I am designing a Facial Recognition system for college, essentially a driver needs to be recognised. I am using Node-Red as a tool to sequence the chain of events.
I have a working Python script on my Pi. Using OpenCV. Iv tried numerous ways to get it to run but keep getting errors.
And this is my code in the function
#! /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
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, 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()
