ini

.idea/.gitignore

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+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/Digit_Image_Process_System.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Digit_Image_Process_System.iml" filepath="$PROJECT_DIR$/.idea/Digit_Image_Process_System.iml" />
+    </modules>
+  </component>
+</project>

.idea/other.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PySciProjectComponent">
+    <option name="PY_SCI_VIEW_SUGGESTED" value="true" />
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

Basic.py

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+import math
+import random
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+counter = 1
+
+
+def turnToBinary(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    Binary = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+    return Binary
+
+
+def segmented_linear_transformation(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    hist = cv2.calcHist([img], [0], None, [256], [0, 256])
+    left = -1
+    right = 256
+    for i in range(1, 256):
+        hist[i] = hist[i - 1] + hist[i]
+    for i in range(0, 256):
+        if hist[i] >= hist[255] * 0.1 and left == -1:
+            left = i
+        elif hist[i] >= hist[255] * 0.9 and right == 256:
+            right = i
+    h, w = img.shape
+    out = np.zeros(img.shape, np.uint8)
+    for i in range(h):
+        for j in range(w):
+            pix = img[i][j]
+            if pix < left:
+                out[i][j] = 0.5 * pix
+            elif left <= pix < right:
+                out[i][j] = 3.6 * pix - 310
+            else:
+                out[i][j] = 0.238 * pix + 194
+    out = np.around(out)
+    out = out.astype(np.uint8)
+    return out
+
+
+def hsv_ajust(img, k, t):
+    img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
+    for i in range(img.shape[0]):
+        for j in range(img.shape[1]):
+            tmp = float(img[i][j][t] * k)
+            if tmp > 255:
+                tmp = 255
+            img[i][j][t] = int(tmp)
+    img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
+    return img
+
+
+def size_change(img, x, y, z):  # x weight y height z interpolation
+    itp = {'最近邻插值': cv2.INTER_NEAREST, '双线性插值': cv2.INTER_LINEAR, '基于局部像素的重采样': cv2.INTER_AREA,
+           '基于4x4像素邻域的3次插值法': cv2.INTER_CUBIC, '基于8x8像素邻域的Lanczos插值': cv2.INTER_LANCZOS4}
+    return cv2.resize(img, (int(y), int(x)), itp.get(z))
+
+
+def image_panning(img, x, y):  # 图片平移
+    M = np.float32([[1, 0, y], [0, 1, x]])
+    return cv2.warpAffine(img, M, (img.shape[1], img.shape[0]))
+
+
+def image_rotate(img, x):
+    height, width, channel = img.shape
+    M = cv2.getRotationMatrix2D((width / 2, height / 2), x, 1)
+    return cv2.warpAffine(img, M, (width, height))
+
+
+def get_gray_hist(img):
+    global counter
+    filename = 'src/' + str(counter) + '_gray.jpg'
+    counter = counter + 1
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    plt.figure(counter)
+    plt.hist(img.ravel(), 256)
+    plt.savefig(filename)
+    return cv2.cvtColor(cv2.imread(filename, 1), cv2.COLOR_BGR2RGB)
+
+
+def get_colorful_hist(img):
+    global counter
+    filename = 'src/' + str(counter) + '_colorful.jpg'
+    counter = counter + 1
+    plt.figure(counter)
+    color = ['b', 'g', 'r']
+    for index, c in enumerate(color):
+        hist = cv2.calcHist([img], [index], None, [256], [0, 256])
+        print(hist)
+        plt.plot(hist, color=c)
+        plt.xlim([0, 256])
+    plt.savefig(filename)
+    return cv2.cvtColor(cv2.imread(filename, 1), cv2.COLOR_BGR2RGB)
+
+
+def Hough_transform(img):
+    img = cv2.GaussianBlur(img, (3, 3), 0)  # 高斯模糊
+    edges = cv2.Canny(img, 50, 150, apertureSize=3)  # Canny边缘检测
