使用 scikit-image 计算图像中特定区域的平均灰度值答案

【问题标题】：Calculate the mean gray value of specific areas in an image with scikit-image使用 scikit-image 计算图像中特定区域的平均灰度值
【发布时间】：2020-02-28 22:41:24
【问题描述】：

我正在尝试计算下图所示图像中 5 个区域的灰度。但是我没有设法在skimage 中找到任何好的命令。

我首先通过处理以下代码来屏蔽图像：

import numpy as np
import matplotlib as mpl
from matplotlib.path import Path
from matplotlib import patches
import matplotlib.pyplot as plt

import skimage.io as io
from skimage import data_dir 

img = io.imread('/media/rene/Windows8_OS/PROMON/Recorded Sequences/PNG/0rpm_p000.png')

vertices = np.asarray([( 947, 1959),
                       (1762, 1959),
                       (1762, 2241),
                       ( 947, 2241),
                       ( 947, 1089),
                       (1762, 1089),
                       (1762, 1371),
                       ( 947, 1371),
                       ( 947,  797),
                       (1762,  797),
                       (1762, 1079),
                       ( 947, 1079),
                       ( 947,  505),
                       (1762,  505),
                       (1762,  787),
                       ( 947,  787),
                       ( 947,  213),
                       (1762,  213),
                       (1762,  495),
                       ( 947,  495)])

# reshape into smaller path for faster debugging
# vertices = vertices // 20

# matplotlib path
path = Path(vertices)
xmin, ymin, xmax, ymax = np.asarray(path.get_extents(), dtype=int).ravel()

# create a mesh grid of the shape of the final mask
x, y = np.mgrid[:img.shape[1], :img.shape[0]]
# mesh grid to points
points = np.vstack((x.ravel(), y.ravel())).T

# mask for the point included in the path
mask1 = path.contains_points(points)
path_points = points[np.where(mask1)]

# reshape mask for display 
img_mask1 = mask1.reshape(x.shape).T

# selecting all but black pixels
# black_pixels_mask = np.all(img_mask1 == [0, 0, 0], axis=-1)
# non_black_pixels_mask = ~black_pixels_mask

# plots
f, ax = plt.subplots()
# if more thn one plot 
# gs = mpl.gridspec.GridSpec(2,2)
# gs.update(wspace=0.2, hspace= 0.2)
# masked image 
ax.imshow(img * img_mask1, cmap="gray")
ixs = np.indices(img.shape)

但现在我不知道如何获取 5 个区域中每个区域的灰度值。我的结果应该看起来像这样： Finding the average pixel values of a list of blobs identified by scikit-image's blob_log (Laplacian of Gaussian) method

蒙面图像：

【问题讨论】：

图像应该是numpy数组所以得到面积img[y1:y2,x1:x2]然后转换为灰色并计算mean()
嘿，感谢您的快速回答！所以我应该在区域 1 到区域 5 中而不是顶点，对吧？ img[y1:y2,x1:x2] 不是一行吗？
不，它是带角的矩形 (y1, x1) 和 (y2, x2)。有关详细信息，请参阅此文档：numpy.org/devdocs/reference/arrays.indexing.html
我不明白。你能在我图片中的一个矩形区域做一个例子吗？会有很大帮助...

标签： python image image-processing scikit-image grayscale

【解决方案1】：

根据您的要求，这是一个玩具示例，它向您展示如何计算由对角坐标定义的两个矩形区域的平均值：

import numpy as np
from skimage import io
import matplotlib.pyplot as plt
import matplotlib.patches as patches

img = io.imread('https://i.stack.imgur.com/2YAwu.png')

vertices = [(200, 200),  # upper left corner
            (400, 400),  # lower right corner
            (200, 1200), # and so on...
            (800, 1400)]

fig, ax = plt.subplots(1)
ax.imshow(img, cmap='gray')

for (upper, left), (lower, right) in zip(vertices[0:-1:2], vertices[1::2]):
    subimg = img[upper:lower, left:right]
    avg = subimg.mean()
    print(f'Mean of img[{upper}:{lower}, {left}:{right}]={avg}')
    rect = patches.Rectangle((left, upper), right-left, lower-upper, 
                             linewidth=2, edgecolor='r', facecolor='none')
    ax.add_patch(rect)

plt.show()

输出

Mean of img[200:400, 200:400]=134.5452
Mean of img[200:800, 1200:1400]=0.0

【讨论】：