Add files via upload

eastmountyxz · web-flow · commit bba0945d83dd · 2019-10-17T23:43:55.000+08:00
diff --git a/blog18-Canny/Lena.png b/blog18-Canny/Lena.png
diff --git a/blog18-Canny/blog18-image02.py b/blog18-Canny/blog18-image02.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#高斯滤波降噪
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0)
+ 
+#Canny算子
+Canny = cv2.Canny(gaussian, 50, 150) 
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Canny算子']  
+images = [lenna_img, Canny]  
+for i in xrange(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog18-Canny/blog18-image03.py b/blog18-Canny/blog18-image03.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#先通过高斯滤波降噪
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0)
+ 
+#再通过拉普拉斯算子做边缘检测
+dst = cv2.Laplacian(gaussian, cv2.CV_16S, ksize = 3)
+LOG = cv2.convertScaleAbs(dst)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'LOG算子']  
+images = [lenna_img, LOG]  
+for i in xrange(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog18-Canny/blog18-image04.py b/blog18-Canny/blog18-image04.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#高斯滤波
+gaussianBlur = cv2.GaussianBlur(grayImage, (3,3), 0)
+
+#阈值处理
+ret, binary = cv2.threshold(gaussianBlur, 127, 255, cv2.THRESH_BINARY)
+
+#Scharr算子
+x = cv2.Scharr(grayImage, cv2.CV_32F, 1, 0) #X方向
+y = cv2.Scharr(grayImage, cv2.CV_32F, 0, 1) #Y方向
+absX = cv2.convertScaleAbs(x)       
+absY = cv2.convertScaleAbs(y)
+Scharr = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Canny算子
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0) #高斯滤波降噪
+Canny = cv2.Canny(gaussian, 50, 150) 
+
+#LOG算子
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0) #先通过高斯滤波降噪
+dst = cv2.Laplacian(gaussian, cv2.CV_16S, ksize = 3) #再通过拉普拉斯算子做边缘检测
+LOG = cv2.convertScaleAbs(dst)
+
+#效果图
+titles = ['Source Image', 'Gray Image', 'Binary Image',
+          'Scharr Image','Canny Image', 'LOG Image']  
+images = [lenna_img, grayImage, binary, Scharr, Canny, LOG]  
+for i in np.arange(6):  
+   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
