Below is the code for app.py:
import cv2
import streamlit as st
import numpy as np
from keras.models import model_from_json
import tempfile
st.set_page_config(page_title="FYP1",page_icon="😀")
st.title("Facial Expressions Recognition System")
option = st.sidebar.selectbox(
"Select an option",
("Real-time Recognition", "Upload Video")
)
FRAME_WINDOW = st.image([])
if option == 'Real-time Recognition':
start = st.checkbox('Start')
while start:
camera = cv2.VideoCapture(0)
_, frame = camera.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# load json and create model
json_file = open('./model/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
# load weights into new model
emotion_model.load_weights("./model/model.h5")
face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces available on camera
num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
# take each face available on the camera and Preprocess it
for (x, y, w, h) in num_faces:
cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
roi_gray_frame = gray_frame[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
# predict the emotions
emotion_prediction = emotion_model.predict(cropped_img)
maxindex = int(np.argmax(emotion_prediction))
cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
FRAME_WINDOW.image(frame)
else:
st.write("Stopped")
else:
uploaded_video = st.file_uploader("Please upload a video.")
if uploaded_video is not None:
tfile = tempfile.NamedTemporaryFile(delete=False)
tfile.write(uploaded_video.read())
camera = cv2.VideoCapture(tfile.name)
while camera.isOpened():
_, frame = camera.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
# load json and create model
json_file = open('./model/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
emotion_model = model_from_json(loaded_model_json)
# load weights into new model
emotion_model.load_weights("./model/model.h5")
face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces available on camera
num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
# take each face available on the camera and Preprocess it
for (x, y, w, h) in num_faces:
cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
roi_gray_frame = gray_frame[y:y + h, x:x + w]
cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
# predict the emotions
emotion_prediction = emotion_model.predict(cropped_img)
maxindex = int(np.argmax(emotion_prediction))
cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
FRAME_WINDOW.image(frame)
# stframe.image(frame)
else:
st.write(" ")
