Python helper files

Install OpenCV on Raspberry Pi (Buster version of OS)

(reference: https://www.tomshardware.com/how-to/raspberry-pi-facial-recognition and https://pyimagesearch.com/2019/09/16/install-opencv-4-on-raspberry-pi-4-and-raspbian-buster/

NOTE: First change to your python folder: cd ~/python

sudo apt update
sudo apt upgrade

sudo apt-get install build-essential cmake pkg-config
sudo apt-get install libjpeg-dev libtiff5-dev libjasper-dev libpng-dev
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
sudo apt-get install libxvidcore-dev libx264-dev
sudo apt-get install libfontconfig1-dev libcairo2-dev
sudo apt-get install libgtk2.0-dev libgtk-3-dev
sudo apt-get install libatlas-base-dev gfortran
sudo apt-get install libhdf5-dev libhdf5-serial-dev libhdf5-103
sudo apt-get install libqtgui4 libqtwebkit4 libqt4-test python3-pyqt5
sudo apt-get install python3-dev

sudo rm -rf ~/.cache/pip
pip3 install opencv-contrib-python==4.1.0.25

git clone https://github.com/carolinedunn/facial_recognition

pip3 install imutils
pip3 install face_recognition









##############################################
# headshots.py (updated by JPW to allow user to enter their name
#   and auto-create directory)
############################################## 
import cv2
import os

name = input("Enter the name of the person to train on: ")
path = "dataset/"+ name
print("Creating directory at " + path)

try: os.mkdir(path) except OSError as error: print("Error making directory! " + error) 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()

############################################## 
# facial_req.py (updated by JPW to select camera 0 by default) 
############################################## 
# 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"

# 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())

# initialize the video stream and allow the camera sensor to warm up
# Set the ser to the followng
# src = 0 : for the build in single web cam, could be your laptop webcam
# src = 2 : I had to set it to 2 inorder to use the USB webcam attached to my laptop
vs = VideoStream(src=0,framerate=10).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)
# Detect the fce boxes
boxes = face_recognition.face_locations(frame)
# compute the facial embeddings for each face bounding box
encodings = face_recognition.face_encodings(frame, 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, 225), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
.8, (0, 255, 255), 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()

Contents

Python helper files

Install OpenCV on Raspberry Pi (Buster version of OS)