Code for How to Make a Facial Recognition System in Python Tutorial
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facial_rec_live_camera.py
import tkinter as tk, numpy as np, cv2, os, face_recognition
from datetime import datetime
# Initialize empty lists to store images and people's names.
known_faces = []
face_labels = []
# Get a list of all images in the TrainingImages directory.
image_files = os.listdir("TrainingImages")
# Loop through the images in the directory.
for image_name in image_files:
# Read each image and add it to the known_faces list.
current_image = cv2.imread(f'TrainingImages/{image_name}')
known_faces.append(current_image)
# Extract the person's name by removing the file extension and add it to the face_labels list.
face_labels.append(os.path.splitext(image_name)[0])
# Function to get face encodings from a list of images.
def get_face_encodings(images):
encoding_list = []
for image in images:
# Convert the image to RGB format. RGB is Red Green Blue.
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Get the face encoding for the first face found in the image.
face_encoding = face_recognition.face_encodings(image)[0]
encoding_list.append(face_encoding)
return encoding_list
# Define a function to document the recognized face.
def document_recognised_face(name, filename='records.csv'):
# Get the current date in the YYYY-MM-DD format.
capture_date = datetime.now().strftime("%Y-%m-%d")
# Check if the specified CSV file exists.
if not os.path.isfile(filename):
# If the file doesn't exist, create it and write the header.
with open(filename, 'w') as f:
f.write('Name,Date,Time') # Create the file and write the header.
# Open the CSV file for reading and writing ('r+')
with open(filename, 'r+') as file:
# Read all lines from the file into a list.
lines = file.readlines()
# Extract the names from existing lines in the CSV.
existing_names = [line.split(",")[0] for line in lines]
# Check if the provided name is not already in the existing names.
if name not in existing_names:
# Get the current time in the HH:MM:SS format.
now = datetime.now()
current_time = now.strftime("%H:%M:%S")
# Write the new entry to the CSV file including name, capture date, and time.
file.write(f'\n{name},{capture_date},{current_time}')
# Get face encodings for known images.
known_face_encodings = get_face_encodings(known_faces)
# Function to start the Facial recognition program.
def start_recognition_program():
# Open a webcam for capturing video. If you are using your computer's webcam, change 1 to 0.
# If using an external webcam, leave it as 1.
video_capture = cv2.VideoCapture(1)
while True:
# Read a frame from the webcam.
frame = video_capture.read()
# Check if the frame is not None (indicating a successful frame capture).
if frame is not None:
frame = frame[1] # The frame is usually the second element of the tuple returned by video_capture.read().
# Resize the image to a smaller size.
resized_frame = cv2.resize(frame, (0, 0), None, 0.25, 0.25)
resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)
# Detect faces in the current frame.
face_locations = face_recognition.face_locations(resized_frame)
# Get face encodings for the faces detected in the current frame.
current_face_encodings = face_recognition.face_encodings(resized_frame, face_locations)
# Loop through the detected faces in the current frame.
for face_encoding, location in zip(current_face_encodings, face_locations):
# Compare the current face encoding with the known encodings.
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
# Find the index of the best match. That is, the best resemblance.
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
# If a match is found, get the name of the recognized person.
recognized_name = face_labels[best_match_index].upper()
# Extract face location coordinates.
top, right, bottom, left = location
top, right, bottom, left = top * 4, right * 4, bottom * 4, left * 4
# Draw a rectangle around the recognized face.
cv2.rectangle(frame, (left, top), (right, bottom), (0, 255, 0), 2)
