中值滤波python代码实现可处理彩色图像和二值图像
中值滤波
中值滤波的原理:
中值滤波,其原理在于使用卷积核内的中值来代替中心点的值。例如,当一个点值为255时,而周围其他点全都低于120,这时候它就是一个明显的噪点。使用中值滤波时,就会用其他的点代替该点的值。不敢说替换的点就是真值,但是它一定比原来的点更接近真值。
下面将介绍如何使用python代码来实现。(文中所使用的图像为网图,如有侵权请及时联系删除)
from PIL import Image import numpy as np class Mdiean_Filter: def __init__(self, source_img): self.source_img = source_img #原图 self.noise_img = './salt_noise.jpg' #加了椒盐噪声之后的图像 self.mdiean_img = './mdiean_filter.jpg' #中值滤波的图像 self.k = 3 def Add_Salt_Noise(self): # 加椒盐噪声 img = Image.open(self.source_img) imgarray = np.array(img) height,width = imgarray.shape[0], imgarray.shape[1] for i in range(height): for j in range(width): if np.random.random(1) < 0.05: if np.random.random(1) < 0.3: imgarray[i][j] = 0 else: imgarray[i][j] = 255 new_img = Image.fromarray(imgarray) new_img.save(self.noise_img) return imgarray def Mdiean_Filtering(self, padding = None): #中值滤波会变为二值图 img = Image.open(self.noise_img) imgarray = np.array(img) height, width = imgarray.shape[0], imgarray.shape[1] print(imgarray.shape) if not padding: edge = int((self.k -1)/2) if height -1 -edge <=edge or width -1-edge<=edge: print("the kenerl is to long") return None new_arr = np.zeros((height, width,3), dtype='uint8') print(new_arr.shape) for i in range(height): for j in range(width): if i <=edge -1 or i >= height -1 -edge or j <=edge -1 or j >= height -1 -edge: new_arr[i, j] = imgarray[i, j] else: new_arr[i, j] = np.median(imgarray[i - edge:i + edge + 1, j -edge:j+edge + 1]) #numpy的代码计算时会把矩阵三个数值变成一样,这也是色彩损失的原因 new_img = Image.fromarray(new_arr) new_img.save(self.mdiean_img) def Mdiean_Filter1(self, padding = None): #中值滤波不损失色彩 imgarray = self.Add_Salt_Noise() height, width = imgarray.shape[0], imgarray.shape[1] if not padding: edge = int((self.k -1)/2) if height -1 -edge <=edge or width -1-edge<=edge: print("the kenerl is to long") return None for i in range(height): for j in range(width): if i <=edge -1 or i >= height -1 -edge or j <=edge -1 or j >= height -1 -edge: imgarray[i][j] = imgarray[i][j] else: num = [] for m in range(i - edge, i + edge + 1): for n in range(j -edge, j+edge + 1): num.append((imgarray[m][n])[0]) #这里通过彩色图像第一个值计算中值也可以改为第二个或者第三个 temp = np.median(num) idex_tem = num.index(temp) #获取中值在数组中的坐标 l1 = int((idex_tem / self.k ))- edge + i #根据进制转换反推出中值在图像中的坐标 l2 = (idex_tem % self.k )- edge + j print(imgarray[l1][l2]) imgarray[i][j] = imgarray[l1][l2] #赋值 #num1 = np.sort(num)[int(self.k * self.k / 2)] #print(idex_tem) #temp = np.median(imgarray[i][j], imgarray[i - edge][j], imgarray[i - edge][j]) #imgarray[i][j] = np.median(imgarray[i - edge:i + edge + 1, j -edge:j+edge + 1]) new_img = Image.fromarray(imgarray) new_img.save(self.mdiean_img) if __name__=="__main__": example = 'example.jpg' filter = Mdiean_Filter(example) #filter.Add_Salt_Noise() filter.Mdiean_Filter1()