OpenCV与AI深度学习 | 实战 | 粘连物体分割与计数应用(二)--基于距离变换+分水岭算法 Halcon/OpenCV实现比较

本文来源公众号“OpenCV与AI深度学习”，仅用于学术分享，侵权删，干货满满。

原文链接：实战 | 粘连物体分割与计数应用(二)--基于距离变换+分水岭算法 Halcon/OpenCV实现比较

导读

本文主要介绍基于基于距离变换+分水岭算法实现粘连物体的分割与计数方法，并对比Halcon与OpenCV实现差异。

OpenCV与AI深度学习 | 实战 | 粘连物体分割与计数应用(一)--基于形态学+连通域处理 Halcon/OpenCV实现比较-优快云博客

背景介绍

在实际的视觉应用场景中，我们常常会遇到物体/元件的计数问题，而计数时比较常见的情形就是物体相邻或粘连，对相邻或粘连物体的分割将直接影响着最终计数的准确性。后面将分篇介绍粘连物体分割计数的常用方法，包括：

【1】形态学 + 连通域处理方法

【2】距离变换 + 分水岭分割方法

【3】其他方法

本文将对第【2】种方法分别用Halcon和OpenCV实现并做简单对比。

实例演示与实现步骤

* 实例一：糖豆分割与计数

测试图像(图片来源--Halcon例程图)：

实现步骤：

【1】阈值处理：区间固定阈值或OTSU阈值

【2】距离变换

【3】分水岭算法

【4】求区域重叠部分(交集)

【5】结果标记与显示

Halcon实现代码：

dev_clear_window()
dev_get_window (WindowHandle)
*读取图片
read_image (Image, 'pellets')
get_image_size (Image, Width, Height)
*自动阈值分割
rgb1_to_gray (Image, GrayImage)
threshold (GrayImage, Regions, 110, 255)
connection (Regions, ConnectedRegions)
select_shape (ConnectedRegions, SelectedRegions, 'area', 'and', 40, 50000)
*欧式距离函数的距离变换
distance_transform (SelectedRegions, DistanceImage, 'octagonal', 'true', Width, Height)
*int4转byte
convert_image_type (DistanceImage, ImageConverted, 'byte')
*图像取反
invert_image (ImageConverted, ImageInvert)
*图像比例增强 按最大比例增强对比度
scale_image_max (ImageInvert, ImageScaleMax)
*分水岭算法
watersheds_threshold (ImageScaleMax, Basins, 30)
select_shape (Basins, SelectedBasins, 'area', 'and', 2000, 50000)
gen_contour_region_xld (SelectedBasins, Contours, 'border')
*取出两个区域中重叠的部分
intersection (SelectedBasins, SelectedRegions, RegionIntersection)
dev_display(Image)
dev_display(RegionIntersection)
dev_display(Image)
dev_set_draw ('margin')
dev_set_line_width (3)
dev_display (RegionIntersection)
dev_set_color ('red')
dev_set_line_width (2)
area_center (RegionIntersection, Area, Row, Column)
gen_cross_contour_xld (Cross, Row, Column, 15, 0.785398)
count_obj (RegionIntersection, Number)
*设置字体颜色
dev_set_color ('green')
*设置文字大小
set_display_font (WindowHandle, 30, 'mono', 'true', 'false')
*设置文字位置
set_tposition (WindowHandle, 15, 220)
write_string(WindowHandle, 'count=' + Number)

OpenCV实现代码与效果：

# 公众号：OpenCV与AI深度学习
# 作者：Color Space
import cv2
import numpy as np
import random as rd

def watershed_algorithm(image):
    src = image.copy()
    # 边缘保留滤波EPF  去噪
    blur = cv2.pyrMeanShiftFiltering(image, sp=21, sr=55)
    cv2.imshow("blur", blur)
    # 转成灰度图像
    gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
    # 得到二值图像区间阈值
    ret, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    cv2.imshow('thres image', binary)

    # 距离变换
    dist = cv2.distanceTransform(binary, cv2.DIST_L2, 3)
    dist_out = cv2.normalize(dist, 0, 1.0, cv2.NORM_MINMAX)
    cv2.imshow('distance-Transform', dist_out * 100)
    ret, surface = cv2.threshold(dist_out, 0.5 * dist_out.max(), 255, cv2.THRESH_BINARY)
    cv2.imshow('surface', surface)
    sure_fg = np.uint8(surface)  # 转成8位整型
    cv2.imshow('Sure foreground', sure_fg)

    # Marker labelling
    ret, markers = cv2.connectedComponents(sure_fg)  # 连通区域
    print(ret)
    markers = markers + 1  # 整个图+1，使背景不是0而是1值

    # 未知区域标记(不能确定是前景还是背景)
    kernel = np.ones((3, 3), np.uint8)
    binary = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel, iterations=1)
    unknown = binary - sure_fg
    cv2.imshow('unknown', unknown)

    # 未知区域标记为0
    markers[unknown == 255] = 0
    # 区域标记结果
    markers_show = np.uint8(markers)
    cv2.imshow('markers', markers_show * 100)

    # 分水岭算法分割
    markers = cv2.watershed(image, markers=markers)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(markers)
    markers_8u = np.uint8(markers)
    # print(max_val)
    colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0),
              (255, 0, 255), (0, 255, 255), (255, 128, 0), (255, 0, 128),
              (128, 255, 0), (128, 0, 255), (255, 128, 128), (128, 255, 255)]
    for i in range(2, int(max_val + 1)):
        ret, thres1 = cv2.threshold(markers_8u, i - 1, 255, cv2.THRESH_BINARY)
        ret2, thres2 = cv2.threshold(markers_8u, i, 255, cv2.THRESH_BINARY)
        mask = thres1 - thres2
        cv2.imshow('mask', mask)
        # color = (rd.randint(0,255), rd.randint(0,255), rd.randint(0,255))
        # image[markers == i] = [rd.randint(0,255), rd.randint(0,255), rd.randint(0,255)]
        # image[markers == i] = [colors[i-2]]
        contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        cv2.drawContours(image, contours, -1, colors[(i - 2) % 12], -1)
        # cv2.drawContours(src, contours, -1, colors[(i-2)%12], -1)
        M = cv2.moments(contours[0])
        cx = int(M['m10'] / M['m00'])
        cy = int(M['m01'] / M['m00'])  # 轮廓重心
        cv2.drawMarker(image, (cx, cy), (0, 0, 255), 1, 10, 2)
        cv2.drawMarker(src, (cx, cy), (0, 0, 255), 1, 10, 2)

    cv2.putText(src, "count=%d" % (int(max_val - 1)), (220, 30), 0, 1, (0, 255, 0), 2)
    cv2.putText(image, "count=%d" % (int(max_val - 1)), (220, 30), 0, 1, (0, 255, 0), 2)
    cv2.imshow('regions', image)
    result = cv2.addWeighted(src, 0.6, image, 0.5, 0)  # 图像权重叠加
    cv2.imshow('result', result)

src = cv2.imread('./pellets.png')
cv2.imshow('src', src)
watershed_algorithm(src)
cv2.waitKey(0)
cv2.destroyAllWindows()

OpenCV标记结果：