使用 python openCV 绘制的封闭等高线面积
Area of a closed contour on a plot using python openCV
我试图找到 python 中绘制的任意形状闭合曲线内的区域（下图示例）。到目前为止，我已经尝试使用 alphashape 和多边形方法来实现这一点，但都失败了。我现在正尝试使用 OpenCV 和 floodfill 方法来计算曲线内的像素数量，然后我稍后会将其转换为一个区域，给定单个像素在图上所包围的区域。
示例图像：
testplot.jpg
为了做到这一点，我正在做以下事情，我改编自另一个关于 OpenCV 的 post。
import cv2
import numpy as np
# Input image
img = cv2.imread('testplot.jpg', cv2.IMREAD_GRAYSCALE)
# Dilate to better detect contours
temp = cv2.dilate(temp, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
# Find largest contour
cnts, _ = cv2.findContours(255-temp, cv2.RETR_TREE , cv2.CHAIN_APPROX_NONE) #255-img and cv2.RETR_TREE is to account for how cv2 expects the background to be black, not white, so I convert the background to black.
largestCnt = [] #I expect this to yield the blue contour
for cnt in cnts:
if (len(cnt) > len(largestCnt)):
largestCnt = cnt
# Determine center of area of largest contour
M = cv2.moments(largestCnt)
x = int(M["m10"] / M["m00"])
y = int(M["m01"] / M["m00"])
# Initial mask for flood filling, should cover entire figure
width, height = temp.shape
mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255
mask[1:width, 1:height] = 0
# Generate intermediate image, draw largest contour onto it, flood fill this contour
temp = np.zeros(temp.shape, np.uint8)
temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED)
_, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255)
temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
area = cv2.countNonZero(temp) #Number of pixels encircled by blue line
我希望从这里得到与上面相同的图像，但轮廓的中心填充白色，背景和原始蓝色轮廓填充黑色。我最终得到这个：
result.jpg
虽然乍一看似乎准确地将轮廓内的区域变成了白色，但白色区域实际上比轮廓内的区域大，所以我得到的结果是高估了其中的像素数。
对此的任何输入将不胜感激。我是 OpenCV 的新手，所以我可能误解了一些东西。
编辑：
感谢下面的评论，我进行了一些编辑，现在这是我的代码，并注明了编辑内容：
import cv2
import numpy as np
# EDITED INPUT IMAGE: Input image
img = cv2.imread('testplot2.jpg', cv2.IMREAD_GRAYSCALE)
# EDIT: threshold
_, temp = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY_INV)
# EDIT, REMOVED: Dilate to better detect contours
# Find largest contour
cnts, _ = cv2.findContours(temp, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)
largestCnt = [] #I expect this to yield the blue contour
for cnt in cnts:
if (len(cnt) > len(largestCnt)):
largestCnt = cnt
# Determine center of area of largest contour
M = cv2.moments(largestCnt)
x = int(M["m10"] / M["m00"])
y = int(M["m01"] / M["m00"])
# Initial mask for flood filling, should cover entire figure
width, height = temp.shape
mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255
mask[1:width, 1:height] = 0
# Generate intermediate image, draw largest contour, flood filled
temp = np.zeros(temp.shape, np.uint8)
temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED)
_, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255)
temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
area = cv2.countNonZero(temp) #Number of pixels encircled by blue line
我输入了一个不同的 image with the axes and the frame that python adds by default removed for ease. I get what I expect at the second step, so this image. However, in the enter image description here 原来的轮廓和它包围的区域似乎都变成了白色，而我想要原来的轮廓是黑色的，只有它包围的区域是白色的。我该如何实现？
问题出在你最后的 opening 操作上。这个形态学操作在末尾包含一个 dilation 来扩展白色轮廓，增加它的面积。让我们尝试一种不涉及形态学的不同方法。这些是步骤：
- 将图像转换为灰度
- 应用Otsu 的阈值 得到二值图像，让我们只处理黑白像素。
- 在图像位置
(0,0) 应用第一个 flood-fill 操作以去除外部白色 space.
