使用 CV2 在弯曲对象中绘制中心曲线
问题描述 投票：0回答：1
我想在弯曲物体的中心画一条线。
举个例子：给定一张香蕉的图像，不同图像的方向可能会改变，并且对象中可能有不止一条曲线，但任务是相同的。
要确定物体的长度，需要计算（插值）物体从头到尾的轮廓中心线，然后才能计算出插值线的长度。这是我目前的想法。
但是现在有一个棘手的部分，使用 python 和 cv2 确定对象的轮廓没有问题，这很好用。但计算中心线以确定其长度是一件很困难的事情。
编辑
脚本的目标应该是测量蠕虫的长度和面积，因此我不必手动测量数百张图像的值。
输入图片：
到目前为止我对轮廓（绿线）的计算：
我想要的（仅以手绘为例）：
到目前为止使用的代码（没有我想要的“中心线”，因为我不知道如何开始）。我的想法使用凸包，构建骨架并使用它并不能按预期工作，因为凸包太大（由凹部分引起），将其与polyDP结合也不起作用，因为polyDP经常错过部分蠕虫，综合结果也很糟糕。
import numpy as np
import cv2
import os
draw_windows = True ## change fo False for no windows only calc
def calc_values(filename):
img = cv2.imread(filename)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
drawWindow('thresh', thresh)
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
drawWindow('edged', edged)
contours, _ = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Assume the largest contour corresponds to the worm
if contours:
largest_contour = max(contours, key=cv2.contourArea)
# Draw the contour on the original image
image_with_contour = cv2.cvtColor(edged, cv2.COLOR_GRAY2BGR)
cv2.drawContours(image_with_contour, [largest_contour], -1, (0, 255, 0), 2)
cv2.drawContours(image_with_contour, contours, -1, color=(255, 255, 255), thickness=cv2.FILLED)
# Display the original image with the detected contour
drawWindow('Worm with Contour', image_with_contour)
def drawWindow(window_name, image):
if draw_windows:
cv2.imshow(window_name, image)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
directory = "input"
for filename in os.listdir(directory):
file = os.path.join(directory, filename)
calc_values(file)
if __name__ == "__main__":
main()
（我知道到目前为止代码质量不是最好的，但它开始时是一个快速而肮脏的“项目”：D）
1个回答
1
投票
投票
我在谷歌上搜索了很多，以找出可用的内容。我最终使用了上面的输入
input_77560561.jpg
有关代码，请参阅问题和我之前的答案以及使用 fil_finder 库
FilFinder _ GitHub: 的 Python Image -Findinglargest Branch from Image rack 答案
import numpy as np
import cv2
draw_windows = True ## change fo False for no windows only calc
def ResizeWithAspectRatio(image, width=None, height=None, inter=cv2.INTER_AREA):
dim = None
(h, w) = image.shape[:2]
if width is None and height is None:
return image
if width is None:
r = height / float(h)
dim = (int(w * r), height)
else:
r = width / float(w)
dim = (width, int(h * r))
return cv2.resize(image, dim, interpolation=inter)
def calc_values(filename):
img = cv2.imread(filename, 1)
print('FILENAME : ', type(img) , img.shape)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
drawWindow('thresh', thresh)
cv2.imwrite("thresh_worm.png", thresh)
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)
drawWindow('edged', edged)
contours, hierarchy = cv2.findContours(edged, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if contours:
cntsSorted = sorted(contours, key=lambda x: cv2.contourArea(x) , reverse = True)
for index , i in enumerate(cntsSorted) :
print('i ************************************************************ ' ,index ,type(index))
print('# \ncontour : ' ,index , 'Area ?? : ', cv2.contourArea(i) , "size : ", i.shape)
# Assume the largest contour corresponds to the worm
if contours:
# largest_contour = max(contours, key=cv2.contourArea)
largest_contour = cntsSorted[0]
# Draw the contour on the original image
image_with_contour = cv2.cvtColor(edged, cv2.COLOR_GRAY2BGR)
cv2.drawContours(image_with_contour, [largest_contour], -1, (0, 255, 0), 2)
cv2.drawContours(image_with_contour, contours, -1, color=(255, 255, 255), thickness=cv2.FILLED)
print('CV2.filled : ' , cv2.FILLED) # CV2.filled : -1
# Display the original image with the detected contour
drawWindow('Worm with Contour', image_with_contour)
print('largest_contour : ' ,largest_contour , type(largest_contour))
# drawing = np.zeros((largest_contour.shape[0], largest_contour.shape[1] , 3))
drawing = np.zeros((edged.shape[0], edged.shape[1] , 3))
print(drawing.shape)
#### SAME THING before had largest_contour = cntsSorted[0]
# cv2.drawContours(drawing, [largest_contour] , -1 , color = (0,255,0) , thickness = cv2.FILLED)
cv2.drawContours(drawing, [cntsSorted[0]] , -1 , color = (0,255,0) , thickness = cv2.FILLED)
cv2.imwrite("drawing.png", drawing)
drawWindow('Worm Contour', drawing)
drawing_gray = cv2.imread( 'drawing.png' , 0 )
thinned = cv2.ximgproc.thinning(drawing_gray, thinningType = cv2.ximgproc.THINNING_ZHANGSUEN)
cv2.imwrite("thinned_worm.png", thinned)
drawWindow('thinned_worm', thinned)
#### code from https://stackoverflow.com/questions/53481596/python-image-finding-largest-branch-from-image-skeleton
from fil_finder import FilFinder2D
import astropy.units as u
skeleton = thinned
fil = FilFinder2D(skeleton, distance=250 * u.pc, mask=skeleton)
fil.preprocess_image(flatten_percent=85)
fil.create_mask(border_masking=True, verbose=False,
use_existing_mask=True)
fil.medskel(verbose=False)
fil.analyze_skeletons(branch_thresh=40* u.pix, skel_thresh=10 * u.pix, prune_criteria='length')
drawWindow('skeleton', fil.skeleton_longpath)
cv2.imwrite("skeleton.png", fil.skeleton_longpath*255)
skel = fil.skeleton
print('\nSkel : ',type(skel) , skel.shape, skel.size , skel.ndim , np.max(skel) , np.min(skel) , np.unique(skel))
original = img
mask = fil.skeleton_longpath
print('\nmask : ',type(mask) , mask.shape, mask.size , mask.ndim , np.max(mask) , np.min(mask) , np.unique(mask))
mask_dilated = cv2.dilate(mask, np.ones((4, 4)))
result = original.copy()
for i in range(original.shape[0]):
for j in range(original.shape[1]):
result[i, j] = [0,0,255] if mask_dilated[i, j] == 1.0 else result[i, j]
print('RESULT : ', result.shape)
cv2.imwrite('overlay_1.png', result) # saves modified image to result.png
drawWindow('overlay_1', result)
original = drawing
result = original.copy()
for i in range(original.shape[0]):
for j in range(original.shape[1]):
result[i, j] = [0,0,255] if mask_dilated[i, j] == 1.0 else result[i, j]
print('RESULT : ', result.shape)
cv2.imwrite('overlay_2.png', result) # saves modified image to result.png
drawWindow('overlay_2', result)
def drawWindow(window_name, image):
if draw_windows:
resize = ResizeWithAspectRatio(image, width=600)
cv2.imshow(window_name, resize)
cv2.moveWindow(window_name, 600, 200)
cv2.waitKey(0)
cv2.destroyAllWindows()
def main():
calc_values('input_77560561.jpg')
if __name__ == "__main__":
main()
按图像顺序输出，脚本显示的部分图像丢失：
Tresh 图像：
边缘图像，脚本中未显示。
有轮廓的蠕虫，脚本中显示缺失。
蠕虫轮廓填充：
细化蠕虫轮廓填充：
骨骼，这是由
FilFinder
覆盖输入并填充轮廓，线条由 OpenCV
dilate2：
1：
正如前面的答案注释掉
# gray = cv2.GaussianBlur(gray, (7, 7), 0)
带有覆盖层：
至于线的长度***是否可以通过骨架的像素总数来近似（即
print('\n\nskeleton_lenght_approx : ', np.sum(mask))*** 值得一读的注意事项：
为了计算骨骼长度，从保存的文件
skeleton.pngimport numpy as np
import cv2
import math
from PIL import Image
def neighbors_coords(matrix: np.ndarray, x: int, y: int):
"""
stolen from https://stackoverflow.com/questions/73811239/query-the-value-of-the-four-neighbors-of-an-element-in-a-numpy-2d-array
"""
x_len, y_len = np.array(matrix.shape) - 1
nbr = []
if x > x_len or y > y_len:
return nbr
if x != 0 :
if matrix[x-1][y] == 1:
nbr.append((x-1,y))
if y != 0:
if matrix[x-1][y-1] == 1 :
nbr.append((x-1,y-1))
if y != y_len:
if matrix[x-1][y+1] == 1:
nbr.append((x-1,y+1))
if y != 0:
if matrix[x][y-1] == 1:
nbr.append((x, y-1))
if x != x_len:
if matrix[x+1][y-1] == 1 :
nbr.append((x+1 , y-1))
if x != x_len:
if matrix[x+1][y] == 1 :
nbr.append((x+1 , y))
if y != y_len:
if matrix[x+1][y+1] == 1 :
nbr.append((x+1 ,y+1))
if y != y_len:
if matrix[x][y+1] == 1:
nbr.append((x, y+1))
# print('nbr : ', nbr , x_len, y_len)
nbr_dist = []
for i in nbr :
dist = math.dist([x,y], [i[0],i[1]])
nbr_dist.append(((i[0],i[1]) , dist))
nbr_dist.sort(key=lambda tup: tup[1] , reverse = False) # sort points to get closest one first
# print('nbr_dist : ', nbr_dist , x , y)
return nbr_dist
img = cv2.imread("skeleton.png", cv2.IMREAD_UNCHANGED)
img[img==255] = 1
neighbor_kernel = np.uint8([
[1, 1, 1],
[1, 0, 1],
[1, 1, 1]])
neighbors_count = cv2.filter2D(img.astype(np.uint8), cv2.CV_8U, neighbor_kernel)
endpoint_indices = [ (i, (y,x)) for (y,x) , i in np.ndenumerate(img) if img[y,x] == 1 and neighbors_count[y,x] == 1]
print('\n\nendpoint_indices on neighbors_count : ', endpoint_indices, type(endpoint_indices) , len(endpoint_indices))
start = endpoint_indices[0][1]
img[start[0]][start[1]] = 0
print('\nstart , : ', start)
cnt = 0
coords = []
lenght = 0
coords.append((start , 0))
while np.sum(img) > 0 :
# print('first : ' , first)
second_next = neighbors_coords(img, start[0] , start[1])[0]
# print('second_next : ', second_next)
img[second_next[0][0]][second_next[0][1]] = 0
# print('second_next : ', second_next)
start = second_next[0]
# print('start : ', start)
coords.append(second_next)
lenght += second_next[1]
cnt +=1
print('cnt : ', cnt )
# print('\n\nCoordinates : ' , coords ,'len(coords) : ', len(coords))
print('\n\nlen(coords) : ' , len(coords))
print('\n\nendpoint_indices: ' , endpoint_indices)
print('\n
"""
this bit just to save a file containing all identified coordinates
to check that my script is working right
"""
\nlenght : ', lenght , ' len(coords) : ', len(coords))
points_img = np.zeros((img.shape[0], img.shape[1], 4)).astype(np.uint8)
for i in coords :
# print(i)
points_img[i[0][0]] [i[0][1]] = (255,0,0,255)
image3 = Image.fromarray(points_img.astype(np.uint8) , 'RGBA')
image3.save('check_Test_WALK.png')
关于
fil.FilFinder2Dfil.analyze_skeletonsfil.skeleton_longpaththinned_worm.png
使用以下代码：
import numpy as np
import cv2
import math
from PIL import Image
from fil_finder import FilFinder2D
import astropy.units as u
def neighbors_coords(matrix: np.ndarray, x: int, y: int):
"""
stolen from https://stackoverflow.com/questions/73811239/query-the-value-of-the-four-neighbors-of-an-element-in-a-numpy-2d-array
"""
x_len, y_len = np.array(matrix.shape) - 1
nbr = []
if x > x_len or y > y_len:
return nbr
if x != 0 :
if matrix[x-1][y] == 1:
nbr.append((x-1,y))
if y != 0:
if matrix[x-1][y-1] == 1 :
nbr.append((x-1,y-1))
if y != y_len:
if matrix[x-1][y+1] == 1:
nbr.append((x-1,y+1))
if y != 0:
if matrix[x][y-1] == 1:
nbr.append((x, y-1))
if x != x_len:
