cv2.destroyAllWindows() 3. 高斯滤波(Gaussian Filtering) 高斯滤波是一种基于高斯函数的平滑滤波器,它通过计算像素及其邻域内像素的加权平均值来平滑图像。高斯滤波在减少噪声的同时,能够较好地保留图像的边缘信息。 Python实现 # 使用高斯滤波 smoothed_image = cv2.GaussianBlur(image, (kernel_size, kernel_size),...
情况二:gaussianImage=cv.GaussianBlur(image,(0,0),2)ksize.width和ksize.height它们是零,然后根据sigma计算归一化后权重,效果如下: 情况三:gaussianImage=cv.GaussianBlur(image,(5,5),0)此时标准差sigma都是零,则从ksize.width和ksize.height计算归一化权重,效果如下: 图像自定义滤波: 函数:cv.filter2D(src, ...
getGaussianKernel cv::Mat cv::getGaussianKernel( int n, double sigma, int ktype ) { const int SMALL_GAUSSIAN_SIZE = 7; //定义了固定的filter即Kernels. static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] = { {1.f}, {0.25f, 0.5f, 0.25f}, {0.0625f, 0.25f, 0.375f, 0.25...
importcv2importnumpy src=cv2.imread("image/1.jpg")# 1.自定义锐化核kernel=numpy.float32([[0,-1,0],[-1,5,-1],[0,-1,0]])dst1=cv2.filter2D(src,-1,kernel)# 2.USM锐化(UnsharpMask)gaussian=cv2.GaussianBlur(src,(5,5),6)dst2=cv2.addWeighted(src,2,gaussian,-1,0)cv2.imshow("src...
现在,请注意,常见的脉冲整形滤波器包括: 1、Raised-cosine filter 升余弦滤波器 2、Root raised-cosine filter 根升余弦滤波器 3、Sinc filter 辛格滤波器 4、Gaussian filter高斯滤波器这些滤波器通常有一个参数,您可以调整该参数以减少使用的带宽。下面演示了具有不同值 ...
self.kernel = self.gaussian_kernel() def gaussian_kernel(self): kernel = np.zeros(shape=(self.kernel_size, self.kernel_size), dtype=np.float) radius = self.kernel_size//2 for y in range(-radius, radius + 1): # [-r, r]
import cv2 import numpy as np def enhance_text(image_path): # 读取图像 img = cv2.imread(image_path) # 转换为灰度图像 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 应用自适应阈值处理 thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11...
signal import convolve2d# Create a list of kernel names for labelingkernel_names = ['Original Image', 'Grayscale', 'Horizontal Sobel', 'Vertical Sobel', 'Left Diagonal', 'Right Diagonal', 'Edge Detection', 'Sharpen', 'Box Blur', 'Gaussian Blur']# Create a 2x5 subplot gridfig...
dtype=int) x = cv.filter2D(gaussian_blur, cv.CV_16S, kernelx) y = cv.filter2D(gaussian_blur, cv.CV_16S, kernely) absX = cv.convertScaleAbs(x) absY = cv.convertScaleAbs(y) Prewitt = cv.addWeighted(absX, 0.5, absY, 0.5, 0) # Sobel 算子 x = cv.Sobel(gaussian_blur, cv....
cv.imshow("Gaussian Blur", dst) cv.waitKey(0) cv.destroyAllWindows() 运行结果: 注意: 1.高斯模糊实质上就是一种均值模糊,只是高斯模糊是按照加权平均的,距离越近的点权重越大,距离越远的点权重越小。通俗的讲,高斯滤波就是对整幅图像进行加权平均的过程,每一个像素点的值,都由其本身和邻域内的其他像...