def restart_visualisation():
# initialize the bounding box coordinates of the object we are going to track
initBB = None
# initialize the FPS throughput estimator
fps = None
# id of img
img_id = 0
video_capture = cv2.VideoCapture(camera_port) # + cv2.CAP_DSHOW)
# video_capture.set(cv2.CAP_PROP_FRAME_WIDTH,camera_width)
# video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT,camera_height)
faces,faceID,name_dict=fr.labels_for_training_data('../face_reco_app/dataset')
face_recognizer=fr.train_classifier(faces,faceID)
param_track = False
state = 0
while state == 0:
# Capture frame-by-frame
ret, test_img = video_capture.read()
if test_img is None:
video_capture = cv2.VideoCapture(camera_port) # + cv2.CAP_DSHOW)
# video_capture.set(cv2.CAP_PROP_FRAME_WIDTH,camera_width)
# video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT,camera_height)
ret, test_img = video_capture.read()
if test_img is not None:
faces_detected,gray_img = fr.detect_face(test_img)
key = cv2.waitKey(1) & 0xFF
# face detect par
if param_track == False :
for face in faces_detected:
(x,y,w,h)=face
roi_gray=gray_img[y:y+h,x:x+w]
label,confidence=face_recognizer.predict(roi_gray)
print("\nconfidence:",confidence)
print("label :",label)
if confidence < 100:
fr.draw_rect(test_img,face)
predicted_name=name_dict[label]
fr.put_text(test_img,predicted_name,x,y)
else:
fr.draw_rect(test_img,face)
predicted_name="John Doe"
fr.put_text(test_img,predicted_name,x,y)
ret, jpeg = cv2.imencode('.jpg', test_img)
yield (b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + jpeg.tobytes() + b'\r\n')
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
################################################################testing#########################################################################
#for (x,y,w,h) in faces_detected:
# cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),thickness=3)
#resized_img = cv2.resize(test_img,(1000,700))
#cv2.imshow("Face Detection",resized_img)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
################################################################################################################################################
#Comment belows lines when running this program second time.Since it saves training.yml file in directory
faces, faceID = fr.labels_for_training_data(
'C:\\Users\\Yadvi\\PycharmProjects\\face\\training')
face_recognizer = fr.train_classifier(faces, faceID)
#face_recognizer.write('trainingData.yml')
name = {
0: "Virat Kohli",
1: "Barack Obama"
} #creating dictionary containing names for each label
############################################################ testing #########################################################################
#face_recognizer = cv2.face.LBPHFaceRecognizer_create()
#face_recognizer.read('C:\\Users\\Yadvi\\PycharmProjects\\face\\trainingData.yml')
#test_img=cv2.imread('C:\\Users\\Yadvi\\Desktop\\bo_test.jpg')
################################################################################################################################################
for face in faces_detected:
import numpy as np
import cv2
import os
import face_recognition as FR
print(FR)
test_img=cv2.imread('G:\Data Science Project\LBPH Face Recongition\Test_image.jpg')
faces_detected,gray_img=FR.faceDetection(test_img)
cv2.imshow
print("Faces Detected",faces_detected)
#Initializing the training
faces,face_ID=FR.labels_for_training_data(r'G:\Data Science Project\LBPH Face Recongition\capture\0')
face_recognizer=FR.train_classifier(faces,face_ID)
face_recognizer.save(r'G:\Data Science Project\LBPH Face Recongition\trainingData.yml')
name={0:'Yashraj/nData Scientist'}
for face in faces_detected:
(x,y,w,h)=face
roi_gray=gray_img[y:y+w,x:x+h]
label,confidence=face_recognizer.predict(roi_gray)
print(label)
print(confidence)
FR.draw_rect(test_img,face)
predict_name=name[label]
FR.put_text1(test_img,predict_name,x,y)
resized_img=cv2.resize(test_img,(1000,700))