Raspberry Pi

Face Recognition Using OpenCV on Raspberry Pi 400

by suadanwar
November 13, 2020

Raspberry Pi

Introduction

A face recognition system is a technology that able to match human faces from digital images or video frames to facial databases. Although humans can recognize faces without much effort, facial recognition is a challenging pattern recognition problem in computing. The face recognition system seeks to identify the human face, which is three-dimensional and changes appearance with facial lighting and expression, based on its two-dimensional image. To complete this computational task, the face recognition system performs four steps. The first face detection is used to segment the face from the background of the image. In the second step, segmented facial images are adjusted to take into account facial poses, image sizes, and photographic properties, such as lighting and grayscale. The purpose of the alignment process is to enable proper localization of facial features in the third step, extraction of facial features. Features such as eyes, nose, and mouth are shown and measured in pictures to represent the face. The vector features a robust face then, in the fourth step, is matched to the face database.

Video

This video shows how I use OpenCV to make a simple face recognition system on Raspberry Pi 400.

Hardware Preparation

This is the list of items used in the video.

RPi 400 Wireless Computer Kit-US Layout & UK Power Plug

Raspberry Pi 400 Keyboard Computer

Official RPi 15W (5V/3A) PSU USB C UK Plug-Black

Sample Program

This is python3 sample program for OpenCV Face Recognition using Raspberry Pi. You can use it with Thonny Python IDE.

#! /usr/bin/python

# import the necessary packages

from imutils.video import VideoStream

from imutils.video import FPS

import face_recognition

import imutils

import pickle

import time

import cv2

#Initialize 'currentname' to trigger only when a new person is identified.

currentname = "unknown"

#Determine faces from encodings.pickle file model created from train_model.py

encodingsP = "encodings.pickle"

#use this xml file

#https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml

cascade = "haarcascade_frontalface_default.xml"

# load the known faces and embeddings along with OpenCV's Haar

# cascade for face detection

print("[INFO] loading encodings + face detector…")

data = pickle.loads(open(encodingsP, "rb").read())

detector = cv2.CascadeClassifier(cascade)

# initialize the video stream and allow the camera sensor to warm up

print("[INFO] starting video stream…")

vs = VideoStream(src=0).start()

#vs = VideoStream(usePiCamera=True).start()

time.sleep(2.0)

# start the FPS counter

fps = FPS().start()

# loop over frames from the video file stream

while True:

	# grab the frame from the threaded video stream and resize it

	# to 500px (to speedup processing)

	frame = vs.read()

	frame = imutils.resize(frame, width=500)

	# convert the input frame from (1) BGR to grayscale (for face

	# detection) and (2) from BGR to RGB (for face recognition)

	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	# detect faces in the grayscale frame

	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 

		minNeighbors=5, minSize=(30, 30),

		flags=cv2.CASCADE_SCALE_IMAGE)

	# OpenCV returns bounding box coordinates in (x, y, w, h) order

	# but we need them in (top, right, bottom, left) order, so we

	# need to do a bit of reordering

	boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]

	# compute the facial embeddings for each face bounding box

	encodings = face_recognition.face_encodings(rgb, boxes)

	names = []

	# loop over the facial embeddings

	for encoding in encodings:

		# attempt to match each face in the input image to our known

		# encodings

		matches = face_recognition.compare_faces(data["encodings"],

			encoding)

		name = "Unknown" #if face is not recognized, then print Unknown

		# check to see if we have found a match

		if True in matches:

			# find the indexes of all matched faces then initialize a

			# dictionary to count the total number of times each face

			# was matched

			matchedIdxs = [i for (i, b) in enumerate(matches) if b]

			counts = {}

			# loop over the matched indexes and maintain a count for

			# each recognized face face

			for i in matchedIdxs:

				name = data["names"][i]

				counts[name] = counts.get(name, 0) + 1

			# determine the recognized face with the largest number

			# of votes (note: in the event of an unlikely tie Python

			# will select first entry in the dictionary)

			name = max(counts, key=counts.get)

			#If someone in your dataset is identified, print their name on the screen

			if currentname != name:

				currentname = name

				print(currentname)

		# update the list of names

		names.append(name)

	# loop over the recognized faces

	for ((top, right, bottom, left), name) in zip(boxes, names):

		# draw the predicted face name on the image – color is in BGR

		cv2.rectangle(frame, (left, top), (right, bottom),

			(0, 255, 0), 2)

		y = top – 15 if top – 15 > 15 else top + 15

		cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,

			.8, (255, 0, 0), 2)

	# display the image to our screen

	cv2.imshow("Facial Recognition is Running", frame)

	key = cv2.waitKey(1) & 0xFF

	# quit when 'q' key is pressed

	if key == ord("q"):

		break

	# update the FPS counter

	fps.update()

# stop the timer and display FPS information

fps.stop()

print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))

print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup

cv2.destroyAllWindows()

vs.stop()

view raw
face_rec.py
hosted with ❤ by GitHub

import cv2

name = 'Suad' #replace with your name

cam = cv2.VideoCapture(0)

cv2.namedWindow("press space to take a photo", cv2.WINDOW_NORMAL)

cv2.resizeWindow("press space to take a photo", 500, 300)

img_counter = 0

while True:

    ret, frame = cam.read()

    if not ret:

        print("failed to grab frame")

        break

    cv2.imshow("press space to take a photo", frame)

    k = cv2.waitKey(1)

    if k%256 == 27:

        # ESC pressed

        print("Escape hit, closing…")

        break

    elif k%256 == 32:

        # SPACE pressed

        img_name = "dataset/"+ name +"/image_{}.jpg".format(img_counter)

        cv2.imwrite(img_name, frame)

        print("{} written!".format(img_name))

        img_counter += 1

cam.release()

cv2.destroyAllWindows()

view raw
face_shot.py
hosted with ❤ by GitHub

#! /usr/bin/python

# import the necessary packages

from imutils import paths

import face_recognition

#import argparse

import pickle

import cv2

import os

# our images are located in the dataset folder

print("[INFO] start processing faces…")

imagePaths = list(paths.list_images("dataset"))

# initialize the list of known encodings and known names

knownEncodings = []

knownNames = []

# loop over the image paths

for (i, imagePath) in enumerate(imagePaths):

	# extract the person name from the image path

	print("[INFO] processing image {}/{}".format(i + 1,

		len(imagePaths)))

	name = imagePath.split(os.path.sep)[–2]

	# load the input image and convert it from RGB (OpenCV ordering)

	# to dlib ordering (RGB)

	image = cv2.imread(imagePath)

	rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

	# detect the (x, y)-coordinates of the bounding boxes

	# corresponding to each face in the input image

	boxes = face_recognition.face_locations(rgb,

		model="hog")

	# compute the facial embedding for the face

	encodings = face_recognition.face_encodings(rgb, boxes)

	# loop over the encodings

	for encoding in encodings:

		# add each encoding + name to our set of known names and

		# encodings

		knownEncodings.append(encoding)

		knownNames.append(name)

# dump the facial encodings + names to disk

print("[INFO] serializing encodings…")

data = {"encodings": knownEncodings, "names": knownNames}

f = open("encodings.pickle", "wb")

f.write(pickle.dumps(data))

f.close()