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main.py
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main.py
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print("""
_ _ _ ____ ___ ____
| | | | __ _ ___| | _| ___| / _ \| ___|
| |_| |/ _` |/ __| |/ /___ \| | | |___ \
| _ | (_| | (__| < ___) | |_| |___) |
|_| |_|\__,_|\___|_|\_\____/ \___/|____/
""")
# Import necessary libraries
import cv2
import numpy as np
import face_recognition
import os
# Define the directory where your images are stored
path = "faces"
# Create lists to store images, class names, and face encodings
images = []
classnames = []
# Get the list of image files in the directory
mylist = os.listdir(path)
# Print the list of image files for reference
print("List of image files:", mylist)
# Loop through the images and store them in the 'images' list, along with class names
for cl in mylist:
curimg = cv2.imread(f"{path}/{cl}")
images.append(curimg)
classnames.append(os.path.splitext(cl)[0])
# Print the class names for reference
print("Class names:", classnames)
# Function to find face encodings for a list of images
def find_encodings(images):
encodelist = []
for img in images:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
encode = face_recognition.face_encodings(img)[0]
encodelist.append(encode)
return encodelist
# Get the known face encodings
encodelistknow = find_encodings(images)
print("Encoding completed...")
# Open the webcam (you can change the argument to '1' if '0' doesn't work)
cap = cv2.VideoCapture(0)
while True:
success, img = cap.read()
# Resize the captured frame for faster processing
imgs = cv2.resize(img, (0, 0), None, 0.25, 0.25)
imgs = cv2.cvtColor(imgs, cv2.COLOR_BGR2RGB)
# Find face locations and encodings in the current frame
facecurframe = face_recognition.face_locations(imgs)
encodecurframe = face_recognition.face_encodings(imgs, facecurframe)
for encodeface, faceloc in zip(encodecurframe, facecurframe):
# Compare face encodings with known encodings
macthes = face_recognition.compare_faces(encodelistknow, encodeface)
facedis = face_recognition.face_distance(encodelistknow, encodeface)
matchindex = np.argmin(facedis)
if macthes[matchindex]:
# Get the name of the recognized person
name = classnames[matchindex].upper()
y1, x2, y2, x1 = faceloc
# Scale the coordinates for the original frame
y1, x2, y2, x1 = y1 * 4, x2 * 4, y2 * 4, x1 * 4
# Draw a rectangle around the recognized face
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
cv2.rectangle(img, (x1, y2 - 35), (x2, y2), (0, 255, 0), cv2.FILLED)
# Display the name of the recognized person
cv2.putText(img, name, (x1 + 6, y2 - 6), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
# Display the frame with recognized faces
cv2.imshow("Face Recognition", img)
# Exit the loop when the 'q' key is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the webcam and close all OpenCV windows
cap.release()
cv2.destroyAllWindows()