增加拖动条的椒盐噪声生成器源码

本文介绍了一个使用OpenCV库实现的椒盐噪声生成器,该生成器通过调整参数n来控制噪声密度,演示了如何读取图像、添加椒盐噪声并保存结果。程序提供了交互界面,允许用户实时预览不同噪声密度的效果。

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#椒盐噪声生成器

//=====================这个函数作用是对输入图像输出椒盐噪声=====================
//输入src源图像,输入格式图像名+图像格式,要求图像和程序处于相同文件夹
//调整椒盐噪声参数,输入s进行保存,输入q退出;
#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;

void SrcImgRead();
void DstImgWrite();
void AddSaltPepperNoise(int,void*);
Mat addSaltNoise(const Mat srcImage, int n);

string srcName;
Mat srctImg,dstImg;
string dstImgName;
int n = 0;//椒盐噪声参数
char key;//操作识别,输入q退出,输入s保存

int main()
{
	SrcImgRead();
	if (!testImg.data) {
		cout << "读取图片失败,请检查文件路径或文件名" << endl;
		getchar();
		return false;
	}
	namedWindow("输入图像");
	imshow("输入图像", testImg);
	namedWindow("输出图像");
	dstImg = testImg;
	imshow("输出图像", dstImg);
	createTrackbar("n*100", "输出图像", &n, 100, AddSaltPepperNoise);
	
	while (waitKey(1) != 'q') {
		if (waitKey(1) == 's') {
			dstImgName = to_string(n*100);
			dstImgName.append(".jpg");
			imwrite(dstImgName,dstImg);
		}
	}
	return 0;
}

/*图像输入*/
void SrcImgRead() {
	cout << "请输入文件名" << endl;
	getline(cin, srcName);
	testImg = imread(srcName);
}
void AddSaltPepperNoise(int,void*) {
	dstImg = addSaltNoise(testImg, 500 * n);
	imshow("输出图像", dstImg);
}

