602 lines
26 KiB
Markdown
602 lines
26 KiB
Markdown
<h1><center>实验报告</center></h1>
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<div style="text-align: center;">
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<div><span style="display: inline-block; width: 65px; text-align: center;">课程名称</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">数字图像处理</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">实验题目</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">均值滤波、中值滤波的计算机实现</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">学号</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">21281280</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">姓名</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">柯劲帆</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">班级</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">物联网2101班</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">指导老师</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">安高云</span></div>
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<div><span style="display: inline-block; width: 65px; text-align: center;">报告日期</span><span style="display: inline-block; width: 25px;">:</span><span style="display: inline-block; width: 250px; font-weight: bold; text-align: left;">2024年1月10日</span></div>
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</div>
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---
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**目录**
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[TOC]
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---
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# 1. 均值、中值滤波程序
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本实验中我使用Python实现均值、中值滤波。对于图像的读取、处理和保存，我都使用了按字节进行读写的方式，符合实验要求。
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BMP格式图片的读写已经写在实验二中，在此不再赘述。
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## 1.1. 均值滤波
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均值滤波就是将图片中某一像素点的值用该点和其周围九宫格内的八个点的均值来替代。这样可以有效滤去高斯噪声。
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如果直接进行滤波，滤波结果尺寸将会缩减为$(originalHeight - 2) \times (originalWidth - 2)$。因此，需要先将原图像填充至$(originalHeight + 2) \times (originalWidth + 2)$，再进行滤波，滤波后的尺寸才能保持$originalHeight \times originalWidth$。
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这里填充的内容选择重复原图片边缘的像素点。
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代码实现如下：
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```python
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def meanfilter(self):
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self.meanfiltered_img = np.zeros_like(self.gray)
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padded_img = np.zeros((abs(self.biHeight) + 2, self.biWidth + 2), dtype=np.int16)
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padded_img[1:-1, 1:-1] = self.gray
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padded_img[0, 1:-1] = self.gray[0, :]
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padded_img[-1, 1:-1] = self.gray[-1, :]
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padded_img[1:-1, 0] = self.gray[:, 0]
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padded_img[1:-1, -1] = self.gray[:, -1]
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padded_img[0][0] = self.gray[0][0]
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padded_img[0][-1] = self.gray[0][-1]
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padded_img[-1][0] = self.gray[-1][0]
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padded_img[-1][-1] = self.gray[-1][-1]
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for x in range(abs(self.biHeight)):
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for y in range(self.biWidth):
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self.meanfiltered_img[x][y] = np.around(np.mean(padded_img[x:x+3, y:y+3]))
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return self.meanfiltered_img
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```
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## 1.2. 中值滤波
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中值滤波就是将图片中某一像素点的值用该点和其周围九宫格内的八个点的中值来替代。这样可以有效滤去椒盐噪声。
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填充的方法与均值滤波相同。
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代码实现如下：
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```python
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def medianfilter(self):
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self.medianfiltered_img = np.zeros_like(self.gray)
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padded_img = np.zeros((abs(self.biHeight) + 2, self.biWidth + 2), dtype=np.int16)
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padded_img[1:-1, 1:-1] = self.gray
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padded_img[0, 1:-1] = self.gray[0, :]
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padded_img[-1, 1:-1] = self.gray[-1, :]
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padded_img[1:-1, 0] = self.gray[:, 0]
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padded_img[1:-1, -1] = self.gray[:, -1]
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padded_img[0][0] = self.gray[0][0]
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padded_img[0][-1] = self.gray[0][-1]
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padded_img[-1][0] = self.gray[-1][0]
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padded_img[-1][-1] = self.gray[-1][-1]
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for x in range(abs(self.biHeight)):
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for y in range(self.biWidth):
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self.medianfiltered_img[x][y] = np.around(np.median(padded_img[x:x+3, y:y+3]))
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return self.medianfiltered_img
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```
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## 1.3. GUI界面设计和程序逻辑
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由于相比实验二多了两个功能，UI界面和控件函数需要新增和修改。
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新增和修改的代码如下：
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```python
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def medianfilter():
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if path_ == '':
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return
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image = BmpData(path_)
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medianfiltered_img = image.medianfilter()
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medianfiltered_img = Image.fromarray(medianfiltered_img.astype(np.uint8))
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# medianfiltered_img.show()
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name_parts = path_.split('.')
