opencv 使用cv2.connectedComponents并消除像素数量较少的元素

dw1jzc5e 于 2023-08-06 发布在其他

关注(0)|答案(2)|浏览(87)

我想使用函数cv2.connectedComponents来连接二进制映像上的组件，如下所示...

的数据
我已经把这个特性添加到了cv2。connectedComponents以消除具有少量像素的元素。
不幸的是，该算法是非常缓慢的大图像，由于扩展。有没有办法重写扩展来加速算法？

import cv2
import numpy as np

def zerolistmaker(n):
    listofzeros = [0] * n
    return listofzeros

img = cv2.imread('files/motorway/gabor/eGaIy.jpg', 0)

img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary
retval, labels = cv2.connectedComponents(img)

##################################################
# ENLARGEMENT
##################################################
sorted_labels = labels.ravel()
sorted_labels = np.sort(sorted_labels)

maxPixel = 50  # eliminate elements with less than maxPixel

# detect how often an element occurs
i=0
counter=0
counterlist = zerolistmaker(retval)

while i < len(sorted_labels):
    if sorted_labels[i] == counter:
        counterlist[counter] = counterlist[counter] + 1
    else:
        counter = counter + 1
        i = i - 1

    i = i + 1

# delete small pixel values
i=0
while i < len(counterlist):
    if counterlist[i] < maxPixel:
        counterlist[i] = 0
    i = i + 1

i=0
counterlisthelper = []
while i < len(counterlist):
    if counterlist[i] == 0:
        counterlisthelper.append(i)
    i = i + 1

i=0
j=0
k=0
while k < len(counterlisthelper):
    while i < labels.shape[0]:
        while j < labels.shape[1]:
            if labels[i,j] == counterlisthelper[k]:
                labels[i,j] = 0
            else:
                labels[i,j] = labels[i,j]
            j = j + 1
        j = 0
        i = i + 1
    i = 0
    j = 0
    k = k + 1

##################################################
##################################################

# Map component labels to hue val
label_hue = np.uint8(179*labels/np.max(labels))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

# cvt to BGR for display
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

# set bg label to black
labeled_img[label_hue==0] = 0

cv2.imshow('labeled.png', labeled_img)
cv2.waitKey()

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opencv

来源：https://stackoverflow.com/questions/48303309/use-cv2-connectedcomponents-and-eliminate-elements-with-a-small-number-of-pixels

2条答案

按热度按时间

iqjalb3h1#

在Python中，你应该避免深度循环。使用numpy而不是python-loop。
改进：

##################################################
ts = time.time()
num = labels.max()

N = 50

## If the count of pixels less than a threshold, then set pixels to `0`.
for i in range(1, num+1):
    pts =  np.where(labels == i)
    if len(pts[0]) < N:
        labels[pts] = 0

print("Time passed: {:.3f} ms".format(1000*(time.time()-ts)))
# Time passed: 4.607 ms

##################################################

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测试结果：
100d1x

的字符串
整个代码：

#!/usr/bin/python3
# 2018.01.17 22:36:20 CST
import cv2
import numpy as np
import time

img = cv2.imread('test.jpg', 0)
img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary
retval, labels = cv2.connectedComponents(img)

##################################################
ts = time.time()
num = labels.max()

N = 50
for i in range(1, num+1):
    pts =  np.where(labels == i)
    if len(pts[0]) < N:
        labels[pts] = 0

print("Time passed: {:.3f} ms".format(1000*(time.time()-ts)))
# Time passed: 4.607 ms

##################################################

# Map component labels to hue val
label_hue = np.uint8(179*labels/np.max(labels))
blank_ch = 255*np.ones_like(label_hue)
labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])

# cvt to BGR for display
labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)

# set bg label to black
labeled_img[label_hue==0] = 0

cv2.imshow('labeled.png', labeled_img)
cv2.imwrite("labeled.png", labeled_img)
cv2.waitKey()

型

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ttp71kqs2#

我知道我已经很晚了，但我发现了一个解决方案，它比Kinght 金的解决方案快两倍多。此解决方案在我的计算机上运行600×800图像的时间约为350ms，而其他解决方案的时间约为850ms。

功能：

def remove_small_groups(image: np.ndarray, minimum: int) -> np.ndarray:
    """
    Removes all the groups of connected components smaller than the minimum
    from a binary image

    :param image: The binary image to process
    :param minimum: The minimum size of groups of connected components
    """

    labels, vals = cv2.connectedComponentsWithStats(image)[1:3]
    num = labels.max()

    vals = vals[1:, cv2.CC_STAT_AREA]

    new_img = np.zeros_like(labels)
    for i in filter(lambda v: vals[v] >= minimum, range(0, num)):
        pts = np.where(labels == i + 1)
        new_img[pts] = 255

    return new_img

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支持代码：

import numpy as np
import time
import cv2

image = cv2.imread('test.jpg', 0)
image = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY)[1]

avg_time = 0
for i in range(25):
    ts = time.time()
    vals = x(image, 50)
    avg_time += time.time() - ts

print("Time passed: {:.3f} ms".format(1000 * (avg_time / 25)))

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赞(0）回复(0）举报 2023-08-06

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opencv 使用cv2.connectedComponents并消除像素数量较少的元素

2条答案

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