[feat] 3.6

3-6.py

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

readme.md

@@ -21,4 +21,5 @@ FaceRandomSelector 是一款人脸随机选择工具。软件采用opencv的人
 6. 3-control.py：由3-3.py改编 用户可以修改相关参数
 7. 3-4.py：使用YuNet模型 加强对小人脸识别准确度
 8. 3-5.py：优化加载性能
+9. 3-6.py：优化加载性能 添加静止模式 删去置信度与选中绿框
 > 软件务必保存在纯英文路径中！