]

    # append this into known_face_encodings
    known_face_encodings.append(face_encoding)
    
print("Training Started...")
generate_known_face_encodings()
print("Training Completed...")

# load the image to be detected
image_to_recognize = cv2.imread('modi-obama-1.jpg')

# locate all faces from the image
all_face_locations = face_recognition.face_locations(image_to_recognize, number_of_times_to_upsample = 2 ,model="cnn")

# generate encoding for all detected faces
all_face_encodings = face_recognition.face_encodings(image_to_recognize,all_face_locations)

for face_location, face_encoding in zip(all_face_locations,all_face_encodings):

  # split the tuple
  top_pos, right_pos, bottom_pos, left_pos = face_location

  # find the distance of current encoding with all known encodings
  face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

  for i,face_distance in enumerate(face_distances):
    print("\nThe calculated distance is {:.2} from sample image {}".format(face_distance, known_face_names[i]))

    # print matching percentage
    print("The matching percentage is {}% against the sample image {}".format(round((1-float(face_distance))*100, 2), known_face_names[i]))

calculate-distance.py

# load the image to be detected
image_to_recognize = cv2.imread('modi-obama-1.jpg')

# locate all faces from the image
all_face_locations = face_recognition.face_locations(image_to_recognize, number_of_times_to_upsample = 2 ,model="cnn")

# generate encoding for all detected faces
all_face_encodings = face_recognition.face_encodings(image_to_recognize,all_face_locations)

for face_location, face_encoding in zip(all_face_locations,all_face_encodings):

  # split the tuple
  top_pos, right_pos, bottom_pos, left_pos = face_location

  # find the distance of current encoding with all known encodings
  face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)

  for i,face_distance in enumerate(face_distances):
    print("\nThe calculated distance is {:.2} from sample image {}".format(face_distance, known_face_names[i]))

    # print matching percentage
    print("The matching percentage is {}% against the sample image {}".format(round((1-float(face_distance))*100, 2), known_face_names[i]))

import-libraries.py

# importing required libraries
import cv2
import  face_recognition
import os
import numpy as np

train-model-on-labeled-images.py

# initialize the empty list of name label
known_face_names = []

# initialize the empty list for storing the encoding of each face
known_face_encodings = []

# initialize the folder path consisting of training dataset
training_dataset_path = 'Training data/'

# read all the image file with extension .JPG present under the training_dataset_path
training_dataset = filter(lambda x: x.endswith('.jpg'), os.listdir(training_dataset_path))

# Sort in alphabetical order
training_dataset = sorted(training_dataset)

# define function for generating known face encodings
def generate_known_face_encodings():

  # Get full paths to training images
  paths_to_training_images = [training_dataset_path + x for x in training_dataset]

  for index,image_file in enumerate(paths_to_training_images):

    # Get list of names of people by eliminating the .JPG extension from image filenames
    known_face_names.append(image_file[image_file.rindex('/')+1:][:-4])

    # read image file
    image = cv2.imread(image_file)
    
    # detect face from image and get the face encoding
    face_encoding = face_recognition.face_encodings(image)[0]

    # append this into known_face_encodings
    known_face_encodings.append(face_encoding)
    
print("Training Started...")
generate_known_face_encodings()
print("Training Completed...")