diff --git a/mainwindow(1).py b/mainwindow(1).py
new file mode 100644
index 0000000..2073040
--- /dev/null
+++ b/mainwindow(1).py
@@ -0,0 +1,956 @@
+import sys
+from PyQt5 import QtCore, QtGui, QtWidgets
+from PyQt5.QtWidgets import QFileDialog, QGraphicsScene, QGraphicsPixmapItem
+import cv2
+import numpy as np
+from PyQt5.QtGui import QImage, QPixmap
+from mainwindow_ui import Ui_Image_processing
+import vessel_detection
+import os
+import io
+from PIL import Image
+from PIL import ImageFile
+
+class MainWindow(QtWidgets.QMainWindow, Ui_Image_processing):
+    def __init__(self, parent=None):
+        super(MainWindow, self).__init__(parent)
+        self.setupUi(self)
+        self.original_image = None
+        self.modified_image = None
+        self.image_history = []
+
+        # Connect buttons to functions
+        #图片存取
+        self.pushButton_open_image.clicked.connect(self.open_image)
+        self.pushButton_undo_image.clicked.connect(self.undo_last_action)
+        self.pushButton_close_image.clicked.connect(self.close_image)
+        self.pushButton_save_image.clicked.connect(self.save_image)
+        self.pushButton_reset_image.clicked.connect(self.reset_image)
+        # 基础功能
+        self.pushButton_symmetry.clicked.connect(self.symmetry)
+        self.pushButton_rotate.clicked.connect(self.rotate)
+        self.pushButton_to_grayscale.clicked.connect(self.to_grayscale)
+        self.pushButton_invert_colors.clicked.connect(self.invert_colors)
+        self.size_controller.valueChanged.connect(self.change_size)
+        #算术运算
+        self.pushButton_add.clicked.connect(self.image_add)
+        self.pushButton_minus.clicked.connect(self.image_subtract)
+        self.pushButton_multi.clicked.connect(self.image_multiply)
+        self.pushButton_complex_change.clicked.connect(self.complex_change)
+        #二值化
+        self.pushButton_to_binary.clicked.connect(self.to_binary)
+        #hough线条预测
+        self.pushButton_hough.clicked.connect(self.hough)
+        self.pushButton_hist_equ.clicked.connect(self.equalize_histogram)
+        self.pushButton_brightness_change.clicked.connect(self.brightness_change)
+        self.pushButton_hist_equ_2.clicked.connect(self.histogram_normalization)
+        #空间滤波器
+        self.pushButton_mean_filter.clicked.connect(self.mean_filter)
+        self.pushButton_gaussian_filter.clicked.connect(self.gaussian_filter)
+        self.pushButton_median_filter.clicked.connect(self.median_filter)
+        #频域滤波
+        self.pushButton_band_stop_filter.clicked.connect(self.band_stop_filter)
+        self.pushButton_band_pass_filter.clicked.connect(self.band_pass_filter)
+        self.pushButton_ideal_high_pass_filter.clicked.connect(self.ideal_high_pass_filter)
+        self.pushButton_ideal_low_pass_filter.clicked.connect(self.ideal_low_pass_filter)
+        self.pushButton_butterworth_high_pass_filter.clicked.connect(self.butterworth_high_pass_filter)
+        self.pushButton_butterworth_low_pass_filter.clicked.connect(self.butterworth_low_pass_filter)
+        self.pushButton_gaussian_high_pass_filter.clicked.connect(self.gaussian_high_pass_filter)
+        self.pushButton_gaussian_low_pass_filter.clicked.connect(self.gaussian_low_pass_filter)
+        #添加噪声
+        self.pushButton_add_gaussian_noise.clicked.connect(self.add_gaussian_noise)
+        self.pushButton_add_poisson_noise.clicked.connect(self.add_poisson_noise)
+        self.pushButton_add_salt_and_pepper_noise.clicked.connect(self.add_salt_and_pepper_noise)
+        #形态学操作
+        self.pushButton_apply_erosion.clicked.connect(self.apply_erosion)
+        self.pushButton_apply_dilation.clicked.connect(self.apply_dilation)
+        #边缘检测
+        self.pushButton_log_edge_detection.clicked.connect(self.log_edge_detection)
+        self.pushButton_roberts_edge_detection.clicked.connect(self.roberts_edge_detection)
+        self.pushButton_sobel_edge_detection.clicked.connect(self.sobel_edge_detection)
+        self.pushButton_laplacian_edge_detection.clicked.connect(self.laplacian_edge_detection)
+        self.pushButton_canny_edge_detection.clicked.connect(self.canny_edge_detection)
+        #锐化
+        self.pushButton_apply_roberts.clicked.connect(self.apply_roberts)
+        self.pushButton_apply_sobel.clicked.connect(self.apply_sobel)
+        self.pushButton_apply_prewitt.clicked.connect(self.apply_prewitt)
+        self.pushButton_sobel_edge_detection_5.clicked.connect(self.apply_laplacian)
+        #实际应用
+        self.pushButton_vessel_division.clicked.connect(self.vessel_division)
+        self.pushButton_repair_image.clicked.connect(self.repair_image)
+        self.pushButton_compress_image.clicked.connect(self.compress_image)
+        #测试专用
