import streamlit as st from PIL import Image import face_recognition import cv2 import numpy as np import requests import os st.title("Project_1 - Face Recognition") # create list of encoding of all images in photos folder # Load images for face recognition Images = [] # List to store Images classnames = [] # List to store 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", ".jfif"]: 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("Take a picture") #st.file_uploader("Upload image ") def update_data(name): url = "https://final-predict-CV.glitch.me/adduserdata1" #?rollno=222&name="+name data = {'name': name} response = requests.post(url , data=data ) if response.status_code == 200: st.success("Your attendance is marked.Have a good day!") else: st.warning("Sorry attendance is not marked") 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) name = "Unknown" # Default name for unknown faces match_found = False # Flag to track if a match is found # 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) matchIndex = np.argmin(faceDis) if matches[matchIndex]: name = classnames[matchIndex].upper() match_found = True # Set the flag to True y1, x2, y2, x1 = faceLoc y1, x2, y2, x1 = (y1 * 4), (x2 * 4), (y2 * 4) ,(x1 * 4) # Make a copy of the image array before drawing on it image_copy = image.copy() cv2.rectangle(image_copy, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.rectangle(image_copy, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED) cv2.putText(image_copy, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2) # update the database update_data(name) st.image(image_copy, use_column_width=True, output_format="PNG") else: st.warning("No faces detected in the image. Face recognition failed.") # image = Image.open(file_name) # col1.image(image, use_column_width=True)