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import streamlit as st
from PIL import Image
import face_recognition
import cv2
import numpy as np
import os
from datetime import datetime
st.title("AIMLJan24 - Face Recognition")
# Load images for face recognition
Images = []
classnames = []
directory = "photos"
myList = os.listdir(directory)
st.write("Photographs found in folder : ")
for cls in myList:
if os.path.splitext(cls)[1] in [".jpg", ".jpeg"]:
img_path = os.path.join(directory, cls)
curImg = cv2.imread(img_path)
Images.append(curImg)
st.write(os.path.splitext(cls)[0])
classnames.append(os.path.splitext(cls)[0])
# Load images for face recognition
encodeListknown = [face_recognition.face_encodings(img)[0] for img in Images]
# camera to take photo of user in question
file_name = st.camera_input("Upload image")
def add_attendance(name):
username = name
current_datetime = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(current_datetime)
if not os.path.isdir('Attendance'):
os.makedirs('Attendance')
if f'Attendance-{current_datetime}.csv' not in os.listdir('Attendance'):
with open(f'Attendance/Attendance-{current_datetime}.csv', 'w') as f:
f.write('Name,Time')
if file_name is not None:
col1, col2 = st.columns(2)
test_image = Image.open(file_name)
image = np.asarray(test_image)
imgS = cv2.resize(image, (0, 0), None, 0.25, 0.25)
imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)
facesCurFrame = face_recognition.face_locations(imgS)
encodesCurFrame = face_recognition.face_encodings(imgS, facesCurFrame)
# List to store recognized names for all faces in the image
recognized_names = []
# Checking if faces are detected
if len(encodesCurFrame) > 0:
for encodeFace, faceLoc in zip(encodesCurFrame, facesCurFrame):
# Assuming that encodeListknown is defined and populated in your code
matches = face_recognition.compare_faces(encodeListknown, encodeFace)
faceDis = face_recognition.face_distance(encodeListknown, encodeFace)
# Initialize name as Unknown
name = "Unknown"
# Check if there's a match with known faces
if True in matches:
matchIndex = np.argmin(faceDis)
name = classnames[matchIndex].upper()
# Append recognized name to the list
recognized_names.append(name)
# Draw rectangle around the face
y1, x2, y2, x1 = faceLoc
y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4)
image = image.copy()
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.putText(image, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
# Display the image with recognized faces
st.image(image, use_column_width=True, output_format="PNG")
# Display recognized names
st.write("Recognized Names:")
for i, name in enumerate(recognized_names):
st.write(f"Face {i+1}: {name}")
else:
st.warning("No faces detected in the image. Face recognition failed.") |