+        self.pushButton_test.clicked.connect(self.repair_image)
+
+    def open_image(self):
+        options = QFileDialog.Options()
+        fileName, _ = QFileDialog.getOpenFileName(self, "打开图片", "", "All Files (*);;Image Files (*.jpg *.png)", options=options)
+        if fileName:
+            self.original_image = cv2.imread(fileName)
+            self.modified_image = self.original_image.copy()
+            self.image_history.clear()  # Clear history when a new image is opened
+            self.display_image(self.original_image, self.graphicsView)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def close_image(self):
+        self.original_image = None
+        self.modified_image = None
+        self.image_history.clear()  # Clear history when image is closed
+        self.graphicsView.scene().clear()
+        self.graphicsView_2.scene().clear()
+
+    def save_image(self):
+        if self.modified_image is not None:
+            options = QFileDialog.Options()
+            fileName, _ = QFileDialog.getSaveFileName(self, "保存图片", "", "Image Files (*.jpg *.png)", options=options)
+            if fileName:
+                cv2.imwrite(fileName, self.modified_image)
+
+    def reset_image(self):
+        if self.original_image is not None:
+            self.modified_image = self.original_image.copy()
+            self.image_history.clear()  # Clear history when image is reset
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def add_to_history(self):
+        if self.modified_image is not None:
+            self.image_history.append(self.modified_image.copy())
+
+    def undo_last_action(self):
+        if self.image_history:
+            self.modified_image = self.image_history.pop()
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def symmetry(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            self.modified_image = cv2.flip(self.modified_image, 1)  # Horizontal flip
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def rotate(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            self.modified_image = cv2.rotate(self.modified_image, cv2.ROTATE_90_CLOCKWISE)  # Rotate 90 degrees clockwise
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def to_grayscale(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            if len(self.modified_image.shape) == 3:  # Image has 3 channels (BGR)
+                self.modified_image = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def to_binary(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            if len(self.original_image.shape) == 3:  # Image has 3 channels (BGR)
+                gray_image = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            else:
+                gray_image = self.modified_image
+        
+            unique_values = set(gray_image.flatten())
+            if unique_values.issubset({0, 255}):  # Image is already binary
+                self.modified_image = gray_image
+            else:
+                _, self.modified_image = cv2.threshold(gray_image, self.spinBox_threshold_for_to_binary.value(), 255, cv2.THRESH_BINARY)
+        
+            self.display_image(self.modified_image, self.graphicsView_2)
+    
+    def image_add(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            options = QFileDialog.Options()
+            fileName, _ = QFileDialog.getOpenFileName(self, "打开图片", "", "All Files (*);;Image Files (*.jpg *.png)", options=options)
+            if fileName:
+                obj_image = cv2.imread(fileName)
+                
+                # 获取原始图像的尺寸
+                original_shape = self.original_image.shape
+                original_height, original_width = original_shape[:2]
+                
+                # 调整新图像的尺寸与原始图像相同
+                resized_obj_image = cv2.resize(obj_image, (original_width, original_height))
+                
+                # 确保通道数匹配，如果不匹配，进行相应调整
+                if len(original_shape) == 2 and len(resized_obj_image.shape) == 3:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_BGR2GRAY)
+                elif len(original_shape) == 3 and len(resized_obj_image.shape) == 2:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_GRAY2BGR)
+                
+                # 执行图像加法操作
+                self.modified_image = cv2.add(self.original_image, resized_obj_image)
+                
+                # 显示结果图像
+                self.display_image(self.modified_image, self.graphicsView_2)
+
+    def image_subtract(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            options = QFileDialog.Options()
+            fileName, _ = QFileDialog.getOpenFileName(self, "打开图片", "", "All Files (*);;Image Files (*.jpg *.png)", options=options)
+            if fileName:
+                obj_image = cv2.imread(fileName)
