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')
import numpy as np
import cv2
import os
import face_recognition as fr
#print(fr)
test_img = cv2.imread(r'E:\Projects\Test_image.jpg')
faces_detected, gray_img = fr.faceDetection(test_img)
print("Face Detected: ", faces_detected)
#Training
faces, faceID = fr.labels_for_training_data(r'E:\Projects\images')
face_recognizer = fr.trainClassifier(faces, faceID)
face_recognizer.save(r'E:\Projects\trainingData.yml')
name = {0: 'Yash', 1: 'Diksha', 2: 'Yana'}
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_text(test_img, predict_name, x, y)
resized_img = cv2.resize(test_img, (1000, 700))
'C:\\Users\\Yadvi\\PycharmProjects\\face\\training\\0\\289002.1.jpg'
) #test_img path
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')
################################################################################################################################################
import numpy as np
import cv2
import os
import face_recognition as fr
print(fr)
test_img = cv2.imread(r'D:\Python\Projects\Face Recognition\test_image5.jfif'
) # Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print('Face Detected: ', faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
"D:\Python\Projects\Face Recognition\Image")
face_recogniser = fr.train_Classifier(faces, faceID)
face_recogniser.save('D:/Python/Projects/Face Recognition/TrainingData.yml'
) # To save the trained model. Just give the path.
# Assign labels to images folder
name = {
0: "Rhythem Jain",
1: "Mark Zuckerberg",
2: "Hrithik Roshan",
3: "Deepika Padukone",
4: "Emma Watson"
}
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
"""
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)
import numpy as numpy
import cv2
import os
import face_recognition as fr
test_img=cv2.imread(r"D:\project\face recognition\test_img.jpeg")
faces_detected,gray_img=fr.faceDetection(test_img)
print("Face Detected: ",faces_detected)
faces,faceID=fr.labels_for_training_data(r"D:\project\face recognition\images")
face_recognizer=fr.train_Classifier(faces,faceID)
face_recognizer.save(r"D:\project\face recognition\trainingData.yml")
name={0:'pranay'}
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_text(test_img,predict_name,x,y)
resized_img=cv2.resize(test_img,(1000,700))
import cv2
import os
import face_recognition as fr
print(fr)
#Give path to the image which you want to test
test_img = cv2.imread(r'D:\FaceRecognitionLBPH\Test_img.jpg')
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training begin
#Give path to the train-images folder which has both labeled folder as 0 and 1
faces, faceID = fr.labels_for_training_data(r'D:\FaceRecognitionLBPH\Image')
face_recognizer = fr.train_classifier(faces, faceID)
#It will save the trained model. Just give path to where you want to save
face_recognizer.save(r'D:\FaceRecognitionLBPH\trainedData.yml')
#Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
name = {0: "Bibek", 1: "2nd img bibek"}
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
label, confidence = face_recognizer.predict(roi_gray)
print("Confidence :", confidence)
print("label :", label)
fr.draw_rect(test_img, face)
import cv2
import os
import face_recognition as fr
#print(fr)
test_img = cv2.imread(
r'C:\Users\Harsha\AppData\Local\Programs\Python\Python38\python project\face recognition\test_img2.jpeg'
)
faces_detected, gray_img = fr.faceDetection(test_img)
#cv2.imshow(faces_detected)
print("face Detected: ", faces_detected)
#training will being from here
faces, faceID = fr.labels_for_training_data(
r'D:\python project datasets\images')
face_recognizer = fr.train_Classifier(faces, faceID)
face_recognizer.save(
r'C:\Users\Harsha\AppData\Local\Programs\Python\Python38\python project\face recognition\trainingData.yml'
)
name = {0: 'Harsha'}
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]
import numpy as np
import cv2
import os
import face_recognition as fr
print(fr)
test_img = cv2.imread(r'C:\Users\msath\Desktop\LBPH\pykara.jpg')
faces_detected, gray_img = fr.faceDetection(test_img)
print("Face Detected: ", faces_detected)
#Training
faces, faceID = fr.labels_for_training_data(
r'C:\Users\msath\Desktop\LBPH\images')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(r'C:\Users\msath\Desktop\LBPH\trainingData.yml')
name = {0: 'Saan'}
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
label, confidence = face_recognizer.predict(roi_gray)
print("Confidence: ", confidence)
print("Label: ", label)
fr.draw_rect(test_img, face)
predicted_name = name[label]
fr.put_text(test_img, predicted_name, x, y)
import numpy as np
import cv2
import os
import face_recognition as fr
test_img=cv2.imread(r'C:\Users\denio\Desktop\Courses & Certificates\courses\DataScience\face recog own\test.jpeg')
faces_detected,gray_img=fr.faceDetection(test_img)
print("Face Detected: ",faces_detected)
faces,face_id=fr.labels_for_training_data(r'C:\Users\denio\Desktop\Courses & Certificates\courses\DataScience\face recog own\images')
face_recognizer=fr.train_Classifier(faces,face_id)
face_recognizer.save(r'C:\Users\denio\Desktop\Courses & Certificates\courses\DataScience\face recog own\trainingdata.yml')
name={0:'Denio'}
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_rectangle(test_img,face)
predict_name=name[label]
fr.put_text(test_img,predict_name,x,y)
resized_img=cv2.resize(test_img,(1000,700))
import os
import face_recognition as fr
test_img = cv2.imread(
r'/Users/nirajpaliwal/Documents/Python For Data Science & ML/Data-Science-Projects/06 - Face Recognition/test_img.jpg'
)
faces_detected, gray_img = fr.faceDetection(test_img)
# cv2.imshow(gray_img)
print('Face Detected : ', faces_detected)
# Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'/Users/nirajpaliwal/Documents/Python For Data Science & ML/Data-Science-Projects/06 - Face Recognition/images'
)
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'/Users/nirajpaliwal/Documents/Python For Data Science & ML/Data-Science-Projects/06 - Face Recognition/trainingData.yml'
)
name = {0: 'Neeraj'}
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_rectangle(test_img, face)