-
Notifications
You must be signed in to change notification settings - Fork 0
/
dataset_creator.py
40 lines (32 loc) · 1.12 KB
/
dataset_creator.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
import cv2
import numpy as np
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cap = cv2.VideoCapture(0)
id = raw_input('Enter user id ')
sampleNum = 0
while True:
# cap.read will return one status variable and the captured image
ret, img = cap.read()
# classifier will work on gray scale images
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# This will detect all the faces in the current frame and returns the co-ordinates of the faces
# gray = input image
# 1.3,5 some parameters for getting accurate values
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# we will be having multiple faces so we need get each and every faces and draw rectangle
for (x,y,w,h) in faces:
sampleNum = sampleNum + 1
cv2.imwrite('dataSet/User.'+str(id)+'.'+str(sampleNum)+'.jpg',gray[y:y+h,x:x+w])
# input = colored image
# x,y - first point : x+w,y+h - end point
# 2 - thickness
cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)
cv2.waitKey(100)
# to show the image
cv2.imshow('img',img)
cv2.waitKey(1)
if(sampleNum > 20):
break
# release camera
cap.release()
cv2.destroyAllWindows()