处理流程

# -*- coding: utf-8 -*-
# @note    : 形态学 开运算 + 圆形内核 处理
# --------------------------------import cv2 as cv
import numpy as npdef opening_circle(img_bin, kernel_size=10):# 形态学kernel = np.zeros((kernel_size, kernel_size), np.uint8)center_radius = int(kernel_size / 2)print(center_radius)cv.circle(kernel, (center_radius, center_radius), center_radius, (1, 1, 1), -1, cv.LINE_AA)img_open_circle = cv.morphologyEx(img_bin, cv.MORPH_OPEN, kernel)return img_open_circledef main():# 1.打开图片 灰度化img_src = cv.imread("./wiresFilled.jpg", cv.IMREAD_GRAYSCALE)img_show = cv.cvtColor(img_src, cv.COLOR_GRAY2BGR)img_src[0:60, :] = 255# 2.二值化ret, img_bin = cv.threshold(img_src, 35, 255, cv.THRESH_BINARY_INV)# 3.形态学 执行开操作img_open = opening_circle(img_bin, 20)# 4.随机颜色index = 0color1 = (255, 0, 0)color2 = (0, 255, 0)color3 = (0, 0, 255)color4 = (255, 255, 0)color5 = (255, 0, 255)color6 = (0, 255, 255)colors = [color1, color2, color3, color4, color5, color6]# 5.连通域筛选font = cv.FONT_HERSHEY_SIMPLEXcontours, hierarchy = cv.findContours(img_open, cv.RETR_LIST, cv.CHAIN_APPROX_SIMPLE)img_result = img_show.copy()for cnt in contours:area = cv.contourArea(cnt)center, radius = cv.minEnclosingCircle(cnt)circularity = area / (np.pi * radius * radius)print('真圆度', circularity, "面积", area)if circularity > 0.65 and area < 1000:# 绘制结果random_color = colors[index % len(colors)]cv.circle(img_result, (int(center[0]), int(center[1])), int(radius), random_color, -1, cv.LINE_AA)cv.putText(img_result, ("D=%0.2f" % (radius * 2)),(int(center[0] - 5), int(center[1] - 25)),font, 0.45,(255, 255, 255), 2)index = index + 1# 显示图片cv.imshow('img_bin', img_bin)cv.imshow('img_open', img_open)cv.imshow('img_result', img_result)cv.imwrite('img_bin.jpg', img_bin)cv.imwrite('img_open.jpg', img_open)cv.imwrite('img_result.jpg', img_result)cv.waitKey()cv.destroyAllWindows()if __name__ == '__main__':main()