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空域锐化.py

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+import cv2
+import numpy as np
+#三个一阶算子是从底层实现的，而拉普拉斯算子通过调库实现
+
+def robert(img):
+    h,w=img.shape[:2]
+    r=[[-1,-1],[1,1]]
+    for i in range(h):
+        for j in range(w):
+            if (j+2<w) and (i+2<=h):
+                process_img=img[i:i+2,j:j+2]
+                list_robert=r*process_img
+                img[i,j]=abs(list_robert.sum())
+
+    return img
+
+img=cv2.imread("da.jpg",cv2.IMREAD_GRAYSCALE)
+cv2.imshow('orl_img',img)
+img=robert(img)
+cv2.imshow('robert',img)
+cv2.waitKey(0)
+
+
+def sobel(img):
+    h,w=img.shape
+    new_img=np.zeros([h,w])
+    x_img=np.zeros(img.shape)
+    y_img=np.zeros(img.shape)
+    sobel_x=np.array([[-1,0,1],[-2,0,2],[-1,0,1]])
+    sobel_y=np.array([[-1,-2,-1],[0,0,0],[1,2,1]])
+    for i in range(h-2):
+        for j in range(w-2):
+            x_img[i+1,j+1]=abs(np.sum(img[i:i+3,j:j+3]*sobel_x))
+            y_img[i+1,j+1]=abs(np.sum(img[i:i+3,j:j+3]*sobel_y))
+            new_img[i+1,j+1]=np.sqrt(np.square(x_img[i+1,j+1])+np.square(y_img[i+1,j+1]))
+
+    return np.uint8(new_img)
+
+img=cv2.imread("da.jpg",cv2.IMREAD_GRAYSCALE)
+img=sobel(img)
+cv2.imshow('sobel',img)
+cv2.waitKey(0)
+
+
+def prewitt(img):
+    h,w=img.shape
+    new_img=np.zeros([h,w])
+    x_img=np.zeros(img.shape)
+    y_img=np.zeros(img.shape)
+    prewitt_x=np.array([[-1,0,1],[-1,0,1],[-1,0,1]])
+    prewitt_y=np.array([[-1,-1,-1],[0,0,0],[1,1,1]])
+    for i in range(h-2):
+        for j in range(w-2):
+            x_img[i+1,j+1]=abs(np.sum(img[i:i+3,j:j+3]*prewitt_x))
+            y_img[i+1,j+1]=abs(np.sum(img[i:i+3,j:j+3]*prewitt_y))
+            new_img[i+1,j+1]=np.sqrt(np.square(x_img[i+1,j+1])+np.square(y_img[i+1,j+1]))
+
+    return np.uint8(new_img)
+
+img=cv2.imread("da.jpg",cv2.IMREAD_GRAYSCALE)
+img=prewitt(img)
+cv2.imshow('prewitt',img)
+cv2.waitKey(0)
+
+
+
+img=cv2.imread("da.jpg",cv2.IMREAD_GRAYSCALE)
+img_lap=cv2.Laplacian(src=img,ddepth=-1,ksize=3)
+cv2.imshow('Laplacian',img_lap)
+cv2.waitKey(0)