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')
test_img=cv2.imread(r'C:\Users\msath\Desktop\LBPH\Test.jpeg') #Give path to the image which you want to test
faces_detected,gray_img=fr.faceDetection(test_img)
print("face Detected: ",faces_detected)
face_recognizer=cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read(r'C:\Users\msath\Desktop\LBPH\trainingData.yml') #Give path of where trainingData.yml is saved
name={0:"Saan"} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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]
if(confidence>60):
fr.put_text(test_img,'Unknown',x,y)
continue
fr.put_text(test_img,predicted_name,x,y)
resized_img=cv2.resize(test_img,(1000,700))
cv2.imshow("face detection ", resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows
# Detecting the face.
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
for (x, y, w, h) in faces_detected:
# Draws bounding boxes.
cv2.rectangle(test_img, (x, y), (x + w, y + h), (0, 255, 0),
thickness=2)
for face in faces_detected:
# faces is a list having x,y coordinates and width and height of the img.
(x, y, w, h) = face
# Refer: https://stackoverflow.com/questions/57068928/opencv-rect-conventions-what-is-x-y-width-height to know about conventions.
roi_gray = gray_img[y:y + h, x:x + h]
# face_recogniser.predict() returns a tuple having label and confidence score
label, confidence = face_recognizer.predict(roi_gray)
print("Confidence :", confidence)
print("label :", label)
fr.draw_rect(test_img, face) # Draws bounding boxes
# Extracting value from the dictionary "name"
predicted_name = name[label]
fr.put_text(test_img, predicted_name, x, y)
resized_img = cv2.resize(test_img, (700, 700))
cv2.imshow("face detection ", resized_img)
if cv2.waitKey(10) == ord('q'):
break
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))
cv2.imshow("face detection ", resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows
