I want to implement the laplacian of gaussian filter for my image.I test this 2 method which give me completely different answer. Please tell me which I made mistake.In the first method I implement the LOG filter from it's function and in the second I use opencv functions. Here is my ...
示例11: LaplacianOfGaussian ▲点赞 5▼ # 需要导入模块: import cv2 [as 别名]# 或者: from cv2 importLaplacian[as 别名]defLaplacianOfGaussian(image):LoG_image = cv2.GaussianBlur(image, (3,3),0)# paramtergray = cv2.cvtColor( LoG_image, cv2.COLOR_BGR2GRAY) LoG_image = cv2.Laplacian( gra...
defapplygaussian(imgs):gauss_f=call2dtensorgaussfilter()gauss_f=tf.expand_dims(gauss_f,axis=2)gauss_f=tf.expand_dims(gauss_f,axis=3)result=tf.nn.conv2d(imgs,gauss_f*4,strides=[1,1,1,1],padding="VALID")result=tf.squeeze(result,axis=0)result=tf.squeeze(result,axis=2)returnresult ...
最后Laplacian算子不能检测边缘的方向;所以Laplacian在分割中所起的作用包括:(1)利用它的零交叉性质进行边缘定位;(2)确定一个像素是在一条边缘暗的一面还是亮的一面;一般使用的是高斯型拉普拉斯算子(Laplacian of a Gaussian,LoG),由于二阶导数是线性运算,利用LoG卷积一幅图像与首先使用高斯型平滑函数卷积改图像,...
def LaplacianOfGaussian(image): LoG_image = cv2.GaussianBlur(image, (3,3), 0) # paramter gray = cv2.cvtColor( LoG_image, cv2.COLOR_BGR2GRAY) LoG_image = cv2.Laplacian( gray, cv2.CV_8U,3,3,2) # parameter LoG_image = cv2.convertScaleAbs(LoG_image) return LoG_image Example...
filter2D(img, -1, kernel.T) cv2.imshow('img', np.hstack((img, dst_v, dst_h))) cv2.waitKey(0) Sobel算子 sobel 算子市高斯平滑和微分操作的结合体,因此它的抗噪声能力比较好。 它先在垂直方向计算梯度 $G_x=k_1×src$,再在水平方向计算梯度 $G_y=k_2×src$ ,最后求出总梯度: $G=\...
Image Processing techniques using OpenCV and Python. python open-source opencv image-processing gaussian video-processing image-segmentation transformation digital-image-processing opencv-python sobel laplacian otsu-thresholding box-filter morphological-processing laplacian-gaussian interpolations-inverse-mapping cont...
图像边缘是图像最基本的特征,所谓边缘(Edge) 是指图像局部特性的不连续性。灰度或结构等信息的突变处称之为边缘。例如,灰度级的突变、颜色的突变,、纹理结构的突变等。边缘是一个区域的结束,也是另一个区域的开始,利用该特征可以提取图像边缘。 图(a)是一个理想的边缘所具备的特性。每个灰度级跃变到一个垂直的台...
the blending picture will be very unnatural, and we will see the middle line split between apple and orange. The main idea is that we need to use Gaussian filter (low pass) and Laplacian filter (high pass) to generate pyramids and then smoothly blend two different images into one. Here ...
Bilateral filter is used for reducing various noises especially Additive White Gaussian Noise which occur more in standard test images. The important feature of this nonlinear bilateral filter is the preservation of the edges, while reducing the noise in the images. The main idea is to replace ...