Merge pull request #104 from anirudh-248/main

Added Face Recognition System project

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/face_rec.py

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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'])

Computer Vision/smart_attendance_system/requirements.txt

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+altair==5.4.1
+antlr4-python3-runtime==4.9.3
+attrs==24.2.0
+blinker==1.8.2
+cachetools==5.5.0
+certifi==2024.8.30
+charset-normalizer==3.3.2
+click==8.1.7
+cmake==3.30.4
+colorama==0.4.6
+contourpy==1.3.0
+cycler==0.12.1
+dill==0.3.9
+dlib @ file:///C:/Users/aniru/Documents/Projects/gssoc_ext_2024/ML-Nexus/Computer%20Vision/smart_attendance_system/dlib-19.24.99-cp312-cp312-win_amd64.whl#sha256=20c62e606ca4c9961305f7be3d03990380d3e6c17f8d27798996e97a73271862
+face-recognition==1.3.0
+face_recognition_models==0.3.0
+filelock==3.16.1
+fonttools==4.54.1
+fsspec==2024.9.0
+gitdb==4.0.11
+GitPython==3.1.43
+idna==3.10
+Jinja2==3.1.4
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.2
+mdurl==0.1.2
+mpmath==1.3.0
+narwhals==1.9.1
+networkx==3.3
+numpy==1.26.4
+omegaconf==2.3.0
+opencv-python==4.10.0.84
+packaging==24.1
+pandas==2.2.3
+pillow==10.4.0
+protobuf==5.28.2
+psutil==6.0.0
+py-cpuinfo==9.0.0
+pyarrow==17.0.0
+pydeck==0.9.1
+Pygments==2.18.0
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+pytz==2024.2
+PyYAML==6.0.2
+referencing==0.35.1
+requests==2.32.3
+rich==13.9.2
+rpds-py==0.20.0
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.1.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.39.0
+sympy==1.13.3
+tenacity==9.0.0
+toml==0.10.2
+torch==2.4.1
+torchvision==0.19.1
+tornado==6.4.1
+tqdm==4.66.5
+typing_extensions==4.12.2
+tzdata==2024.2
+ultralytics==8.3.5
+ultralytics-thop==2.0.9
+urllib3==2.2.3
+watchdog==5.0.3

Computer Vision/smart_attendance_system/smart_attendance.py

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+import face_recognition
+import cv2
+import numpy as np
+import csv
+from datetime import datetime
+import os
+from ultralytics import YOLO
+
+# Load YOLO model
+yolo_model = YOLO("yolov8/yolov8n-face.pt")
+
+# Start video capture
+video_capture = cv2.VideoCapture(0)
+
+# 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)
+    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    encoding = face_recognition.face_encodings(img)
+    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()]
+
+students = known_face_names.copy()
+
+face_locations = []
+face_encodings = []
+face_names = []
+s = True
+
+now = datetime.now()
+current_date = now.strftime("%Y-%m-%d")
+
+# Create or open a CSV file for the current date
+with open(current_date + '.csv', 'w+', newline='') as f:
+    lnwriter = csv.writer(f)
+
+    while True:
+        ret, frame = video_capture.read()
+        if not ret:
+            break
+
+        small_frame = cv2.resize(frame, (0, 0), fx=1, fy=1)
+        rgb_small_frame = np.ascontiguousarray(small_frame[:, :, ::-1])
+
+        # Get YOLO results
+        yolo_results = yolo_model(rgb_small_frame)
+        yolo_boxes = yolo_results[0].boxes
+
+        # Draw rectangles for YOLO-detected faces
+        for box in yolo_boxes:
+            top_left_x, top_left_y = int(box.xyxy.tolist()[0][0]), int(box.xyxy.tolist()[0][1])
+            bottom_right_x, bottom_right_y = int(box.xyxy.tolist()[0][2]), int(box.xyxy.tolist()[0][3])
+            cv2.rectangle(frame, (top_left_x, top_left_y), (bottom_right_x, bottom_right_y), (50, 200, 129), 2)
+
+        if s:
+            face_locations = face_recognition.face_locations(rgb_small_frame)
+            if face_locations:
+                face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
+                face_names = []
+                for face_encoding in face_encodings:
+                    matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+                    name = ""
+                    face_distance = face_recognition.face_distance(known_face_encodings, face_encoding)
+                    best_match_index = np.argmin(face_distance)
+                    if matches[best_match_index]:
+                        name = known_face_names[best_match_index]
+
+                    face_names.append(name)
+
+                    # Display the name on the frame
+                    font = cv2.FONT_HERSHEY_SIMPLEX
+                    bottomLeftCornerOfText = (10, 50)
+                    fontScale = 1.5
+                    fontColor = (0, 255, 0)
+                    thickness = 3
+                    lineType = 2
+
+                    if name in known_face_names:
+                        cv2.putText(frame, name + ' present', 
+                            bottomLeftCornerOfText, 
+                            font, 
+                            fontScale,
+                            fontColor,
+                            thickness,
+                            lineType)
+
+                        if name in students:
+                            students.remove(name)
+                            print(students)
+                            lnwriter.writerow([name, 'present'])
+                    else:
+                        cv2.putText(frame, 'unknown',
+                            bottomLeftCornerOfText, 
+                            font, 
+                            fontScale,
+                            fontColor,
+                            thickness,
+                            lineType)
+
+        cv2.imshow("Attendance System", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Write absent students to CSV
+    for name in students:
+        lnwriter.writerow([name, 'absent'])
+
+# Release video capture and close windows
+video_capture.release()
+cv2.destroyAllWindows()