如何在opencv python中使用分水岭分割

Question

我有一个问题，即如何使用 python 中的分水岭分割单独分割该图像中的粒子。我的主要目标是通过应用过滤器 medianBlur 然后应用 Canny 边缘检测方法来去除噪声。[![img = cv2.imread('sands.jpg')img = cv2.medianBlur(img,7)img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)imo = cv2.Canny(img,140,255)][1]][1]我想增强 Canny 边缘检测功能产生的轮廓，因为我使用这些图像来检测图像中粒子的区域属性以估计面积。

Answer 1

这是改编自这篇博文的一种方法

• 将图像转换为灰度

• 获得二值图像的 Otsu 阈值

• 计算欧几里得距离变换

• 执行连通分量分析

• 应用分水岭

• 遍历标签值并提取对象

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这是结果

在遍历每个轮廓时，您可以累积总面积

1388903.5

import cv2

import numpy as np

from skimage.feature import peak_local_max

from skimage.morphology import watershed

from scipy import ndimage

# Load in image, convert to gray scale, and Otsu's threshold

image = cv2.imread('1.jpg')

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Compute Euclidean distance from every binary pixel

# to the nearest zero pixel then find peaks

distance_map = ndimage.distance_transform_edt(thresh)

local_max = peak_local_max(distance_map, indices=False, min_distance=20, labels=thresh)

# Perform connected component analysis then apply Watershed

markers = ndimage.label(local_max, structure=np.ones((3, 3)))[0]

labels = watershed(-distance_map, markers, mask=thresh)

# Iterate through unique labels

total_area = 0

for label in np.unique(labels):

    if label == 0:

        continue

    # Create a mask

    mask = np.zeros(gray.shape, dtype="uint8")

    mask[labels == label] = 255

    # Find contours and determine contour area

    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    cnts = cnts[0] if len(cnts) == 2 else cnts[1]

    c = max(cnts, key=cv2.contourArea)

    area = cv2.contourArea(c)

    total_area += area

    cv2.drawContours(image, [c], -1, (36,255,12), 4)

print(total_area)

cv2.imshow('image', image)

cv2.waitKey()