如何在OpenCV Python中使用分水岭分割

Question

我有一个问题，如何使用python中的分水岭分割对该图像中的粒子进行单独分割。我的主要目标是通过应用滤镜meanBlur然后应用Canny边缘检测方法来消除噪声。

[![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，因为我使用该图像检测图像中粒子的区域特性以估计面积。

Answer 1

以下是根据此blog post

改编的一种方法

• 将图像转换为灰度
• 大津获取二值图像的阈值
• 计算欧几里得距离变换
• 执行连接的组件分析
• 应用分水岭
• 遍历标签值并提取对象

---

这是结果

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()