import cv2
from ELABORAZIONE_IMMAGINI.CV import facerecognition as fr
test_img = cv2.imread(
r"C:\Users\daniel\PycharmProjects\tensorEnv\CV\immagini\0\img0.jpg")
face, gray = fr.faceDetection(test_img)
#faces,facesID=fr.training_data('C:/Users/daniel/PycharmProjects/tensorEnv/CV/immagini')
#faceRecognizer=fr.train_classifier(faces,facesID)
#faceRecognizer.save("trainedData.yml")
faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
faceRecognizer.read(
r"C:\Users\daniel\PycharmProjects\tensorEnv\ELABORAZIONE_IMMAGINI\CV\trainedData.yml"
)
name = {0: "Elon", 1: "Daniel"}
for face in face:
(x, y, w, h) = face
region = gray[y:y + h, x:x + w]
label, confidence = faceRecognizer.predict(region)
print("confidence", confidence)
fr.draw_rect(test_img, face)
predicted_name = name[label]
print("Name: ", predicted_name)
if (label != 0 and label != 1):
print("non lo conosco")
if (confidence > 40):
continue
fr.put_name(test_img, predicted_name, x, y)
import cv2
from ELABORAZIONE_IMMAGINI.CV import facerecognition as fr
faceRecognizer = cv2.face.LBPHFaceRecognizer_create()
faceRecognizer.read(
r"C:\Users\daniel\PycharmProjects\tensorEnv\ELABORAZIONE_IMMAGINI\CV\trainedData.yml"
)
name = {0: "Elon", 1: "Daniel"}
capture = cv2.VideoCapture(0)
while True:
ret, test_img = capture.read()
faces_detected, gray = fr.faceDetection(test_img)
#for (x,y,w,h) in faces_detected:
# cv2.rectangle(test_img,(x,y),(x+w,y+h),(0,0,255),thickness=3)
#resized = cv2.resize(test_img,(600,600))
#cv2.imshow("face",resized)
#cv2.waitKey(10)
for face in faces_detected:
(x, y, w, h) = face
region = gray[y:y + w, x:x + h]
label, confidence = faceRecognizer.predict(region)
print("confidence", confidence)
predictedName = name[label]
fr.draw_rect(test_img, face)
if confidence > 35: