OpenCV教程02：图像处理系统1.0（翻转+形态学+滤波+缩放+旋转）

-------------OpenCV教程集合-------------

Python教程99：一起来初识OpenCV（一个跨平台的计算机视觉库）

OpenCV教程01：图像的操作（读取+显示+保存+属性+获取和修改像素值）

OpenCV教程02：图像处理系统1.0（翻转+形态学+滤波+缩放+旋转）
OpenCV教程03：绘制图形（线段、矩形、圆形、椭圆、多边形、箭头线）+水印文本

1.准备一张要输入的图像素材，没有的话也可以拿下面的图片练习一下，大小为250250或300300。基于OpenCV+tkinter的图像处理系统1.0，主要功能，翻转+形态学+滤波+缩放+旋转的示例用法，代码已跑亲测有效。感兴趣的朋友，自己学习一下。当然运行该代码，一些必要的第三方安装包是必不可少的，自己看着源码，逐一模块安装一下。

2.py源码不足的地方：
a.仅支持png和jpe的图片格式，如果你要添加其他的图片格式，自己在源码里面的通用对话框增加一些格式，默认是显示png格式。
b.没有办法，保存生成的新图片，当然你可以参考，历史pillow教程博文中，tk是如何显示和保存图片的。
Pillow教程10：设计博文的文字背景封面图，再也不担心找不到素材了
c.显示图片的窗口大小是固定的，输入的图像最好为250*250，这样刚好显示。如果你要显示更大的，自己调一下tk窗口组件的大小。

