Python/OpenCV——细菌簇中的质心确定
Rai*_*ury 9 python opencv image-processing computer-vision python-3.x
我目前正在开发一种算法来确定细菌簇的（明场）显微镜图像的质心位置。这是目前图像处理中的一个主要未解决的问题。
这个问题是对：Python/OpenCV — 匹配两个图像中细菌的质心点的后续问题。
目前，该算法对稀疏、间隔开的细菌有效。然而，当细菌聚集在一起时，它变得完全无效。
在这些图像中，请注意细菌质心是如何有效定位的。
然而，当细菌聚集在不同的水平时，算法就会失败。
原始图像
我想优化我当前的算法，使其对这些类型的图像更加健壮。这是我正在运行的程序。
import cv2
import numpy as np
import os
kernel = np.array([[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]], dtype=np.uint8)
def e_d(image, it):
image = cv2.erode(image, kernel, iterations=it)
image = cv2.dilate(image, kernel, iterations=it)
return image
path = r"(INSERT IMAGE DIRECTORY HERE)"
img_files = [file for file in os.listdir(path)]
def segment_index(index: int):
segment_file(img_files[index])
def segment_file(img_file: str):
img_path = path + "\\" + img_file
print(img_path)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Applying adaptive mean thresholding
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
# Removing small noise
th = e_d(th.copy(), 1)
# Finding contours with RETR_EXTERNAL flag and removing undesired contours and
# drawing them on a new image.
cnt, hie = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cntImg = th.copy()
for contour in cnt:
x, y, w, h = cv2.boundingRect(contour)
# Eliminating the contour if its width is more than half of image width
# (bacteria will not be that big).
if w > img.shape[1] / 2:
continue
cntImg = cv2.drawContours(cntImg, [cv2.convexHull(contour)], -1, 255, -1)
# Removing almost all the remaining noise.
# (Some big circular noise will remain along with bacteria contours)
cntImg = e_d(cntImg, 3)
# Finding new filtered contours again
cnt2, hie2 = cv2.findContours(cntImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Now eliminating circular type noise contours by comparing each contour's
# extent of overlap with its enclosing circle.
finalContours = [] # This will contain the final bacteria contours
for contour in cnt2:
# Finding minimum enclosing circle
(x, y), radius = cv2.minEnclosingCircle(contour)
center = (int(x), int(y))
radius = int(radius)
# creating a image with only this circle drawn on it(filled with white colour)
circleImg = np.zeros(img.shape, dtype=np.uint8)
circleImg = cv2.circle(circleImg, center, radius, 255, -1)
# creating a image with only the contour drawn on it(filled with white colour)
contourImg = np.zeros(img.shape, dtype=np.uint8)
contourImg = cv2.drawContours(contourImg, [contour], -1, 255, -1)
# White pixels not common in both contour and circle will remain white
# else will become black.
union_inter = cv2.bitwise_xor(circleImg, contourImg)
# Finding ratio of the extent of overlap of contour to its enclosing circle.
# Smaller the ratio, more circular the contour.
ratio = np.sum(union_inter == 255) / np.sum(circleImg == 255)
# Storing only non circular contours(bacteria)
if ratio > 0.55:
finalContours.append(contour)
finalContours = np.asarray(finalContours)