if matrix[x+1][y-1] == 1 :
nbr.append((x+1 , y-1))
if x != x_len:
if matrix[x+1][y] == 1 :
nbr.append((x+1 , y))
if y != y_len:
if matrix[x+1][y+1] == 1 :
nbr.append((x+1 ,y+1))
if y != y_len:
if matrix[x][y+1] == 1:
nbr.append((x, y+1))
# print('nbr : ', nbr , x_len, y_len)
nbr_dist = []
for i in nbr :
dist = math.dist([x,y], [i[0],i[1]])
nbr_dist.append(((i[0],i[1]) , dist))
nbr_dist.sort(key=lambda tup: tup[1] , reverse = False) # sort points to get closest one first
# print('nbr_dist : ', nbr_dist , x , y)
return nbr_dist
def main(filename, thinned) :
for i in (range(10)) :
skeleton = cv2.imread(thinned , cv2.IMREAD_UNCHANGED)
skeleton[skeleton == 255] = 1
fil = FilFinder2D(skeleton, distance=250 * u.pc, mask=skeleton)
fil.preprocess_image(flatten_percent=85)
fil.create_mask(border_masking=True, verbose=False,
use_existing_mask=True)
fil.medskel(verbose=False)
fil.analyze_skeletons(branch_thresh=40* u.pix, skel_thresh=10 * u.pix, prune_criteria='length')
# drawWindow('skeleton', fil.skeleton_longpath)
cv2.imwrite("skeleton"+str(i)+'.png', fil.skeleton_longpath*255)
lenghts_calculated = []
for i in (range(10)) :
img = cv2.imread(filename.split('.')[0]+str(i)+'.png' , cv2.IMREAD_UNCHANGED)
img[img==255] = 1
neighbor_kernel = np.uint8([
[1, 1, 1],
[1, 0, 1],
[1, 1, 1]])
neighbors_count = cv2.filter2D(img.astype(np.uint8), cv2.CV_8U, neighbor_kernel)
endpoint_indices = [ (i, (y,x)) for (y,x) , i in np.ndenumerate(img) if img[y,x] == 1 and neighbors_count[y,x] == 1]
print('\n\nendpoint_indices on neighbors_count : ', endpoint_indices, type(endpoint_indices) , len(endpoint_indices))
start = endpoint_indices[0][1]
img[start[0]][start[1]] = 0
print('\nstart , : ', start)
cnt = 0
coords = []
lenght = 0
coords.append((start , 0))
while np.sum(img) > 0 :
# print('first : ' , first)
second_next = neighbors_coords(img, start[0] , start[1])[0]
# print('second_next : ', second_next)
img[second_next[0][0]][second_next[0][1]] = 0
# print('second_next : ', second_next)
start = second_next[0]
# print('start : ', start)
coords.append(second_next)
lenght += second_next[1]
cnt +=1
print('cnt : ', cnt )
# print('\n\nCoordinates : ' , coords ,'len(coords) : ', len(coords))
print('\n\nlen(coords) : ' , len(coords))
print('\n\nendpoint_indices: ' , endpoint_indices)
print('\n\nlenght : ', lenght , ' len(coords) : ', len(coords))
lenghts_calculated.append((lenght, len(coords)))
# """
# this bit just to save a file containing all identtified coordinates
# to check that my script is working right
# """
# points_img = np.zeros((img.shape[0], img.shape[1], 4)).astype(np.uint8)
# for i in coords :
# # print(i)
# points_img[i[0][0]] [i[0][1]] = (255,0,0,255)
# image3 = Image.fromarray(points_img.astype(np.uint8) , 'RGBA')
# image3.save('check_Test_WALK.png')
for i , value in enumerate(lenghts_calculated) :
print(i+1, '____________' , value[0] ,' vs number of pixels ' , value[1])
return lenghts_calculated
filename = "skeleton.png"
thinned = "thinned_worm.png"
val = main(filename, thinned)
val_measure = [i[0] for i in val]
val_numb = [i[1] for i in val]
def get_change(current, previous):
if current == previous:
return 100.0
try:
return (abs(current - previous) / previous) * 100.0
except ZeroDivisionError:
return 0
def mean(data):
"""Return the sample arithmetic mean of data."""