+                
+                # 获取原始图像的尺寸
+                original_shape = self.original_image.shape
+                original_height, original_width = original_shape[:2]
+                
+                # 调整新图像的尺寸与原始图像相同
+                resized_obj_image = cv2.resize(obj_image, (original_width, original_height))
+                
+                # 确保通道数匹配，如果不匹配，进行相应调整
+                if len(original_shape) == 2 and len(resized_obj_image.shape) == 3:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_BGR2GRAY)
+                elif len(original_shape) == 3 and len(resized_obj_image.shape) == 2:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_GRAY2BGR)
+                
+                # 执行图像加法操作
+                self.modified_image = cv2.subtract(self.original_image, resized_obj_image)
+                
+                # 显示结果图像
+                self.display_image(self.modified_image, self.graphicsView_2)
+
+    def image_multiply(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            options = QFileDialog.Options()
+            fileName, _ = QFileDialog.getOpenFileName(self, "打开图片", "", "All Files (*);;Image Files (*.jpg *.png)", options=options)
+            if fileName:
+                obj_image = cv2.imread(fileName)
+                
+                # 获取原始图像的尺寸
+                original_shape = self.original_image.shape
+                original_height, original_width = original_shape[:2]
+                
+                # 调整新图像的尺寸与原始图像相同
+                resized_obj_image = cv2.resize(obj_image, (original_width, original_height))
+                
+                # 确保通道数匹配，如果不匹配，进行相应调整
+                if len(original_shape) == 2 and len(resized_obj_image.shape) == 3:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_BGR2GRAY)
+                elif len(original_shape) == 3 and len(resized_obj_image.shape) == 2:
+                    resized_obj_image = cv2.cvtColor(resized_obj_image, cv2.COLOR_GRAY2BGR)
+                
+                # 执行图像加法操作
+                self.modified_image = cv2.multiply(self.original_image, resized_obj_image)
+                
+                # 显示结果图像
+                self.display_image(self.modified_image, self.graphicsView_2)
+    
+    def complex_change(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            
+            # 图像放缩
+            self.modified_image = cv2.resize(self.original_image, (256, 256))
+            # 获取图像shape
+            rows, cols = self.modified_image.shape[: 2]
+
+            ########Begin########
+            # 设置图像仿射变化矩阵
+
+            post1 = np.float32([[50, 50], [200, 50], [50, 200]])
+            post2 = np.float32([[10, 100], [200, 50], [100,250]])
+            M = cv2.getAffineTransform(post1, post2)
+
+            # 图像仿射变换，及保存
+            self.modified_image = cv2.warpAffine(self.modified_image, M, (rows, cols))
+            self.display_image(self.modified_image, self.graphicsView_2)
+    
+    
+    def change_size(self):
+        def resize_image(image, scale):
+            original_height, original_width = image.shape[:2]
+
+            if scale > 1:
+                # 截取中心区域
+                new_width = int(original_width / scale)
+                new_height = int(original_height / scale)
+                start_x = (original_width - new_width) // 2
+                start_y = (original_height - new_height) // 2
+                cropped_image = image[start_y:start_y + new_height, start_x:start_x + new_width]
+                resized_image = cv2.resize(cropped_image, (original_width, original_height), interpolation=cv2.INTER_LINEAR)
+            elif scale < 1:
+                # 填充白边
+                new_width = int(original_width * scale)
+                new_height = int(original_height * scale)
+                resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_LINEAR)
+                
+                # 创建带有白边的新图像
+                padded_image = np.ones((original_height, original_width, 3), dtype=np.uint8) * 255
+                start_x = (original_width - new_width) // 2
+                start_y = (original_height - new_height) // 2
+                padded_image[start_y:start_y + new_height, start_x:start_x + new_width] = resized_image
+                resized_image = padded_image
+            else:
+                resized_image = image.copy()
+
+            return resized_image
+        
+        if self.modified_image is not None:
+            self.add_to_history()
+            
+            # Get the factor from the slider value
+            factor = self.size_controller.value() / 10.0
+            
+            if self.radioButton_bigger.isChecked():
+                changed=resize_image(self.modified_image,factor)
+            else:
+               changed=resize_image(self.modified_image,1/factor)
+            
+            self.display_image(changed, self.graphicsView_2)
+
+    def hough(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            self.modified_image = cv2.GaussianBlur(self.modified_image, (3, 3), 0)
+            edges = cv2.Canny(self.modified_image, 50, 150, apertureSize=3)
+
+            minLineLength = 200
+            maxLineGap = 15
+
+            linesP = cv2.HoughLinesP(edges, 1, np.pi/180, 80, minLineLength, maxLineGap)
+            self.modified_image = self.modified_image.copy()
+            for i_P in linesP:
+                for x1, y1, x2, y2 in i_P:
+                    cv2.line(self.modified_image, (x1, y1), (x2, y2), (0, 255, 0), 3)
+            self.display_image(self.modified_image, self.graphicsView_2)
+    
+    def equalize_histogram(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            #判断是不是灰度图
+            if len(self.modified_image.shape) == 3:
+                ycrcb_img = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2YCrCb)
+    
+                # 对Y通道进行直方图均衡化
+                ycrcb_img[:, :, 0] = cv2.equalizeHist(ycrcb_img[:, :, 0])
+                
+                # 将图像转换回BGR颜色空间
+                self.modified_image = cv2.cvtColor(ycrcb_img, cv2.COLOR_YCrCb2BGR)
+            else:  # 是灰度图
+                self.modified_image = cv2.equalizeHist(self.modified_image)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def brightness_change(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            self.modified_image=cv2.convertScaleAbs(self.modified_image, alpha=1, beta=self.spinBox_threshold_for_brightness_change.value())
+            self.display_image(self.modified_image, self.graphicsView_2)
+    
+    def histogram_normalization(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+           
+            options = QFileDialog.Options()
+            fileName, _ = QFileDialog.getOpenFileName(self, "打开图片", "", "All Files (*);;Image Files (*.jpg *.png)", options=options)
+            if fileName:
+                self.modified_image=cv2.imread(fileName)
+                img=self.original_image.copy()
+                scr=self.modified_image.copy()
+                mHist1=[]
+                mNum1=[]
+                inhist1=[]
+                mHist2=[]
+                mNum2=[]
+                inhist2=[]
+                ########Begin########
+                # 对原图像进行均衡化
+                for i in range(256):
+                    mHist1.append(0)
+                # 获取原图像像素点的宽度和高度
+                row, col = img.shape
+                for i in range(row):
+                    for j in range(col):
+                        mHist1[img[i, j]] = mHist1[img[i, j]] + 1  # 统计灰度值的个数
+                mNum1.append(mHist1[0] / img.size)
+                for i in range(0, 255):
+                    mNum1.append(mNum1[i] + mHist1[i + 1] / img.size)
+                for i in range(256):
+                    inhist1.append(round(255 * mNum1[i]))
+                # 对目标图像进行均衡化
+                for i in range(256):
+                    mHist2.append(0)
+                rows, cols = scr.shape  # 获取目标图像像素点的宽度和高度
+                for i in range(rows):
+                    for j in range(cols):
+                        mHist2[scr[i, j]] = mHist2[scr[i, j]] + 1  # 统计灰度值的个数
+                mNum2.append(mHist2[0] / scr.size)
+                for i in range(0, 255):
+                    mNum2.append(mNum2[i] + mHist2[i + 1] / scr.size)
+                for i in range(256):
+                    inhist2.append(round(255 * mNum2[i]))
+                # 进行规定化
+                # 用于放入规定化后的图片像素
+                g = []
+                for i in range(256):
+                    a = inhist1[i]
+                    flag = True
+                    for j in range(256):
+                        if inhist2[j] == a:
+                            g.append(j)
+                            flag = False
+                            break
+                    if flag == True:
+                        minp = 255
+                        for j in range(256):
+                            b = abs(inhist2[j] - a)
+                            if b < minp:
+                                minp = b
+                                jmin = j
+                        g.append(jmin)
+                for i in range(row):
+                    for j in range(col):
+                        img[i, j] = g[img[i, j]]
+                self.modified_image=img
+                self.display_image(self.modified_image, self.graphicsView_2)
+
+
+
+    def invert_colors(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            self.modified_image = cv2.bitwise_not(self.modified_image)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def display_image(self, image, graphicsView):
