Python 查找由scikit image'识别的斑点列表的平均像素值;s blob_log(高斯拉普拉斯)方法
输入为uint16灰度.tif图像,512 x 512像素。正如这个问题的标题所暗示的,我想计算由blob_log方法(参见:)识别的blob的平均像素强度,但我不确定如何访问每个blob的像素值。平均强度值必须在uint16范围(0到65535)内返回 以下是我到目前为止的情况。如果我不够清楚,请提前道歉。我已经学习python(3.6.x)大约三个月了,这是我的第一个项目。任何帮助都将不胜感激Python 查找由scikit image'识别的斑点列表的平均像素值;s blob_log(高斯拉普拉斯)方法,python,image-segmentation,scikit-image,scientific-computing,Python,Image Segmentation,Scikit Image,Scientific Computing,输入为uint16灰度.tif图像,512 x 512像素。正如这个问题的标题所暗示的,我想计算由blob_log方法(参见:)识别的blob的平均像素强度,但我不确定如何访问每个blob的像素值。平均强度值必须在uint16范围(0到65535)内返回 以下是我到目前为止的情况。如果我不够清楚,请提前道歉。我已经学习python(3.6.x)大约三个月了,这是我的第一个项目。任何帮助都将不胜感激 from skimage import io from math import sqrt fro
from skimage import io
from math import sqrt
from skimage.feature import blob_log
import numpy as np
from pandas import DataFrame
def start():
while True:
us_input = input(
"Please enter the name of the file you'd like to analyze.\n> "
)
try:
im = io.imread(us_input)
break
except FileNotFoundError:
print(
"That file doesn't seem to exist or has been entered incorrectly."
)
neur_detect(im)
def neur_detect(im):
neurons = blob_log(im, min_sigma=1, max_sigma=30, threshold=.02, overlap=.1)
neur_props(neurons)
def neur_props(blobs):
num_neurons = len(blobs)
print("\nNumber of potential neurons detected: {}\n".format(num_neurons))
results = []
for blob in blobs:
y_row, x_col, r = blob
properties = []
properties.append(x_col / .769230769230769) # convert pixels to um
properties.append(y_row / .769230769230769) # convert pixels to um
properties.append(r * sqrt(2)) # compute radii in 3rd column of DataFrame
mean_intensity = ????
properties.append(mean_intensity)
results.append(properties)
results = DataFrame(results, columns = ['x_coor', 'y_coor', 'radius', 'mean_intensity'])
results.index = results.index + 1
print(results)
start()
日志blob检测器返回一个数组,其中每一行表示一个blob。它给出了中心坐标(行和列)以及高斯博客宽度sigma。二维水滴的半径约为
sqrt(2)*sigma
import numpy as np
from skimage import draw, feature
import matplotlib.pyplot as plt
# Create a test image
image = np.zeros((200, 200))
# Parameters for test image blobs
positions_r, positions_c = (50, 100, 150), (50, 100, 150)
radii = (20, 15, 30)
values = (.5, .75, 1)
# We'll make the blobs vary a bit with noise, to make it
# more realistic. Here we'll store their known average values.
blob_avg = []
for (r, c, radius, val) in zip(positions_r, positions_c,
radii, values):
# Get the coordinates for a circle at the specified coordinates
r_ix, c_ix = draw.circle(r, c, radius)
# Generate values for all pixels inside the circle, varying
# between val/2 and val.
noisy_vals = val * 0.5 + np.random.random(size=len(r_ix)) / 2
# Put those values into our test image
image[r_ix, c_ix] = noisy_vals
# Save the average value of this blob
blob_avg.append(noisy_vals.mean())
# Compute the blobs in the image, setting the desired sigma range,
# and lowering the threshold so that we also grab our faintest blob
blobs_log = feature.blob_log(image, min_sigma=5, max_sigma=50,
threshold=0.3, num_sigma=50)
# `blob_log` returns the blobs in reverse order (in this case),
# so to compare with our test data we reverse the list of blob
# averages
blob_avg = blob_avg[::-1]
# Compute each blob's radius, by multiplying its sigma by sqrt(2)
blobs_log[:, 2] = blobs_log[:, 2] * np.sqrt(2)
# Create a plot, and display our test data
f, ax = plt.subplots(figsize=(15, 10))
ax.imshow(image, cmap='gray');
# Generate all row and column coordinates for our test image
# For an `(N, M)` test image, `ixs` will have shape `(N, M, 2)`,
# since it stores row and column coordinates.
ixs = np.indices(image.shape)
# Now, we plot each detected blob and estimate its average intensity
for i, blob in enumerate(blobs_log):
y, x, r = blob
c = plt.Circle((x, y), r, color='red', linewidth=2, fill=False)
ax.add_patch(c)
# Define an array of shape `[2, 1, 1]`, containing
# the center of the blob
blob_center = np.array([y, x])[:, np.newaxis, np.newaxis]
# Using the formula for a circle, `x**2 + y**2 < r**2`,
# generate a mask for this blob.
mask = ((ixs - blob_center)**2).sum(axis=0) < r**2
# Calculate the average intensity of pixels under the mask
blob_avg_est = image[mask].mean()
print(f'Blob {i} average value: true={blob_avg[i]:.2f}, estimated={blob_avg_est:.2f}')
嗨,Stefan,谢谢你的代码!消化需要一些时间,但我会马上开始的。嗨,斯蒂芬!我有别的事情要做,但我想为此感谢你。我已经能够在我的代码中成功地适应这一点,并且非常感谢您花时间帮助一个绝对的初学者。谢谢-肖恩
Blob 0 average value: true=0.75, estimated=0.75
Blob 1 average value: true=0.63, estimated=0.63
Blob 2 average value: true=0.50, estimated=0.49