425 lines
15 KiB
Python
425 lines
15 KiB
Python
import sys
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import os
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import random
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import cv2
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import numpy as np
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from PySide6.QtCore import QTimer, Qt, QThread, Signal
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from PySide6.QtGui import QImage, QPixmap
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from PySide6.QtWidgets import QApplication, QWidget, QLabel, QPushButton, QSlider, QVBoxLayout, QProgressBar
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# ======================================================
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# 加载动画界面
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# ======================================================
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class LoadingScreen(QWidget):
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def __init__(self):
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super().__init__()
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self.setWindowTitle("Face Random Selector")
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self.setFixedSize(300, 150)
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self.setWindowFlags(Qt.FramelessWindowHint | Qt.WindowStaysOnTopHint)
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self.setStyleSheet("""
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background-color: #1E90FF;
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border-radius: 10px;
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""")
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# 创建布局
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layout = QVBoxLayout()
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layout.setAlignment(Qt.AlignCenter)
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layout.setSpacing(15)
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# 加载提示文字
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self.loading_label = QLabel("正在加载模型...")
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self.loading_label.setAlignment(Qt.AlignCenter)
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self.loading_label.setStyleSheet("""
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color: white;
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font-size: 18px;
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font-weight: bold;
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""")
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layout.addWidget(self.loading_label)
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# 进度条 - 设置为不确定模式，显示加载动画
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self.progress_bar = QProgressBar()
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self.progress_bar.setRange(0, 0) # 设置为0,0表示不确定模式
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self.progress_bar.setFixedHeight(10)
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self.progress_bar.setStyleSheet("""
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QProgressBar {
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border: 1px solid rgba(255, 255, 255, 100);
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border-radius: 5px;
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background-color: rgba(255, 255, 255, 50);
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}
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QProgressBar::chunk {
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background-color: white;
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border-radius: 4px;
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}
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""")
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layout.addWidget(self.progress_bar)
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self.setLayout(layout)
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# ======================================================
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# 模型加载线程
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# ======================================================
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class ModelLoader(QThread):
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loaded = Signal(object, str) # 模型加载完成信号
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progress = Signal(str) # 进度更新信号
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def __init__(self, model_path):
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super().__init__()
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self.model_path = model_path
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def run(self):
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try:
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# 模拟进度更新
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self.progress.emit("正在初始化模型...")
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# 启用 OpenCV 的优化并设置线程数，减少模型加载/运算开销
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try:
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cv2.setUseOptimized(True)
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except Exception:
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pass
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# 延迟加载检测器。
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detector = cv2.FaceDetectorYN.create(
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self.model_path,
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"",
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(320, 240),
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score_threshold=0.6,
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nms_threshold=0.3,
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top_k=5000
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)
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self.progress.emit("模型加载完成")
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self.loaded.emit(detector, "")
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except Exception as e:
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self.loaded.emit(None, str(e))
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# ======================================================
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# 主窗口
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# ======================================================
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class FaceRandomApp(QWidget):
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def __init__(self):
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super().__init__()
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# 先显示加载页面
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self.loading_screen = LoadingScreen()
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self.loading_screen.show()
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# 强制立即处理GUI事件，确保加载页面能立即显示
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QApplication.processEvents()
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# 延迟初始化主界面
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QTimer.singleShot(100, self.initialize_app)
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def initialize_app(self):
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"""初始化应用程序"""
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# 状态
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self.state = "normal"
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self.selected_face_index = -1
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self.static_frame = None
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self.faces_snapshot = []
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self.faces = []
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self.detection_confidence = 0.6
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self.is_static_mode = False
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# 初始化摄像头
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self.cap = cv2.VideoCapture(0)
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if self.cap.isOpened():
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self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
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self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
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# 提高图像质量设置
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self.cap.set(cv2.CAP_PROP_FPS, 30)
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# UI
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self.setup_ui()
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# 启动模型加载
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self.start_model_loading()
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def start_model_loading(self):
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"""启动模型加载"""
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model_path = self.get_yunet_model_path()
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if model_path:
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self.loader = ModelLoader(model_path)
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self.loader.loaded.connect(self.on_model_loaded)
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self.loader.progress.connect(self.on_loading_progress)
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self.loader.start()
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else:
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# 如果没有模型，跳过模型加载
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self.loading_screen.close()
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self.show()
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self.timer = QTimer()
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self.timer.timeout.connect(self.update_frame)
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self.timer.start(30)
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def get_yunet_model_path(self):
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"""获取模型路径"""
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base_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = os.path.join(base_dir, "model", "face_detection_yunet_2023mar.onnx")
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return model_path if os.path.exists(model_path) else None
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def on_loading_progress(self, message):
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"""加载进度更新"""
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self.loading_screen.loading_label.setText(message)
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def on_model_loaded(self, detector, error):
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"""模型加载完成回调"""
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if detector and not error:
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self.detector = detector
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# 更新置信度
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self.detector.setScoreThreshold(self.detection_confidence)
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else:
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print(f"模型加载失败: {error}")
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# 关闭加载界面并显示主窗口
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self.loading_screen.close()
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self.show()
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# 启动视频更新定时器
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self.timer = QTimer()
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self.timer.timeout.connect(self.update_frame)
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self.timer.start(30)
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# ======================================================
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# UI
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# ======================================================
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def setup_ui(self):
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self.setWindowTitle("Face Random Selector")
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self.resize(1280, 720)
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self.video_label = QLabel(self)
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self.video_label.setGeometry(0, 0, self.width(), self.height())
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self.video_label.setStyleSheet("background: black;")
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# 随机按钮
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self.btn = QPushButton("随机", self)
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self.btn.setFixedSize(140, 55)
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self.btn.move(20, self.height() - 75)
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self.btn.clicked.connect(self.on_random_clicked)
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self.btn.setStyleSheet("""
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QPushButton {
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background-color: rgba(0, 0, 0, 180);
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color: white;
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font-size: 20px;
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font-weight: bold;
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border: none;
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border-radius: 8px;
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}
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QPushButton:hover {
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background-color: rgba(30, 30, 30, 200);
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}
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QPushButton:pressed {
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background-color: rgba(50, 50, 50, 220);
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}
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""")
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# 置信度滑条
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self.slider = QSlider(Qt.Horizontal, self)
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self.slider.setRange(30, 90)
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self.slider.setValue(int(self.detection_confidence * 100))
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self.slider.setFixedWidth(200)
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self.slider.move(120, 20)
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self.slider.valueChanged.connect(self.on_confidence_change)
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self.slider.setStyleSheet("""
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QSlider::groove:horizontal {
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background: rgba(0, 0, 0, 120);
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height: 4px;
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border-radius: 2px;
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}
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QSlider::handle:horizontal {
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background: white;
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width: 14px;
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height: 14px;
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border-radius: 7px;
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margin: -5px 0;
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}
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""")
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# 置信度标签
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self.conf_label = QLabel("置信度", self)
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self.conf_label.move(20, 20)
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self.conf_label.setStyleSheet("""
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color: white;
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font-size: 16px;
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font-weight: bold;
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background: transparent;
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""")
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# 静止按钮 (圆形半透明)
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self.static_btn = QPushButton("静", self)
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self.static_btn.setFixedSize(55, 55)
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self.static_btn.move(self.btn.x() + self.btn.width() + 20, self.height() - 75)
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self.static_btn.clicked.connect(self.on_static_clicked)
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self.static_btn.setStyleSheet("""
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QPushButton {
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background-color: rgba(0, 0, 0, 140);
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color: white;
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font-size: 18px;
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font-weight: bold;
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border: none;
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border-radius: 27px;
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}
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QPushButton:hover {
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background-color: rgba(30, 30, 30, 180);
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}
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QPushButton:pressed {
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background-color: rgba(50, 50, 50, 200);
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}
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""")
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def resizeEvent(self, event):
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self.video_label.setGeometry(0, 0, self.width(), self.height())
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self.btn.move(20, self.height() - 75)
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if hasattr(self, 'static_btn'):
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self.static_btn.move(self.btn.x() + self.btn.width() + 20, self.height() - 75)
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super().resizeEvent(event)
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def on_confidence_change(self, value):
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self.detection_confidence = value / 100.0
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if hasattr(self, 'detector'):
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self.detector.setScoreThreshold(self.detection_confidence)
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def on_random_clicked(self):
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if self.state == "normal":
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self.state = "random"