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name_parts[-2] += "_medianfiltered"
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new_file_name = '.'.join(name_parts)
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image.save_medianfiltered_img(new_file_name)
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medianfiltered_img = medianfiltered_img.resize((int(256 * 0.8), int(256 * 0.8)))
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medianfiltered_img = ImageTk.PhotoImage(medianfiltered_img)
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label_image2.config(image=medianfiltered_img)
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label_image2.image = medianfiltered_img # 处理后的图片的显示
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def meanfilter():
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if path_ == '':
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return
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image = BmpData(path_)
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meanfiltered_img = image.meanfilter()
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meanfiltered_img = Image.fromarray(meanfiltered_img.astype(np.uint8))
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# meanfiltered_img.show()
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name_parts = path_.split('.')
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name_parts[-2] += "_meanfiltered"
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new_file_name = '.'.join(name_parts)
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image.save_meanfiltered_img(new_file_name)
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meanfiltered_img = meanfiltered_img.resize((int(256 * 0.8), int(256 * 0.8)))
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meanfiltered_img = ImageTk.PhotoImage(meanfiltered_img)
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label_image2.config(image=meanfiltered_img)
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label_image2.image = meanfiltered_img # 处理后的图片的显示
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if __name__ == "__main__":
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root = tkinter.Tk()
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root.title('21281280柯劲帆') # 标题
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width, height = 600, 400
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width_max, height_max = root.maxsize()
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s_center = '%dx%d+%d+%d' % (width, height, (width_max - width) / 2, (height_max - height) / 2) # 将页面显示在正中间
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root.geometry(s_center)
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root.resizable(width=False, height=False) # 窗口不可移动
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l = tkinter.Label(root, text='实验三', width=60, height=2, fg='black', font=("微软雅黑", 16), anchor=tkinter.CENTER)
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l.pack()
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label_image1 = tkinter.Label(root, width=int(256 * 0.8), height=int(256 * 0.8), bg='whitesmoke', anchor=tkinter.NE)
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label_image1.pack()
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label_image1.place(x=45, y=70, width=int(256 * 0.8), height=int(256 * 0.8))
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label_image2 = tkinter.Label(root, width=int(256 * 0.8), height=int(256 * 0.8), bg='whitesmoke', anchor=tkinter.NE)
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label_image2.place(x=350, y=70, width=int(256 * 0.8), height=int(256 * 0.8))
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# 文本按钮
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Image_Input = tkinter.Button(root, text='Choose', command=choosepic)
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Image_Input.place(x=50, y=300, width=90, height=30)
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# 直方图均衡化
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Fun1 = tkinter.Button(root, text='直方图均衡化', command=equalize)
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Fun1.place(x=150, y=300, width=90, height=30)
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# 中值滤波
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Fun1 = tkinter.Button(root, text='中值滤波', command=medianfilter) # 添加对应按钮
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Fun1.place(x=250, y=300, width=90, height=30)
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# 均值滤波
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Fun2 = tkinter.Button(root, text='均值滤波', command=meanfilter) # 添加对应按钮
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Fun2.place(x=350, y=300, width=90, height=30)
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# 退出
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Quit = tkinter.Button(root, text='Quit', command=sys.exit)