- 使用区域过滤器过滤 小斑点
- 找到“曲线Canvas”（包围曲线的白色space）并将其起点定位并存储在
(targetX, targetY)
- 应用第二个 flood-fill al location
(targetX, targetY)
- 用
cv2.countNonZero 得到孤立 blob 的 面积
我们来看看代码：
import cv2
import numpy as np
# Set image path
path = "C:/opencvImages/"
fileName = "cLIjM.jpg"
# Read Input image
inputImage = cv2.imread(path+fileName)
inputCopy = inputImage.copy()
# Convert BGR to grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Threshold via Otsu + bias adjustment:
threshValue, binaryImage = cv2.threshold(grayscaleImage, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
这是您得到的二值图像：
现在，让我们 flood-fill 在 (0,0) 的拐角处用黑色去掉第一个白色 space。这一步非常简单：
# Flood-fill background, seed at (0,0) and use black color:
cv2.floodFill(binaryImage, None, (0, 0), 0)
这是结果，请注意第一个大的白色区域是如何消失的：
让我们用区域过滤器去除小斑点。 100 区域以下的所有内容都将被删除：
# Perform an area filter on the binary blobs:
componentsNumber, labeledImage, componentStats, componentCentroids = \
cv2.connectedComponentsWithStats(binaryImage, connectivity=4)
# Set the minimum pixels for the area filter:
minArea = 100
# Get the indices/labels of the remaining components based on the area stat
# (skip the background component at index 0)
remainingComponentLabels = [i for i in range(1, componentsNumber) if componentStats[i][4] >= minArea]
# Filter the labeled pixels based on the remaining labels,
# assign pixel intensity to 255 (uint8) for the remaining pixels
filteredImage = np.where(np.isin(labeledImage, remainingComponentLabels) == True, 255, 0).astype('uint8')
这是筛选的结果：
现在，剩下的是第二个白色区域，我需要定位它的起点，因为我想在这个位置应用第二个flood-fill操作。我将遍历图像以找到第一个白色像素。像这样：
# Get Image dimensions:
height, width = filteredImage.shape
# Store the flood-fill point here:
targetX = -1
targetY = -1
for i in range(0, width):
for j in range(0, height):
# Get current binary pixel:
currentPixel = filteredImage[j, i]
# Check if it is the first white pixel:
if targetX == -1 and targetY == -1 and currentPixel == 255:
targetX = i
targetY = j
print("Flooding in X = "+str(targetX)+" Y: "+str(targetY))
可能有一种更优雅、面向 Python 的方法，但我仍在学习这门语言。随时改进脚本（并在此处分享）。然而，循环让我找到了第一个白色像素的位置，所以我现在可以在这个确切位置应用第二个 flood-fill：
# Flood-fill background, seed at (targetX, targetY) and use black color:
cv2.floodFill(filteredImage, None, (targetX, targetY), 0)
你最终得到这个：
如你所见，只需计算非零像素的数量：
# Get the area of the target curve:
area = cv2.countNonZero(filteredImage)
print("Curve Area is: "+str(area))
结果是：
Curve Area is: 1510
这是另一种使用 Python/OpenCV 的方法。
- 读取输入
- 转换为 HSV 色彩空间
- 蓝色的颜色范围阈值
- 找到最大的轮廓
- 获取它的面积并打印出来
- 将轮廓绘制为黑色背景上的白色填充轮廓
- 保存结果
输入：
import cv2
import numpy as np
# read image as grayscale
img = cv2.imread('closed_curve.jpg')
# convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#select blu color range in hsv
lower = (24,128,115)
upper = (164,255,255)
# threshold on blue in hsv
thresh = cv2.inRange(hsv, lower, upper)
# get largest contour
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
area = cv2.contourArea(c)
print("Area =",area)
# draw filled contour on black background
result = np.zeros_like(thresh)
cv2.drawContours(result, [c], -1, 255, cv2.FILLED)
# save result
cv2.imwrite("closed_curve_thresh.jpg", thresh)
cv2.imwrite("closed_curve_result.jpg", result)
# view result
cv2.imshow("threshold", thresh)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
阈值图像：
黑色背景上的结果填充轮廓：
区域结果：
面积 = 2347.0
我试图找到 python 中绘制的任意形状闭合曲线内的区域（下图示例）。到目前为止，我已经尝试使用 alphashape 和多边形方法来实现这一点，但都失败了。我现在正尝试使用 OpenCV 和 floodfill 方法来计算曲线内的像素数量，然后我稍后会将其转换为一个区域，给定单个像素在图上所包围的区域。 示例图像： testplot.jpg
为了做到这一点，我正在做以下事情，我改编自另一个关于 OpenCV 的 post。
import cv2
import numpy as np
# Input image
img = cv2.imread('testplot.jpg', cv2.IMREAD_GRAYSCALE)
# Dilate to better detect contours
temp = cv2.dilate(temp, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
# Find largest contour
cnts, _ = cv2.findContours(255-temp, cv2.RETR_TREE , cv2.CHAIN_APPROX_NONE) #255-img and cv2.RETR_TREE is to account for how cv2 expects the background to be black, not white, so I convert the background to black.
