Я занимаюсь проектом по обнаружению лиц и хочу перейти с малиновой камеры на USB-камеру, что мне нужно изменить в этом к ⇐ Python

[Anonymous]

Это мой код. Я попытался запустить его на USB-камере, но цвета установлены неправильно. Я пробовал разные методы, но не смог получить правильный набор цветов. Мне нужно, чтобы он был полностью записан на USB-камеру. Пожалуйста, помогите
Я думаю, что изменения в основном необходимо вносить в самом начале и в самом начале пути

Код: Выделить всё

import face_recognition
import cv2
import numpy as np
from picamera2 import Picamera2
import time
import pickle
from gpiozero import LED
from gpiozero import AngularServo
servo = AngularServo(18, min_pulse_width=0.0006, max_pulse_width=0.0023)
# 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"]
servo.angle = 90
# Initialize the camera
picam2 = Picamera2(0)
picam2.configure(picam2.create_preview_configuration(main={"format": 'XRGB8888', "size": (640, 480)}))
picam2.start()

# Initialize GPIO
#output = LED(14)

# Initialize our variables
cv_scaler = 5 # this has to be a whole number

face_locations = []
face_encodings = []
face_names = []
frame_count = 0
start_time = time.time()
fps = 0

# List of names that will trigger the GPIO pin
authorized_names = ["Damir", "Amir"]  # Replace with names you wish to authorise THIS IS CASE-SENSITIVE

Код: Выделить всё

def process_frame(frame):
global face_locations, face_encodings, face_names

# Resize the frame using cv_scaler to increase performance (less pixels processed, less time spent)
resized_frame = cv2.resize(frame, (0, 0), fx=(1/cv_scaler), fy=(1/cv_scaler))

# Convert the image from BGR to RGB colour space, the facial recognition library uses RGB, OpenCV uses BGR
rgb_resized_frame = cv2.cvtColor(resized_frame, cv2.COLOR_BGR2RGB)

# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_resized_frame)
face_encodings = face_recognition.face_encodings(rgb_resized_frame, face_locations, model='large')

face_names = []
authorized_face_detected = False

for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown"

# Use the known face with the smallest distance to the new face
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]
# Check if the detected face is in our authorized list
if name in authorized_names:
authorized_face_detected = True
face_names.append(name)

# Control the GPIO pin based on face detection
if authorized_face_detected:
# Turn on Pin
servo.angle = -90

servo.angle = 90

else:
servo.angle = -90
# Turn off Pin

return frame

Код: Выделить всё

def draw_results(frame):
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled
top *= cv_scaler
right *= cv_scaler
bottom *= cv_scaler
left *= cv_scaler

# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (244, 42, 3), 3)

# Draw a label with a name below the face
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)

# Add an indicator if the person is authorized
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

Код: Выделить всё

while True:
# Capture a frame from camera
frame = picam2.capture_array()

# Process the frame with the function
processed_frame = process_frame(frame)

# Get the text and boxes to be drawn based on the processed frame
display_frame = draw_results(processed_frame)

# Calculate and update FPS
current_fps = calculate_fps()

# Attach FPS counter to the text and boxes
cv2.putText(display_frame, f"FPS: {current_fps:.1f}", (display_frame.shape[1] - 150, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

# Display everything over the video feed.
cv2.imshow('Video', display_frame)

# Break the loop and stop the script if 'q' is pressed
if cv2.waitKey(1) == ord("q"):
break

# By breaking the loop we run this code here which closes everything
cv2.destroyAllWindows()
picam2.stop()
output.off()  # Make sure to turn off the GPIO pin when exiting

Проект находится на камере Raspberry, и я хочу, чтобы он был на камере USB

Подробнее здесь: https://stackoverflow.com/questions/792 ... -camera-to