# Draw a filled rectangle and display the name above the face.
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 255, 0), cv2.FILLED)
cv2.putText(frame, recognized_name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255), 2)
document_recognised_face(recognized_name)
# Display the image with recognized faces.
cv2.imshow("Webcam", frame)
# Check for key press
key = cv2.waitKey(1) & 0xFF
# Check if the 'q' key is pressed to exit the program.
if key == ord('q'):
break
# Release the video capture and close all OpenCV windows.
video_capture.release()
cv2.destroyAllWindows()
# Create the main application window.
root = tk.Tk()
root.title("Face Recognition Program")
# Create a label
label = tk.Label(root, text="Click the button to start the facial recognition program")
label.pack(pady=10)
# Create a button to start the program
start_button = tk.Button(root, text="Start Recognition", command=start_recognition_program)
start_button.pack(pady=10)
# Function to quit the application. This is for quitting the entire program. To quit the webcam stream, hit q.
def quit_app():
root.quit()
cv2.destroyAllWindows()
# Create a quit button to exit the application.
exit_button = tk.Button(root, text="Close", command=quit_app)
exit_button.pack(pady=10)
# Start the Tkinter event loop.
root.mainloop()
facial_rec_image_upload.py
import cv2, numpy as np, face_recognition, os, tkinter as tk
from tkinter import filedialog
# Initialize empty lists to store images and people's names.
known_faces = []
face_labels = []
# Get a list of all images in the TrainingImages directory.
image_files = os.listdir("TrainingImages")
# Loop through the images in the directory.
for image_name in image_files:
# Read each image and add it to the known_faces list.
current_image = cv2.imread(f'TrainingImages/{image_name}')
known_faces.append(current_image)
# Extract the person's name by removing the file extension and add it to the face_labels list.
face_labels.append(os.path.splitext(image_name)[0])
# Function to get face encodings from a list of images.
def get_face_encodings(images):
encoding_list = []
for image in images:
# Convert the image to RGB format. RGB is Red Green Blue.
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Get the face encoding for the first face found in the image.
face_encoding = face_recognition.face_encodings(image)[0]
encoding_list.append(face_encoding)
return encoding_list
# Get face encodings for known images.
known_face_encodings = get_face_encodings(known_faces)
# Function to handle image selection and recognition
def select_and_recognize_image():
# Use a file dialog to let the user select an image.
selected_file = filedialog.askopenfilename()
if selected_file:
# Read the selected image.
selected_image = cv2.imread(selected_file)
# Convert the image to RGB format.
selected_image_rgb = cv2.cvtColor(selected_image, cv2.COLOR_BGR2RGB)
# Get face encodings for the selected image.
selected_face_encodings = face_recognition.face_encodings(selected_image_rgb)
match_found = False # Flag to track if a match is found.
if not selected_face_encodings:
print("No faces found in the selected image.")
else:
# Loop through the detected faces in the selected image.
for face_encoding in selected_face_encodings:
# Compare the current face encoding with the known encodings.
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
# Find the index of the best match. That is, the best resemblance.
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
# If a match is found, get the name of the recognized person.
recognized_name = face_labels[best_match_index].upper()
# Draw a green rectangle around the recognized face.
top, right, bottom, left = face_recognition.face_locations(selected_image_rgb)[0]
cv2.rectangle(selected_image, (left, top), (right, bottom), (0, 255, 0), 2,)
# Display the name below the face.
cv2.putText(selected_image, recognized_name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 0.5,
(0, 255, 0), 2)
match_found = True # Match found flag.
break # Exit loop as soon as a match is found.
if not match_found:
# If no match is found, draw a red rectangle and display No Match.
top, right, bottom, left = face_recognition.face_locations(selected_image_rgb)[0]
cv2.rectangle(selected_image, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.putText(selected_image, "No match", (left + 6, bottom - 6), cv2.FONT_HERSHEY_COMPLEX, 1,
(0, 0, 255), 2)
# Show the image with the rectangle and name.
cv2.imshow("Recognized Image", selected_image)
known_faces.clear()# To prevent the program from slowing down due to excess unnecessary encodings.
cv2.waitKey(0)
cv2.destroyAllWindows()
# Create the main application window.
root = tk.Tk()
root.title("Face Recognition Program")
# Create a button to select an image for recognition.
select_button = tk.Button(root, text="Select Image for Recognition", command=select_and_recognize_image)
select_button.pack(pady=10)
# Function to quit the application.
def quit_app():
root.quit()
# Create a quit button to exit the application.
quit_button = tk.Button(root, text="Quit", command=quit_app)
quit_button.pack(pady=10)
# Start the Tkinter event loop.
root.mainloop()