+    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 30)
+    img_empty = np.zeros((img.shape[0], img.shape[1], 3), np.uint8)
+    for i in lines:
+        for x1, y1, x2, y2 in i:
+            cv2.line(img_empty, (x1, y1), (x2, y2), (255, 255, 255), 1)
+    return img_empty
+
+
+def gradient_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    h, w = img.shape
+    img.astype('float')
+    gradient = np.zeros([h, w], dtype='float')
+    for x in range(h):
+        for y in range(w):
+            if x == h - 1:
+                gx = abs(float(img[x][y]) - float(img[x - 1][y]))
+            else:
+                gx = abs(float(img[x + 1][y]) - float(img[x][y]))
+            if y == w - 1:
+                gy = abs(float(img[x][y]) - float(img[x][y - 1]))
+            else:
+                gy = abs(float(img[x][y + 1]) - float(img[x][y]))
+            gradient[x][y] = float(gx) + float(gy)
+    sharp = gradient
+    sharp = np.where(sharp > 255, 255, sharp)
+    sharp = np.where(sharp < 0, 0, sharp)
+    sharp = sharp.astype('uint8')
+    return sharp
+
+
+def Roberts_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
+    kernely = np.array([[0, -1], [1, 0]], dtype=int)
+    x = cv2.filter2D(img, cv2.CV_16S, kernelx)
+    y = cv2.filter2D(img, cv2.CV_16S, kernely)
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+    Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+    return Roberts
+
+
+def Prewitt_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    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(img, cv2.CV_16S, kernelx)
+    y = cv2.filter2D(img, cv2.CV_16S, kernely)
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+    Prewitt = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+    return Prewitt
+
+
+def Sobel_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
+    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
+    absX = cv2.convertScaleAbs(x)
+    absY = cv2.convertScaleAbs(y)
+    Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+    return Sobel
+
+
+def Laplacian_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    gray = cv2.GaussianBlur(img, (5, 5), 0)
+    dst = cv2.Laplacian(gray, cv2.CV_16S, ksize=3)
+    Laplacian = cv2.convertScaleAbs(dst)
+    return Laplacian
+
+
+def LoG_img_sharpen(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    gray = cv2.copyMakeBorder(img, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
+    gray = cv2.GaussianBlur(gray, (3, 3), 0, 0)
+    LoG = np.array([[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]])
+    h = gray.shape[0]
+    w = gray.shape[1]
+    image = np.zeros([h, w], dtype=int)
+    for i in range(2, h - 2):
+        for j in range(2, w - 2):
+            image[i, j] = np.sum(LoG * gray[i - 2:i + 3, j - 2:j + 3, 1])
+    image = cv2.convertScaleAbs(image)
+    return image
+
+
+def Canny_img_sharpen(img):
+    img = cv2.GaussianBlur(img, (3, 3), 0)
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gx = cv2.Sobel(img, cv2.CV_16SC1, 1, 0)
+    gy = cv2.Sobel(img, cv2.CV_16SC1, 0, 1)
+    edge_output = cv2.Canny(gx, gy, 50, 100)
+    return edge_output
+
+
+def neighberhood_average_smooth(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    h, w = img.shape
+    out = np.zeros((h, w))
+    for i in range(h):
+        for j in range(w):
+            if i == 0 or j == 0 or i == h - 1 or j == w - 1:
+                out[i][j] = img[i][j]
+            else:
+                ans = 0
+                for k in range(-1, 2):
+                    for s in range(-1, 2):
+                        ans = ans + img[i + k][j + s]
+                ans = ans / 9
+                out[i][j] = ans
+    return out
+
+