+        if len(image.shape) == 2:
+            height, width = image.shape
+            bytesPerLine = width
+            qImg = QImage(image.data.tobytes(), width, height, bytesPerLine, QImage.Format_Grayscale8)
+        else:
+            height, width, channel = image.shape
+            bytesPerLine = 3 * width
+            qImg = QImage(image.data.tobytes(), width, height, bytesPerLine, QImage.Format_RGB888).rgbSwapped()
+        pixmap = QPixmap.fromImage(qImg)
+        scene = QGraphicsScene()
+        scene.addItem(QGraphicsPixmapItem(pixmap))
+        graphicsView.setScene(scene)
+        graphicsView.fitInView(scene.itemsBoundingRect(), QtCore.Qt.KeepAspectRatio)
+
+    def mean_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel=self.spinBox_kernel_for_mean_filter.value()
+            self.modified_image = cv2.blur(self.modified_image, (kernel, kernel))
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def gaussian_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel=self.spinBox_kernel_for_gaussian_filter.value()
+            self.modified_image = cv2.GaussianBlur(self.modified_image, (kernel,kernel), 0)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def median_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel=self.spinBox_kernel_for_median_filter.value()
+            self.modified_image = cv2.medianBlur(self.modified_image, kernel)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def low_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel = np.array([[-1, -1, -1],
+                                [-1, 8, -1],
+                                 [-1, -1, -1]])
+
+            # 使用 cv2.filter2D() 进行高通滤波
+            self.modified_image = cv2.filter2D(self.modified_image, -1, kernel)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def high_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            self.modified_image= cv2.blur(self.modified_image, (5, 5))
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def band_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            # 将图像转换为灰度图像
+            gray_image = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY) if len(self.original_image.shape) == 3 else self.original_image
+            rows, cols = gray_image.shape
+            crow, ccol = rows // 2, cols // 2
+
+            dft = cv2.dft(np.float32(gray_image), flags=cv2.DFT_COMPLEX_OUTPUT)
+            dft_shift = np.fft.fftshift(dft)
+            
+            low_thresh=self.spinBox_min_for_band_pass_filter.value()
+            high_thresh=self.spinBox_max_for_band_pass_filter.value()
+
+            mask = np.zeros((rows, cols, 2), np.float32)
+            for u in range(rows):
+                for v in range(cols):
+                    d = np.sqrt((u - crow)**2 + (v - ccol)**2)
+                    if low_thresh < d < high_thresh:
+                        mask[u, v] = 1
+
+            fshift = dft_shift * mask
+            f_ishift = np.fft.ifftshift(fshift)
+            img_back = cv2.idft(f_ishift)
+            self.modified_image = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
+
+            self.display_image(self.modified_image, self.graphicsView_2)
+            
+    def band_stop_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            # 将图像转换为灰度图像
+            gray_image = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY) if len(self.original_image.shape) == 3 else self.original_image
+            rows, cols = gray_image.shape
+            crow, ccol = rows // 2, cols // 2
+
+            dft = cv2.dft(np.float32(gray_image), flags=cv2.DFT_COMPLEX_OUTPUT)
+            dft_shift = np.fft.fftshift(dft)
+
+            low_thresh=self.spinBox_min_for_band_stop_filter.value()
+            high_thresh=self.spinBox_max_for_band_stop_filter.value()
+            
+            mask = np.ones((rows, cols, 2), np.float32)
+            for u in range(rows):
+                for v in range(cols):
+                    d = np.sqrt((u - crow)**2 + (v - ccol)**2)
+                    if low_thresh < d < high_thresh:
+                        mask[u, v] = 0
+
+            fshift = dft_shift * mask
+            f_ishift = np.fft.ifftshift(fshift)
+            img_back = cv2.idft(f_ishift)
+            self.modified_image = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
+
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def add_gaussian_noise(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            row, col, ch = self.modified_image.shape
+            mean = self.doubleSpinBox_mean_for_add_gaussian_noise.value()
+            sigma = self.doubleSpinBox_sigma_for_add_gaussian_noise.value()
+            gauss = np.random.normal(mean, sigma, (row, col, ch))
