如何使二進制影像中的所有背景都變白

在影像中，我希望只有黑色的肺而不是背景。背景[影像的頂部黑色和底部黑色區域]必須是白色而不是黑色。我怎么能在python中做到這一點？從原始灰度影像（“影像”）生成上圖中的代碼是：

from skimage.filters import sobel

img = cv2.GaussianBlur(image, (5, 5), 0)
img = cv2.erode(img, None, iterations=2)
img = cv2.dilate(img, None, iterations=2)

elevation_map = sobel(img)

markers = np.zeros_like(image)

markers[image < 70] = 1
markers[image > 254] = 2
segmentation = skimage.morphology.watershed(elevation_map, markers)

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('segmentation')

uj5u.com熱心網友回復：

假設我們有segmentation上面發布的影像，并且我們想用 while 填充周圍的背景。

一種選擇是迭代邊界，并floodFill在像素為黑色的地方應用：

import cv2
import numpy as np

gray = cv2.imread('segmentation.png', cv2.IMREAD_GRAYSCALE)  # Read image as grayscale

for x in range(gray.shape[1]):
    # Fill dark top pixels:
    if gray[0, x] == 0:
        cv2.floodFill(gray, None, seedPoint=(x, 0), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

    # Fill dark bottom pixels:
    if gray[-1, x] == 0:
        cv2.floodFill(gray, None, seedPoint=(x, gray.shape[0]-1), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

for y in range(gray.shape[0]):
    # Fill dark left side pixels:
    if gray[y, 0] == 0:
        cv2.floodFill(gray, None, seedPoint=(0, y), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

    # Fill dark right side pixels:
    if gray[y, -1] == 0:
        cv2.floodFill(gray, None, seedPoint=(gray.shape[1]-1, y), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

cv2.imshow('gray', gray)
cv2.waitKey()
cv2.destroyAllWindows()

其他選擇是使用

為了去除中心的黑點（如果需要），我們可以使用connectedComponentsWithStats，并根據區域填充小“點”。

編輯：

將上述解決方案與您的代碼結合的示例：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import numpy as np
import matplotlib.pyplot as plt
import skimage.io
import skimage.color
import skimage.filters
from skimage.io import imread
from skimage.color import rgb2gray
from skimage.filters import sobel
import cv2

from skimage import data

image = cv2.imread('/content/drive/MyDrive/Covid_data/Training Set/covid/covid/ct_scan_100/22.jpg')

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('Original Image')

# Image in grayscale
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Blurring the image [img]
img = cv2.GaussianBlur(image, (5, 5), 0)
img = cv2.erode(img, None, iterations=2)
img = cv2.dilate(img, None, iterations=2)
#img = image
elevation_map = sobel(img)

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(elevation_map, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('elevation_map')

# we find markers of the background and the coins based on the extreme parts of the histogram of grey values
markers = np.zeros_like(image)
# Choosing extreme parts of the histogram of grey values
markers[image < 70] = 1
markers[image > 254] = 2

#we use the watershed transform to fill regions of the elevation map starting from the markers determined above:
segmentation = skimage.morphology.watershed(elevation_map, markers)

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(segmentation, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('segmentation')



#gray = cv2.imread('segmentation.png', cv2.IMREAD_GRAYSCALE)  # Read image as grayscale
# Convert segmentation to uint8 image, where 255 is white and 0 is black (OpenCV style mask).
ret, gray = ret, gray = cv2.threshold(segmentation.astype(np.uint8), 1, 255, cv2.THRESH_BINARY)

for x in range(gray.shape[1]):
    # Fill dark top pixels:
    if gray[0, x] == 0:
        cv2.floodFill(gray, None, seedPoint=(x, 0), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

    # Fill dark bottom pixels:
    if gray[-1, x] == 0:
        cv2.floodFill(gray, None, seedPoint=(x, gray.shape[0]-1), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

for y in range(gray.shape[0]):
    # Fill dark left side pixels:
    if gray[y, 0] == 0:
        cv2.floodFill(gray, None, seedPoint=(0, y), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

    # Fill dark right side pixels:
    if gray[y, -1] == 0:
        cv2.floodFill(gray, None, seedPoint=(gray.shape[1]-1, y), newVal=255, loDiff=3, upDiff=3)  # Fill the background with white color

fig, ax = plt.subplots(figsize=(7, 7))
ax.imshow(gray, cmap=plt.cm.gray, interpolation='nearest')
ax.axis('off')
ax.set_title('gray')

使用：用ret, gray = cv2.threshold(segmentation.astype(np.uint8), 1, 255, cv2.THRESH_BINARY)
替換所有白色像素，segmentation用替換所有255黑色像素0。

標籤：Python 图像处理 图像分割 索贝尔 二值图像

上一篇：為出現在其他影像中的第一個影像中的每個物件的像素找到正確的坐標

下一篇：需要使用opencv識別“托盤是否為空”