56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
from skimage import feature
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import cv2
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import numpy as np
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import os
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# 图像路径
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img_path = "2.jpg"
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# ✅ 1. 检查文件是否存在
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if not os.path.exists(img_path):
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raise FileNotFoundError(f"图像文件不存在，请检查路径: {img_path}")
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# ✅ 2. 以灰度模式读取图像
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img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
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if img is None:
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raise ValueError(f"无法读取图像，可能是文件损坏或格式问题: {img_path}")
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print(f"✅ 图像加载成功，原始尺寸: {img.shape}")
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'''
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height, width = img.shape
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start_row = height // 3 * 2 # 从 2/3 处开始，到底部
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bottom_region = img[start_row:, :] # 所有列，只取底部 1/3 行
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print(f"✅ 裁剪出底部 1/3 区域，尺寸: {bottom_region.shape}")
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'''
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# ✅ 3. 提取最下方三分之一的区域
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bottom_region = img
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# ✅ 4. 计算 GLCM（建议降低 levels 或使用压缩）
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# 注意：levels=256 对计算资源要求高，可考虑先归一化到 32 或 64 灰度级
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# 这里我们先保持 256，若报内存错误可改用降级方法
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glcm = feature.graycomatrix(
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bottom_region,
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distances=[1],
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angles=[0, np.pi/4, np.pi/2, 3*np.pi/4], # 多方向
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levels=256,
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symmetric=True,
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normed=True
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)
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# ✅ 5. 提取平均纹理特征（在所有方向上取均值）
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properties = ['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation']
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features = {}
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for prop in properties:
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values = feature.graycoprops(glcm, prop)[0]
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features[prop] = np.mean(values) # 对多个角度取平均
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# ✅ 6. 输出结果
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print("\n📊 图像底部 1/3 区域的纹理特征:")
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print(f"Contrast: {features['contrast']:.3f}") # 越大越粗糙230
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print(f"Dissimilarity: {features['dissimilarity']:.3f}") # 越大差异越大7.265
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print(f"Homogeneity: {features['homogeneity']:.3f}") # 越小越不均匀（粗糙）0.291
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print(f"Energy: {features['energy']:.3f}") # 越小越复杂（纹理丰富）0.033
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print(f"Correlation: {features['correlation']:.3f}") # 灰度线性相关性0.941 |