+            gauss = gauss.reshape(row, col, ch)
+            noisy = self.modified_image + gauss * 255
+            self.modified_image = np.clip(noisy, 0, 255).astype(np.uint8)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def add_poisson_noise(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            vals = len(np.unique(self.modified_image))
+            vals = 2 ** np.ceil(np.log2(vals))
+            noisy = np.random.poisson(self.modified_image * vals) / float(vals)
+            self.modified_image = np.clip(noisy, 0, 255).astype(np.uint8)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def add_salt_and_pepper_noise(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            noisy_img = self.modified_image.copy()
+            total_pixels = self.modified_image.size // self.modified_image.shape[2]  # 计算总像素数
+            num_noise_pixels = int(total_pixels * self.doubleSpinBox_occupation_for_add_salt_and_pepper_noise.value())  # 计算噪声像素数
+
+            if self.radioButton_salt_and_pepper_for_add_salt_and_pepper_noise.isChecked():
+                # 随机分布椒盐噪声
+                for _ in range(num_noise_pixels):
+                    x = np.random.randint(0, self.modified_image.shape[0])
+                    y = np.random.randint(0, self.modified_image.shape[1])
+                    if np.random.randint(2) == 0:
+                        noisy_img[x, y] = [0, 0, 0]  # 椒噪声
+                    else:
+                        noisy_img[x, y] = [255, 255, 255]  # 盐噪声
+            elif self.radioButton_salt_for_add_salt_and_pepper_noise.isChecked():
+                # 全盐噪声
+                for _ in range(num_noise_pixels):
+                    x = np.random.randint(0, self.modified_image.shape[0])
+                    y = np.random.randint(0, self.modified_image.shape[1])
+                    noisy_img[x, y] = [255, 255, 255]  # 盐噪声
+            elif self.radioButton_pepper_for_add_salt_and_pepper_noise.isChecked():
+                # 全椒噪声
+                for _ in range(num_noise_pixels):
+                    x = np.random.randint(0, self.modified_image.shape[0])
+                    y = np.random.randint(0, self.modified_image.shape[1])
+                    noisy_img[x, y] = [0, 0, 0]  # 椒噪声
+            self.modified_image=noisy_img
+            self.display_image(self.modified_image, self.graphicsView_2)
+                    
+
+    def apply_erosion(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel = np.ones((5,5), np.uint8)
+            self.modified_image = cv2.erode(self.modified_image, kernel, iterations = 1)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def apply_dilation(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            kernel = np.ones((5,5), np.uint8)
+            self.modified_image = cv2.dilate(self.modified_image, kernel, iterations = 1)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def log_edge_detection(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            blurred = cv2.GaussianBlur(gray, (3, 3), 0)
+            log = cv2.Laplacian(blurred, cv2.CV_64F)
+            self.modified_image = cv2.convertScaleAbs(log)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def roberts_edge_detection(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            kernelx = np.array([[1, 0], [0, -1]], dtype=int)
+            kernely = np.array([[0, 1], [-1, 0]], dtype=int)
+            x = cv2.filter2D(gray, cv2.CV_16S, kernelx)
+            y = cv2.filter2D(gray, cv2.CV_16S, kernely)
+            absX = cv2.convertScaleAbs(x)
+            absY = cv2.convertScaleAbs(y)
+            self.modified_image = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def sobel_edge_detection(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            grad_x = cv2.Sobel(gray, cv2.CV_16S, 1, 0)
+            grad_y = cv2.Sobel(gray, cv2.CV_16S, 0, 1)
+            abs_grad_x = cv2.convertScaleAbs(grad_x)
+            abs_grad_y = cv2.convertScaleAbs(grad_y)
+            self.modified_image = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def laplacian_edge_detection(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            laplacian = cv2.Laplacian(gray, cv2.CV_16S)
+            self.modified_image = cv2.convertScaleAbs(laplacian)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def canny_edge_detection(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            self.modified_image = cv2.Canny(gray, 100, 200)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def ideal_high_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            threshold=self.spinBox_threshold_for_ideal_high_pass_filter.value()
+
+            if self.radioButton_circle_for_ideal_high_pass_filter.isChecked():
