Merge pull request #104 from anirudh-248/main

Added Face Recognition System project

Author: SaiNivedh26

Computer Vision/smart_attendance_system/.gitignore

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+cmake-3.30.3-windows-x86_64
+*.csv
+yolov8
+runs
+myenv

Computer Vision/smart_attendance_system/README.md

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+# Steps to run the project:
+
+1) Create a virtual environment.
+2) Before installing the requirements, install dlib separately using `pip install "dlib_file_path"`
+3) Install the requirements using `pip install -r requirements.txt`

Computer Vision/smart_attendance_system/dlib-19.24.99-cp312-cp312-win_amd64.whl

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+import face_recognition
+import cv2
+import numpy as np
+import csv
+import os
+import streamlit as st
+from datetime import datetime
+from ultralytics import YOLO
+
+# Load YOLO model
+yolo_model = YOLO("yolov8/yolov8n-face.pt")
+
+# Load and encode images
+image_dir = "photos/"
+encodings = {}
+known_face_names = []
+
+for fname in os.listdir(image_dir):
+    file_path = os.path.join(image_dir, fname)
+    image = face_recognition.load_image_file(file_path)
+    encoding = face_recognition.face_encodings(image)
+    if encoding:
+        encodings[fname] = encoding[0]
+        known_face_names.append(fname.split('.')[0])  # Assuming the names are derived from file names
+
+# Known face encodings and names
+known_face_encodings = list(encodings.values())
+known_face_names = [name.split('.')[0] for name in encodings.keys()]
+
+# Streamlit app
+st.title("Face Recognition Attendance System")
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    # Read and process the uploaded image
+    img = face_recognition.load_image_file(uploaded_image)
+
+    # Create or open a CSV file for the current date
+    now = datetime.now()
+    current_date = now.strftime("%Y-%m-%d")
+    attendance_file = current_date + '.csv'
+
+    # Initialize students list
+    students = known_face_names.copy()
+    attendance_records = []  # Store attendance records
+
+    # Get YOLO results
+    yolo_results = yolo_model(img)
+    yolo_boxes = yolo_results[0].boxes
+
+    # Find face locations and encodings for detected faces
+    face_locations = face_recognition.face_locations(img)
+    face_encodings = face_recognition.face_encodings(img, face_locations)
+    
+    face_names = []  # List to hold names for all detected faces
+
+    for face_encoding in face_encodings:
+        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+        name = "Unknown"  # Default to "Unknown" for unmatched faces
+
+        if True in matches:  # If there is a match
+            best_match_index = np.argmin(face_recognition.face_distance(known_face_encodings, face_encoding))
+            name = known_face_names[best_match_index]  # Get the name of the matched face
+
+            # Attendance tracking
+            if name in students:
+                students.remove(name)
+                # Record attendance in the list
+                attendance_records.append([name, 'present'])
+        face_names.append(name)  # Append the name to the list (either known or "Unknown")
+
+    # Draw rectangles and labels for all detected faces
+    for (top, right, bottom, left), name in zip(face_locations, face_names):
+        cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2)
+        cv2.putText(img, name, (left, bottom + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 2)
+
+    # Display the image in Streamlit
+    st.image(img, caption='Processed Image', channels="RGB")
+
+    # Write to CSV
+    with open(attendance_file, 'w', newline='') as f:
+        lnwriter = csv.writer(f)
+        lnwriter.writerow(["Name", "Status"])  # Write header
+        # Write present records
+        lnwriter.writerows(attendance_records)
+        # Write absent students
+        for name in students:
+            lnwriter.writerow([name, 'absent'])