largestCnt = [] #I expect this to yield the blue contour
for cnt in cnts:
if (len(cnt) > len(largestCnt)):
largestCnt = cnt
# Determine center of area of largest contour
M = cv2.moments(largestCnt)
x = int(M["m10"] / M["m00"])
y = int(M["m01"] / M["m00"])
# Initial mask for flood filling, should cover entire figure
width, height = temp.shape
mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255
mask[1:width, 1:height] = 0
# Generate intermediate image, draw largest contour onto it, flood fill this contour
temp = np.zeros(temp.shape, np.uint8)
temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED)
_, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255)
temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
area = cv2.countNonZero(temp) #Number of pixels encircled by blue line
我希望从这里得到与上面相同的图像，但轮廓的中心填充白色，背景和原始蓝色轮廓填充黑色。我最终得到这个：
result.jpg
虽然乍一看似乎准确地将轮廓内的区域变成了白色，但白色区域实际上比轮廓内的区域大，所以我得到的结果是高估了其中的像素数。 对此的任何输入将不胜感激。我是 OpenCV 的新手，所以我可能误解了一些东西。
编辑： 感谢下面的评论，我进行了一些编辑，现在这是我的代码，并注明了编辑内容：
import cv2
import numpy as np
# EDITED INPUT IMAGE: Input image
img = cv2.imread('testplot2.jpg', cv2.IMREAD_GRAYSCALE)
# EDIT: threshold
_, temp = cv2.threshold(img, 250, 255, cv2.THRESH_BINARY_INV)
# EDIT, REMOVED: Dilate to better detect contours
# Find largest contour
cnts, _ = cv2.findContours(temp, cv2.RETR_EXTERNAL , cv2.CHAIN_APPROX_NONE)
largestCnt = [] #I expect this to yield the blue contour
for cnt in cnts:
if (len(cnt) > len(largestCnt)):
largestCnt = cnt
# Determine center of area of largest contour
M = cv2.moments(largestCnt)
x = int(M["m10"] / M["m00"])
y = int(M["m01"] / M["m00"])
# Initial mask for flood filling, should cover entire figure
width, height = temp.shape
mask = img2 = np.ones((width + 2, height + 2), np.uint8) * 255
mask[1:width, 1:height] = 0
# Generate intermediate image, draw largest contour, flood filled
temp = np.zeros(temp.shape, np.uint8)
temp = cv2.drawContours(temp, largestCnt, -1, 255, cv2.FILLED)
_, temp, mask, _ = cv2.floodFill(temp, mask, (x, y), 255)
temp = cv2.morphologyEx(temp, cv2.MORPH_OPEN, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)))
area = cv2.countNonZero(temp) #Number of pixels encircled by blue line
我输入了一个不同的 image with the axes and the frame that python adds by default removed for ease. I get what I expect at the second step, so this image. However, in the enter image description here 原来的轮廓和它包围的区域似乎都变成了白色，而我想要原来的轮廓是黑色的，只有它包围的区域是白色的。我该如何实现？
问题出在你最后的 opening 操作上。这个形态学操作在末尾包含一个 dilation 来扩展白色轮廓，增加它的面积。让我们尝试一种不涉及形态学的不同方法。这些是步骤：
- 将图像转换为灰度
- 应用Otsu 的阈值 得到二值图像，让我们只处理黑白像素。
- 在图像位置
(0,0)应用第一个 flood-fill 操作以去除外部白色 space. - 使用区域过滤器过滤 小斑点
- 找到“曲线Canvas”（包围曲线的白色space）并将其起点定位并存储在
(targetX, targetY) - 应用第二个 flood-fill al location
(targetX, targetY) - 用
cv2.countNonZero 得到孤立 blob 的 面积
我们来看看代码：
import cv2
import numpy as np
# Set image path
path = "C:/opencvImages/"
fileName = "cLIjM.jpg"
# Read Input image
inputImage = cv2.imread(path+fileName)
inputCopy = inputImage.copy()