# Finding center of bacteria and showing it.
bacteriaImg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
for bacteria in finalContours:
M = cv2.moments(bacteria)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
bacteriaImg = cv2.circle(bacteriaImg, (cx, cy), 5, (0, 0, 255), -1)
cv2.imshow("bacteriaImg", bacteriaImg)
cv2.waitKey(0)
# Segment Each Image
for i in range(len(img_files)):
segment_index(i)
理想情况下，我希望至少改进一些已发布的图片。
面具始终是识别物体的薄弱环节，也是最重要的一步。这将改进具有大量细菌的识别图像。我通过添加一个 OPEN 和另一个带有内核的 ERODE 传递修改了您的 e_d 函数，并更改了它（迭代次数）变量（为 1, 2 而不是 1,3），以便您的代码执行此操作。这绝不是一个完成的工作，但我希望它能让您了解您可以尝试进一步增强它的方法。我使用了你提供的图像，因为它们已经有一个红点，这可能会干扰我的结果图像......但你可以看到它能够识别出更多的细菌。我的一些结果显示了两个点，我错过了只有一种细菌的图像，每个点很可能是因为它已经被标记了。用原始图像尝试一下，看看效果如何。
此外，由于细菌在大小和形状上都相对均匀，我认为您可以使用每个细菌的高宽比和/或平均值来过滤掉极端形状（小或大）和瘦长的形状也。你可以测量足够多的细菌，看看平均轮廓长度是多少，或者高度和宽度，或者高度/宽度比等等，以找到合理的公差而不是图像大小本身的比例。另一个建议是重新考虑如何将所有图像屏蔽在一起，可能分两步进行尝试。一是找到含有细菌的长条形的边界，然后找到里面的细菌。这假设您的所有图像都与这些图像相似，如果是这样，它可能有助于消除此边界之外的杂散命中，这些杂散命中绝不是细菌。
#!usr/bin/env python
# /sf/ask/4422745281/
import cv2
import numpy as np
import os
kernel = np.array([[0, 0, 1, 0, 0],
[0, 1, 1, 1, 0],
[1, 1, 1, 1, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]], dtype=np.uint8)
def e_d(image, it):
print(it)
image = cv2.erode(image, kernel, iterations=it)
image = cv2.dilate(image, kernel, iterations=it)
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel, iterations = 1)
image = cv2.morphologyEx(image, cv2.MORPH_ERODE, kernel, iterations = 1)
return image
#path = r"(INSERT IMAGE DIRECTORY HERE)"
path = r"E:\stackimages"
img_files = [file for file in os.listdir(path)]
def segment_index(index: int):
segment_file(img_files[index])
def segment_file(img_file: str):
img_path = path + "\\" + img_file
print(img_path)
head, tail = os.path.split(img_path)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("bacteriaImg-1", img)
cv2.waitKey(0)
# Applying adaptive mean thresholding
th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 11, 2)
# Removing small noise
th = e_d(th.copy(), 1)
# Finding contours with RETR_EXTERNAL flag and removing undesired contours and
# drawing them on a new image.
cnt, hie = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cntImg = th.copy()
for contour in cnt:
x, y, w, h = cv2.boundingRect(contour)
# Eliminating the contour if its width is more than half of image width
# (bacteria will not be that big).
if w > img.shape[1] / 2:
continue
else:
cntImg = cv2.drawContours(cntImg, [cv2.convexHull(contour)], -1, 255, -1)
# Removing almost all the remaining noise.
# (Some big circular noise will remain along with bacteria contours)
cntImg = e_d(cntImg, 2)
cv2.imshow("bacteriaImg-2", cntImg)
cv2.waitKey(0)
# Finding new filtered contours again
cnt2, hie2 = cv2.findContours(cntImg, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# Now eliminating circular type noise contours by comparing each contour's
# extent of overlap with its enclosing circle.
finalContours = [] # This will contain the final bacteria contours
for contour in cnt2:
# Finding minimum enclosing circle
(x, y), radius = cv2.minEnclosingCircle(contour)
center = (int(x), int(y))
radius = int(radius)
# creating a image with only this circle drawn on it(filled with white colour)
circleImg = np.zeros(img.shape, dtype=np.uint8)
circleImg = cv2.circle(circleImg, center, radius, 255, -1)
# creating a image with only the contour drawn on it(filled with white colour)
contourImg = np.zeros(img.shape, dtype=np.uint8)
contourImg = cv2.drawContours(contourImg, [contour], -1, 255, -1)
# White pixels not common in both contour and circle will remain white
# else will become black.
union_inter = cv2.bitwise_xor(circleImg, contourImg)
# Finding ratio of the extent of overlap of contour to its enclosing circle.
# Smaller the ratio, more circular the contour.
ratio = np.sum(union_inter == 255) / np.sum(circleImg == 255)
# Storing only non circular contours(bacteria)
if ratio > 0.55:
finalContours.append(contour)
finalContours = np.asarray(finalContours)
# Finding center of bacteria and showing it.
bacteriaImg = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
for bacteria in finalContours:
M = cv2.moments(bacteria)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
bacteriaImg = cv2.circle(bacteriaImg, (cx, cy), 5, (0, 0, 255), -1)
cv2.imshow("bacteriaImg", bacteriaImg)
cv2.waitKey(0)
# Segment Each Image
for i in range(len(img_files)):
segment_index(i)
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