由于粒子是白色的，背景是黑色的，我们可以使用Kmeans颜色量化将图像分为两组，cluster=2。这将使我们能够轻松区分粒子和背景。由于粒子可能非常微小，我们应该尽量避免模糊，膨胀或任何可能改变粒子轮廓的形态学操作。下面是一种方法：
1.**Kmeans颜色量化。**我们用两个聚类、灰度、然后大津阈值来执行Kmeans，以获得二值图像。
1.**过滤掉超微小的噪声。**接下来我们找到轮廓，使用轮廓区域过滤去除微小的噪声，并收集每个粒子(x, y)坐标及其面积。我们通过“填充”这些轮廓来有效地删除二进制掩码上的微小粒子。
1.**将蒙版应用到原始图像上。**现在我们将过滤后的蒙版逐位和到原始图像上以突出显示粒子簇。
K均值clusters=2

的数据
结果

的

Number of particles: 204
Average particle size: 30.537

字符串
代码

import cv2
import numpy as np
import pylab

# Kmeans 
def kmeans_color_quantization(image, clusters=8, rounds=1):
    h, w = image.shape[:2]
    samples = np.zeros([h*w,3], dtype=np.float32)
    count = 0

    for x in range(h):
        for y in range(w):
            samples[count] = image[x][y]
            count += 1

    compactness, labels, centers = cv2.kmeans(samples,
            clusters, 
            None,
            (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001), 
            rounds, 
            cv2.KMEANS_RANDOM_CENTERS)

    centers = np.uint8(centers)
    res = centers[labels.flatten()]
    return res.reshape((image.shape))

# Load image
image = cv2.imread('1.png')
original = image.copy()

# Perform kmeans color segmentation, grayscale, Otsu's threshold
kmeans = kmeans_color_quantization(image, clusters=2)
gray = cv2.cvtColor(kmeans, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find contours, remove tiny specs using contour area filtering, gather points
points_list = []
size_list = []
cnts, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:]
AREA_THRESHOLD = 2
for c in cnts:
    area = cv2.contourArea(c)
    if area < AREA_THRESHOLD:
        cv2.drawContours(thresh, [c], -1, 0, -1)
    else:
        (x, y), radius = cv2.minEnclosingCircle(c)
        points_list.append((int(x), int(y)))
        size_list.append(area)

# Apply mask onto original image
result = cv2.bitwise_and(original, original, mask=thresh)
result[thresh==255] = (36,255,12)

# Overlay on original
original[thresh==255] = (36,255,12)

print("Number of particles: {}".format(len(points_list)))
print("Average particle size: {:.3f}".format(sum(size_list)/len(size_list)))

# Display
cv2.imshow('kmeans', kmeans)
cv2.imshow('original', original)
cv2.imshow('thresh', thresh)
cv2.imshow('result', result)
cv2.waitKey()

型