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空域平滑.py

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+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+ #当使用的平滑模板尺寸增大时，对噪声的消除效果有所增强，
+ #但是图像更模糊，可视细节逐渐减少，运算量逐步增大
+
+
+#加椒盐噪声的函数
+def saltPepper(image, salt, pepper):
+    height = image.shape[0]
+    width = image.shape[1]
+    pertotal = salt + pepper    #总噪声占比
+    noiseImage = image.copy()
+    noiseNum = int(pertotal * height * width)
+    for i in range(noiseNum):
+        rows = np.random.randint(0, height-1)
+        cols = np.random.randint(0,width-1)
+        if(np.random.randint(0,100)<salt*100):
+            noiseImage[rows,cols] = 255
+        else:
+            noiseImage[rows,cols] = 0
+    return noiseImage
+ 
+ 
+#显示函数
+def matplotlib_multi_pic1(images):
+    for i in range(len(images)):
+        img = images[i]
+        title = "("+str(i+1)+")"
+        #行，列，索引
+        plt.subplot(2, 2, i+1)
+        plt.imshow(img, cmap="gray")
+        plt.title(title,fontsize=10)
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+
+def show(imagess):
+    for i in range(len(imagess)):
+        img = imagess[i]
+        title = "("+str(i+1)+")"
+        #行，列，索引
+        plt.subplot(2, 2, i+1)
+        plt.imshow(img, cmap="gray")
+        plt.title(title,fontsize=10)
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+ 
+#邻域平均法
+image = cv2.imread("da.jpg", cv2.IMREAD_GRAYSCALE)
+imageNoise = saltPepper(image, 0.1, 0.1)
+imageAver3 = cv2.blur(imageNoise, (3, 3))
+imageAver5 = cv2.blur(imageNoise, (5, 5))
+images = [image, imageNoise, imageAver3, imageAver5]
+matplotlib_multi_pic1(images)
+
+# 中值滤波 medianx 即模板为 x行x列
+median3 = cv2.medianBlur(image, 3)
+median5 = cv2.medianBlur(image, 5)
+median7 = cv2.medianBlur(image, 7)
+imagess = [image, imageNoise, median3, median5]
+show(imagess)
+
+
+#先显示邻域平均法，后显示中值滤波法