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face_datasets.py
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face_datasets.py
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####################################################
# Modified by Sacha Arbonel #
# Original code: http://thecodacus.com/ #
# All right reserved to the respective owner #
####################################################
from urllib.request import urlopen
from ssl import SSLContext,PROTOCOL_TLSv1
import numpy as np
# Import OpenCV2 for image processing
import cv2
# Detect object in video stream using Haarcascade Frontal Face
face_detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# For each person, one face id
face_id = 1
# Initialize sample face image
count = 0
# Ip of the IP webcam server (on phone). The phone and your computer must be in the same LAN (connected to the same WiFi)
url = 'https://192.168.1.93:8080/shot.jpg'
# Start looping
while(True):
# Read the video frame from the url
gcontext = SSLContext(PROTOCOL_TLSv1) # Only for gangstars
info = urlopen(url, context=gcontext).read()
imgNp=np.array(bytearray(info),dtype=np.uint8)
image_frame=cv2.imdecode(imgNp,-1)
# Convert frame to grayscale
gray = cv2.cvtColor(image_frame, cv2.COLOR_BGR2GRAY)
# Detect frames of different sizes, list of faces rectangles
faces = face_detector.detectMultiScale(gray, 1.3, 5)
# Loops for each faces
for (x,y,w,h) in faces:
# Crop the image frame into rectangle
cv2.rectangle(image_frame, (x,y), (x+w,y+h), (255,0,0), 2)
# Increment sample face image
count += 1
# Save the captured image into the datasets folder
cv2.imwrite("dataset/User." + str(face_id) + '.' + str(count) + ".jpg", gray[y:y+h,x:x+w])
print(count)
# Display the video frame, with bounded rectangle on the person's face
cv2.imshow('frame', image_frame)
k = cv2.waitKey(33)
if k==27: # Esc key to stop
break
# If image taken reach 100, stop taking video
elif count>100:
break