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deploy ekspresi.py
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deploy ekspresi.py
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import streamlit as st
import cv2
from PIL import Image
def face_detection(image):
# Tambahkan kode deteksi wajah menggunakan OpenCV atau alat deteksi wajah lainnya
# ...
face_classifier = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
classifier =load_model('model.h5')
class_labels = ['Angry','Disgusted','Fearful','happy','Neutral','Sad','Surprised']
cap = cv2.VideoCapture(0)
while True:
# Grab a single frame of video
ret, frame = cap.read()
labels = []
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray,1.3,5)
for (x,y,w,h) in faces:
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h,x:x+w]
roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)
if np.sum([roi_gray])!=0:
roi = roi_gray.astype('float')/255.0
roi = img_to_array(roi)
roi = np.expand_dims(roi,axis=0)
# make a prediction on the ROI, then lookup the class
preds = classifier.predict(roi)[0]
print("\nprediction = ",preds)
label=class_labels[preds.argmax()]
print("\nprediction max = ",preds.argmax())
print("\nlabel = ",label)
label_position = (x,y)
cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
else:
cv2.putText(frame,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)
print("\n\n")
cv2.imshow('Aplikasi Deteksi Ekspresi',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
st.title('Aplikasi Deteksi Wajah')
uploaded_file = st.file_uploader("Unggah Gambar", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption='Gambar yang diunggah', use_column_width=True)
st.write("")
st.write("Deteksi Wajah:")
# Panggil fungsi deteksi wajah
faces = face_detection(image)
# Tampilkan hasil deteksi
# ...