Mat addSaltNoise(const Mat srcImage, int n)
{
	Mat dstImage = srcImage.clone();
	for (int k = 0; k < n; k++)
	{
		int i = rand() % dstImage.rows;//??t
		int j = rand() % dstImage.cols;
		if (dstImage.channels() == 1)
		{
			dstImage.at<uchar>(i, j) = 255;		//盐噪声
		}
		else
		{
			dstImage.at<Vec3b>(i, j)[0] = 255;
			dstImage.at<Vec3b>(i, j)[1] = 255;
			dstImage.at<Vec3b>(i, j)[2] = 255;
		}
	}
	for (int k = 0; k < n; k++)
	{
		int i = rand() % dstImage.rows;
		int j = rand() % dstImage.cols;
		if (dstImage.channels() == 1)
		{
			dstImage.at<uchar>(i, j) = 0;		//椒噪声
		}
		else
		{
			dstImage.at<Vec3b>(i, j)[0] = 0;
			dstImage.at<Vec3b>(i, j)[1] = 0;
			dstImage.at<Vec3b>(i, j)[2] = 0;
		}
	}
	return dstImage;
}
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) # 初始化变量 self.original_image = None # 原始图像 self.processed_image = None # 处理后的图像 self.current_image = None # 当前显示的图像 self.history = [] # 操作历史 self.history_index = -1 # 当前历史位置 # 创建中心部件 self.central_widget = QWidget() self.setCentralWidget(self.central_widget) # 创建主布局 self.main_layout = QHBoxLayout(self.central_widget) # 创建左侧面板 self.left_panel = QVBoxLayout() # 创建工具栏 self.create_toolbar() # 创建图像显示区域 self.create_image_viewer() # 创建右侧面板 self.right_panel = QVBoxLayout() # 创建处理历史标签页 self.create_history_tabs() # 添加分割器 self.splitter = QSplitter(Qt.Horizontal) left_widget = QWidget() left_widget.setLayout(self.left_panel) right_widget = QWidget() right_widget.setLayout(self.right_panel) self.splitter.addWidget(left_widget) self.splitter.addWidget(right_widget) self.splitter.setSizes([800, 400]) # 初始大小 self.main_layout.addWidget(self.splitter) # 创建菜单 self.create_menu() # 状态栏 self.statusBar().showMessage("就绪") def create_menu(self): """创建菜单栏""" # 文件菜单 file_menu = self.menuBar().addMenu("文件") open_action = QAction("打开", self) open_action.setShortcut(QKeySequence.Open) open_action.triggered.connect(self.open_image) file_menu.addAction(open_action) save_action = QAction("保存", self) save_action.setShortcut(QKeySequence.Save) save_action.triggered.connect(self.save_image) file_menu.addAction(save_action) save_as_action = QAction("另存为", self) save_as_action.setShortcut(QKeySequence.SaveAs) save_as_action.setShortcut("Ctrl+Shift+S") save_as_action.triggered.connect(self.save_image_as) file_menu.addAction(save_as_action) file_menu.addSeparator() exit_action = QAction("退出", self) exit_action.setShortcut(QKeySequence.Quit) exit_action.triggered.connect(self.close) file_menu.addAction(exit_action) # 编辑菜单 edit_menu = self.menuBar().addMenu("编辑") undo_action = QAction("撤销", self) undo_action.setShortcut(QKeySequence.Undo) undo_action.triggered.connect(self.undo) edit_menu.addAction(undo_action) redo_action = QAction("重做", self) redo_action.setShortcut(QKeySequence.Redo) redo_action.triggered.connect(self.redo) edit_menu.addAction(redo_action) # 处理菜单 process_menu = self.menuBar().addMenu("图像处理") # 图像转换子菜单 convert_menu = QMenu("图像转换", self) rgb_to_gray_action = QAction("RGB转灰度", self) rgb_to_gray_action.triggered.connect(self.rgb_to_gray) convert_menu.addAction(rgb_to_gray_action) resize_action = QAction("调整分辨率", self) resize_action.triggered.connect(self.resize_image) convert_menu.addAction(resize_action) process_menu.addMenu(convert_menu) # 图像增强子菜单 enhance_menu = QMenu("图像增强", self) histogram_equalization_action = QAction("直方图均衡化", self) histogram_equalization_action.triggered.connect(self.histogram_equalization) enhance_menu.addAction(histogram_equalization_action) log_transform_action = QAction("对数变换", self) log_transform_action.triggered.connect(self.log_transform) enhance_menu.addAction(log_transform_action) power_law_action = QAction("幂律变换", self) power_law_action.triggered.connect(self.power_law_transform) enhance_menu.addAction(power_law_action) blur_menu = QMenu("平滑滤波", self) mean_blur_action = QAction("均值滤波", self) mean_blur_action.triggered.connect(lambda: self.spatial_filtering("均值滤波")) blur_menu.addAction(mean_blur_action) gaussian_blur_action = QAction("高斯滤波", self) gaussian_blur_action.triggered.connect(lambda: self.spatial_filtering("高斯滤波")) blur_menu.addAction(gaussian_blur_action) median_blur_action = QAction("中值滤波", self) median_blur_action.triggered.connect(lambda: self.spatial_filtering("中值滤波")) blur_menu.addAction(median_blur_action) enhance_menu.addMenu(blur_menu) sharpen_menu = QMenu("锐化滤波", self) sobel_action = QAction("Sobel算子", self) sobel_action.triggered.connect(lambda: self.spatial_filtering("Sobel算子")) sharpen_menu.addAction(sobel_action) prewitt_action = QAction("Prewitt算子", self) prewitt_action.triggered.connect(lambda: self.spatial_filtering("Prewitt算子")) sharpen_menu.addAction(prewitt_action) laplacian_action = QAction("Laplacian算子", self) laplacian_action.triggered.connect(lambda: self.spatial_filtering("Laplacian算子")) sharpen_menu.addAction(laplacian_action) enhance_menu.addMenu(sharpen_menu) process_menu.addMenu(enhance_menu) # 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)
最新发布
06-09
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