extract frames on a given time in order to run face recognition

AttendanceApp/camera.py

@@ -6,10 +6,14 @@ import imutils
 import cv2,os,urllib.request
 import numpy as np
 from django.conf import settings
+from time import sleep
+import random
+
 face_detection_videocam = cv2.CascadeClassifier(os.path.join(
 			settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
 face_detection_webcam = cv2.CascadeClassifier(os.path.join(
 			settings.BASE_DIR,'opencv_haarcascade_data/haarcascade_frontalface_default.xml'))
+
 # load our serialized face detector model from disk
 prototxtPath = os.path.sep.join([settings.BASE_DIR, "face_detector/deploy.prototxt"])
 weightsPath = os.path.sep.join([settings.BASE_DIR,"face_detector/res10_300x300_ssd_iter_140000.caffemodel"])
@@ -17,28 +21,6 @@ faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)
 maskNet = load_model(os.path.join(settings.BASE_DIR,'face_detector/mask_detector.model'))
 
 
-# class VideoCamera(object):
-# 	def __init__(self):
-# 		self.video = cv2.VideoCapture(0)
-#
-# 	def __del__(self):
-# 		self.video.release()
-#
-# 	def get_frame(self):
-# 		success, image = self.video.read()
-# 		# We are using Motion JPEG, but OpenCV defaults to capture raw images,
-# 		# so we must encode it into JPEG in order to correctly display the
-# 		# video stream.
-#
-# 		gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-# 		faces_detected = face_detection_videocam.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5)
-# 		for (x, y, w, h) in faces_detected:
-# 			cv2.rectangle(image, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2)
-# 		frame_flip = cv2.flip(image,1)
-# 		ret, jpeg = cv2.imencode('.jpg', frame_flip)
-# 		return jpeg.tobytes()
-
-
 class IPWebCam(object):
 	def __init__(self):
 		self.url = "http://192.168.8.100:8080/shot.jpg"
@@ -47,6 +29,8 @@ class IPWebCam(object):
 		cv2.destroyAllWindows()
 
 	def get_frame(self):
+		fourcc = cv2.VideoWriter_fourcc(*'XVID')
+		out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
 		imgResp = urllib.request.urlopen(self.url)
 		imgNp = np.array(bytearray(imgResp.read()),dtype=np.uint8)
 		img= cv2.imdecode(imgNp,-1)
@@ -60,6 +44,9 @@ class IPWebCam(object):
 		resize = cv2.resize(img, (640, 480), interpolation = cv2.INTER_LINEAR) 
 		frame_flip = cv2.flip(resize,1)
 		ret, jpeg = cv2.imencode('.jpg', frame_flip)
+		count = random.randint(0, 9)
+		# capture frame and save on a given time in order to run the face recognition
+		sleep(3); cv2.imwrite("lecture%d.jpg" % count, img)
 		return jpeg.tobytes()
 
 
@@ -161,16 +148,3 @@ class MaskDetect(object):
 			cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
 		ret, jpeg = cv2.imencode('.jpg', frame)
 		return jpeg.tobytes()
-		
-# class LiveWebCam(object):
-# 	def __init__(self):
-# 		self.url = cv2.VideoCapture("rtsp://admin:Mumbai@123@203.192.228.175:554/")
-#
-# 	def __del__(self):
-# 		cv2.destroyAllWindows()
-#
-# 	def get_frame(self):
-# 		success,imgNp = self.url.read()
-# 		resize = cv2.resize(imgNp, (640, 480), interpolation = cv2.INTER_LINEAR)
-# 		ret, jpeg = cv2.imencode('.jpg', resize)
-# 		return jpeg.tobytes()