+def median_filtering_smooth(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    h, w = img.shape
+    out = np.zeros((h, w))
+    for i in range(h):
+        for j in range(w):
+            if i == 0 or j == 0 or i == h - 1 or j == w - 1:
+                out[i][j] = img[i][j]
+            else:
+                pix = []
+                for k in range(-1, 2):
+                    for s in range(-1, 2):
+                        pix.append(img[i + k][j + s])
+                pix.sort()
+                out[i][j] = pix[4]
+    return out
+
+
+def Ideal_low_pass_filtering_smooth(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    img = np.float32(img)
+    dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
+    dft = np.fft.fftshift(dft)
+    h, w = img.shape
+    mask = np.zeros((h, w, 2), dtype=np.uint8)
+    mask[int(h / 2) - 20:int(h / 2) + 20, int(w / 2) - 20:int(w / 2) + 20] = 1
+    f = dft * mask
+    f = np.fft.ifftshift(f)
+    img = cv2.idft(f)
+    img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
+    global counter
+    filename = 'src/' + str(counter) + '_ideal_low_pass.png'
+    plt.imsave(filename, img, cmap='gray')
+    counter = counter + 1
+    img = cv2.imread(filename, 1)
+    return img
+
+
+def ButterWorth_filtering_smooth(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    img = np.float32(img)
+    dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
+    dft = np.fft.fftshift(dft)
+    h, w = img.shape
+    mask = np.zeros((h, w, 2), dtype=np.float32)
+    for i in range(h):
+        for j in range(w):
+            x = i - h / 2
+            y = j - w / 2
+            D = np.sqrt(x ** 2 + y ** 2)
+            k = D / 20
+            k = k ** 4
+            k = k + 1.0
+            k = 1.0 / k
+            mask[i, j, 0] = mask[i, j, 1] = k
+    f = dft * mask
+    f = np.fft.ifftshift(f)
+    img = cv2.idft(f)
+    img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
+    global counter
+    filename = 'src/' + str(counter) + '_butterworth_low_pass.png'
+    plt.imsave(filename, img, cmap='gray')
+    counter = counter + 1
+    img = cv2.imread(filename, 1)
+    return img
+
+
+def Gaussian_filtering_smooth(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    img = np.float32(img)
+    dft = cv2.dft(img, flags=cv2.DFT_COMPLEX_OUTPUT)
+    dft = np.fft.fftshift(dft)
+    h, w = img.shape
+    mask = np.zeros((h, w, 2), dtype=np.float32)
+    for i in range(h):
+        for j in range(w):
+            x = i - h / 2
+            y = j - w / 2
+            D = np.sqrt(x ** 2 + y ** 2)
+            k = D / 20
+            k = k * k
+            k = k * - 0.5
+            k = 2.7182818 ** k
+            mask[i][j][0] = mask[i][j][1] = k
+    f = dft * mask
+    f = np.fft.ifftshift(f)
+    img = cv2.idft(f)
+    img = cv2.magnitude(img[:, :, 0], img[:, :, 1])
+    global counter
+    filename = 'src/' + str(counter) + '_gaussian_low_pass.png'
+    plt.imsave(filename, img, cmap='gray')
+    counter = counter + 1
+    img = cv2.imread(filename, 1)
+    return img
+
+
+def produce_pepper_salt_noise(img):
+    h, w, t = img.shape
+    for i in range(h):
+        for j in range(w):
+            rnd = random.random()
+            if rnd < 0.05:
+                img[i][j] = [0, 0, 0]
+            elif rnd > 0.95:
+                img[i][j] = [255, 255, 255]
+    return img
+
+
+def produce_Gaussian_noise(img):
+    img = np.array(img / 255, dtype=float)
+    noise = np.random.normal(0, 0.1, img.shape)
+    out = img + noise
+    out = np.clip(out, 0.0, 1.0)
+    out = np.uint8(out * 255)
+    return out
+
+
+def image_erode(img):
+    if len(img.shape) == 3:
+        img = turnToBinary(img)
+    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5), (-1, -1))
