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identify_face.py
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identify_face.py
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import face_recognition as fr
import sys
import pickle
import matplotlib.pyplot as plt
import numpy as np
import math
import os
# import cv2
from sklearn.neighbors import KNeighborsClassifier
from face_detection import *
def recognize_faces(img_path):
target = dict()
a = 0
for p in os.listdir(r'.\dataset'):
target[p] = a
a += 1
reverse_target = dict([(v,k) for k,v in target.items()])
try:
f = open("./important files/facenet_knn.pickle", 'rb')
model = pickle.load(f)
f.close()
except:
print("Something went wrong!")
os._exit(1)
img = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB)
img = np.uint8(img)
cropped_faces, img = face_detection_2(img)
# print(len(cropped_faces), cropped_faces[0].shape)
# plt.imshow(img)
# plt.show()
ans = dict()
face_vs_name = []
for i,face in enumerate(cropped_faces):
# plt.imshow(face)
# plt.show()
unknown_encoding = fr.face_encodings(face ,num_jitters=3 , model='large')
if len(unknown_encoding):
ans = model.predict(unknown_encoding)
face_vs_name.append(reverse_target[ans[0]])
else :
face_vs_name.append("~ENCODING")
fig, axes = plt.subplots( math.ceil(len(face_vs_name)/2) , 2 )
for i,face in enumerate(cropped_faces):
axes.ravel()[i].imshow(face)
axes.ravel()[i].set_title(face_vs_name[i])
axes.ravel()[i].axis('off')
fig.tight_layout()
plt.show()
return face_vs_name
if __name__ == '__main__':
recognize_faces(r"./test images/1.jpg")
recognize_faces(r"./test images/2.jpg")
recognize_faces(r"./test images/3.jpg")
recognize_faces(r"./test images/4.jpg")
recognize_faces(r"./test images/5.jpg")
recognize_faces(r"./test images/6.jpg")
recognize_faces(r"./test images/7.jpg")