关于双边滤波的详细内容可参见【图像处理】——双边滤波 2.均值迁移 API接口 pyrMeanShiftFiltering def pyrMeanShiftFiltering(src, sp, sr,...termcrit=None) 参数含义 sp,定义的漂移物理空间半径大小; 测试用例: def shift_demo(image): # 均值迁移 dst = cv.pyrMeanShiftFiltering
算法过程 API pyrMeanShiftFiltering函数其实也是一种滤波,只能达到平滑图像的效果,无法直接分割图像。但是平滑后的图像更方便我们进行图像分割。...public static void pyrMeanShiftFiltering(Mat src, Mat dst, double sp, double sr, int maxLevel, TermCriteria...doMeanShift() } } private fun doMeanShift() { val dst = Mat() Imgproc.pyrMeanShiftFiltering
,dst) cv.waitKey(0) cv.destroyAllWindows() 另一个EPF的经典实现是均值迁移 image.png 比起高斯双边,均值迁移有时候过度模糊 原型: void pyrMeanShiftFiltering...cv import numpy as np image1=cv.imread("D://bil.png") cv.imshow("yuantu",image1) dst=cv.pyrMeanShiftFiltering
Opencv中对应的均值偏移函数是pyrMeanShiftFiltering。...函数API void pyrMeanShiftFiltering( InputArray src, OutputArray dst,...定义的漂移色彩空间半径大小; ---- maxLevel:定义金字塔的最大层数; ---- termcrit:定义的漂移迭代终止条件,可以设置为迭代次数满足终止,迭代目标与中心点偏差满足终止,或者两者的结合; ---- pyrMeanShiftFiltering
import pytesseract from PIL import Image def recognize_text(image): # 边缘保留滤波 去噪 dst = cv.pyrMeanShiftFiltering...import pytesseract from PIL import Image def recognize_text(image): # 边缘保留滤波 去噪 blur =cv.pyrMeanShiftFiltering...import pytesseract from PIL import Image def recognize_text(image): # 边缘保留滤波 去噪 blur = cv.pyrMeanShiftFiltering
OpenCV 4中提供了实现Mean-Shift算法分割图像的pyrMeanShiftFiltering()函数,该函数的函数原型在代码清单8-23中给出。...代码清单8-23 pyrMeanShiftFiltering()函数原型 void cv::pyrMeanShiftFiltering(InputArray src,
基于颜色分布峰值进行这种分割的函数是cv2.pyrMeanShiftFiltering()。...可以利用均值偏移算法的这个特性,实现彩色图像分割. cv2.pyrMeanShiftFiltering Mean-Shift 分割算法 官方文档 函数使用 cv2.pyrMeanShiftFiltering...(img, sp=20, sr=20, maxLevel=3) res_20_50 = cv2.pyrMeanShiftFiltering(img, sp=20, sr=50, maxLevel=3)...res_50_20 = cv2.pyrMeanShiftFiltering(img, sp=50, sr=20, maxLevel=3) res_50_50 = cv2.pyrMeanShiftFiltering...(img, sp=50, sr=50, maxLevel=3) res_80_80 = cv2.pyrMeanShiftFiltering(img, sp=80, sr=80, maxLevel=3)
cv.namedWindow("input", cv.WINDOW_AUTOSIZE) cv.imshow("input", src) h, w = src.shape[:2] dst = cv.pyrMeanShiftFiltering
, 255, cv.THRESH_BINARY | cv.THRESH_OTSU) return binary def method_3(image): blurred = cv.pyrMeanShiftFiltering
# 均值偏移滤波降噪处理 mean_filter_img = cv.pyrMeanShiftFiltering(image, 10, 100) cv.imshow("mean_filter_img",...'' 作用:圆形检测 参数:需要检测圆的图片 返回:检测出圆形的信息 ''' # 均值偏移滤波降噪处理 mean_filter_img = cv.pyrMeanShiftFiltering
np.uint8(center)plt.subplot(222); plt.imshow(g, cmap=plt.cm.gray_r)# @Date : 2020/4/18result3 = cv2.pyrMeanShiftFiltering
random as rd def watershed_algorithm(image): src = image.copy() # 边缘保留滤波EPF 去噪 blur = cv2.pyrMeanShiftFiltering...有时候还可能出现难以分割的情况,比如下图: Halcon分割结果: OpenCV分割结果(分割失败): 这种情况下,就直接换形态学 + 连通域方法处理即可: 【3】OpenCV实现代码里面用到了cv2.pyrMeanShiftFiltering
python与 opencv 实现均值迁移滤波,调用 pyrMeanShiftFiltering 这个API。...cv2.pyrMeanShiftFiltering(src, sp, sr, dst=None, maxLevel=None, termcrit=None) - src:输入图像 - sp:定义迁移物理空间的半径大小...import cv2 as cv def pyr_meanshift_filter(image): # 均值偏移 dst = cv.pyrMeanShiftFiltering(image
filtering # to aid the thresholding step image = cv2.imread(image_path) shifted = cv2.pyrMeanShiftFiltering
python与opencv实现均值迁移滤波,可以调用pyrMeanShiftFiltering这个API。...cv2.pyrMeanShiftFiltering(src, sp, sr, dst=None, maxLevel=None, termcrit=None) - src:输入图像 - sp:定义迁移物理空间的半径大小...import cv2 as cv def pyr_meanshift_filter(image): # 均值偏移 dst = cv.pyrMeanShiftFiltering(image
OpenCV中均值迁移滤波函数处于Imgproc模块中, 其还可以被用作图像自动分割方法之一, 解释具体如下: pyrMeanShiftFiltering(Mat src, Mat dst, double...使用该函数的代码: Imgproc.pyrMeanShiftFiltering(src, dst, 10, 50); 补充:关于maxLevel的金字塔的层数的意义以及termcrit的解释等,
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