for anyone who may want it for servo control, built on the CORE LED control script.. This is a working version, i did not say a perfect version… but it does work…
import face_recognition
import cv2
import numpy as np
from picamera2 import Picamera2
import time
import pickle
from gpiozero import Servo
from time import sleep
# Load pre-trained face encodings
print("[INFO] Loading encodings...")
with open("encodings.pickle", "rb") as f:
data = pickle.loads(f.read())
known_face_encodings = data["encodings"]
known_face_names = data["names"]
# Initialize the camera
print("[INFO] Initializing camera...")
picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(main={"format": 'XRGB8888', "size": (640, 480)})) # Reduced resolution
picam2.start()
# Initialize GPIO and Servo
myGPIO = 14
servo = Servo(myGPIO)
servo.detach() # Servo starts in detached (off) state
# Processing variables
cv_scaler = 6
face_locations = []
face_encodings = []
face_names = []
frame_count = 0
start_time = time.time()
fps = 0
process_every_n_frames = 5
# Authorized users
authorized_names = ["THE NAME YOU TRAINED IT ON"]
def process_frame(frame):
global face_locations, face_encodings, face_names
resized_frame = cv2.resize(frame, (0, 0), fx=(1/cv_scaler), fy=(1/cv_scaler))
rgb_resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)
face_locations = face_recognition.face_locations(rgb_resized_frame, model='hog') # Use faster model
face_encodings = face_recognition.face_encodings(rgb_resized_frame, face_locations)
face_names = []
authorized_face_detected = False
for face_encoding in face_encodings:
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_match_index]
if name in authorized_names:
authorized_face_detected = True
face_names.append(name)
# Trigger servo if authorized face detected
if authorized_face_detected:
print("HI YOU!!!")
servo.mid()
sleep(0.5)
servo.min()
sleep(0.5)
servo.max()
sleep(0.5)
servo.detach()
else:
print("NOT YOU!!!")
servo.detach()
return frame
def draw_results(frame):
for (top, right, bottom, left), name in zip(face_locations, face_names):
top *= cv_scaler
right *= cv_scaler
bottom *= cv_scaler
left *= cv_scaler
cv2.rectangle(frame, (left, top), (right, bottom), (244, 42, 3), 3)
cv2.rectangle(frame, (left - 3, top - 35), (right + 3, top), (244, 42, 3), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, top - 6), font, 1.0, (255, 255, 255), 1)
if name in authorized_names:
cv2.putText(frame, "Authorized", (left + 6, bottom + 23), font, 0.6, (0, 255, 0), 1)
return frame
def calculate_fps():
global frame_count, start_time, fps
frame_count += 1
elapsed_time = time.time() - start_time
if elapsed_time > 1:
fps = frame_count / elapsed_time
frame_count = 0
start_time = time.time()
return fps
# Main loop
print("[INFO] Running facial recognition. Press 'q' to quit.")
try:
while True:
frame = picam2.capture_array()
if frame_count % process_every_n_frames == 0:
processed_frame = process_frame(frame)
else:
processed_frame = frame
display_frame = draw_results(processed_frame)
current_fps = calculate_fps()
cv2.putText(display_frame, f"FPS: {current_fps:.1f}", (display_frame.shape[1] - 150, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('Video', display_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
except KeyboardInterrupt:
print("\n[INFO] Exiting...")
# Cleanup
cv2.destroyAllWindows()
picam2.stop()
servo.detach()