+                shape = "圆形"
+            elif self.radioButton_rectangle_for_ideal_high_pass_filter.isChecked():
+                shape = "矩形"
+            else:
+                shape = "圆形" 
+            
+            mask = np.ones((rows, cols), np.uint8)
+            if shape == "圆形":
+                for i in range(rows):
+                    for j in range(cols):
+                        dist = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                        if dist <= threshold:
+                            mask[i, j] = 0
+            elif shape == "矩形":
+                mask[int(crow - threshold):int(crow + threshold), int(ccol - threshold):int(ccol + threshold)] = 0
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def butterworth_high_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            # 设置阈值和阶数
+            threshold=self.spinBox_threshold_for_butterworth_high_pass_filter.value()
+            
+            order=self.spinBox_order_for_butterworth_high_pass_filter.value()
+            
+            mask = np.zeros((rows, cols), np.float32)
+            for i in range(rows):
+                for j in range(cols):
+                    dist = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                    mask[i, j] = 1 / (1 + (threshold / dist) ** (2 * order))
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+            
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def gaussian_high_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            # 设置标准差
+            threshold=self.spinBox_sigma_for_gaussian_high_pass_filter.value()
+            
+            mask = np.zeros((rows, cols), np.float32)
+            for i in range(rows):
+                for j in range(cols):
+                    dist = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                    mask[i, j] = 1 - np.exp(-dist ** 2 / (2 * (threshold ** 2)))
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+            
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def ideal_low_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            threshold=self.spinBox_threshold_for_ideal_low_pass_filter.value()
+
+            if self.radioButton_circle_for_ideal_low_pass_filter.isChecked():
+                shape = "圆形"
+            
+            elif self.radioButton_rectangle_for_ideal_low_pass_filter.isChecked():
+                shape = "矩形"
+            else:
+                shape = "圆形" 
+            
+            mask = np.zeros((rows, cols), np.uint8)
+            if shape == "圆形":
+                for i in range(rows):
+                    for j in range(cols):
+                        dist = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                        if dist <= threshold:
+                            mask[i, j] = 1
+            elif shape == "矩形":
+                mask[int(crow - threshold):int(crow + threshold), int(ccol - threshold):int(ccol + threshold)] = 1
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+            
+    def butterworth_low_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            threshold=self.spinBox_threshold_for_butterworth_low_pass_filter.value()
+            
+            order=self.spinBox_order_for_butterworth_low_pass_filter.value()
+            
+            mask = np.zeros((rows, cols))
+            for i in range(rows):
+                for j in range(cols):
+                    distance = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                    mask[i, j] = 1 / (1 + (distance / threshold) ** (2 * order))
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def gaussian_low_pass_filter(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)
+            rows, cols = gray.shape
+            crow, ccol = rows // 2, cols // 2
+
+            sigma=self.spinBox_sigma_for_gaussian_low_pass_filter.value()
+
+            mask = np.zeros((rows, cols))
+            for i in range(rows):
+                for j in range(cols):
+                    distance = np.sqrt((i - crow) ** 2 + (j - ccol) ** 2)
+                    mask[i, j] = np.exp(-(distance ** 2) / (2 * (sigma ** 2)))
+
+            fshift = np.fft.fftshift(np.fft.fft2(gray))
+            fshift = fshift * mask
+
+            img_back = np.fft.ifft2(np.fft.ifftshift(fshift))
+            img_back = np.abs(img_back)
+
+            self.modified_image = np.uint8(img_back)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    
+
+
+    def apply_roberts(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            kernelx = np.array([[1, 0], [0, -1]], dtype=int)
+            kernely = np.array([[0, 1], [-1, 0]], dtype=int)
+            x = cv2.filter2D(gray, cv2.CV_16S, kernelx)
+            y = cv2.filter2D(gray, cv2.CV_16S, kernely)
