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face_recognition.py
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face_recognition.py
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# -*- coding: utf-8 -*-
"""face_recognition.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1rf0USLPFafybXqveIhmrBIor4uBOGNU-
"""
import face_recognition
import pandas as pd
import os
import numpy as np
test_dir = './test_dataset'
test_data = os.listdir(test_dir)
# test_data.sort()
# train_data[:10]
train_dir = './train_dataset'
train_data = os.listdir(train_dir)
train_data.sort()
encodings_list = pd.DataFrame()
encodings_list['Person'] = []
encodings_list['encoding'] = []
"""Trained for classified faces
"""
for i in train_data[1:]:
image = face_recognition.load_image_file(os.path.join(train_dir ,i))
face_encoding_list = face_recognition.face_encodings(image, model='cnn')
encodings_list.loc[len(encodings_list)] = [i.split('.')[0], face_encoding_list[0]]
result = pd.DataFrame()
result['S.No'] = []
for i in encodings_list['Person']:
result[i] = []
result[i] = result[i].astype(int)
#result
for i in test_data:
image = face_recognition.load_image_file(os.path.join(test_dir ,i))
face_encoding_list = face_recognition.face_encodings(image, model='cnn')
if len(face_encoding_list) > 0:
dict__ = []
for j in face_encoding_list:
matches = face_recognition.compare_faces(list(encodings_list['encoding']), j)
dict__.append([int(item) for item in matches])
dict__ = np.asarray(dict__)
for k in dict__[1:]:
dict__[0] += k
final__dict = [i]
for ele in dict__[0]:
final__dict.append(int(ele))
result.loc[len(result)] = final__dict
else:
result.loc[len(result)] = [i , 0, 0, 0, 0, 0, 0]
result
result.to_csv('result.csv')