Нейросеть для опознавания лиц

Question

Нейросеть для опознавания лиц

Alpha Cep Ученик (128), на голосовании 2 недели назад

Что нужно добавить в код, чтобы он определял лицо и пол человека.
обучение:
from ultralytics import YOLO
import subprocess
import os
import glob
import sys

bl = "run/detect/train/weight/ best.pt "

subprocess.run (f" yolo task = detect mode = train data={"dataset"}/data.yaml model = ' yolo11n.pt ' epochs = 50 imgsz = 640", shell=True)
subprocess.run (f" yolo task = detect mode = val model = {bl} data = {"dataset"}/data.yaml", shell=True)
subprocess.run (f" yolo task = detect mode = predict model = {bl} conf = 0.25 source = {"dataset"}/train/images save = True", shell=True)

Сервер:
from flask import Flask, request, send_file, jsonify
from ultralytics import YOLO
from PIL import Image
import subprocess
import io
import glob
import os

app = Flask(__name__)
model = YOLO("runs/detect/train/weights/ best.pt ")
@app.route('/detect_faces', methods=['POST'])
def detect_faces():
if 'image' not in request.files:
return jsonify({"error": "No image provided"}), 400
image = Image.open (request.files ['image'] ).convert('RGB')
temp_image_path = "temp_image.jpg"
image.save(temp_image_path)
subprocess.run (f"yolo task=detect mode=predict model='runs/detect/train/weights/ best.pt ' conf=0.1 source={temp_image_path} save=True", shell=True)
predict_dirs = glob.glob("runs/detect/predict*/")
if predict_dirs:
latest_predict_dir = max(predict_dirs, key=os.path.getmtime)
img_path = os.path.join(latest_predict_dir, os.path.basename(temp_image_path))
img_path_with_new_extension = os.path.splitext(img_path)[0] + '.jpg'

result_image = Image.open (img_path_with_new_extension)
byte_io = io.BytesIO()
result_image.save(byte_io, 'JPEG')
byte_ io.seek (0)

return send_file(byte_io, mimetype='image/jpeg')
return jsonify({"error": "Prediction output not found."}), 500
@app.route('/', methods=['GET'])
def index():
return jsonify({"message": "API для обнаружения объектов. Отправьте POST запрос на /detect_faces с изображением."})

if __name__ == '__main__':
app.run (debug=True)

Дополнен 1 месяц назад

import sys
from PyQt5.QtWidgets import QApplication, QLabel, QPushButton, QVBoxLayout, QHBoxLayout, QFileDialog, QMainWindow,QWidget
from PyQt5.QtGui import QPixmap, QImage
import requests
from io import BytesIO
from PIL import Image

class FaceDetectionApp(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Распознавание лиц")
self.resize(800, 600)
self.init_ui()

def init_ui(self):
self.image_label = QLabel("Загрузите изображение")
self.image_label.setFixedSize(600, 400)
self.image_label.setStyleSheet("border: 1px solid black;")
self.image_label.setScaledContents(True)

self.upload_button = QPushButton("Загрузить изображение")
self.upload_button.clicked.connect(self.load_image)

self.detect_button = QPushButton("Отправить на сервер")
self.detect_button.clicked.connect(self.send_image)
self.detect_button.setEnabled(False)

layout = QVBoxLayout()
layout.addWidget(self.image_label)
layout.addWidget(self.upload_button)
layout.addWidget(self.detect_button)

container = QWidget()
container.setLayout(layout)
self.setCentralWidget(container)

Дополнен 1 месяц назад

def load_image(self):
options = QFileDialog.Options()
file_path, _ = QFileDialog.getOpenFileName(self, "Выберите изображение", "", "Images (*.png *.xpm *.jpg)",options=options)
if file_path:
self.image_path = file_path
pixmap = QPixmap(file_path)
self.image_label.setPixmap(pixmap)
self.detect_button.setEnabled(True)

def send_image(self):

url = "http://127.0.0.1:5000/detect_faces"

try:

with open(self.image_path, 'rb') as image_file:

response = requests.post(url, files={"image": image_file})

if response.status_code == 200:

content_type = response.headers.get('Content-Type')

if content_type == 'image/jpeg':

result_image = Image.open(BytesIO(response.content))

result_image = result_image.convert("RGBA")

data = result_image.tobytes("raw", "RGBA")

qimage = QImage(data, result_image.width, result_image.height, QImage.Format_RGBA8888)

pixmap = QPixmap.fromImage(qimage)

self.image_label.setPixmap(pixmap)

else:

self.image_label.setText("Ответ не является изображением")

else:

self.image_label.setText(f"Ошибка сервера: {response.status_code}")

except Exception as e:

self.image_label.setText(f"Ошибка: {str(e)}")

if __name__ == "__main__":

app = QApplication(sys.argv)

window = FaceDetectionApp()

window.show()

sys.exit(app.exec_())

Answer 1

???? Гуру (2578) 1 месяц назад

много чего нужно

Alpha CepУченик (128) 1 месяц назад

import unittest
from PyQt5.QtWidgets import QApplication
from local import FaceDetect
import sys

class TestFaceDetect(unittest.TestCase):
def test_send_image(self):
app = QApplication(sys.argv)
window = FaceDetect()
window.image_path = r'C:\Users\user\Desktop\test\rrr.jpg'
window.send_image()
self.assertIsNotNone(window.image_label.pixmap())
if __name__ == '__main__':
unittest.main()

Alpha Cep Ученик (128) Alpha Cep, import unittest from PyQt5.QtWidgets import QApplication from local import FaceDetect import sys import os class TestFaceDetect(unittest.TestCase): def test_send_image_to_server(self): app = QApplication(sys.argv) window = FaceDetect() image_path = r'C:\Users\user\Desktop\test\rrr.jpg' self.assertTrue(os.path.exists(image_path), f"Файл {image_path} не найден") window.image_path = image_path window.send_image() self.assertIsNotNone(window.image_label.pixmap()) if __name__ == '__main__': unittest.main()