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Quit.place(x=450, y=300, width=90, height=30)
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end = tkinter.Label(root, text='21281280 柯劲帆', fg='silver', font=("微软雅黑", 10))
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end.place(x=215, y=360, width=200, height=20)
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root.mainloop()
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```
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# 2. 实验过程
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编好代码后，对Python代码进行封装，变成exe可执行程序。
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在命令行中配置环境并封装：
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```sh
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> pip install pyinstaller
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> Pyinstaller -F -w read_bmp.py
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```
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在文件资源管理器窗口中双击exe文件，即可运行。
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# 3. 实验结果及分析
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这里我准备了手机拍摄的1张图片，对其添加高斯噪声和椒盐噪声：
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```python
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import skimage
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from PIL import Image
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import numpy as np
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if __name__ == '__main__':
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origin = skimage.img_as_float(Image.open('./test_images/my_image_normal.bmp'))
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noisy = skimage.util.random_noise(origin, mode='gaussian', var=0.01)
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noisy = (noisy * 255).astype(np.uint8)
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skimage.io.imsave('./test_images/my_image_gaussiannoise.bmp', noisy)
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origin = skimage.img_as_float(Image.open('./test_images/my_image_normal.bmp'))
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noisy = skimage.util.random_noise(origin, mode='salt')
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noisy = (noisy * 255).astype(np.uint8)
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skimage.io.imsave('./test_images/my_image_saltnoise.bmp', noisy)
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```
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进行中值滤波、均值滤波过程和结果如下：
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<table>
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<tr>
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<td></td>
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<td>正常</td>
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<td>加高斯噪声</td>
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<td>加椒盐噪声</td>
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</tr>
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<tr>
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<td>原图</td>
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<td><img src="my_image_normal.bmp" alt="my_image_normal"></td>
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<td><img src="my_image_gaussiannoise.bmp" alt="my_image_gaussiannoise"></td>
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<td><img src="my_image_saltnoise.bmp" alt="my_image_saltnoise"></td>
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</tr>
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<tr>
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<td>中值滤波结果</td>
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<td><img src="my_image_normal_medianfiltered.bmp" alt="my_image_normal_medianfiltered"></td>
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<td><img src="my_image_gaussiannoise_medianfiltered.bmp" alt="my_image_gaussiannoise_medianfiltered"></td>
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<td><img src="my_image_saltnoise_medianfiltered.bmp" alt="my_image_saltnoise_medianfiltered"></td>
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</tr>
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<tr>
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<td>均值滤波结果</td>
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<td><img src="my_image_normal_meanfiltered.bmp" alt="my_image_normal_meanfiltered"></td>
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<td><img src="my_image_gaussiannoise_meanfiltered.bmp" alt="my_image_gaussiannoise_meanfiltered"></td>
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<td><img src="my_image_saltnoise_meanfiltered.bmp" alt="my_image_saltnoise_meanfiltered"></td>
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</tr>
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<tr>
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<td>中值滤波过程</td>
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<td><img src="normal_median.png" alt="normal_median"></td>
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<td><img src="gaussian_median.png" alt="gaussian_median"></td>
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<td><img src="salt_median.png" alt="salt_median"></td>
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</tr>
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<tr>
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<td>均值滤波过程</td>
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<td><img src="normal_mean.png" alt="normal_mean"></td>
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<td><img src="gaussian_mean.png" alt="gaussian_mean"></td>
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<td><img src="salt_mean.png" alt="salt_mean"></td>
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</tr>
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</table>
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可见正常的图进行中值滤波后，细节有所丢失，蓝色斑点几乎消失；进行均值滤波后，蓝色斑点有所保留；
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加了高斯噪声的图进行中值滤波和均值滤波的效果都并不好，还是很模糊；
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加了椒盐噪声的图进行中值滤波后，噪声能明显去除；进行均值滤波，噪声去除效果不佳。
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# 4. 心得体会
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通过本次实验，我掌握了图像的均值滤波和中值滤波的原理及实现方法。
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均值滤波可以有效滤除图像中的高斯噪声，它通过用像素点周围区域的平均值来替代该像素点的值，由于高斯噪声具有零均值的特点，所以能够有效地减弱噪声；中值滤波可以有效去除图像中的椒盐噪声，它通过用像素点周围区域的中值来替代该像素点的值，由于椒盐噪声往往处于区域灰度值的两端，使用中值可以将其排除在外。
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通过测试不同的图像，我观察到均值滤波对正常图像会造成一定的模糊，而中值滤波可以很好地保持边缘细节。所以在不需要强烈平滑图像的情况下，中值滤波更好。当图像包含不同类型的噪声时，需要权衡使用均值滤波还是中值滤波。
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通过本次实验，我对数字图像处理滤波方法有了更深入的理解。
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# 5. 源代码
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```python
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import numpy as np
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from struct import unpack
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from PIL import Image, ImageTk
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import sys
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import tkinter
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import tkinter.filedialog
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class BmpData:
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def __init__(self, file_path:str):
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with open(file_path, "rb") as file:
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self.file = file
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self.bfType = unpack("<H", file.read(2))[0] # 0x00 文件类型
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self.bfSize = unpack("<i", file.read(4))[0] # 0x02 文件大小
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self.bfReserved1 = unpack("<H", file.read(2))[0] # 0x06 保留，必须设置为0
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self.bfReserved2 = unpack("<H", file.read(2))[0] # 0x08 保留，必须设置为0
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self.bfOffBits = unpack("<i", file.read(4))[0] # 0x0a 从头到位图数据的偏移
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self.biSize = unpack("<i", file.read(4))[0] # 0x0e 信息头的大小
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self.biWidth = unpack("<i", file.read(4))[0] # 0x12 图像的宽度(以像素为单位)
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self.biHeight = unpack("<i", file.read(4))[0] # 0x16 图像的高度(以像素为单位)(负说明图像是倒立的)
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self.biPlanes = unpack("<H", file.read(2))[0] # 0x1a 颜色平面数
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self.biBitCount = unpack("<H", file.read(2))[0] # 0x1c 比特数/像素数
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self.biCompression = unpack("<i", file.read(4))[0] # 0x1e 压缩类型
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self.biSizeImage = unpack("<i", file.read(4))[0] # 0x22 位图数据的大小
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self.biXPelsPerMeter = unpack("<i", file.read(4))[0] # 0x26 水平分辨率
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self.biYPelsPerMeter = unpack("<i", file.read(4))[0] # 0x2a 垂直分辨率
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self.biClrUsed = unpack("<i", file.read(4))[0] # 0x2e 位图使用的调色板中的颜色索引数
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self.biClrImportant = unpack("<i", file.read(4))[0] # 0x32 对图像显示有重要影响的颜色索引数(0说明都重要)
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self.color_palette = self.get_color_palette()
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self.img_np = self.get_numpy_img()
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self.gray = self.get_gray_img()
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file.close()