+    img = cv2.erode(img, kernel)
+    return img
+
+
+def image_dilate(img):
+    if len(img.shape) == 3:
+        img = turnToBinary(img)
+    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5), (-1, -1))
+    img = cv2.dilate(img, kernel)
+    return img

advanced.py

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+import cv2
+import numpy as np
+
+
+def face_detect_function(img):
+    if len(img.shape) == 3:
+        img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    face_detect = cv2.CascadeClassifier(
+        'haarcascade_frontalface_default.xml')  # 人脸识别分类器
+    face = face_detect.detectMultiScale(img)
+    for x, y, w, h in face:
+        cv2.rectangle(img, (x, y, w, h), color=(0, 0, 255), thickness=2)
+    return img
+
+
+def removeBG(img, back):
+    imgt = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    back = cv2.resize(back, (imgt.shape[1], imgt.shape[0]))
+    if len(back.shape) == 3:
+        back = cv2.cvtColor(back, cv2.COLOR_RGB2GRAY)
+    bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
+    img = cv2.bilateralFilter(img, 5, 50, 100)  # smoothing filter
+    fgmask = bgModel.apply(img, learningRate=0.01)
+    kernel = np.ones((3, 3), np.uint8)
+    fgmask = cv2.erode(fgmask, kernel, iterations=1)
+    img = cv2.bitwise_and(img, img, mask=fgmask)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = cv2.GaussianBlur(gray, (41, 41), 0)
+    _, img = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)
+    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (17, 17), (-1, -1))
+    img = cv2.dilate(img, kernel, iterations=5)
+    result = imgt & img
+    img = ~img
+    result = (back & img) + result
+    result = cv2.GaussianBlur(result, (5, 5), 0)
+    return result

main.py

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+import tkinter as tk
+import tkinter.filedialog
+from tkinter.ttk import *
+import cv2
+import numpy as np
+from PIL import Image, ImageTk
+
+import Basic
+import advanced
+
+root = tk.Tk()
+root.title('Galaxy_Eyes')
+img = cv2.imread('origin.jpg')
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+img2 = Image.fromarray(img)
+photo = ImageTk.PhotoImage(img2)
+imageLabel = tk.Label(root, image=photo)
+imageLabel.pack(side=tk.LEFT)
+logical_var = tk.IntVar()
+arithmetic_var = tk.IntVar()
+imgx = np.zeros((img.shape[1], img.shape[0]))
+
+
+def img_change(img_t):
+    global img
+    global img2
+    global photo
+    global imageLabel
+    img = img_t
+    img2 = Image.fromarray(img)
+    photo = ImageTk.PhotoImage(img2)
+    imageLabel.config(image=photo)
+
+
+def ope():
+    filename = tkinter.filedialog.askopenfilename()
+    if filename != '':
+        i = cv2.imread(filename)
+        i = cv2.cvtColor(i, cv2.COLOR_BGR2RGB)
+        img_change(i)
+
+
+def sav(imgt):
+    filename = tkinter.filedialog.asksaveasfilename(defaultextension='.png',
+                                                    filetypes=(('PNG文件', '*.png'),
+                                                               ('JPEG文件', '*.jpg'),
+                                                               ('全部类型文件', '*.*')))
+    if filename != '':
+        img_t = imgt
+        if len(imgt.shape) >= 3:
+            img_t = cv2.cvtColor(img_t, cv2.COLOR_RGB2BGR)
+        cv2.imwrite(filename, img_t)
+
+
+def otsu():
+    global img
+    img_change(Basic.turnToBinary(img))
+
+
+def logical_operation(winNew, imx, imy):
+    if logical_var.get() == 1:
+        result = imx & imy
+    elif logical_var.get() == 2:
+        result = imx | imy
+    elif logical_var.get() == 3:
+        result = imx ^ imy
+    else:
+        return
+    result = cv2.resize(result, (600, 600))
+    img_change(result)
+    winNew.destroy()
+
+