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self.btn.setText("重置")
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if len(self.faces) > 0:
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self.selected_face_index = random.randint(0, len(self.faces) - 1)
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# 捕获静态帧
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if self.cap and self.cap.isOpened():
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ret, img = self.cap.read()
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if ret:
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img = cv2.flip(img, 1)
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img = self.resize_cover(img, self.video_label.width(), self.video_label.height())
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self.static_frame = img.copy()
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self.faces_snapshot = self.faces.copy()
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else:
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self.state = "normal"
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self.btn.setText("随机")
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self.selected_face_index = -1
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self.static_frame = None
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def on_static_clicked(self):
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""" 静止模式 """
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if not getattr(self, 'is_static_mode', False):
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# 静止模式：保留已加载的模型，不释放摄像头，只停止读取/检测
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self.is_static_mode = True
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try:
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self.static_btn.setText("恢复")
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except Exception:
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pass
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else:
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# 退出静止模式，恢复摄像头和模型
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self.is_static_mode = False
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try:
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self.static_btn.setText("静")
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except Exception:
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pass
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# 重新打开摄像头（仅在当前没有打开时）
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if not hasattr(self, 'cap') or self.cap is None or not self.cap.isOpened():
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self.cap = cv2.VideoCapture(0)
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if self.cap.isOpened():
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self.cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
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self.cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
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self.cap.set(cv2.CAP_PROP_FPS, 30)
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# 保留 detector（如果之前加载过则直接可用），无需重新加载。
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# 重新打开摄像头，检测器若存在则继续使用；否则重新加载模型。
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if not hasattr(self, 'detector') or self.detector is None:
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# 仅在没有 detector 时重新加载
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self.start_model_loading()
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def resize_cover(self, img, target_w, target_h):
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h, w = img.shape[:2]
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scale = max(target_w / w, target_h / h)
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new_w = int(w * scale)
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new_h = int(h * scale)
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resized = cv2.resize(img, (new_w, new_h))
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x_start = (new_w - target_w) // 2
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y_start = (new_h - target_h) // 2
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return resized[y_start:y_start + target_h, x_start:x_start + target_w]
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def draw_face_with_confidence(self, img, face, color, thickness=2):
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"""绘制人脸框和置信度"""
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x, y, w, h = map(int, face[:4])
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# 仅绘制人脸矩形框，不显示置信度文本
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cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness)
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def update_frame(self):
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# 如果处于静止模式，不使用摄像头和模型，摄像头区域黑屏以节省资源
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if getattr(self, 'is_static_mode', False):
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h = self.video_label.height()
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w = self.video_label.width()
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black = np.zeros((h, w, 3), dtype=np.uint8)
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self.display(black)
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return
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# 非静止模式，需要摄像头
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if not self.cap or not self.cap.isOpened():
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return
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ret, img = self.cap.read()
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if not ret:
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return
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# 镜像翻转
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img = cv2.flip(img, 1)
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# resize_cover方法，保持视野大小
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img = self.resize_cover(img, self.video_label.width(), self.video_label.height())
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if self.state == "normal":
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if hasattr(self, 'detector') and self.detector is not None:
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# 设置检测器输入大小
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self.detector.setInputSize((img.shape[1], img.shape[0]))
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# 检测人脸
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_, detected = self.detector.detect(img)
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self.faces = detected if detected is not None else []
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# 绘制检测结果（正常模式绘制全部绿色框）
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for face in self.faces:
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self.draw_face_with_confidence(img, face, (0, 255, 0), 2)
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else:
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# 如果没有模型，只显示原始图像
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pass
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else: # random 模式
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if self.static_frame is not None:
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img = self.static_frame.copy()
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# 仅绘制被选中的红色框，其他不显示
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if self.selected_face_index != -1 and len(self.faces_snapshot) > self.selected_face_index:
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face = self.faces_snapshot[self.selected_face_index]
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self.draw_face_with_confidence(img, face, (0, 0, 255), 3)
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self.display(img)
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def display(self, img):
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# 图像显示
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rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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h, w, c = rgb.shape
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qimg = QImage(rgb.data, w, h, c * w, QImage.Format_RGB888)
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self.video_label.setPixmap(QPixmap.fromImage(qimg))
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def closeEvent(self, event):
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if self.cap and self.cap.isOpened():
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self.cap.release()
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if hasattr(self, 'loader') and self.loader.isRunning():
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self.loader.quit()
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self.loader.wait()
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event.accept()
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# ======================================================
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def main():
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app = QApplication(sys.argv)
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window = FaceRandomApp()
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# 注意：这里不再调用 window.show()，因为 FaceRandomApp 内部会处理显示逻辑
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sys.exit(app.exec())
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if __name__ == "__main__":
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main() |