-
Notifications
You must be signed in to change notification settings - Fork 0
/
faces.py
55 lines (46 loc) · 1.54 KB
/
faces.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
import numpy as np
import cv2
import pickle
face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("./trainner.yml")
labels = {"person_name" : 1}
with open("labels.pickle", 'rb') as f:
og_labels = pickle.load(f)
labels = {v:k for k,v in og_labels.items()}
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
for (x, y, w, h) in faces:
# print(x,y,w,h)
roi_gray = gray[y:y+h, x:x+w] #(ycord_start, ycord_end)
roi_color = frame[y:y+h, x:x+w]
#recognize? deep learned model predict keras tensorflow pytorch scikit learn
id_, conf = recognizer.predict(roi_gray)
# if conf>=45: # and conf <= 85:
if conf>=4 and conf <= 85:
# print(id_)
print(labels[id_])
font = cv2.FONT_HERSHEY_SIMPLEX
name = labels[id_]
color = (255, 255, 255)
stroke = 2
cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)
# img_item = "my-image.png"
# cv2.imwrite(img_item, roi_color) #Save face to file
# Draw rectangle around face
color = (255, 0, 0) #BGR 0-255
stroke = 2
end_cord_x = x + w
end_cord_y = y + h
cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)
# Display the resulting frame
cv2.imshow('frame',frame)
if cv2.waitKey(20) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()