+            absX = cv2.convertScaleAbs(x)
+            absY = cv2.convertScaleAbs(y)
+            roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+            self.modified_image = cv2.cvtColor(roberts, cv2.COLOR_GRAY2BGR)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def apply_sobel(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            x = cv2.Sobel(gray, cv2.CV_16S, 1, 0)
+            y = cv2.Sobel(gray, cv2.CV_16S, 0, 1)
+            absX = cv2.convertScaleAbs(x)
+            absY = cv2.convertScaleAbs(y)
+            sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+            self.modified_image = cv2.cvtColor(sobel, cv2.COLOR_GRAY2BGR)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def apply_prewitt(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            kernelx = np.array([[1, 0, -1], [1, 0, -1], [1, 0, -1]], dtype=int)
+            kernely = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]], dtype=int)
+            x = cv2.filter2D(gray, cv2.CV_16S, kernelx)
+            y = cv2.filter2D(gray, cv2.CV_16S, kernely)
+            absX = cv2.convertScaleAbs(x)
+            absY = cv2.convertScaleAbs(y)
+            prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+            self.modified_image = cv2.cvtColor(prewitt, cv2.COLOR_GRAY2BGR)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def apply_laplacian(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+
+            gray = cv2.cvtColor(self.modified_image, cv2.COLOR_BGR2GRAY)
+            laplacian = cv2.Laplacian(gray, cv2.CV_16S)
+            laplacian = cv2.convertScaleAbs(laplacian)
+            self.modified_image = cv2.cvtColor(laplacian, cv2.COLOR_GRAY2BGR)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def vessel_division(self):
+        if self.modified_image is not None:
+            self.add_to_history()
+            
+            self.modified_image=vessel_detection.vessel_division(self.original_image)
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def repair_image(self):
+        if  self.modified_image is not None:
+            self.add_to_history()
+
+            # 读取图片
+            img = self.original_image
+            # 对图片进行噪声去除
+            blurred = cv2.medianBlur(img, 5)
+            noise = cv2.fastNlMeansDenoisingColored(blurred, None, 10, 10, 7, 21)
+            # 对图片进行细节修复
+            detail_enhancer = cv2.detailEnhance(noise)
+            detail_enhancer = detail_enhancer.astype(np.uint8)
+            self.modified_image=detail_enhancer
+            self.display_image(self.modified_image, self.graphicsView_2)
+
+    def compress_image(self): # 通常你只需要修改mb大小
+        if  self.modified_image is not None:
+            self.add_to_history()
+
+             # 如果输入图像是 numpy 数组，将其转换为 PIL 图像
+            if isinstance(self.original_image, np.ndarray):
+                self.modified_image = Image.fromarray(self.original_image)
+           # 创建一个BytesIO对象，用于存储压缩后的图像数据
+            img_bytes = io.BytesIO()
+            
+            # 初始压缩质量
+            quality = 95
+            target_size_kb=self.spinBox_size_to_transform.value()
+            quality_step=5
+            # 获取当前文件大小
+            def get_size_kb(byte_data):
+                return len(byte_data) / 1024
+            
+            while quality > 0:
+                # 清空BytesIO对象
+                img_bytes.seek(0)
+                img_bytes.truncate()
+                
+                # 压缩图像
+                self.modified_image.save(img_bytes, format='JPEG', quality=quality)
+                
+                # 获取压缩后图像的大小
+                current_size_kb = get_size_kb(img_bytes.getvalue())
+                
+                # 如果压缩后的图像大小在目标范围内，则返回图像对象
+                if current_size_kb <= target_size_kb:
+                    img_bytes.seek(0)
+                    self.modified_image = Image.open(img_bytes)
+                    self.modified_image =np.array(self.modified_image)
+                    self.display_image(self.modified_image, self.graphicsView_2)
+                    return 
+                
+                # 否则，降低质量并继续压缩
+                quality -= quality_step
+    
+            # 如果无法压缩到目标大小，返回None并提示
+            print("无法压缩到目标大小，请检查目标大小是否合理。")
+            return None
+        
+if __name__ == '__main__':
+    def scale_ui(widget, scale_factor):
+        widget.resize(widget.size() * scale_factor)
+        for child in widget.findChildren(QtWidgets.QWidget):
+            child.resize(child.size() * scale_factor)
+            child.move(child.pos() * scale_factor)
+
+    app = QtWidgets.QApplication(sys.argv)
+    window=MainWindow()
+
+    scale_factor = 1.8
+    scale_ui(window, scale_factor)
+
+    window.show()
+    sys.exit(app.exec_())