OpenCV半小时掌握基本操作之分水岭算法

cv2.connectedComponents(image, labels=None, connectivity=None, ltype=None)

cv2.watershed(image, markers)

import numpy as np
import cv2
from matplotlib import pyplot as plt


def watershed(image):
    """分水岭算法"""

    # 卷积核
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))

    # 均值迁移滤波
    blur = cv2.pyrMeanShiftFiltering(image, 10, 100)

    # 转换成灰度图
    image_gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)

    # 二值化
    ret1, thresh1 = cv2.threshold(image_gray, 0, 255, cv2.THRESH_OTSU)

    # 开运算
    open = cv2.morphologyEx(thresh1, cv2.MORPH_OPEN, kernel, iterations=2)

    # 膨胀
    dilate = cv2.dilate(open, kernel, iterations=3)

    # 距离变换
    dist = cv2.distanceTransform(dilate, cv2.DIST_L2, 3)
    dist = cv2.normalize(dist, 0, 1.0, cv2.NORM_MINMAX)
    print(dist.max())

    # 二值化
    ret2, thresh2 = cv2.threshold(dist, dist.max() * 0.6, 255, cv2.THRESH_BINARY)
    thresh2 = np.uint8(thresh2)

    # 分水岭计算
    unknown = cv2.subtract(dilate, thresh2)
    ret3, component = cv2.connectedComponents(thresh2)
    print(ret3)

    # 分水岭计算
    markers = component + 1
    markers[unknown == 255] = 0
    result = cv2.watershed(image, markers=markers)
    image[result == -1] = [0, 0, 255]

    # 图片展示
    image_show((image, blur, image_gray, thresh1, open, dilate), (dist, thresh2, unknown, component, markers, image))

    return image


def image_show(graph1, graph2):
    """绘制图片"""

    # 图像1
    original, blur, gray, binary1, open, dilate = graph1

    # 图像2
    dist, binary2, unknown, component, markers, result = graph2

    f, ax = plt.subplots(3, 2, figsize=(12, 16))

    # 绘制子图
    ax[0, 0].imshow(cv2.cvtColor(original, cv2.COLOR_BGR2RGB))
    ax[0, 1].imshow(cv2.cvtColor(blur, cv2.COLOR_BGR2RGB))
    ax[1, 0].imshow(gray, "gray")
    ax[1, 1].imshow(binary1, "gray")
    ax[2, 0].imshow(open, "gray")
    ax[2, 1].imshow(dilate, "gray")

    # 标题
    ax[0, 0].set_title("original")
    ax[0, 1].set_title("image blur")
    ax[1, 0].set_title("image gray")
    ax[1, 1].set_title("image binary1")
    ax[2, 0].set_title("image open")
    ax[2, 1].set_title("image dilate")

    plt.show()

    f, ax = plt.subplots(3, 2, figsize=(12, 16))

    # 绘制子图
    ax[0, 0].imshow(dist, "gray")
    ax[0, 1].imshow(binary2, "gray")
    ax[1, 0].imshow(unknown, "gray")
    ax[1, 1].imshow(component, "gray")
    ax[2, 0].imshow(markers, "gray")
    ax[2, 1].imshow(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))

    # 标题
    ax[0, 0].set_title("image distance")
    ax[0, 1].set_title("image binary2")
    ax[1, 0].set_title("image unknown")
    ax[1, 1].set_title("image component")
    ax[2, 0].set_title("image markers")
    ax[2, 1].set_title("result")

    plt.show()


if __name__ == "__main__":
    # 读取图片
    image = cv2.imread("coin.jpg")

    # 分水岭算法
    result = watershed(image)

    # 保存结果
    cv2.imwrite("result.jpg", result)