运行后的效果

# -*- coding: utf-8 -*-
# @Author : 小红牛
# 微信公众号：WdPython
import tkinter.messagebox as messagebox
import tkinter as tk
from tkinter import ttk
from PIL import Image, ImageTk
import os
from tkinter.filedialog import askopenfilename, asksaveasfilename
import cv2
import numpy as npfile_path = os.path.dirname(__file__)
test_file_path = file_path + os.sep + 'example.png'WIN_WIDTH = 700
WIN_HEIGHT = 400class Image_sys():def __init__(self):self.root = tk.Tk()self.root.geometry('700x400+80+80')self.root.title('图像处理系统1.0——微信公众号：WdPython')  # 设置窗口标题# self.root.iconbitmap('icon/icon.ico')  # 设置窗口图标# scnWidth, scnHeight = self.root.maxsize()# 屏幕中心居中# center = '%dx%d+%d+%d' % (WIN_WIDTH, WIN_HEIGHT, (scnWidth - WIN_WIDTH) / 2, (scnHeight - WIN_HEIGHT) / 2)# print(center)# 设置窗口的大小宽x高+偏移量# self.root.geometry(center)# 调用方法会禁止根窗体改变大小self.root.resizable(False, False)menubar = tk.Menu(self.root)  # 创建菜单栏 (Menu)self.root.config(menu=menubar)# 创建文件下拉菜单# 文件菜单下 tearoff=0 表示有没有分隔符，默认为有分隔符file_menu = tk.Menu(menubar, tearoff=0)# 为顶级菜单实例添加菜单，并级联相应的子菜单实例menubar.add_cascade(label="文件", menu=file_menu)file_menu.add_command(label="打开测试文件", command=self.open_test_file)file_menu.add_command(label="打开自定义文件", command=self.open_file)file_menu.add_command(label="复原", command=self.recover)file_menu.add_command(label="清除", command=self.clear)file_menu.add_command(label="退出", command=self.exit_sys)# 创建翻转下拉菜单turn_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="翻转", menu=turn_menu)turn_menu.add_command(label="水平", command=self.flip_horizontal)turn_menu.add_command(label="垂直", command=self.flip_vertical)turn_menu.add_command(label="水平&垂直", command=self.flip_hor_ver)# 形态学morph_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="形态学", menu=morph_menu)morph_menu.add_command(label="腐蚀", command=self.mor_corrosion)morph_menu.add_command(label="膨胀", command=self.mor_expand)morph_menu.add_command(label="开运算", command=self.mor_open_operation)morph_menu.add_command(label="闭运算", command=self.mor_close_operation)morph_menu.add_command(label="Morphological Gradient", command=self.mor_gradient)morph_menu.add_command(label="顶帽", command=self.mor_top_hat)morph_menu.add_command(label="黑帽", command=self.mor_black_hat)# 滤波filter_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="滤波", menu=filter_menu)filter_menu.add_command(label="均值", command=self.filter_mean)filter_menu.add_command(label="方框", command=self.filter_box)filter_menu.add_command(label="高斯", command=self.filter_gauss)filter_menu.add_command(label="中值", command=self.filter_mid_value)filter_menu.add_command(label="双边", command=self.filter_bilateral)# 缩放scale_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="缩放", menu=scale_menu)scale_menu.add_command(label="放大PyrUp", command=self.scale_pyrup)scale_menu.add_command(label="缩小PyrDown", command=self.scale_pyrdown)scale_menu.add_command(label="放大Resize", command=self.scale_zoom_in)scale_menu.add_command(label="缩小Resize", command=self.scale_zoom_out)# 旋转rotate_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="旋转", menu=rotate_menu)rotate_menu.add_command(label="平移", command=self.rotate_offset)rotate_menu.add_command(label="仿射", command=self.rotate_affine)rotate_menu.add_command(label="透射", command=self.rotate_transmission)rotate_menu.add_command(label="顺时针-无缩放", command=self.rotate_clockwise)rotate_menu.add_command(label="顺时针-缩放", command=self.rotate_clockwise_zoom)rotate_menu.add_command(label="逆时针-缩放", command=self.rotate_anti_zoom)rotate_menu.add_command(label="零旋转-缩放", command=self.rotate_zero_zoom)# 帮助help_menu = tk.Menu(menubar, tearoff=0)menubar.add_cascade(label="帮助", menu=help_menu)help_menu.add_command(label="版权", command=self.help_copyright)help_menu.add_command(label="关于", command=self.help_about)# 创建一个容器,其父容器为self.rootself.frame_scr = ttk.LabelFrame(self.root, text="Scour image:")# padx  pady   该容器外围需要留出的空余空间self.frame_scr.place(x=80, y=30, width=250, height=250)# 创建一个容器,其父容器为self.rootself.frame_des = ttk.LabelFrame(self.root, text="Destination image:")# padx  pady   该容器外围需要留出的空余空间self.frame_des.place(x=370, y=30, width=250, height=250)# 创建两个labellabel_scr = ttk.Label(self.root, text='源图像', font=25, foreground='blue', anchor='center')label_scr.place(x=150, y=280, width=100, height=50)label_des = ttk.Label(self.root, text='目标图像', font=25, foreground='blue', anchor='center')label_des.place(x=450, y=280, width=100, height=50)self.label_scr_image = Noneself.label_des_image = Noneself.path = ''self.root.mainloop()def open_test_file(self):self.path = test_file_pathimage = Image.open(self.path)test_image = ImageTk.PhotoImage(image)if (self.label_des_image != None):self.label_des_image.pack_forget()  # 隐藏控件self.label_des_image = Noneif (self.label_scr_image == None):self.label_scr_image = tk.Label(self.frame_scr, image=test_image)self.label_scr_image.configure(image=test_image)self.label_scr_image.pack()self.root.mainloop()def open_file(self):# 打开文件对话框open_img_path = askopenfilename(initialdir=file_path,filetypes=[("png格式", "png"), ("jpg格式", "jpg"), ("bmp格式", "bmp")],parent=self.root,title='打开自定义图片')if (open_img_path == ''):returnelse:if (self.label_des_image != None):self.label_des_image.pack_forget()  # 隐藏控件self.label_des_image = Noneself.path = open_img_pathimage = Image.open(self.path)tk_image = ImageTk.PhotoImage(image)if (self.label_scr_image == None):self.label_scr_image = tk.Label(self.frame_scr, image=tk_image)self.label_scr_image.configure(image=tk_image)self.label_scr_image.pack()  # 显示控件self.root.mainloop()def recover(self):if (self.path == ''):returnimage = Image.open(self.path)tk_image = ImageTk.PhotoImage(image)if (self.label_des_image == None):returnself.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()def clear(self):if (self.label_scr_image != None):self.label_scr_image.pack_forget()  # 隐藏控件self.label_scr_image = Noneself.path = ''if (self.label_des_image != None):self.label_des_image.pack_forget()  # 隐藏控件self.label_des_image = Noneself.path = ''def exit_sys(self):quit_root = messagebox.askokcancel('提示', '真的要退出么!~')if (quit_root == True):self.root.destroy()returndef flip_horizontal(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# Flipped Horizontally 水平翻转image_hflip = cv2.flip(image, 1)image_pil_hflip = Image.fromarray(image_hflip)tk_image = ImageTk.PhotoImage(image_pil_hflip)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()def flip_vertical(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# Flipped Horizontally 水平翻转image_hflip = cv2.flip(image, 0)  # 垂直翻转image_pil_hflip = Image.fromarray(image_hflip)tk_image = ImageTk.PhotoImage(image_pil_hflip)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()def flip_hor_ver(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# Flipped Horizontally 水平翻转image_hflip = cv2.flip(image, -1)  # 水平垂直翻转image_pil_hflip = Image.fromarray(image_hflip)tk_image = ImageTk.PhotoImage(image_pil_hflip)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()def mor_corrosion(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# kernel = np.ones((5, 5), np.uint8)# 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_erosion = cv2.erode(image, kernel)  # 腐蚀image_pil_erosion = Image.fromarray(img_erosion)tk_image = ImageTk.PhotoImage(image_pil_erosion)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 膨胀def mor_expand(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# kernel = np.ones((5, 5), np.uint8)# 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_dilation = cv2.dilate(image, kernel)  # 膨胀image_pil_dilation = Image.fromarray(img_dilation)tk_image = ImageTk.PhotoImage(image_pil_dilation)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 开运算def mor_open_operation(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# kernel = np.ones((5, 5), np.uint8)# 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_open_operation = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)  # 开运算image_pil_open = Image.fromarray(img_open_operation)tk_image = ImageTk.PhotoImage(image_pil_open)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 闭运算def mor_close_operation(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# kernel = np.ones((5, 5), np.uint8)# 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_close_operation = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel)  # 闭运算image_pil_close = Image.fromarray(img_close_operation)tk_image = ImageTk.PhotoImage(image_pil_close)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 形态学梯度：膨胀图减去腐蚀图，dilation - erosion，这样会得到物体的轮廓：def mor_gradient(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并# kernel = np.ones((5, 5), np.uint8)# 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_gradient = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel)  # 形态学梯度image_pil_gradient = Image.fromarray(img_gradient)tk_image = ImageTk.PhotoImage(image_pil_gradient)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 顶帽def mor_top_hat(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并kernel = np.ones((7, 7), np.uint8)  # 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_top_hat = cv2.morphologyEx(image, cv2.MORPH_TOPHAT, kernel)  # 顶帽image_pil_top_hat = Image.fromarray(img_top_hat)tk_image = ImageTk.PhotoImage(image_pil_top_hat)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 黑帽def mor_black_hat(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并kernel = np.ones((7, 7), np.uint8)  # 指定核大小# kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))  # 矩形结构# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))  # 椭圆结构# kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))  # 十字形结构img_black_hat = cv2.morphologyEx(image, cv2.MORPH_BLACKHAT, kernel)  # 黑帽image_pil_black_hat = Image.fromarray(img_black_hat)tk_image = ImageTk.PhotoImage(image_pil_black_hat)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()'''常见噪声有椒盐噪声和高斯噪声，椒盐噪声可以理解为斑点，随机出现在图像中的黑点或白点；高斯噪声可以理解为拍摄图片时由于光照等原因造成的噪声；这样解释并不准确，只要能简单分辨即可。'''# 均值滤波def filter_mean(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_mean = cv2.blur(image, (3, 3))  # 均值滤波image_pil_mean = Image.fromarray(img_mean)tk_image = ImageTk.PhotoImage(image_pil_mean)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 方框滤波方框滤波跟均值滤波很像，当可选参数normalize为True的时候，方框滤波就是均值滤波，# 如3×3的核，a就等于1/9；normalize为False的时候，a=1，相当于求区域内的像素和。def filter_box(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_box = cv2.boxFilter(image, -1, (3, 3), normalize=False)  # 方框滤波image_pil_box = Image.fromarray(img_box)tk_image = ImageTk.PhotoImage(image_pil_box)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 高斯滤波与两种滤波方式，卷积核内的每个值都一样，相当于图像区域中每个像素的权重也就一样。# 高斯滤波的卷积核权重并不相同，中间像素点权重最高，越远离中心的像素权重越小。# 高斯滤波相比均值滤波效率要慢，但可以有效消除高斯噪声，能保留更多的图像细节，所以经常被称为最有用的滤波器。def filter_gauss(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_gauss = cv2.GaussianBlur(image, (1, 1), 1)  # 方框滤波image_pil_gauss = Image.fromarray(img_gauss)tk_image = ImageTk.PhotoImage(image_pil_gauss)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 中值滤波，中值又叫中位数，是所有值排序后取中间的值。# 中值滤波就是用区域内的中值来代替本像素值，所以那种孤立的斑点，# 如0或255很容易消除掉，适用于去除椒盐噪声和斑点噪声。中值是一种非线性操作，效率相比前面几种线性滤波要慢。# 斑点噪声图，用中值滤波显然更好：def filter_mid_value(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_mid_value = cv2.medianBlur(image, 5)  # 中值滤波image_pil_mid_value = Image.fromarray(img_mid_value)tk_image = ImageTk.PhotoImage(image_pil_mid_value)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 双边滤波，模糊操作基本都会损失掉图像细节信息，尤其前面介绍的线性滤波器，图像的边缘信息很难保留下来。# 然而，边缘edge信息是图像中很重要的一个特征，所以这才有了双边滤波。def filter_bilateral(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_bilateral = cv2.bilateralFilter(image, 9, 75, 75)  # 双边滤波image_pil_bilateral = Image.fromarray(img_bilateral)tk_image = ImageTk.PhotoImage(image_pil_bilateral)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()# 图像金字塔操作的将是图像的像素问题（图像变清晰了还是模糊了）# 图像金字塔主要有两类：高斯金字塔和拉普拉斯金字塔。def scale_pyrup(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_pyrup = cv2.pyrUp(image)  # 高斯金字塔image_pil_pyrup = Image.fromarray(img_pyrup)tk_image = ImageTk.PhotoImage(image_pil_pyrup)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.pack()self.root.mainloop()def scale_pyrdown(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并img_pyrdown = cv2.pyrDown(image)  # 高斯金字塔image_pil_pyrdown = Image.fromarray(img_pyrdown)tk_image = ImageTk.PhotoImage(image_pil_pyrdown)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(relx=0,rely=0)# 放置组件的不同方式self.label_des_image.pack()self.root.mainloop()# 放大def scale_zoom_in(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并size = (2 * image.shape[1], 2 * image.shape[0])img_zoom_in = cv2.resize(image, size)  # 放大image_pil_zoom_in = Image.fromarray(img_zoom_in)tk_image = ImageTk.PhotoImage(image_pil_zoom_in)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)self.label_des_image.place(x=0, y=0)  # 放置组件的不同方式与金字塔放大相比对齐方式不同显示不同# self.label_des_image.pack()self.root.mainloop()# 缩小def scale_zoom_out(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并size = (int(0.3 * image.shape[1]), int(0.3 * image.shape[0]))img_zoom_out = cv2.resize(image, size)  # 放大image_pil_zoom_out = Image.fromarray(img_zoom_out)tk_image = ImageTk.PhotoImage(image_pil_zoom_out)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 平移def rotate_offset(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]direction = np.float32([[1, 0, 50], [0, 1, 50]])  # 沿x轴移动50，沿y轴移动50img_offset = cv2.warpAffine(image, direction, (width, height))image_pil_offset = Image.fromarray(img_offset)tk_image = ImageTk.PhotoImage(image_pil_offset)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 仿射-需要三个点坐标def rotate_affine(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]pts1 = np.float32([[50, 50], [200, 50], [50, 200]])pts2 = np.float32([[10, 100], [200, 50], [100, 250]])rot_mat = cv2.getAffineTransform(pts1, pts2)  # 沿x轴移动50，沿y轴移动50img_affine = cv2.warpAffine(image, rot_mat, (width, height))image_pil_affine = Image.fromarray(img_affine)tk_image = ImageTk.PhotoImage(image_pil_affine)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 透射 -需要四个点的坐标def rotate_transmission(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]pts1 = np.float32([[56, 65], [238, 52], [28, 237], [239, 240]])pts2 = np.float32([[0, 0], [250, 0], [0, 250], [250, 250]])rot_mat = cv2.getPerspectiveTransform(pts1, pts2)img_clockwise = cv2.warpPerspective(image, rot_mat, (250, 250))  # 透射与仿射的函数不一样image_pil_clockwise = Image.fromarray(img_clockwise)tk_image = ImageTk.PhotoImage(image_pil_clockwise)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 顺时针无缩放def rotate_clockwise(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]rotate_center = (width // 2, height // 2)rot_mat = cv2.getRotationMatrix2D(rotate_center, angle=-45, scale=1)  # 旋转中心rotate_center，角度degree， 缩放scaleimg_clockwise = cv2.warpAffine(image, rot_mat, (width, height))image_pil_clockwise = Image.fromarray(img_clockwise)tk_image = ImageTk.PhotoImage(image_pil_clockwise)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 顺时针-缩放def rotate_clockwise_zoom(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]rotate_center = (width // 2, height // 2)rot_mat = cv2.getRotationMatrix2D(rotate_center, angle=-45, scale=0.6)  # 旋转中心rotate_center，角度degree， 缩放scaleimg_clockwise_zoom = cv2.warpAffine(image, rot_mat, (width, height))image_pil_clockwise_zoom = Image.fromarray(img_clockwise_zoom)tk_image = ImageTk.PhotoImage(image_pil_clockwise_zoom)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 逆时针-缩放def rotate_anti_zoom(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]rotate_center = (width // 2, height // 2)rot_mat = cv2.getRotationMatrix2D(rotate_center, angle=45, scale=0.6)  # 旋转中心rotate_center，角度degree， 缩放scaleimg_clockwise_zoom = cv2.warpAffine(image, rot_mat, (width, height))image_pil_clockwise_zoom = Image.fromarray(img_clockwise_zoom)tk_image = ImageTk.PhotoImage(image_pil_clockwise_zoom)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()# 零旋转-缩放def rotate_zero_zoom(self):if (self.path == ''):returnif (self.label_scr_image == None):returnimage = cv2.imdecode(np.fromfile(self.path, dtype=np.uint8), 1)  # 读取图片b, g, r = cv2.split(image)  # 三通道分离image = cv2.merge([r, g, b])  # 三通道合并width, height = image.shape[1], image.shape[0]rotate_center = (width // 2, height // 2)rot_mat = cv2.getRotationMatrix2D(rotate_center, angle=0, scale=0.6)  # 旋转中心rotate_center，角度degree， 缩放scaleimg_zero_zoom = cv2.warpAffine(image, rot_mat, (width, height))image_pil_zero_zoom = Image.fromarray(img_zero_zoom)tk_image = ImageTk.PhotoImage(image_pil_zero_zoom)if (self.label_des_image == None):self.label_des_image = tk.Label(self.frame_des, image=tk_image)self.label_des_image.configure(image=tk_image)# self.label_des_image.place(x=0, y=0)self.label_des_image.pack()self.root.mainloop()def help_copyright(self):tk.messagebox.showinfo(title='版权', message='微信公众号：WdPython！~')def help_about(self):tk.messagebox.showinfo(title='关于', message='微信公众号：WdPython！~！~')if __name__ == '__main__':Image_sys()

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