+def logical_image_choose(fr, ph):
+    filename = tkinter.filedialog.askopenfilename()
+    if filename != '':
+        imgt = cv2.imread(filename)
+        imgt = cv2.cvtColor(imgt, cv2.COLOR_BGR2RGB)
+        imgt = Basic.turnToBinary(imgt)
+        imgt = cv2.resize(imgt, (150, 150))
+        fr.photot = ImageTk.PhotoImage(Image.fromarray(imgt))
+        ph.config(image=fr.photot)
+        fr.imgy = imgt
+
+
+def logical_window_setup():
+    global logical_var
+    logical_winNew = tk.Toplevel(root)
+    logical_winNew.wm_attributes('-topmost', 1)
+    logical_winNew.title('Logical_operation')
+    logical_winNew.geometry('400x400')
+    frame1 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
+    operation_and = tk.Radiobutton(frame1, text='逻辑与', variable=logical_var, value=1)
+    operation_or = tk.Radiobutton(frame1, text='逻辑或', variable=logical_var, value=2)
+    operation_xor = tk.Radiobutton(frame1, text='逻辑异或', variable=logical_var, value=3)
+    operation_and.pack()
+    operation_or.pack()
+    operation_xor.pack()
+    frame1.pack()
+    frame2 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
+    global img
+    img_Binary = Basic.turnToBinary(img)
+    img_Binary = cv2.resize(img_Binary, (150, 150))  # 缩小至100x100
+    frame2.imgx = img_Binary
+    frame2.imgy = img_Binary
+    frame2.photo1 = ImageTk.PhotoImage(Image.fromarray(img_Binary))
+    frame2.photo2 = frame2.photo1
+    photo1 = tk.Label(frame2, image=frame2.photo1)
+    photo2 = tk.Label(frame2, image=frame2.photo2)
+    photo1.pack(side=tk.LEFT)
+    photo2.pack(side=tk.RIGHT)
+    frame2.pack()
+    frame3 = tk.Frame(logical_winNew, relief=tkinter.RAISED, borderwidth=2)
+    choose_image = tk.Button(frame3, text='请选择操作图', command=lambda: logical_image_choose(frame2, photo2))
+    operation_button = tk.Button(frame3, text='执行操作',
+                                 command=lambda: logical_operation(logical_winNew, frame2.imgx, frame2.imgy))
+    choose_image.pack(side=tk.LEFT)
+    operation_button.pack(side=tk.RIGHT)
+    frame3.pack()
+
+
+def arithmetic_operation(winNew, imx, imy):
+    if arithmetic_var.get() == 1:
+        result = cv2.add(imx, imy)
+    elif arithmetic_var.get() == 2:
+        result = cv2.subtract(imx, imy)
+    elif arithmetic_var.get() == 3:
+        result = cv2.multiply(imx, imy)
+    elif arithmetic_var.get() == 4:
+        result = cv2.divide(imx, imy)
+    else:
+        return
+    result = cv2.resize(result, (600, 600))
+    img_change(result)
+    winNew.destroy()
+
+
+def arithmetic_image_choose(fr, ph):
+    filename = tkinter.filedialog.askopenfilename()
+    if filename != '':
+        imgt = cv2.imread(filename)
+        imgt = cv2.cvtColor(imgt, cv2.COLOR_BGR2RGB)
+        imgt = cv2.resize(imgt, (150, 150))
+        fr.photot = ImageTk.PhotoImage(Image.fromarray(imgt))
+        ph.config(image=fr.photot)
+        fr.imgy = imgt
+
+
+def arithmetic_window_setup():
+    global arithmetic_var
+    arithmetic_winNew = tk.Toplevel(root)
+    arithmetic_winNew.wm_attributes('-topmost', 1)
+    arithmetic_winNew.title('Logical_operation')
+    arithmetic_winNew.geometry('400x400')
+    frame1 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
+    operation_add = tk.Radiobutton(frame1, text='算术加', variable=arithmetic_var, value=1)
+    operation_sub = tk.Radiobutton(frame1, text='算术减', variable=arithmetic_var, value=2)
+    operation_mul = tk.Radiobutton(frame1, text='算术乘', variable=arithmetic_var, value=3)
+    operation_div = tk.Radiobutton(frame1, text='算术除', variable=arithmetic_var, value=4)
+    operation_add.pack(side=tk.LEFT)
+    operation_sub.pack(side=tk.LEFT)
+    operation_mul.pack(side=tk.LEFT)
+    operation_div.pack(side=tk.LEFT)
+    frame1.pack()
