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import streamlit as st
from utils.levels import complete_level, render_page, initialize_level
from utils.login import get_login, initialize_login
from utils.inference import recognize
import os
import time
import face_recognition
import cv2
import numpy as np
from PIL import Image
initialize_login()
initialize_level()
LEVEL = 4
def step4_page():
st.header("Face Recognition: Trying It Out")
st.write(
"""
Once the face encodings are obtained, they can be stored in a database or used for face recognition tasks.
During face recognition, the encodings of input faces are compared to the stored encodings (our known-face database)
to determine if a match exists. Various similarity metrics, such as Euclidean distance or cosine similarity,
can be utilized to measure the similarity between face encodings and determine potential matches.
"""
)
st.info(
"Now that we know how our face recognition application works, let's try it out!"
)
# Select input type
st.info("Select your input type to analyze!")
input_type = st.radio("Select the Input Type", ["Image upload", "Camera"])
# Put slide to adjust tolerance
tolerance = 0.6
# tolerance = st.slider("Tolerance", 0.0, 1.0, 0.15, 0.01)
# st.info(
# "Tolerance is the threshold for face recognition. The lower the tolerance, the more strict the face recognition. The higher the tolerance, the more loose the face recognition.")
if input_type == "Image upload":
st.title("Face Recognition App")
uploaded_images = st.file_uploader("Please upload image(s) to try it out!", type=['jpg', 'png', 'jpeg'], accept_multiple_files=True)
if len(uploaded_images) != 0:
# Read uploaded image with face_recognition
for image in uploaded_images:
image = face_recognition.load_image_file(image)
image, name, face_id = recognize(image, tolerance)
st.image(image)
else:
st.info("Please upload an image")
elif input_type == "Camera":
st.title("Face Recognition App")
uploaded_image = st.camera_input("Take a picture")
if uploaded_image:
# Read uploaded image with face_recognition
image = face_recognition.load_image_file(uploaded_image)
image, name, face_id = recognize(image, tolerance)
st.image(image)
else:
st.info("Please take an image")
else:
st.title("Face Recognition App")
# Camera Settings
cam = cv2.VideoCapture(0)
cam.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
FRAME_WINDOW = st.image([])
while True:
ret, frame = cam.read()
if not ret:
st.error("Failed to capture frame from camera")
st.info("Please turn off the other app that is using the camera and restart app")
st.stop()
image, name, face_id = recognize(frame, tolerance)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Display name and ID of the person
FRAME_WINDOW.image(image)
st.info("Click on the button below to complete this level!")
if st.button("Complete Level"):
complete_level(LEVEL)
render_page(step4_page, LEVEL)
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