n = len(data)
if n < 1:
raise ValueError('mean requires at least one data point')
return sum(data)/n # in Python 2 use sum(data)/float(n)
def _ss(data):
"""Return sum of square deviations of sequence data."""
c = mean(data)
ss = sum((x-c)**2 for x in data)
return ss
def stddev(data, ddof=0):
"""Calculates the population standard deviation
by default; specify ddof=1 to compute the sample
standard deviation."""
n = len(data)
if n < 2:
raise ValueError('variance requires at least two data points')
ss = _ss(data)
pvar = ss/(n-ddof)
return pvar**0.5
print('value measured , mean : ', mean(val_measure) ,' SD : ' ,stddev(val_measure, ddof=1))
print('value number of pixel , mean : ', mean(val_numb) ,' SD : ' ,stddev(val_numb, ddof=1))
我得到以下输出：
........
.......
lenght : 1316.467170879763 len(coords) : 1129
1 ____________ 1316.4671708797628 vs number of pixels 1129
2 ____________ 1315.295598004509 vs number of pixels 1127
3 ____________ 1317.0529573173897 vs number of pixels 1130
4 ____________ 1315.8813844421359 vs number of pixels 1128
5 ____________ 1318.8103166302703 vs number of pixels 1133
6 ____________ 1314.709811566882 vs number of pixels 1126
7 ____________ 1315.881384442136 vs number of pixels 1128
8 ____________ 1315.295598004509 vs number of pixels 1127
9 ____________ 1312.9524522540014 vs number of pixels 1123
10 ____________ 1316.467170879763 vs number of pixels 1129
value measured , mean : 1315.881384442136 SD : 1.5374956741211723
value number of pixel , mean : 1128.0 SD : 2.6246692913372702
.....
文件中 10 个最长路径（0 到 9 个结果：
skeleton#.png"
和一小部分的放大显示
fil.skeleton_longpath
附录
与 filFinder 开发人员进行了交谈，他对我非常友善，并向我指出了lengths，即：
def lengths(self, unit=u.pix):
'''
Return longest path lengths of the filaments.
Parameters
----------
unit : `~astropy.units.Unit`, optional
Pixel, angular, or physical unit to convert to.
'''
pix_lengths = np.array([fil.length().value
for fil in self.filaments]) * u.pix
return self.converter.from_pixel(pix_lengths, unit)
（它是
class fil_finder.FilFinder2D(...)正如我所解释的，它的值与
fil.skeleton_longpath几何长度与单个发现者从最长路径中找到的长度之间可能存在适度的差异。 FilFinder 概括了沿最长路径的分支之间的交叉点的识别，以允许它们由多个像素组成。它使用的距离是相交像素的中值，添加到沿每个分支的几何距离。
运行 FilFinder2D.find_widths 时还有一个选项，其中 2 * 宽度添加到灯丝长度，以考虑骨架化过程中的缩短：https://fil-finder.readthedocs.io/en/latest/api/fil_finder .FilFinder2D.html#fil_finder.FilFinder2D.find_widths。如果您正在运行该步骤，您可以使用 add_width_to_length=False
禁用此步骤
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