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def get_color_palette(self) -> np.ndarray:
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if (self.bfOffBits == 0x36): # 16/24位图像不需要调色板，起始位置就等于0x36
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return None
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color_alette_size = 2 ** int(self.biBitCount) # 多少字节调色板颜色就有2^n个
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color_palette = np.zeros((color_alette_size, 3), dtype=np.int32)
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self.file.seek(0x36)
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for i in range(color_alette_size):
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b = unpack("B", self.file.read(1))[0]
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g = unpack("B", self.file.read(1))[0]
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r = unpack("B", self.file.read(1))[0]
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alpha = unpack("B", self.file.read(1))[0]
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color_palette[i][0] = b
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color_palette[i][1] = g
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color_palette[i][2] = r
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return color_palette
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def get_numpy_img(self) -> np.ndarray:
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biHeight = abs(self.biHeight)
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img_np = np.zeros((biHeight, self.biWidth, 3), dtype=np.int32)
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self.file.seek(self.bfOffBits)
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for x in range(biHeight):
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row_byte_count = ((self.biWidth * self.biBitCount + 31) >> 5) << 2
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row_bits = self.file.read(row_byte_count)
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row_bits = ''.join(format(byte, '08b') for byte in row_bits)
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for y in range(self.biWidth):
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pixel_data = row_bits[y * self.biBitCount: (y + 1) * self.biBitCount]
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if self.biHeight > 0: # 图像倒立
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img_np[biHeight - 1 - x][y] = self.get_RGB(pixel_data)
|
||
else:
|
||
img_np[x][y] = self.get_RGB(pixel_data)
|
||
return img_np
|
||
|
||
def get_gray_img(self) -> np.ndarray:
|
||
biHeight = abs(self.biHeight)
|
||
gray_img = np.dot(self.img_np.reshape((biHeight * self.biWidth, 3)).astype(np.float32),
|
||
[0.299, 0.587, 0.114]).astype(np.int32)
|
||
gray_img = gray_img.reshape((biHeight, self.biWidth))
|
||
return gray_img
|
||
|
||
def get_RGB(self, pixel_data:str):
|
||
if len(pixel_data) <= 8:
|
||
color_index = int(pixel_data, 2)
|
||
return self.color_palette[color_index]
|
||
elif len(pixel_data) == 16:
|
||
b = int(pixel_data[1:6], 2) * 8
|
||
g = int(pixel_data[6:11], 2) * 8
|
||
r = int(pixel_data[11:16], 2) * 8
|
||
return [r, g, b]
|
||
elif len(pixel_data) == 24:
|
||
b = int(pixel_data[0:8], 2)
|
||
g = int(pixel_data[8:16], 2)
|
||
r = int(pixel_data[16:24], 2)
|
||
return [r, g, b]
|
||
elif len(pixel_data) == 32:
|
||
b = int(pixel_data[0:8], 2)
|
||
g = int(pixel_data[8:16], 2)
|
||
r = int(pixel_data[16:24], 2)
|
||
alpha = int(pixel_data[24:32], 2)
|
||
return [r, g, b]
|
||
|
||
|
||
def equalize(self, level:int):
|
||
biHeight = abs(self.biHeight)
|
||
self.hist = np.zeros(256, dtype=np.int32)
|
||
max_value = self.gray.max()
|
||
min_value = self.gray.min()
|
||
gap = (max_value - min_value + 1) / level
|
||
for x in range(biHeight):
|
||
for y in range(self.biWidth):
|
||
self.hist[self.gray[x, y]] += 1
|
||
hist = np.zeros(level, dtype=np.float32)
|
||
for i in range(level):
|
||
hist[i] = np.sum(self.hist[min_value + int(i * gap) : min_value + int((i + 1) * gap)])
|
||
hist /= biHeight * self.biWidth
|
||
for i in range(1, level):
|
||
hist[i] += hist[i - 1]
|
||
hist *= level
|
||
hist = np.around(hist)
|
||
hist /= level
|
||
hist = np.floor(hist * 255).astype(np.int32)
|
||
self.equalized_img = np.zeros_like(self.gray)
|
||
self.equalized_hist = np.zeros(256, dtype=np.int32)
|
||
for x in range(biHeight):
|
||
for y in range(self.biWidth):
|
||
self.equalized_img[x, y] = hist[int((self.gray[x, y] - min_value) / gap)]
|
||
self.equalized_hist[self.equalized_img[x, y]] += 1
|
||
return self.equalized_img, self.hist, self.equalized_hist
|
||
|
||
|
||
def save_equalized_img(self, save_path:str):
|
||
self.save_img(image=self.equalized_img, save_path=save_path)
|
||
|
||
|
||
def medianfilter(self):
|
||
self.medianfiltered_img = np.zeros_like(self.gray)
|
||
padded_img = np.zeros((abs(self.biHeight) + 2, self.biWidth + 2), dtype=np.int16)
|
||
