faces.py

import numpy as np
import cv2
import pickle

face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read("./trainner.yml")

labels = {"person_name" : 1}
with open("labels.pickle", 'rb') as f:
	og_labels = pickle.load(f)
	labels = {v:k for k,v in og_labels.items()}

cap = cv2.VideoCapture(0)

while(True):
	# Capture frame-by-frame
	ret, frame = cap.read()
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	faces = face_cascade.detectMultiScale(gray, scaleFactor=1.5, minNeighbors=5)
	for (x, y, w, h) in faces: 
		# print(x,y,w,h)
		roi_gray = gray[y:y+h, x:x+w] #(ycord_start, ycord_end)
		roi_color = frame[y:y+h, x:x+w]

		#recognize? deep learned model predict keras tensorflow pytorch scikit learn
		id_, conf = recognizer.predict(roi_gray)
		# if conf>=45: # and conf <= 85:
		if conf>=4 and conf <= 85:
			# print(id_)
			print(labels[id_])
			font = cv2.FONT_HERSHEY_SIMPLEX
			name = labels[id_]
			color = (255, 255, 255)
			stroke = 2
			cv2.putText(frame, name, (x,y), font, 1, color, stroke, cv2.LINE_AA)

		# img_item = "my-image.png"
		# cv2.imwrite(img_item, roi_color) #Save face to file

		# Draw rectangle around face
		color = (255, 0, 0) #BGR 0-255
		stroke = 2
		end_cord_x = x + w
		end_cord_y = y + h
		cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), color, stroke)

	# Display the resulting frame
	cv2.imshow('frame',frame)
	if cv2.waitKey(20) & 0xFF == ord('q'):
		break

# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()