+    frame2 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
+    global img
+    img = cv2.resize(img, (150, 150))
+    frame2.imgx = img
+    frame2.imgy = img
+    frame2.photo1 = ImageTk.PhotoImage(Image.fromarray(img))
+    frame2.photo2 = frame2.photo1
+    photo1 = tk.Label(frame2, image=frame2.photo1)
+    photo2 = tk.Label(frame2, image=frame2.photo2)
+    photo1.pack(side=tk.LEFT)
+    photo2.pack(side=tk.RIGHT)
+    frame2.pack()
+    frame3 = tk.Frame(arithmetic_winNew, relief=tkinter.RAISED, borderwidth=2)
+    choose_image = tk.Button(frame3, text='请选择操作图', command=lambda: arithmetic_image_choose(frame2, photo2))
+    operation_button = tk.Button(frame3, text='执行操作',
+                                 command=lambda: arithmetic_operation(arithmetic_winNew, frame2.imgx, frame2.imgy))
+    choose_image.pack(side=tk.LEFT)
+    operation_button.pack(side=tk.RIGHT)
+    frame3.pack()
+
+
+def size_change_window_setup():
+    size_winNew = tk.Toplevel(root)
+    textlabel = tk.Label(size_winNew, text='选择放缩后高度和宽度的大小 和 使用的线性插值')
+    textlabel.pack()
+    frame1 = tk.Frame(size_winNew, relief=tkinter.RAISED, borderwidth=2)
+    frame1_hw = tk.Frame(frame1, relief=tkinter.RAISED, borderwidth=2)  # 宽高
+    frame1_itp = tk.Frame(frame1, relief=tkinter.RAISED, borderwidth=2)  # 线性插值
+    height_entry = tk.Entry(frame1_hw)
+    weight_entry = tk.Entry(frame1_hw)
+    global img
+    height_entry.insert(0, img.shape[0])
+    weight_entry.insert(0, img.shape[1])
+    frame1.pack()
+    frame1_hw.pack(side=tk.LEFT)
+    frame1_itp.pack(side=tk.LEFT)
+    height_entry.pack()
+    weight_entry.pack()
+    itp_var = tk.StringVar()
+    itp_comb = Combobox(size_winNew, textvariable=itp_var,
+                        values=['最近邻插值', '双线性插值', '基于局部像素的重采样', '基于4x4像素邻域的3次插值法', '基于8x8像素邻域的Lanczos插值'])
+    itp_comb.pack()
+    tk.Button(size_winNew, text='放缩！',
+              command=lambda: img_change(
+                  Basic.size_change(img, height_entry.get(), weight_entry.get(),
+                                    itp_comb.current()))).pack()
+
+
+def panning_window_setup():
+    pan_winNew = tk.Toplevel(root)
+    framex = tk.Frame(pan_winNew, relief=tkinter.RAISED, borderwidth=2)
+    x_direction = tk.StringVar()
+    x_direction_comb = Combobox(framex, textvariable=x_direction, value=['上', '下'])
+    x_direction_comb.pack(side=tk.LEFT)
+    x_direction_comb.current(0)
+    x_direction_entry = tk.Entry(framex)
+    x_direction_entry.insert(0, 0)
+    x_direction_entry.pack(side=tk.LEFT)
+    framex.pack()
+    framey = tk.Frame(pan_winNew, relief=tkinter.RAISED, borderwidth=2)
+    y_direction = tk.StringVar()
+    y_direction_comb = Combobox(framey, textvariable=y_direction, value=['左', '右'])
+    y_direction_comb.pack(side=tk.LEFT)
+    y_direction_comb.current(0)
+    y_direction_entry = tk.Entry(framey)
+    y_direction_entry.pack(side=tk.LEFT)
+    y_direction_entry.insert(0, 0)
+    framey.pack()
+    global img
+    tk.Button(pan_winNew, text='平移！', command=lambda: img_change(
+        Basic.image_panning(img, int(x_direction_entry.get()) * (1 if x_direction.get() == '下' else -1),
+                            int(y_direction_entry.get()) * (1 if y_direction.get() == '右' else -1)))).pack()
+
+
+def rotate_window_setup():
+    rotate_winNew = tk.Toplevel(root)
+    rotate_direction = tk.StringVar()
+    rotate_direction_comb = Combobox(rotate_winNew, textvariable=rotate_direction, value=['逆时针', '顺时针'])
+    rotate_direction_comb.pack(side=tk.LEFT)
+    rotate_direction_comb.current(0)
+    rotate_angle = tk.Entry(rotate_winNew)
+    rotate_angle.insert(0, 0)
+    rotate_angle.pack(side=tk.LEFT)
+    global img
+    tk.Button(rotate_winNew, text='旋转！', command=lambda: img_change(