padded_img[1:-1, 1:-1] = self.gray
|
||
padded_img[0, 1:-1] = self.gray[0, :]
|
||
padded_img[-1, 1:-1] = self.gray[-1, :]
|
||
padded_img[1:-1, 0] = self.gray[:, 0]
|
||
padded_img[1:-1, -1] = self.gray[:, -1]
|
||
padded_img[0][0] = self.gray[0][0]
|
||
padded_img[0][-1] = self.gray[0][-1]
|
||
padded_img[-1][0] = self.gray[-1][0]
|
||
padded_img[-1][-1] = self.gray[-1][-1]
|
||
for x in range(abs(self.biHeight)):
|
||
for y in range(self.biWidth):
|
||
self.medianfiltered_img[x][y] = np.around(np.median(padded_img[x:x+3, y:y+3]))
|
||
return self.medianfiltered_img
|
||
|
||
|
||
def save_medianfiltered_img(self, save_path:str):
|
||
self.save_img(image=self.medianfiltered_img, save_path=save_path)
|
||
|
||
|
||
def meanfilter(self):
|
||
self.meanfiltered_img = np.zeros_like(self.gray)
|
||
padded_img = np.zeros((abs(self.biHeight) + 2, self.biWidth + 2), dtype=np.int16)
|
||
padded_img[1:-1, 1:-1] = self.gray
|
||
padded_img[0, 1:-1] = self.gray[0, :]
|
||
padded_img[-1, 1:-1] = self.gray[-1, :]
|
||
padded_img[1:-1, 0] = self.gray[:, 0]
|
||
padded_img[1:-1, -1] = self.gray[:, -1]
|
||
padded_img[0][0] = self.gray[0][0]
|
||
padded_img[0][-1] = self.gray[0][-1]
|
||
padded_img[-1][0] = self.gray[-1][0]
|
||
padded_img[-1][-1] = self.gray[-1][-1]
|
||
for x in range(abs(self.biHeight)):
|
||
for y in range(self.biWidth):
|
||
self.meanfiltered_img[x][y] = np.around(np.mean(padded_img[x:x+3, y:y+3]))
|
||
return self.meanfiltered_img
|
||
|
||
|
||
def save_meanfiltered_img(self, save_path:str):
|
||
self.save_img(image=self.meanfiltered_img, save_path=save_path)
|
||
|
||
|
||
def save_img(self, image:np.ndarray, save_path:str):
|
||
with open(save_path, "wb") as file:
|
||
file.write(int(self.bfType).to_bytes(2, byteorder='little')) # 0x00 文件类型
|
||
file.write(int(0x36 + 0x100 * 4 + self.biWidth * abs(self.biHeight)).to_bytes(4, byteorder='little')) # 0x02 文件大小
|
||
file.write(int(0).to_bytes(4, byteorder='little')) # 0x06 保留，必须设置为0
|
||
file.write(int(0x36 + 0x100 * 4).to_bytes(4, byteorder='little')) # 0x0a 从头到位图数据的偏移
|
||
file.write(int(40).to_bytes(4, byteorder='little')) # 0x0e 信息头的大小
|
||
file.write(int(self.biWidth).to_bytes(4, byteorder='little')) # 0x12 图像的宽度
|
||
file.write(int(self.biHeight).to_bytes(4, byteorder='little')) # 0x16 图像的高度
|
||
file.write(int(self.biPlanes).to_bytes(2, byteorder='little')) # 0x1a 颜色平面数
|
||
file.write(int(8).to_bytes(2, byteorder='little')) # 0x1c 比特数/像素数
|
||
file.write(int(self.biCompression).to_bytes(4, byteorder='little')) # 0x1e 压缩类型
|
||
file.write(int(self.biSizeImage).to_bytes(4, byteorder='little')) # 0x22 位图数据的大小
|
||
file.write(int(self.biXPelsPerMeter).to_bytes(4, byteorder='little')) # 0x26 水平分辨率
|
||
file.write(int(self.biYPelsPerMeter).to_bytes(4, byteorder='little')) # 0x2a 垂直分辨率
|
||
file.write(int(0x100 * 4).to_bytes(4, byteorder='little')) # 0x2e 位图使用的调色板中的颜色索引数
|
||
file.write(int(0).to_bytes(4, byteorder='little')) # 0x32 对图像显示有重要影响的颜色索引数
|
||
|
||
for i in range(256):
|
||
file.write(int(i).to_bytes(1, byteorder='little'))
|
||
file.write(int(i).to_bytes(1, byteorder='little'))
|
||
file.write(int(i).to_bytes(1, byteorder='little'))
|
||
file.write(int(0).to_bytes(1, byteorder='little'))
|
||
|
||
for x in range(abs(self.biHeight)):
|
||
for y in range(self.biWidth):
|
||
if self.biHeight > 0:
|
||
file.write(int(image[self.biHeight - 1 - x][y]).to_bytes(1, byteorder='little'))
|
||
else:
|
||
file.write(int(image[x][y]).to_bytes(1, byteorder='little'))
|
||
file.write(b'0' * ((((self.biWidth * 8 + 31) >> 5) << 2) - 8 * self.biWidth))
|
||
|
||
file.close()
|
||
|
||
|
||
def choosepic():
|
||
global path_
|
||
path_ = tkinter.filedialog.askopenfilename(title='请选择图片文件', filetypes=[('图片', '.bmp')])
|
||
if path_ == '':
|
||
return
|
||
img_temp = Image.open(path_).resize((int(256 * 0.8), int(256 * 0.8))) # 图片读取和加载
|
||
img = ImageTk.PhotoImage(img_temp)
|
||
label_image1.config(image=img)
|
||
label_image1.image = img
|
||
|
||
|
||
def equalize():
|
||
if path_ == '':
|
||
return
|
||
image = BmpData(path_)
|
||
# img = Image.fromarray(image.img_np.astype(np.uint8))
|
||
# img.show()
|
||
|
||
equalized_img, hist, equalized_hist = image.equalize(8) # 分别为均衡化的图/直方图/均衡化后的直方图
|
||
equalized_img = Image.fromarray(equalized_img.astype(np.uint8))
|
||
# equalized_img.show()
|
||
|
||
name_parts = path_.split('.')
|
||
name_parts[-2] += "_equalized"
|
||
new_file_name = '.'.join(name_parts)
|
||
image.save_equalized_img(new_file_name)
|
||
|
||
equalized_img = equalized_img.resize((int(256 * 0.8), int(256 * 0.8)))
|
||
equalized_img = ImageTk.PhotoImage(equalized_img)
|
||
label_image2.config(image=equalized_img)
|
||
label_image2.image = equalized_img # 处理后的图片的显示
|
||
|
||
|
||
def medianfilter():
|
||
if path_ == '':
|
||
return
|
||
image = BmpData(path_)
|
||
medianfiltered_img = image.medianfilter()
|
||
medianfiltered_img = Image.fromarray(medianfiltered_img.astype(np.uint8))
|
||
# medianfiltered_img.show()
|
||
|
||
name_parts = path_.split('.')
