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.
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() |
| 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() |
| #! /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() |
References:
Thanks for reading this tutorial. If you have any technical inquiries, please post at Cytron Technical Forum.
“Please be reminded, this tutorial is prepared for you to try and learn.
You are encouraged to improve the code for better application.“
2 thoughts on “Face Recognition Using OpenCV on Raspberry Pi 400”
from .cv2 import *
ImportError: libQtGui.so.4: cannot open shared object file: No such file or directory
how to solved this problem?
web camera tu guna brand apa?