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[feat] 3.5

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He
2025-12-03 19:55:35 +08:00
parent 9a015f2358
commit 41a31473f3
3 changed files with 376 additions and 2 deletions
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3-5.py Normal file
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import sys
import os
import random
import cv2
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...")
# 延迟加载检测器
detector = cv2.FaceDetectorYN.create(
self.model_path,
"",
(640, 480),
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
# 初始化摄像头 - 使用原代码的高分辨率设置
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;
""")
def resizeEvent(self, event):
self.video_label.setGeometry(0, 0, self.width(), self.height())
self.btn.move(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 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])
score = face[4]
# 绘制人脸框
cv2.rectangle(img, (x, y), (x + w, y + h), color, thickness)
# 绘制置信度文本
confidence_text = f"{score:.2f}"
text_y = max(y - 10, 20)
# 创建半透明背景
overlay = img.copy()
text_size = cv2.getTextSize(confidence_text, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)[0]
cv2.rectangle(overlay, (x, text_y - text_size[1] - 5),
(x + text_size[0] + 10, text_y + 5), color, -1)
# 应用透明度
alpha = 0.7
cv2.addWeighted(overlay, alpha, img, 1 - alpha, 0, img)
# 绘制文本
cv2.putText(img, confidence_text, (x + 5, text_y),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
def update_frame(self):
if not self.cap or not self.cap.isOpened():
return
ret, img = self.cap.read()
if not ret:
return
# 使用原代码的镜像翻转
img = cv2.flip(img, 1)
# 使用原代码的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'):
# 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
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()
for i, face in enumerate(self.faces_snapshot):
color = (0, 0, 255) if i == self.selected_face_index else (0, 255, 0)
thickness = 3 if i == self.selected_face_index else 2
self.draw_face_with_confidence(img, face, color, thickness)
self.display(img)
def display(self, img):
# 保持原代码的图像显示方式
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()

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5
readme.md
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@@ -10,14 +10,15 @@ FaceRandomSelector 是一款人脸随机选择工具。软件采用opencv的人
2.4版本之后：
` nuitka --standalone --include-package=cv2 --include-package=numpy --enable-plugin=pyside6 --windows-console-mode=disable --windows-icon-from-ico=./text.ico --include-data-dir=F:\python310\lib\site-packages\cv2\data=cv2\data --include-data-files=model/face_detection_yunet_2023mar.onnx=model/face_detection_yunet_2023mar.onnx --output-dir=dist34 --remove-output 3-4.py`
` nuitka --standalone --include-package=cv2 --enable-plugin=pyside6 --windows-console-mode=disable --windows-icon-from-ico=./text.ico --include-data-files=model/face_detection_yunet_2023mar.onnx=model/face_detection_yunet_2023mar.onnx --output-dir=dist34 --remove-output 3-5.py`
## 3.版本说明
1. 1.py 2.py：非稳定版本
2. 3-1.py：存在UI不美观 窗口大小 摄像头清晰度等问题
3. 3-2.py：解决3-1.py的相关问题
4. 3-3.py：添加加载进度条
5. 3-test.py：由3-3.py改编 给出具体的[人脸识别模型无法获取]的相关报错
5. 3-test.py：由3-3.py改编 给出具体的人脸识别模型无法获取的相关报错
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
> 软件务必保存在纯英文路径中！