|
||
name_parts[-2] += "_medianfiltered"
|
||
new_file_name = '.'.join(name_parts)
|
||
image.save_medianfiltered_img(new_file_name)
|
||
|
||
medianfiltered_img = medianfiltered_img.resize((int(256 * 0.8), int(256 * 0.8)))
|
||
medianfiltered_img = ImageTk.PhotoImage(medianfiltered_img)
|
||
label_image2.config(image=medianfiltered_img)
|
||
label_image2.image = medianfiltered_img # 处理后的图片的显示
|
||
|
||
|
||
|
||
def meanfilter():
|
||
if path_ == '':
|
||
return
|
||
image = BmpData(path_)
|
||
meanfiltered_img = image.meanfilter()
|
||
meanfiltered_img = Image.fromarray(meanfiltered_img.astype(np.uint8))
|
||
# meanfiltered_img.show()
|
||
|
||
name_parts = path_.split('.')
|
||
name_parts[-2] += "_meanfiltered"
|
||
new_file_name = '.'.join(name_parts)
|
||
image.save_meanfiltered_img(new_file_name)
|
||
|
||
meanfiltered_img = meanfiltered_img.resize((int(256 * 0.8), int(256 * 0.8)))
|
||
meanfiltered_img = ImageTk.PhotoImage(meanfiltered_img)
|
||
label_image2.config(image=meanfiltered_img)
|
||
label_image2.image = meanfiltered_img # 处理后的图片的显示
|
||
|
||
|
||
if __name__ == "__main__":
|
||
root = tkinter.Tk()
|
||
root.title('21281280柯劲帆') # 标题
|
||
width, height = 600, 400
|
||
width_max, height_max = root.maxsize()
|
||
s_center = '%dx%d+%d+%d' % (width, height, (width_max - width) / 2, (height_max - height) / 2) # 将页面显示在正中间
|
||
root.geometry(s_center)
|
||
root.resizable(width=False, height=False) # 窗口不可移动
|
||
l = tkinter.Label(root, text='实验三', width=60, height=2, fg='black', font=("微软雅黑", 16), anchor=tkinter.CENTER)
|
||
l.pack()
|
||
|
||
label_image1 = tkinter.Label(root, width=int(256 * 0.8), height=int(256 * 0.8), bg='whitesmoke', anchor=tkinter.NE)
|
||
label_image1.pack()
|
||
label_image1.place(x=45, y=70, width=int(256 * 0.8), height=int(256 * 0.8))
|
||
|
||
label_image2 = tkinter.Label(root, width=int(256 * 0.8), height=int(256 * 0.8), bg='whitesmoke', anchor=tkinter.NE)
|
||
label_image2.place(x=350, y=70, width=int(256 * 0.8), height=int(256 * 0.8))
|
||
|
||
# 文本按钮
|
||
Image_Input = tkinter.Button(root, text='Choose', command=choosepic)
|
||
Image_Input.place(x=50, y=300, width=90, height=30)
|
||
|
||
# 直方图均衡化
|
||
Fun1 = tkinter.Button(root, text='直方图均衡化', command=equalize)
|
||
Fun1.place(x=150, y=300, width=90, height=30)
|
||
|
||
# 中值滤波
|
||
Fun1 = tkinter.Button(root, text='中值滤波', command=medianfilter) # 添加对应按钮
|
||
Fun1.place(x=250, y=300, width=90, height=30)
|
||
|
||
# 均值滤波
|
||
Fun2 = tkinter.Button(root, text='均值滤波', command=meanfilter) # 添加对应按钮
|
||
Fun2.place(x=350, y=300, width=90, height=30)
|
||
|
||
# 退出
|
||
Quit = tkinter.Button(root, text='Quit', command=sys.exit)
|
||
Quit.place(x=450, y=300, width=90, height=30)
|
||
|
||
end = tkinter.Label(root, text='21281280 柯劲帆', fg='silver', font=("微软雅黑", 10))
|
||
end.place(x=215, y=360, width=200, height=20)
|
||
root.mainloop()
|
||
```
|
||
|