+        Basic.image_rotate(img, int(rotate_angle.get()) if rotate_direction.get() == '逆时针' else (
+                360 - int(rotate_angle.get()))))).pack(side=tk.BOTTOM)
+
+
+def Matrix_operation(MA):
+    M = [[float for i in range(3)] for i in range(2)]
+    for i in range(0, 2):
+        for j in range(0, 3):
+            M[i][j] = float(MA[i][j].get())
+    M = np.float32(M)
+    global img
+    img_change(cv2.warpAffine(img, M, (img.shape[1], img.shape[0])))
+
+
+def Matrix_window_setup():
+    Matrix_winNew = tk.Toplevel(root)
+    Matrix_A = [[tk.Entry() for i in range(3)] for i in range(2)]
+    for i in range(0, 2):
+        for j in range(0, 3):
+            Matrix_A[i][j] = tk.Entry(Matrix_winNew)
+            Matrix_A[i][j].grid(row=i, column=j)
+            Matrix_A[i][j].insert(0, (1 if i == j else 0))
+    for i in range(0, 3):
+        tk.Label(Matrix_winNew, text=(1 if i == 2 else 0)).grid(row=2, column=i)
+    tk.Button(Matrix_winNew, text='仿射变换', command=lambda: Matrix_operation(Matrix_A)).grid(row=3, column=0,
+                                                                                           columnspan=3)
+
+
+def flip_window_setup():
+    flip_winNew = tk.Toplevel(root)
+    flip_direction = tk.StringVar()
+    flip_direction_comb = Combobox(flip_winNew, textvariable=flip_direction, value=['水平翻转', '垂直翻转', '对角翻转'])
+    flip_direction_comb.current(0)
+    flip_direction_comb.pack()
+    flip_dic = {'水平翻转': 1, '垂直翻转': 0, '对角翻转': -1}
+    global img
+    tk.Button(flip_winNew, text='翻转！',
+              command=lambda: img_change(cv2.flip(img, flip_dic[flip_direction.get()], dst=None))).pack()
+
+
+def img_sharp(x, winNew):
+    if x == 1:
+        img_change(Basic.gradient_img_sharpen(img))
+    elif x == 2:
+        img_change(Basic.Roberts_img_sharpen(img))
+    elif x == 3:
+        img_change(Basic.Prewitt_img_sharpen(img))
+    elif x == 4:
+        img_change(Basic.Sobel_img_sharpen(img))
+    elif x == 5:
+        img_change(Basic.Laplacian_img_sharpen(img))
+    elif x == 6:
+        img_change(Basic.LoG_img_sharpen(img))
+    elif x == 7:
+        img_change(Basic.Canny_img_sharpen(img))
+    winNew.destroy()
+
+
+def sharp_window_setup():
+    sharp_winNew = tk.Toplevel(root)
+    sharp_way = tk.StringVar()
+    sharp_way_comb = Combobox(sharp_winNew, textvariable=sharp_way,
+                              value=['梯度', 'Roberts算子', 'Prewitt算子', 'Sobel算子', 'Laplacian算子', 'LoG算子', 'Canny边缘检测'])
+    sharp_way_comb.current(0)
+    sharp_way_comb.pack()
+    sharp_dic = {'梯度': 1, 'Roberts算子': 2, 'Prewitt算子': 3, 'Sobel算子': 4, 'Laplacian算子': 5, 'LoG算子': 6, 'Canny边缘检测': 7}
+    tk.Button(sharp_winNew, text='检测！', command=lambda: img_sharp(sharp_dic[sharp_way.get()], sharp_winNew)).pack()
+
+
+def maintain_face_detection(camera, winNew, panel, bk):
+    global imgx
+    success, imgx = camera.read()  # 从摄像头读取照片
+    if success:
+        global img
+        imgx = cv2.cvtColor(imgx, cv2.COLOR_BGR2RGB)  # 转换颜色从BGR到RGB
+        if bk.get() == 1:
+            imgx = advanced.face_detect_function(imgx)
+        elif bk.get() == 2:
+            imgx = advanced.removeBG(imgx, img)
+        current_image = Image.fromarray(imgx)  # 将图像转换成Image对象
+        imgtk = ImageTk.PhotoImage(image=current_image)
+        panel.imgtk1 = imgtk
+        panel.config(image=imgtk)
+        winNew.after(1, lambda: maintain_face_detection(camera, winNew, panel, bk))
+
+
+def real_time_face_detection():
+    global imgx
+    face_winNew = tk.Toplevel(root)
+    imageLabelt = tk.Label(face_winNew)
+    imageLabelt.pack()
+    frame1 = tk.Frame(face_winNew)
+    frame1.pack()
+    bk = tk.IntVar()
+    no_background = tk.Radiobutton(frame1, text='无背景,人脸检测', variable=bk, value=1)
