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【python】纤维宽度分布分析与可视化

news 2025/5/22 7:07:06

根据五张显微镜图片（11-1.tif ~ 11-5.tif），统计纤维宽度分布，并画出直方图+正态分布拟合曲线

主要步骤

读取图片

图像预处理（灰度化、二值化、去噪等）

边缘检测/骨架提取

测量纤维宽度

统计宽度分布，绘制直方图和正态分布拟合曲线

图片

代码

import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from skimage import measuredef measure_fiber_width(image_path, pixel_per_um):# 读取图片img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)# 二值化_, binary = cv2.threshold(img, 0, 255, cv2.THRESH_OTSU)# 反色（确保纤维为白色）binary = 255 - binary# 去噪binary = cv2.medianBlur(binary, 5)# 连通域分析labels = measure.label(binary, connectivity=2)props = measure.regionprops(labels)widths = []for prop in props:# 只考虑较大的区域，过滤噪声if prop.area > 100:minr, minc, maxr, maxc = prop.bboxwidth = max(maxr - minr, maxc - minc) / pixel_per_umwidths.append(width)return widthsdef remove_outliers(data, n_std=2):mu = np.mean(data)std = np.std(data)filtered = [x for x in data if (mu - n_std*std) <= x <= (mu + n_std*std)]return filtereddef plot_width_distribution(widths, ax, title):widths = remove_outliers(widths, n_std=1)# 直方图n, bins, patches = ax.hist(widths, bins=7, color='cyan', edgecolor='black', alpha=0.7, density=True)# 拟合正态分布mu, std = norm.fit(widths)xmin, xmax = ax.get_xlim()x = np.linspace(xmin, xmax, 100)p = norm.pdf(x, mu, std)ax.plot(x, p, 'r-', lw=2)ax.set_xlabel('Width (μm)')ax.set_ylabel('Percentage (%)')ax.set_title(f'{title}\nW={mu:.2f}±{std:.2f}')ax.grid(False)# 假设每像素代表的微米数（需根据标尺换算）
# pixel_per_um = 2.0  # 你需要根据图片标尺换算file_list = ['11-1.tif', '11-2.tif', '11-3.tif', '11-4.tif', '11-5.tif']
titles = ['e', 'f', 'g', 'h', 'i']
pixel_per_ums = [(1255-798)/50.0,(1255-748)/20.0,(1255-748)/10.0,(1255-798)/5.0,(1255-849)/2.0]fig, axs = plt.subplots(1, 5, figsize=(20, 4))
for i, (file, title,pixel_per_um) in enumerate(zip(file_list, titles,pixel_per_ums)):widths = measure_fiber_width(file, pixel_per_um)plot_width_distribution(widths, axs[i], title)
plt.tight_layout()
plt.show()