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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 query
import os
import time
import face_recognition
import cv2
import numpy as np
initialize_login()
initialize_level()
LEVEL = 4
def infer(image):
time.sleep(1)
output = query(image)
cols = st.columns(2)
cols[0].image(image, use_column_width=True)
with cols[1]:
for item in output:
st.progress(item["score"], text=item["label"])
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
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!"
)
face_encodings_dir = os.path.join(".sessions", get_login()["username"], "face_encodings")
face_encodings = os.listdir(face_encodings_dir)
known_face_encodings = []
known_face_names = []
if len(face_encodings) > 0:
for i, face_encoding in enumerate(face_encodings):
known_face_encoding = np.load(os.path.join(face_encodings_dir, face_encoding))
face_name = face_encoding.split(".")[0]
known_face_encodings.append(known_face_encoding)
known_face_names.append(face_name)
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_frame = frame[:, :, ::-1]
# Find all the faces and face encodings in the frame of video
face_locations = face_recognition.face_locations(rgb_frame)
face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
# Loop through each face in this frame of video
for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
# If a match was found in known_face_encodings, just use the first one.
# if True in matches:
# first_match_index = matches.index(True)
# name = known_face_names[first_match_index]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
# Draw a label with a name below the face
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
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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