我有这个 python 代码，据说可以填充图像的轮廓，但其中包含的孔未填充。这就是我要的：
但这就是我得到的：
我尝试指定轮廓层次结构来填充 cv2，但我无法得到我想要的结果。
这是我尝试过的：
import numpy as np
import cv2
# Load the PNG image
img = cv2.imread('slice.png')
# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
# Find the contours in the binary image
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)
# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
# Check if the contour has a parent
if hierarchy[0][i][3] == -1:
# If the contour doesn't have a parent, fill it with pixel value 255
cv2.drawContours(filled_img, [contour], -1, 255, cv2.FILLED)
# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
我尝试修改 'if hierarchy[0][i][3] == -1:' 部分的 -1, 0, 1 值，但它要么填充较小的孔，要么填充整个较大的轮廓，例如我发布的第一张照片。
Update
我还想在较小的层次结构轮廓内部填充白色，如下所示：
问题是cv2.drawContours填充闭合轮廓的整个内部部分，无论是否存在内部轮廓。
我们可以从白色轮廓开始，然后用黑色填充没有父项的轮廓，而不是用白色填充没有父项的轮廓。
假设我们知道内部应该是黑色的，我们可以应用以下阶段：
代码示例：
import numpy as np
import cv2
# Load the PNG image
img = cv2.imread('slice.png')
# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)
# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)
# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
# Check if the contour has no child
if hierarchy[0][i][2] < 0:
# If contour has no child, fill the contour with black color
cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)
# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result filled_img:
Note:
如果我们不知道最内部轮廓的颜色，我们可以在黑色背景上绘制白色轮廓，并将结果用作蒙版 - 使用蒙版复制输入图像的原始内容。
支持没有子项的轮廓：
为了支持有子轮廓和没有子轮廓的轮廓，我们可以填充黑色，仅填充符合这两个条件的轮廓：
代码示例：
import numpy as np
import cv2
# Load the PNG image
img = cv2.imread('slice.png')
# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)
# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)
# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
has_grandparent = False
has_parent = hierarchy[0][i][3] >= 0
if has_parent:
# Check if contour has a grandparent
parent_idx = hierarchy[0][i][3]
has_grandparent = hierarchy[0][parent_idx][3] >= 0
# Check if the contour has no child
if hierarchy[0][i][2] < 0 and has_grandparent:
# If contour has no child, fill the contour with black color
cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)
# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
在较小的层次结构轮廓内部填充白色：
在用黑色填充轮廓之前，我们可以检查指定轮廓内部是否有黑色像素。
仅当它没有孩子、有祖父母并且内部有黑色时才填充黑色。
为了测试内部是否有黑色像素，我们可以在临时图像上绘制轮廓（白色）。
然后检查最小值是否为 0（绘制轮廓为白色的值）。
tmp = np.zeros_like(thresh)
cv2.drawContours(tmp, [contour], -1, 255, cv2.FILLED)
has_innder_black_pixels = (thresh[tmp==255].min() == 0)
代码示例：
import numpy as np
import cv2
# Load the PNG image
img = cv2.imread('slice.png')
# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)
# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)
# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
has_grandparent = False
has_parent = hierarchy[0][i][3] >= 0
if has_parent:
# Check if contour has a grandparent
parent_idx = hierarchy[0][i][3]
has_grandparent = hierarchy[0][parent_idx][3] >= 0
# Draw the contour over temporary image first (for testing if it has black pixels inside).
tmp = np.zeros_like(thresh)
cv2.drawContours(tmp, [contour], -1, 255, cv2.FILLED)
has_innder_black_pixels = (thresh[tmp==255].min() == 0) # If the minimum value is 0 (value where draw contour is white) then the contour has black pixels inside
if hierarchy[0][i][2] < 0 and has_grandparent and has_innder_black_pixels:
# If contour has no child and has a grandparent and it has black inside, fill the contour with black color
cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)
# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()