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recognition.py
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recognition.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import face_recognition
import os
import cv2
import numpy as np
# In[2]:
known="known"
tolerance=0.3
frame=3
font=2
model="large"
video=cv2.VideoCapture(0)
print("loading faces")
known_faces=[]
known_names=[]
for name in os.listdir(known):
for filename in os.listdir(f"{known}/{name}"):
image=face_recognition.load_image_file(f"{known}/{name}/{filename}")
encoding=face_recognition.face_encodings(image)
if not len(encoding):
print(filename, "can't be encoded")
continue
known_faces.append(encoding[0])
known_names.append(name)
i=0
while True:
ret,img=video.read()
locations=face_recognition.face_locations(img,model=model)
encodings=face_recognition.face_encodings(img,locations)
for face_encoding , face_location in zip (encodings,locations):
results=face_recognition.compare_faces(known_faces,face_encoding,tolerance)
match=None
if True in results:
match=known_names[results.index(True)]
print(f"match found:::{match}")
top_left=(face_location[3],face_location[0])
bottom_right=(face_location[1],face_location[2])
color=[0,255,0]
cv2.rectangle(img,top_left,bottom_right,color,frame)
top_left=(face_location[3],face_location[0])
bottom_right=(face_location[1],face_location[2])
color=[0,255,0]
cv2.rectangle(img,top_left,bottom_right,cv2.FILLED)
cv2.putText(img,match,(face_location[3]*10,face_location[2]*15),cv2.FONT_HERSHEY_SIMPLEX,0.5,(200,200,0))
i+=1
cv2.imwrite(f"known/{match}/new{i}.jpg",img)
#image=face_recognition.load_image_file(f"{known}/{name}/{filename}")
encoding=face_recognition.face_encodings(img)
if not len(encoding):
print(filename, "can't be encoded")
continue
known_faces.append(encoding[0])
known_names.append(match)
cv2.imshow("recognition",img)
cv2.waitKey(2)
if cv2.waitKey(1) & 0xFF==ord("q"):
break
# In[ ]: