opencv-python QObject::moveToThread报错； 4.6.0.66版本无代码补全；Matplotlib plt.show()无显示

已于 2022-10-27 16:14:22 修改
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分类专栏： OpenCV python相关学习 文章标签： opencv python 人工智能
于 2022-10-27 15:55:49 首次发布
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问题1： opencv-python QT报错

报错
当opencv_python版本高于4.3时，使用imshow可能报以下错误：

QObject::moveToThread: Current thread (0x1186a80) is not the object’s thread (0x272b130).
Cannot move to target thread (0x1186a80)

qt.qpa.plugin: Could not load the Qt platform plugin “xcb” even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.

Available platform plugins are: xcb, eglfs, linuxfb, minimal, minimalegl, offscreen, vnc, wayland-egl, wayland, wayland-xcomposite-egl, wayland-xcomposite-glx, webgl.

意思是QT没有加载到libqxcb这个插件，即使找到了它。opencv4的imshow函数是使用QT

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import sys import cv2 import numpy as np from PySide6.QtWidgets import ( QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, QDialog, QLabel, QPushButton, QFileDialog, QTextEdit, QTabWidget, QMessageBox, QProgressBar, QSlider, QComboBox, QGroupBox, QGridLayout, QToolBar, QStatusBar, QDockWidget, QSplitter, QScrollArea, QMenu, QSpinBox, QDoubleSpinBox, QCheckBox, QRadioButton, QButtonGroup # 添加缺失的导入 ) from PySide6.QtGui import ( QAction, QPixmap, QImage, QPainter, QPen, QColor, QIcon, QKeySequence, QTransform, QCursor ) from PySide6.QtCore import Qt, QThread, Signal, QPoint, QSize, QRect import matplotlib matplotlib.use('Agg') # 使用Agg后端，不显示图形窗口 import matplotlib.pyplot as plt from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.figure import Figure import os import math from scipy import ndimage try: from skimage.feature import graycomatrix, graycoprops except ImportError: from skimage.feature import greycomatrix as graycomatrix, greycoprops as graycoprops plt.rcParams["font.family"] = ["SimHei"] # 仅保留 SimHei（黑体） plt.rcParams["axes.unicode_minus"] = False class ImageProcessingThread(QThread): """图像处理的工作线程，避免界面卡顿""" finished = Signal(object) def __init__(self, function, *args): super().__init__() self.function = function self.args = args def run(self): result = self.function(*self.args) self.finished.emit(result) class ImageViewer(QWidget): """图像显示组件，支持缩放和平移""" def __init__(self, parent=None): super().__init__(parent) self.image = QImage() self.scale_factor = 1.0 self.dragging = False self.last_pos = QPoint() self.setMouseTracking(True) layout = QVBoxLayout(self) self.label = QLabel(self) self.label.setAlignment(Qt.AlignCenter) self.label.setMinimumSize(1, 1) # 允许缩小 layout.addWidget(self.label) def set_image(self, image): self.image = image self.update_pixmap() def update_pixmap(self): if not self.image.isNull(): scaled_pixmap = QPixmap.fromImage(self.image).scaled( self.image.width() * self.scale_factor, self.image.height() * self.scale_factor, Qt.KeepAspectRatio, Qt.SmoothTransformation) self.label.setPixmap(scaled_pixmap) def wheelEvent(self, event): """鼠标滚轮缩放""" delta = event.angleDelta().y() if delta > 0: self.scale_factor *= 1.1 else: self.scale_factor *= 0.9 self.update_pixmap() def mousePressEvent(self, event): """鼠标按下开始拖动""" if event.button() == Qt.LeftButton: self.dragging = True self.last_pos = event.position().toPoint() # 修改后 def mouseMoveEvent(self, event): """鼠标拖动图像""" if self.dragging: delta = event.pos() - self.last_pos scroll_bar = self.parent().horizontalScrollBar() scroll_bar.setValue(scroll_bar.value() - delta.x()) scroll_bar = self.parent().verticalScrollBar() scroll_bar.setValue(scroll_bar.value() - delta.y()) self.last_pos = event.pos() def mouseReleaseEvent(self, event): """鼠标释放结束拖动""" if event.button() == Qt.LeftButton: self.dragging = False def resizeEvent(self, event): """窗口大小变化时更新图像显示""" self.update_pixmap() super().resizeEvent(event) class HistogramWidget(QWidget): """直方图显示组件""" def __init__(self, parent=None): super().__init__(parent) self.figure = Figure(figsize=(5, 3), dpi=100) self.canvas = FigureCanvas(self.figure) layout = QVBoxLayout(self) layout.addWidget(self.canvas) self.axes = self.figure.add_subplot(111) def update_histogram(self, image): """更新直方图显示""" self.axes.clear() if image.ndim == 3: # 彩色图像 colors = ('b', 'g', 'r') for i, color in enumerate(colors): hist = cv2.calcHist([image], [i], None, [256], [0, 256]) self.axes.plot(hist, color=color) else: # 灰度图像 hist = cv2.calcHist([image], [0], None, [256], [0, 256]) self.axes.plot(hist, color='black') self.axes.set_xlim([0, 256]) self.axes.set_title('图像直方图') self.axes.set_xlabel('像素值') self.axes.set_ylabel('像素数量') self.figure.tight_layout() self.canvas.draw() class GLCMWidget(QWidget): """灰度共生矩阵(GLCM)特征显示组件""" def __init__(self, parent=None): super().__init__(parent) layout = QVBoxLayout(self) self.text_edit = QTextEdit() self.text_edit.setReadOnly(True) layout.addWidget(self.text_edit) def update_glcm(self, image): """更新GLCM特征显示""" if image.ndim == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 量化到16级灰度以减少计算量 image_quantized = (image // 16).astype(np.uint8) # 计算GLCM矩阵 (距离=1, 角度=0, 45, 90, 135度) glcm = graycomatrix(image_quantized, distances=[1], angles=[0, np.pi / 4, np.pi / 2, 3 * np.pi / 4], levels=16, symmetric=True, normed=True) # 计算各种特征 contrast = graycoprops(glcm, 'contrast')[0] dissimilarity = graycoprops(glcm, 'dissimilarity')[0] homogeneity = graycoprops(glcm, 'homogeneity')[0] energy = graycoprops(glcm, 'energy')[0] correlation = graycoprops(glcm, 'correlation')[0] asm = graycoprops(glcm, 'ASM')[0] # 显示结果 result = "灰度共生矩阵(GLCM)特征:\n\n" result += f"对比度: {contrast}\n" result += f"相异性: {dissimilarity}\n" result += f"同质性: {homogeneity}\n" result += f"能量: {energy}\n" result += f"相关性: {correlation}\n" result += f"角二阶矩: {asm}\n" self.text_edit.setText(result) class ProcessingDialog(QDialog): def __init__(self, title, parent=None): super().__init__(parent) self.setWindowTitle(title) self.layout = QGridLayout(self) self.setLayout(self.layout) self.row = 0 def add_slider(self, label_text, min_val, max_val, default_val, step=1, callback=None): """添加滑块控件""" label = QLabel(label_text) slider = QSlider(Qt.Horizontal) slider.setMinimum(min_val) slider.setMaximum(max_val) slider.setValue(default_val) slider.setSingleStep(step) spinbox = QSpinBox() # 修正拼写错误 spinbox.setMinimum(min_val) spinbox.setMaximum(max_val) spinbox.setValue(default_val) spinbox.setSingleStep(step) # 同步滑块和数值框 slider.valueChanged.connect(spinbox.setValue) spinbox.valueChanged.connect(slider.setValue) if callback: slider.valueChanged.connect(callback) self.layout.addWidget(label, self.row, 0) self.layout.addWidget(slider, self.row, 1) self.layout.addWidget(spinbox, self.row, 2) self.row += 1 return slider, spinbox def add_double_slider(self, label_text, min_val, max_val, default_val, step=0.1, decimals=1, callback=None): """添加浮点数滑块控件""" label = QLabel(label_text) slider = QSlider(Qt.Horizontal) slider.setMinimum(int(min_val * 10)) slider.setMaximum(int(max_val * 10)) slider.setValue(int(default_val * 10)) spinbox = QDoubleSpinBox() spinbox.setMinimum(min_val) spinbox.setMaximum(max_val) spinbox.setValue(default_val) spinbox.setSingleStep(step) spinbox.setDecimals(decimals) # 同步滑块和数值框 def update_slider(value): slider.setValue(int(value * 10)) def update_spinbox(value): spinbox.setValue(value / 10) slider.valueChanged.connect(update_spinbox) spinbox.valueChanged.connect(update_slider) if callback: slider.valueChanged.connect(lambda: callback(spinbox.value())) self.layout.addWidget(label, self.row, 0) self.layout.addWidget(slider, self.row, 1) self.layout.addWidget(spinbox, self.row, 2) self.row += 1 return slider, spinbox def add_combo_box(self, label_text, items, default_index=0, callback=None): """添加下拉选择框""" label = QLabel(label_text) combo_box = QComboBox() combo_box.addItems(items) combo_box.setCurrentIndex(default_index) if callback: combo_box.currentIndexChanged.connect(callback) self.layout.addWidget(label, self.row, 0) self.layout.addWidget(combo_box, self.row, 1, 1, 2) self.row += 1 return combo_box def add_checkbox(self, label_text, default_state=False, callback=None): """添加复选框""" checkbox = QCheckBox(label_text) checkbox.setChecked(default_state) if callback: checkbox.stateChanged.connect(callback) self.layout.addWidget(checkbox, self.row, 0, 1, 3) self.row += 1 return checkbox def add_radio_buttons(self, label_text, options, default_index=0, callback=None): """添加单选按钮组""" label = QLabel(label_text) button_group = QButtonGroup(self) layout = QHBoxLayout() for i, option in enumerate(options): radio = QRadioButton(option) if i == default_index: radio.setChecked(True) button_group.addButton(radio, i) layout.addWidget(radio) if callback: button_group.buttonClicked.connect(callback) self.layout.addWidget(label, self.row, 0) self.layout.addLayout(layout, self.row, 1, 1, 2) self.row += 1 return button_group def add_button_box(self): """添加确认和取消按钮""" button_layout = QHBoxLayout() ok_button = QPushButton("确定") cancel_button = QPushButton("取消") ok_button.clicked.connect(self.accept) cancel_button.clicked.connect(self.reject) button_layout.addStretch() button_layout.addWidget(ok_button) button_layout.addWidget(cancel_button) self.layout.addLayout(button_layout, self.row, 0, 1, 3) self.row += 1 return ok_button, cancel_button class MainWindow(QMainWindow): """主窗口类""" def __init__(self): super().__init__() self.setWindowTitle("数字图像处理系统") self.setGeometry(100, 100, 1200, 800) # 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图像复原子菜单 restore_menu = QMenu("图像复原", self) motion_deblur_action = QAction("运动模糊复原", self) motion_deblur_action.triggered.connect(self.motion_deblur) restore_menu.addAction(motion_deblur_action) gaussian_noise_removal_action = QAction("高斯噪声去除", self) gaussian_noise_removal_action.triggered.connect(lambda: self.noise_removal("高斯噪声")) restore_menu.addAction(gaussian_noise_removal_action) salt_pepper_noise_removal_action = QAction("椒盐噪声去除", self) salt_pepper_noise_removal_action.triggered.connect(lambda: self.noise_removal("椒盐噪声")) restore_menu.addAction(salt_pepper_noise_removal_action) process_menu.addMenu(restore_menu) # 几何变换子菜单 geometric_menu = QMenu("几何变换", self) translate_action = QAction("平移", self) translate_action.triggered.connect(self.translate_image) geometric_menu.addAction(translate_action) rotate_action = QAction("旋转", self) rotate_action.triggered.connect(self.rotate_image) geometric_menu.addAction(rotate_action) scale_action = QAction("缩放", self) scale_action.triggered.connect(self.scale_image) geometric_menu.addAction(scale_action) flip_action = QAction("镜像", self) flip_action.triggered.connect(self.flip_image) geometric_menu.addAction(flip_action) process_menu.addMenu(geometric_menu) # 形态学处理子菜单 morphology_menu = QMenu("形态学处理", self) erosion_action = QAction("腐蚀", self) erosion_action.triggered.connect(lambda: self.morphological_operation("腐蚀")) morphology_menu.addAction(erosion_action) dilation_action = QAction("膨胀", self) dilation_action.triggered.connect(lambda: self.morphological_operation("膨胀")) morphology_menu.addAction(dilation_action) opening_action = QAction("开运算", self) opening_action.triggered.connect(lambda: self.morphological_operation("开运算")) morphology_menu.addAction(opening_action) closing_action = QAction("闭运算", self) closing_action.triggered.connect(lambda: self.morphological_operation("闭运算")) morphology_menu.addAction(closing_action) edge_extraction_action = QAction("边界提取", self) edge_extraction_action.triggered.connect(self.edge_extraction) morphology_menu.addAction(edge_extraction_action) process_menu.addMenu(morphology_menu) # 图像分割子菜单 segmentation_menu = QMenu("图像分割", self) threshold_action = QAction("阈值分割", self) threshold_action.triggered.connect(self.threshold_segmentation) segmentation_menu.addAction(threshold_action) adaptive_threshold_action = QAction("自适应阈值分割", self) adaptive_threshold_action.triggered.connect(self.adaptive_threshold_segmentation) segmentation_menu.addAction(adaptive_threshold_action) watershed_action = QAction("分水岭分割", self) watershed_action.triggered.connect(self.watershed_segmentation) segmentation_menu.addAction(watershed_action) process_menu.addMenu(segmentation_menu) # 图像描述子菜单 description_menu = QMenu("图像描述", self) hu_moments_action = QAction("计算Hu不变矩", self) hu_moments_action.triggered.connect(self.calculate_hu_moments) description_menu.addAction(hu_moments_action) glcm_action = QAction("计算灰度共生矩阵", self) glcm_action.triggered.connect(self.calculate_glcm) description_menu.addAction(glcm_action) process_menu.addMenu(description_menu) # 视图菜单 view_menu = self.menuBar().addMenu("视图") zoom_in_action = QAction("放大", self) zoom_in_action.setShortcut("Ctrl++") zoom_in_action.triggered.connect(self.zoom_in) view_menu.addAction(zoom_in_action) zoom_out_action = QAction("缩小", self) zoom_out_action.setShortcut("Ctrl+-") zoom_out_action.triggered.connect(self.zoom_out) view_menu.addAction(zoom_out_action) fit_to_window_action = QAction("适应窗口", self) fit_to_window_action.setShortcut("Ctrl+F") fit_to_window_action.triggered.connect(self.fit_to_window) view_menu.addAction(fit_to_window_action) # 帮助菜单 help_menu = self.menuBar().addMenu("帮助") about_action = QAction("关于", self) about_action.triggered.connect(self.about) help_menu.addAction(about_action) help_action = QAction("帮助", self) help_action.triggered.connect(self.show_help) help_menu.addAction(help_action) def create_toolbar(self): """创建工具栏""" toolbar = QToolBar("工具栏") self.addToolBar(toolbar) # 文件操作 open_action = QAction(QIcon.fromTheme("document-open"), "打开", self) open_action.triggered.connect(self.open_image) toolbar.addAction(open_action) save_action = QAction(QIcon.fromTheme("document-save"), "保存", self) save_action.triggered.connect(self.save_image) toolbar.addAction(save_action) toolbar.addSeparator() # 编辑操作 undo_action = QAction(QIcon.fromTheme("edit-undo"), "撤销", self) undo_action.triggered.connect(self.undo) toolbar.addAction(undo_action) redo_action = QAction(QIcon.fromTheme("edit-redo"), "重做", self) redo_action.triggered.connect(self.redo) toolbar.addAction(redo_action) toolbar.addSeparator() # 视图操作 zoom_in_action = QAction(QIcon.fromTheme("zoom-in"), "放大", self) zoom_in_action.triggered.connect(self.zoom_in) toolbar.addAction(zoom_in_action) zoom_out_action = QAction(QIcon.fromTheme("zoom-out"), "缩小", self) zoom_out_action.triggered.connect(self.zoom_out) toolbar.addAction(zoom_out_action) fit_to_window_action = QAction(QIcon.fromTheme("zoom-fit-best"), "适应窗口", self) fit_to_window_action.triggered.connect(self.fit_to_window) toolbar.addAction(fit_to_window_action) toolbar.addSeparator() # 图像比较 compare_action = QAction(QIcon.fromTheme("view-compare"), "比较原图", self) compare_action.triggered.connect(self.compare_with_original) toolbar.addAction(compare_action) def create_image_viewer(self): """创建图像显示区域""" # 创建滚动区域 self.scroll_area = QScrollArea() self.scroll_area.setWidgetResizable(True) # 创建图像查看器 self.image_viewer = ImageViewer() self.scroll_area.setWidget(self.image_viewer) # 添加到左侧面板 self.left_panel.addWidget(self.scroll_area) # 创建直方图显示区域 self.histogram_widget = HistogramWidget() self.left_panel.addWidget(self.histogram_widget) def create_history_tabs(self): """创建历史标签页""" self.history_tabs = QTabWidget() # 原始图像标签页 self.original_tab = QWidget() self.original_tab_layout = QVBoxLayout(self.original_tab) self.original_viewer = ImageViewer() self.original_tab_layout.addWidget(self.original_viewer) self.history_tabs.addTab(self.original_tab, "原始图像") # 处理后图像标签页 self.processed_tab = QWidget() self.processed_tab_layout = QVBoxLayout(self.processed_tab) self.processed_viewer = ImageViewer() self.processed_tab_layout.addWidget(self.processed_viewer) self.history_tabs.addTab(self.processed_tab, "处理后图像") # 描述信息标签页 self.info_tab = QWidget() self.info_tab_layout = QVBoxLayout(self.info_tab) self.info_text = QTextEdit() self.info_text.setReadOnly(True) self.info_tab_layout.addWidget(self.info_text) self.history_tabs.addTab(self.info_tab, "图像信息") # GLCM特征标签页 self.glcm_tab = QWidget() self.glcm_tab_layout = QVBoxLayout(self.glcm_tab) self.glcm_widget = GLCMWidget() self.glcm_tab_layout.addWidget(self.glcm_widget) self.history_tabs.addTab(self.glcm_tab, "GLCM特征") # 添加到右侧面板 self.right_panel.addWidget(self.history_tabs) def open_image(self): """打开图像文件""" file_path, _ = QFileDialog.getOpenFileName( self, "打开图像", "", "图像文件 (*.png *.jpg *.jpeg *.bmp *.gif *.tiff)" ) if file_path: self.statusBar().showMessage(f"正在加载图像: {file_path}") # 在单独的线程中加载图像，避免界面卡顿 thread = ImageProcessingThread(self._load_image, file_path) thread.finished.connect(self._on_image_loaded) thread.start() def _load_image(self, file_path): """在线程中加载图像""" image = cv2.imread(file_path) if image is None: return None, file_path return image, file_path def _on_image_loaded(self, result): """图像加载完成后的回调函数""" image, file_path = result if image is None: QMessageBox.critical(self, "错误", f"无法加载图像: {file_path}") self.statusBar().showMessage("加载图像失败") return self.original_image = image self.processed_image = image.copy() self.current_image = image.copy() # 显示图像 self.display_image(self.current_image) # 更新原始图像查看器 self.original_viewer.set_image(self.cv_to_qimage(self.original_image)) # 更新图像信息 self.update_image_info() # 清空历史 self.history = [self.original_image.copy()] self.history_index = 0 self.statusBar().showMessage(f"已加载图像: {os.path.basename(file_path)}") def save_image(self): """保存当前图像""" if self.processed_image is None: QMessageBox.warning(self, "警告", "没有可保存的图像") return if not hasattr(self, 'current_file_path'): self.save_image_as() else: try: cv2.imwrite(self.current_file_path, self.processed_image) self.statusBar().showMessage(f"已保存图像: {os.path.basename(self.current_file_path)}") except Exception as e: QMessageBox.critical(self, "错误", f"保存图像失败: {str(e)}") self.statusBar().showMessage("保存图像失败") def save_image_as(self): """另存为图像""" if self.processed_image is None: QMessageBox.warning(self, "警告", "没有可保存的图像") return file_path, _ = QFileDialog.getSaveFileName( self, "保存图像", "", "PNG (*.png);;JPEG (*.jpg);;BMP (*.bmp);;TIFF (*.tiff)" ) if file_path: try: # 确保保存的是RGB格式 if len(self.processed_image.shape) == 3: image_to_save = cv2.cvtColor(self.processed_image, cv2.COLOR_BGR2RGB) else: image_to_save = self.processed_image cv2.imwrite(file_path, image_to_save) self.current_file_path = file_path self.statusBar().showMessage(f"已保存图像: {os.path.basename(file_path)}") except Exception as e: QMessageBox.critical(self, "错误", f"保存图像失败: {str(e)}") self.statusBar().showMessage("保存图像失败") def display_image(self, image): """显示图像""" if image is None: return qimage = self.cv_to_qimage(image) self.image_viewer.set_image(qimage) # 更新直方图 self.histogram_widget.update_histogram(image) # 更新处理后图像查看器 self.processed_viewer.set_image(qimage) def cv_to_qimage(self, cv_image): """将OpenCV图像转换为Qt图像""" if len(cv_image.shape) == 3: # 彩色图像 height, width, channel = cv_image.shape bytes_per_line = 3 * width qimage = QImage(cv_image.data, width, height, bytes_per_line, QImage.Format_BGR888) else: # 灰度图像 height, width = cv_image.shape bytes_per_line = width qimage = QImage(cv_image.data, width, height, bytes_per_line, QImage.Format_Grayscale8) return qimage def update_image_info(self): """更新图像信息""" if self.original_image is None: return info = "图像信息:\n\n" info += f"尺寸: {self.original_image.shape[1]} x {self.original_image.shape[0]} 像素\n" if len(self.original_image.shape) == 3: info += f"通道数: {self.original_image.shape[2]}\n" info += "类型: 彩色图像\n" else: info += "通道数: 1\n" info += "类型: 灰度图像\n" info += f"数据类型: {self.original_image.dtype}" self.info_text.setText(info) def add_to_history(self, processed_image, operation_name): """添加操作到历史记录""" # 如果当前不在历史的末尾，删除后面的所有历史 if self.history_index < len(self.history) - 1: self.history = self.history[:self.history_index + 1] # 添加新的历史记录 self.history.append(processed_image.copy()) self.history_index += 1 # 更新处理后图像标签页 self.processed_viewer.set_image(self.cv_to_qimage(processed_image)) # 更新历史标签页标题 self.history_tabs.setTabText(1, f"处理后图像 ({operation_name})") def undo(self): """撤销操作""" if self.history_index > 0: self.history_index -= 1 self.processed_image = self.history[self.history_index].copy() self.display_image(self.processed_image) self.statusBar().showMessage("已撤销操作") def redo(self): """重做操作""" if self.history_index < len(self.history) - 1: self.history_index += 1 self.processed_image = self.history[self.history_index].copy() self.display_image(self.processed_image) self.statusBar().showMessage("已重做操作") def zoom_in(self): """放大图像""" self.image_viewer.scale_factor *= 1.1 self.image_viewer.update_pixmap() def zoom_out(self): """缩小图像""" self.image_viewer.scale_factor *= 0.9 self.image_viewer.update_pixmap() def fit_to_window(self): """适应窗口显示""" if self.current_image is None: return # 计算适应窗口的缩放因子 scroll_area_width = self.scroll_area.width() - 20 # 减去边框 scroll_area_height = self.scroll_area.height() - 20 image_width = self.current_image.shape[1] image_height = self.current_image.shape[0] scale_x = scroll_area_width / image_width scale_y = scroll_area_height / image_height self.image_viewer.scale_factor = min(scale_x, scale_y) self.image_viewer.update_pixmap() def compare_with_original(self): """比较处理后的图像与原始图像""" if self.original_image is None or self.processed_image is None: return # 创建一个新窗口进行比较 compare_window = QMainWindow() compare_window.setWindowTitle("图像比较") compare_window.resize(1000, 500) # 创建分割器 splitter = QSplitter(Qt.Horizontal) # 左侧显示原始图像 left_widget = QWidget() left_layout = QVBoxLayout(left_widget) left_label = QLabel("原始图像") left_label.setAlignment(Qt.AlignCenter) left_viewer = ImageViewer() left_viewer.set_image(self.cv_to_qimage(self.original_image)) left_layout.addWidget(left_label) left_layout.addWidget(left_viewer) # 右侧显示处理后的图像 right_widget = QWidget() right_layout = QVBoxLayout(right_widget) right_label = QLabel("处理后图像") right_label.setAlignment(Qt.AlignCenter) right_viewer = ImageViewer() right_viewer.set_image(self.cv_to_qimage(self.processed_image)) right_layout.addWidget(right_label) right_layout.addWidget(right_viewer) # 添加到分割器 splitter.addWidget(left_widget) splitter.addWidget(right_widget) splitter.setSizes([500, 500]) compare_window.setCentralWidget(splitter) compare_window.show() def about(self): """显示关于对话框""" QMessageBox.about(self, "关于数字图像处理系统", "数字图像处理系统\n\n" "基于OpenCV和PySide6开发\n" "支持图像转换、增强、复原、几何变换、形态学处理、分割和描述等功能\n\n" "版本: 1.0.0" ) def show_help(self): """显示帮助对话框""" help_text = ( "数字图像处理系统帮助文档\n\n" "1. 文件操作:\n" " - 打开: 从文件系统加载图像\n" " - 保存: 保存当前处理的图像\n" " - 另存为: 以新文件名保存图像\n\n" "2. 编辑操作:\n" " - 撤销: 撤销上一步操作\n" " - 重做: 恢复撤销的操作\n\n" "3. 图像处理:\n" " - 图像转换: 支持模式转换和分辨率调整\n" " - 图像增强: 包括直方图均衡化、滤波等\n" " - 图像复原: 处理运动模糊和噪声\n" " - 几何变换: 平移、旋转、缩放和镜像\n" " - 形态学处理: 腐蚀、膨胀、开/闭运算等\n" " - 图像分割: 阈值分割和区域分割\n" " - 图像描述: 计算不变矩和灰度共生矩阵\n\n" "4. 视图操作:\n" " - 放大/缩小: 调整图像显示大小\n" " - 适应窗口: 自动调整图像大小以适应窗口\n\n" "5. 比较功能:\n" " - 比较原图: 同时显示原始图像和处理后图像进行对比" ) QMessageBox.information(self, "帮助", help_text) # 图像处理功能实现 def rgb_to_gray(self): """RGB转灰度""" if self.processed_image is None: return if len(self.processed_image.shape) == 2: QMessageBox.warning(self, "警告", "当前图像已经是灰度图像") return thread = ImageProcessingThread(cv2.cvtColor, self.processed_image, cv2.COLOR_BGR2GRAY) thread.finished.connect(lambda result: self._process_finished(result, "RGB转灰度")) thread.start() def resize_image(self): """调整图像分辨率""" if self.processed_image is None: return dialog = ProcessingDialog("调整分辨率") # 获取当前图像尺寸 current_width = self.processed_image.shape[1] current_height = self.processed_image.shape[0] # 添加宽度和高度输入框 width_spinbox = QSpinBox() width_spinbox.setRange(1, 10000) width_spinbox.setValue(current_width) height_spinbox = QSpinBox() height_spinbox.setRange(1, 10000) height_spinbox.setValue(current_height) # 保持比例复选框 keep_ratio_checkbox = QCheckBox("保持比例") keep_ratio_checkbox.setChecked(True) # 添加到对话框 dialog.layout.addWidget(QLabel("宽度:"), 0, 0) dialog.layout.addWidget(width_spinbox, 0, 1) dialog.layout.addWidget(QLabel("像素"), 0, 2) dialog.layout.addWidget(QLabel("高度:"), 1, 0) dialog.layout.addWidget(height_spinbox, 1, 1) dialog.layout.addWidget(QLabel("像素"), 1, 2) dialog.layout.addWidget(keep_ratio_checkbox, 2, 0, 1, 3) # 添加按钮 ok_button, cancel_button = dialog.add_button_box() # 保持比例功能 ratio = current_width / current_height def update_height(): if keep_ratio_checkbox.isChecked(): height_spinbox.setValue(int(width_spinbox.value() / ratio)) def update_width(): if keep_ratio_checkbox.isChecked(): width_spinbox.setValue(int(height_spinbox.value() * ratio)) width_spinbox.valueChanged.connect(update_height) height_spinbox.valueChanged.connect(update_width) if dialog.exec_(): new_width = width_spinbox.value() new_height = height_spinbox.value() # 添加插值方法选择 interpolation_dialog = ProcessingDialog("选择插值方法") methods = ["最近邻", "双线性", "双三次", "Lanczos"] method_combo = interpolation_dialog.add_combo_box("插值方法", methods) if interpolation_dialog.exec_(): method_index = method_combo.currentIndex() interpolation_methods = [ cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_LANCZOS4 ] interpolation = interpolation_methods[method_index] thread = ImageProcessingThread(cv2.resize, self.processed_image, (new_width, new_height), interpolation=interpolation) thread.finished.connect(lambda result: self._process_finished(result, "调整分辨率")) thread.start() def histogram_equalization(self): """直方图均衡化""" if self.processed_image is None: return if len(self.processed_image.shape) == 3: # 彩色图像需要先转换到YUV空间 def equalize_color(image): yuv = cv2.cvtColor(image, cv2.COLOR_BGR2YUV) yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR) thread = ImageProcessingThread(equalize_color, self.processed_image) else: # 灰度图像直接均衡化 thread = ImageProcessingThread(cv2.equalizeHist, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, "直方图均衡化")) thread.start() def log_transform(self): """对数变换""" if self.processed_image is None: return dialog = ProcessingDialog("对数变换") c_slider, c_spinbox = dialog.add_slider("常数C", 1, 100, 25) if dialog.exec_(): c = c_spinbox.value() def log_transform(image): # 确保图像是浮点类型 image_float = image.astype(np.float32) / 255.0 # 应用对数变换 result = c * np.log(1 + image_float) # 归一化到[0, 1] result = cv2.normalize(result, None, 0, 1, cv2.NORM_MINMAX) # 转回uint8 return (result * 255).astype(np.uint8) thread = ImageProcessingThread(log_transform, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, "对数变换")) thread.start() def power_law_transform(self): """幂律变换""" if self.processed_image is None: return dialog = ProcessingDialog("幂律变换") gamma_slider, gamma_spinbox = dialog.add_double_slider("伽马值", 0.1, 5.0, 1.0, 0.1, 1) if dialog.exec_(): gamma = gamma_spinbox.value() def power_law_transform(image): # 确保图像是浮点类型 image_float = image.astype(np.float32) / 255.0 # 应用幂律变换 result = np.power(image_float, gamma) # 归一化到[0, 1] result = cv2.normalize(result, None, 0, 1, cv2.NORM_MINMAX) # 转回uint8 return (result * 255).astype(np.uint8) thread = ImageProcessingThread(power_law_transform, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"幂律变换 (γ={gamma})")) thread.start() def spatial_filtering(self, filter_type): """空域滤波""" if self.processed_image is None: return dialog = ProcessingDialog(f"{filter_type}参数设置") if filter_type in ["均值滤波", "高斯滤波"]: kernel_size, _ = dialog.add_slider("核大小", 1, 21, 3, 2) # 奇数 elif filter_type == "中值滤波": kernel_size, _ = dialog.add_slider("孔径大小", 1, 21, 3, 2) # 奇数 elif filter_type in ["Sobel算子", "Prewitt算子"]: direction_combo = dialog.add_combo_box("方向", ["水平", "垂直", "两者"]) elif filter_type == "Laplacian算子": ksize_combo = dialog.add_combo_box("核大小", ["1", "3", "5", "7"], 1) if dialog.exec_(): if filter_type == "均值滤波": ksize = kernel_size.value() def mean_filter(image): return cv2.blur(image, (ksize, ksize)) thread = ImageProcessingThread(mean_filter, self.processed_image) elif filter_type == "高斯滤波": ksize = kernel_size.value() def gaussian_filter(image): return cv2.GaussianBlur(image, (ksize, ksize), 0) thread = ImageProcessingThread(gaussian_filter, self.processed_image) elif filter_type == "中值滤波": ksize = kernel_size.value() def median_filter(image): return cv2.medianBlur(image, ksize) thread = ImageProcessingThread(median_filter, self.processed_image) elif filter_type == "Sobel算子": direction = direction_combo.currentIndex() def sobel_filter(image): if len(image.shape) == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if direction == 0: # 水平 sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3) return cv2.convertScaleAbs(sobelx) elif direction == 1: # 垂直 sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3) return cv2.convertScaleAbs(sobely) else: # 两者 sobelx = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3) sobely = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3) return cv2.addWeighted(cv2.convertScaleAbs(sobelx), 0.5, cv2.convertScaleAbs(sobely), 0.5, 0) thread = ImageProcessingThread(sobel_filter, self.processed_image) elif filter_type == "Prewitt算子": direction = direction_combo.currentIndex() def prewitt_filter(image): if len(image.shape) == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) kernelx = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]], dtype=np.float32) kernely = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]], dtype=np.float32) if direction == 0: # 水平 prewittx = cv2.filter2D(image, -1, kernelx) return prewittx elif direction == 1: # 垂直 prewitty = cv2.filter2D(image, -1, kernely) return prewitty else: # 两者 prewittx = cv2.filter2D(image, -1, kernelx) prewitty = cv2.filter2D(image, -1, kernely) return cv2.addWeighted(prewittx, 0.5, prewitty, 0.5, 0) thread = ImageProcessingThread(prewitt_filter, self.processed_image) elif filter_type == "Laplacian算子": ksize = [1, 3, 5, 7][ksize_combo.currentIndex()] def laplacian_filter(image): if len(image.shape) == 3: image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) return cv2.Laplacian(image, cv2.CV_64F, ksize=ksize) thread = ImageProcessingThread(laplacian_filter, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, filter_type)) thread.start() def motion_deblur(self): """运动模糊复原""" if self.processed_image is None: return dialog = ProcessingDialog("运动模糊复原参数设置") length_slider, length_spinbox = dialog.add_slider("运动长度", 1, 100, 15) angle_slider, angle_spinbox = dialog.add_slider("运动角度", 0, 360, 0) gamma_slider, gamma_spinbox = dialog.add_double_slider("噪声功率谱比", 0.01, 10.0, 0.1, 0.01, 2) if dialog.exec_(): length = length_spinbox.value() angle = angle_spinbox.value() gamma = gamma_spinbox.value() def deblur(image): # 确保图像是灰度图 if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() # 创建运动模糊核 kernel = np.zeros((length, length), dtype=np.float32) kernel[int((length - 1) / 2), :] = np.ones(length, dtype=np.float32) # 旋转核 M = cv2.getRotationMatrix2D((length / 2, length / 2), angle, 1.0) kernel = cv2.warpAffine(kernel, M, (length, length)) # 归一化 kernel /= length # 傅里叶变换 fft = np.fft.fft2(gray) fft_kernel = np.fft.fft2(kernel, s=gray.shape) # 维纳滤波 H_conj = np.conj(fft_kernel) H_squared = np.abs(fft_kernel) ** 2 G = (H_conj / (H_squared + gamma)) * fft # 逆傅里叶变换 deblurred = np.fft.ifft2(G) deblurred = np.abs(deblurred) # 归一化到0-255 deblurred = cv2.normalize(deblurred, None, 0, 255, cv2.NORM_MINMAX) deblurred = deblurred.ast(np.uint8) return deblurred thread = ImageProcessingThread(deblur, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, "运动模糊复原")) thread.start() def noise_removal(self, noise_type): """噪声去除""" if self.processed_image is None: return dialog = ProcessingDialog(f"{noise_type}去除") if noise_type == "高斯噪声": kernel_size, _ = dialog.add_slider("核大小", 1, 21, 3, 2) # 奇数 elif noise_type == "椒盐噪声": kernel_size, _ = dialog.add_slider("核大小", 1, 21, 3, 2) # 奇数 if dialog.exec_(): ksize = kernel_size.value() if noise_type == "高斯噪声": def remove_gaussian_noise(image): return cv2.GaussianBlur(image, (ksize, ksize), 0) thread = ImageProcessingThread(remove_gaussian_noise, self.processed_image) elif noise_type == "椒盐噪声": def remove_salt_pepper_noise(image): return cv2.medianBlur(image, ksize) thread = ImageProcessingThread(remove_salt_pepper_noise, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"{noise_type}去除")) thread.start() def translate_image(self): """平移图像""" if self.processed_image is None: return dialog = ProcessingDialog("平移图像") tx_slider, tx_spinbox = dialog.add_slider("水平偏移", -500, 500, 0) ty_slider, ty_spinbox = dialog.add_slider("垂直偏移", -500, 500, 0) if dialog.exec_(): tx = tx_spinbox.value() ty = ty_spinbox.value() def translate(image): M = np.float32([[1, 0, tx], [0, 1, ty]]) return cv2.warpAffine(image, M, (image.shape[1], image.shape[0])) thread = ImageProcessingThread(translate, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"平移 (tx={tx}, ty={ty})")) thread.start() def rotate_image(self): """旋转图像""" if self.processed_image is None: return dialog = ProcessingDialog("旋转图像") angle_slider, angle_spinbox = dialog.add_slider("旋转角度", -180, 180, 0) scale_slider, scale_spinbox = dialog.add_double_slider("缩放比例", 0.1, 5.0, 1.0, 0.1, 1) if dialog.exec_(): angle = angle_spinbox.value() scale = scale_spinbox.value() def rotate(image): center = (image.shape[1] // 2, image.shape[0] // 2) M = cv2.getRotationMatrix2D(center, angle, scale) return cv2.warpAffine(image, M, (image.shape[1], image.shape[0])) thread = ImageProcessingThread(rotate, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"旋转 ({angle}°, 缩放{scale}x)")) thread.start() def scale_image(self): """缩放图像""" if self.processed_image is None: return dialog = ProcessingDialog("缩放图像") scale_slider, scale_spinbox = dialog.add_double_slider("缩放比例", 0.1, 5.0, 1.0, 0.1, 1) if dialog.exec_(): scale = scale_spinbox.value() def scale_image(image): new_width = int(image.shape[1] * scale) new_height = int(image.shape[0] * scale) return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LINEAR) thread = ImageProcessingThread(scale_image, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"缩放 ({scale}x)")) thread.start() def flip_image(self): """镜像图像""" if self.processed_image is None: return dialog = ProcessingDialog("镜像图像") flip_type = dialog.add_combo_box("镜像类型", ["水平", "垂直", "水平和垂直"]) if dialog.exec_(): flip_code = flip_type.currentIndex() def flip(image): return cv2.flip(image, flip_code) flip_types = ["水平", "垂直", "水平和垂直"] thread = ImageProcessingThread(flip, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, f"镜像 ({flip_types[flip_code]})")) thread.start() def morphological_operation(self, operation_type): """形态学操作""" if self.processed_image is None: return dialog = ProcessingDialog(f"{operation_type}参数设置") kernel_size, _ = dialog.add_slider("核大小", 1, 21, 3) kernel_shape = dialog.add_combo_box("核形状", ["矩形", "椭圆", "十字形"]) if dialog.exec_(): ksize = kernel_size.value() shape_index = kernel_shape.currentIndex() kernel_shapes = [ cv2.MORPH_RECT, cv2.MORPH_ELLIPSE, cv2.MORPH_CROSS ] kernel = cv2.getStructuringElement(kernel_shapes[shape_index], (ksize, ksize)) if operation_type == "腐蚀": def erode(image): return cv2.erode(image, kernel) thread = ImageProcessingThread(erode, self.processed_image) elif operation_type == "膨胀": def dilate(image): return cv2.dilate(image, kernel) thread = ImageProcessingThread(dilate, self.processed_image) elif operation_type == "开运算": def opening(image): return cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel) thread = ImageProcessingThread(opening, self.processed_image) elif operation_type == "闭运算": def closing(image): return cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel) thread = ImageProcessingThread(closing, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, operation_type)) thread.start() def edge_extraction(self): """边界提取""" if self.processed_image is None: return dialog = ProcessingDialog("边界提取参数设置") kernel_size, _ = dialog.add_slider("核大小", 1, 21, 3) kernel_shape = dialog.add_combo_box("核形状", ["矩形", "椭圆", "十字形"]) if dialog.exec_(): ksize = kernel_size.value() shape_index = kernel_shape.currentIndex() kernel_shapes = [ cv2.MORPH_RECT, cv2.MORPH_ELLIPSE, cv2.MORPH_CROSS ] kernel = cv2.getStructuringElement(kernel_shapes[shape_index], (ksize, ksize)) def extract_edge(image): if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() # 膨胀 dilated = cv2.dilate(gray, kernel) # 边界提取 return dilated - gray thread = ImageProcessingThread(extract_edge, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, "边界提取")) thread.start() def threshold_segmentation(self): """阈值分割""" if self.processed_image is None: return dialog = ProcessingDialog("阈值分割参数设置") threshold_slider, threshold_spinbox = dialog.add_slider("阈值", 0, 255, 127) max_value_slider, max_value_spinbox = dialog.add_slider("最大值", 0, 255, 255) threshold_type = dialog.add_combo_box("阈值类型", [ "二进制阈值", "反二进制阈值", "截断阈值", "零阈值", "反零阈值", "Otsu算法" ]) if dialog.exec_(): threshold = threshold_spinbox.value() max_value = max_value_spinbox.value() type_index = threshold_type.currentIndex() threshold_types = [ cv2.THRESH_BINARY, cv2.THRESH_BINARY_INV, cv2.THRESH_TRUNC, cv2.THRESH_TOZERO, cv2.THRESH_TOZERO_INV, cv2.THRESH_BINARY + cv2.THRESH_OTSU ] def threshold_segment(image): if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() if type_index == 5: # Otsu算法，忽略手动设置的阈值 _, thresh = cv2.threshold(gray, 0, max_value, threshold_types[type_index]) else: _, thresh = cv2.threshold(gray, threshold, max_value, threshold_types[type_index]) return thresh type_names = ["二进制阈值", "反二进制阈值", "截断阈值", "零阈值", "反零阈值", "Otsu算法"] thread = ImageProcessingThread(threshold_segment, self.processed_image) thread.finished.connect( lambda result: self._process_finished(result, f"阈值分割 ({type_names[type_index]})")) thread.start() def adaptive_threshold_segmentation(self): """自适应阈值分割""" if self.processed_image is None: return dialog = ProcessingDialog("自适应阈值分割参数设置") max_value_slider, max_value_spinbox = dialog.add_slider("最大值", 0, 255, 255) method_combo = dialog.add_combo_box("自适应方法", ["均值", "高斯"]) type_combo = dialog.add_combo_box("阈值类型", ["二进制阈值", "反二进制阈值"]) block_size_slider, block_size_spinbox = dialog.add_slider("块大小", 3, 101, 11, 2) # 奇数 c_slider, c_spinbox = dialog.add_double_slider("常数C", -10, 10, 2, 0.1, 1) if dialog.exec_(): max_value = max_value_spinbox.value() method_index = method_combo.currentIndex() type_index = type_combo.currentIndex() block_size = block_size_spinbox.value() c = c_spinbox.value() adaptive_methods = [ cv2.ADAPTIVE_THRESH_MEAN_C, cv2.ADAPTIVE_THRESH_GAUSSIAN_C ] threshold_types = [ cv2.THRESH_BINARY, cv2.THRESH_BINARY_INV ] def adaptive_threshold(image): if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() return cv2.adaptiveThreshold( gray, max_value, adaptive_methods[method_index], threshold_types[type_index], block_size, c ) method_names = ["均值", "高斯"] type_names = ["二进制阈值", "反二进制阈值"] thread = ImageProcessingThread(adaptive_threshold, self.processed_image) thread.finished.connect(lambda result: self._process_finished( result, f"自适应阈值分割 ({method_names[method_index]}, {type_names[type_index]})" )) thread.start() def watershed_segmentation(self): """分水岭分割""" if self.processed_image is