# Convert BGR to grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Threshold via Otsu + bias adjustment:
threshValue, binaryImage = cv2.threshold(grayscaleImage, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)
这是您得到的二值图像：
现在，让我们 flood-fill 在 (0,0) 的拐角处用黑色去掉第一个白色 space。这一步非常简单：
# Flood-fill background, seed at (0,0) and use black color:
cv2.floodFill(binaryImage, None, (0, 0), 0)
这是结果，请注意第一个大的白色区域是如何消失的：
让我们用区域过滤器去除小斑点。 100 区域以下的所有内容都将被删除：
# Perform an area filter on the binary blobs:
componentsNumber, labeledImage, componentStats, componentCentroids = \
cv2.connectedComponentsWithStats(binaryImage, connectivity=4)
# Set the minimum pixels for the area filter:
minArea = 100
# Get the indices/labels of the remaining components based on the area stat
# (skip the background component at index 0)
remainingComponentLabels = [i for i in range(1, componentsNumber) if componentStats[i][4] >= minArea]
# Filter the labeled pixels based on the remaining labels,
# assign pixel intensity to 255 (uint8) for the remaining pixels
filteredImage = np.where(np.isin(labeledImage, remainingComponentLabels) == True, 255, 0).astype('uint8')
这是筛选的结果：
现在，剩下的是第二个白色区域，我需要定位它的起点，因为我想在这个位置应用第二个flood-fill操作。我将遍历图像以找到第一个白色像素。像这样：
# Get Image dimensions:
height, width = filteredImage.shape
# Store the flood-fill point here:
targetX = -1
targetY = -1
for i in range(0, width):
for j in range(0, height):
# Get current binary pixel:
currentPixel = filteredImage[j, i]
# Check if it is the first white pixel:
if targetX == -1 and targetY == -1 and currentPixel == 255:
targetX = i
targetY = j
print("Flooding in X = "+str(targetX)+" Y: "+str(targetY))
可能有一种更优雅、面向 Python 的方法，但我仍在学习这门语言。随时改进脚本（并在此处分享）。然而，循环让我找到了第一个白色像素的位置，所以我现在可以在这个确切位置应用第二个 flood-fill：
# Flood-fill background, seed at (targetX, targetY) and use black color:
cv2.floodFill(filteredImage, None, (targetX, targetY), 0)
你最终得到这个：
如你所见，只需计算非零像素的数量：
# Get the area of the target curve:
area = cv2.countNonZero(filteredImage)
print("Curve Area is: "+str(area))
结果是：
Curve Area is: 1510
这是另一种使用 Python/OpenCV 的方法。
- 读取输入
- 转换为 HSV 色彩空间
- 蓝色的颜色范围阈值
- 找到最大的轮廓
- 获取它的面积并打印出来
- 将轮廓绘制为黑色背景上的白色填充轮廓
- 保存结果
输入：
import cv2
import numpy as np
# read image as grayscale
img = cv2.imread('closed_curve.jpg')
# convert to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
#select blu color range in hsv
lower = (24,128,115)
upper = (164,255,255)
# threshold on blue in hsv
thresh = cv2.inRange(hsv, lower, upper)
# get largest contour
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
area = cv2.contourArea(c)
print("Area =",area)
# draw filled contour on black background
result = np.zeros_like(thresh)
cv2.drawContours(result, [c], -1, 255, cv2.FILLED)
# save result
cv2.imwrite("closed_curve_thresh.jpg", thresh)
cv2.imwrite("closed_curve_result.jpg", result)
# view result
cv2.imshow("threshold", thresh)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
阈值图像：
黑色背景上的结果填充轮廓：
区域结果：
面积 = 2347.0