+    with_background = tk.Radiobutton(frame1, text='以软件当前显示图片为背景', variable=bk, value=2)
+    no_background.pack(side=tk.LEFT)
+    with_background.pack(side=tk.LEFT)
+    tk.Button(face_winNew, text='拍照', command=lambda: sav(imgx)).pack()
+    camera = cv2.VideoCapture(0)
+    maintain_face_detection(camera, face_winNew, imageLabelt, bk)
+    face_winNew.mainloop()
+    camera.release()
+
+
+mainmenu = tk.Menu(root)  # 菜单栏
+filemenu = tk.Menu(mainmenu)  # 文件菜单
+processmenu = tk.Menu(mainmenu)  # 处理菜单
+calculationmenu = tk.Menu(mainmenu)  # 运算菜单
+histmenu = tk.Menu(mainmenu)  # 直方图
+smoothmenu = tk.Menu(processmenu)
+noisemenu = tk.Menu(processmenu)
+structuringmenu = tk.Menu(mainmenu)
+advancedmenu = tk.Menu(mainmenu)
+mainmenu.add_cascade(label='文件', menu=filemenu)
+mainmenu.add_cascade(label='基本处理', menu=processmenu)
+mainmenu.add_cascade(label='运算', menu=calculationmenu)
+mainmenu.add_cascade(label='直方图', menu=histmenu)
+mainmenu.add_cascade(label='形态学操作', menu=structuringmenu)
+mainmenu.add_cascade(label='进阶操作', menu=advancedmenu)
+filemenu.add_command(label='打开', command=ope)
+filemenu.add_command(label='保存', command=lambda: sav(img))
+processmenu.add_command(label='自适应阈值法二值化', command=otsu)
+processmenu.add_command(label='缩放', command=size_change_window_setup)
+processmenu.add_command(label='平移', command=panning_window_setup)
+processmenu.add_command(label='旋转', command=rotate_window_setup)
+processmenu.add_command(label='翻转', command=flip_window_setup)
+processmenu.add_command(label='平移和旋转（矩阵）——仿射变换', command=Matrix_window_setup)
+processmenu.add_command(label='Hough变换检测直线', command=lambda: img_change(Basic.Hough_transform(img)))
+processmenu.add_command(label='边缘检测', command=sharp_window_setup)
+processmenu.add_cascade(label='平滑/滤噪', menu=smoothmenu)
+processmenu.add_cascade(label='添加噪声', menu=noisemenu)
+smoothmenu.add_command(label='空域平滑-邻域平均法', command=lambda: img_change(Basic.neighberhood_average_smooth(img)))
+smoothmenu.add_command(label='空域平滑-中值滤波法', command=lambda: img_change(Basic.median_filtering_smooth(img)))
+smoothmenu.add_command(label='频域平滑-理想低通滤波', command=lambda: img_change(Basic.Ideal_low_pass_filtering_smooth(img)))
+smoothmenu.add_command(label='频域平滑-巴特沃斯低通滤波', command=lambda: img_change(Basic.ButterWorth_filtering_smooth(img)))
+smoothmenu.add_command(label='频域平滑-高斯低通滤波', command=lambda: img_change(Basic.Gaussian_filtering_smooth(img)))
+noisemenu.add_command(label='添加椒盐噪声', command=lambda: img_change(Basic.produce_pepper_salt_noise(img)))
+noisemenu.add_command(label='添加高斯噪声', command=lambda: img_change(Basic.produce_Gaussian_noise(img)))
+calculationmenu.add_command(label='逻辑运算', command=logical_window_setup)
+calculationmenu.add_command(label='算术运算', command=arithmetic_window_setup)
+histmenu.add_command(label='灰度直方图', command=lambda: img_change(Basic.get_gray_hist(img)))
+histmenu.add_command(label='彩色直方图', command=lambda: img_change(Basic.get_colorful_hist(img)))
+histmenu.add_command(label='分段线性变化', command=lambda: img_change(Basic.segmented_linear_transformation(img)))
+structuringmenu.add_command(label='膨胀', command=lambda: img_change(Basic.image_erode(img)))
+structuringmenu.add_command(label='腐蚀', command=lambda: img_change(Basic.image_dilate(img)))
+structuringmenu.add_command(label='开运算', command=lambda: img_change(Basic.image_dilate(Basic.image_erode(img))))
+structuringmenu.add_command(label='闭运算', command=lambda: img_change(Basic.image_erode(Basic.image_dilate(img))))
+advancedmenu.add_command(label='人脸检测', command=lambda: img_change(advanced.face_detect_function(img)))
+advancedmenu.add_command(label='实时更换背景/拍照', command=real_time_face_detection)
+root.config(menu=mainmenu)
+# root.bind('Button-3', popupmenu)
+root.mainloop()