None: return if len(self.processed_image.shape) != 3: QMessageBox.warning(self, "警告", "分水岭分割需要彩色图像") return dialog = ProcessingDialog("分水岭分割参数设置") threshold_slider, threshold_spinbox = dialog.add_slider("阈值", 0, 255, 100) morph_size_slider, morph_size_spinbox = dialog.add_slider("形态学操作核大小", 1, 21, 3) if dialog.exec_(): threshold = threshold_spinbox.value() morph_size = morph_size_spinbox.value() def watershed(image): # 转换为灰度图 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # 阈值处理 ret, thresh = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # 噪声去除 kernel = np.ones((morph_size, morph_size), np.uint8) opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2) # 确定背景区域 sure_bg = cv2.dilate(opening, kernel, iterations=3) # 确定前景区域 dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5) ret, sure_fg = cv2.threshold(dist_transform, 0.7 * dist_transform.max(), 255, 0) # 找到未知区域 sure_fg = np.uint8(sure_fg) unknown = cv2.subtract(sure_bg, sure_fg) # 标记标签 ret, markers = cv2.connectedComponents(sure_fg) # 为所有标签加1，确保背景不是0而是1 markers = markers + 1 # 将未知区域标记为0 markers[unknown == 255] = 0 # 应用分水岭算法 markers = cv2.watershed(image, markers) image[markers == -1] = [0, 0, 255] # 标记边界为红色 return image thread = ImageProcessingThread(watershed, self.processed_image) thread.finished.connect(lambda result: self._process_finished(result, "分水岭分割")) thread.start() def calculate_hu_moments(self): """计算Hu不变矩""" if self.processed_image is None: return def calculate_moments(image): if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() # 计算矩 moments = cv2.moments(gray) # 计算Hu不变矩 hu_moments = cv2.HuMoments(moments) # 对数变换，方便显示 for i in range(7): hu_moments[i] = -1 * np.copysign(1.0, hu_moments[i]) * np.log10(np.abs(hu_moments[i])) return hu_moments thread = ImageProcessingThread(calculate_moments, self.processed_image) thread.finished.connect(self._on_hu_moments_calculated) thread.start() def _on_hu_moments_calculated(self, hu_moments): """Hu不变矩计算完成后的回调""" result = "Hu不变矩:\n\n" for i, moment in enumerate(hu_moments): result += f"H{i + 1}: {moment[0]:.8f}\n" self.info_text.setText(result) self.history_tabs.setCurrentWidget(self.info_tab) self.statusBar().showMessage("Hu不变矩计算完成") def calculate_glcm(self): """计算灰度共生矩阵""" if self.processed_image is None: return thread = ImageProcessingThread(self._calculate_glcm_thread, self.processed_image) thread.finished.connect(self._on_glcm_calculated) thread.start() def _calculate_glcm_thread(self, image): """在线程中计算GLCM""" if len(image.shape) == 3: gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) else: gray = image.copy() # 量化到16级灰度以减少计算量 gray_quantized = (gray // 16).astype(np.uint8) # 计算GLCM矩阵 (距离=1, 角度=0, 45, 90, 135度) glcm = graycomatrix(gray_quantized, distances=[1], angles=[0, np.pi / 4, np.pi / 2, 3 * np.pi / 4], levels=16, symmetric=True, normed=True) return glcm def _on_glcm_calculated(self, glcm): """GLCM计算完成后的回调""" self.glcm_widget.update_glcm(self.processed_image) self.history_tabs.setCurrentWidget(self.glcm_tab) self.statusBar().showMessage("灰度共生矩阵计算完成") def _process_finished(self, result, operation_name): """图像处理完成后的回调""" self.processed_image = result self.display_image(result) self.add_to_history(result, operation_name) self.statusBar().showMessage(f"{operation_name}完成") if __name__ == "__main__": app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec())为什么运行后打开图片D:\python\python.exe D:\PythonProject\.venv\2.py 插入图片后闪退后显示 进程已结束，退出代码为 -1073740791 (0xC0000409)
06-09
嗯，我收到了一个关于Python程序闪退的问题，用户提到在使用PySide6和OpenCV处理图片时，程序在插入图片后会闪退并返回退出代码-1073740791（0xC0000409）。这个问题很具体，我需要回忆一下这个错误代码的含义并分析...
QObject::moveToThread: Current thread (0x55e9e34cb990) is not the object‘s thread (0x55e9e3692eb0).
终身学习，每天进步一点点
04-26 236
错误原因：OpenCV-Python 和qt版本冲突。解决方法：OpenCV-Python4.2以下的版本。
QObject::moveToThread用例
08-16
QObject调用moveToThread(QThread *targetThread)后， 运行targetThread.start()，此后QObject的事件将在targetThread中处理。 比如：QObject的槽函数，将在targetThread中处理。
QObject::moveToThread: Current thread (0x31a457a0) is not the object‘s thread (0x31a4f060).
buzhisuoyunye的博客
04-19 860
遇到这种问题，是要考虑是不是conda安装与pip安装的包冲突了，把包用统一方法安装就能避免。
QObject
weixin_34166472的博客
06-26 189
QObject Class Reference   void QObject::moveToThread ( QThread * targetThread ) Changes the thread affinity for this object and its children. The object cannot be moved if it has a parent. Event pr...
QObject::moveToThread:
wqsgy的博客
05-17 3264
QObject::moveToThread:Current thread is not the object`s thread. Cannot move to target thread 网上也有许多说法关于pyqt的，本人排除了这方面的问题 大部分说法说，主要问题为opencv-python版本问题，卸载当前版本或是更新版本即可。 sudo pip uninstall opencv-python sudo pip upgrade opencv-python 成功解决。 ...
Ubuntu下OpenCV报错：QObject::moveToThread: Current thread (0x1b97af0) is not the object‘s thread 0x290d.
程序必须为人类阅读而编写，并且仅仅碰巧可以让机器执行
12-21 5890
文章目录项目场景：问题描述：原因分析：解决方案： 项目场景： 之前的系统版本是 Python3.8 版本，在更新到 Python3.9 版本后，之前运行的好好的代码却运行不了了，下载好了相关的库后，仍出现相关报错。 问题描述： 报错如下： QObject::moveToThread: Current thread (0x1b97af0) is not the object's thread (0x290d650). Cannot move to target thread (0x1b97af0)
Qt源码分析之moveToThread
m0_60259116的博客
04-04 1783
Qt源码分析之moveToThread
[已解决]QObject::moveToThread: Current thread is not the object‘s thread...... --carla调试遇到的报错问题（1）
ZHUO__zhuo的博客
04-20 9777
carla安装RGB相机遇到的报错： 1.线程报错：QObject::moveToThread: Current thread (0x7f632833ba40) is not the object's thread (0x7f63283b1ec0). Cannot move to target thread (0x7f632833ba40) 2.ModuleNotFoundError: No module named 'conda' 文章目录 前言 一、pandas是什么？ 二.
QObject::moveToThread
AlphaRui的博客
03-20 339
Solution:
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