76 lines
2.1 KiB
Python
76 lines
2.1 KiB
Python
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import matplotlib
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matplotlib.use('Agg') # 使用非 GUI 后端
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import cv2
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import numpy as np
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import matplotlib.pyplot as plt
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def analyze_surface_roughness(image_path):
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# 读取图像
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img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
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if img is None:
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raise FileNotFoundError(f"无法加载图像: {image_path}")
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# 预处理
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clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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img = clahe.apply(img)
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img = cv2.medianBlur(img, 5)
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# FFT
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f = np.fft.fft2(img)
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fshift = np.fft.fftshift(f)
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magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1e-9)
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# 动态低频区域
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rows, cols = img.shape
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crow, ccol = rows // 2, cols // 2
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radius = min(rows, cols) // 20
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mask = np.ones((rows, cols), np.uint8)
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cv2.circle(mask, (ccol, crow), radius, 0, thickness=-1)
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# 计算能量占比
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total_energy = np.sum(magnitude_spectrum)
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high_freq_energy = np.sum(magnitude_spectrum * mask)
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ratio = high_freq_energy / total_energy
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# 可视化并保存
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plt.figure(figsize=(12, 6))
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plt.subplot(1, 3, 1)
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plt.imshow(img, cmap='gray')
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plt.title('预处理后图像')
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plt.axis('off')
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plt.subplot(1, 3, 2)
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plt.imshow(magnitude_spectrum, cmap='gray')
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plt.title('频谱图（中心为低频）')
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plt.axis('off')
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overlay = magnitude_spectrum.copy()
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if len(overlay.shape) == 2:
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overlay = np.stack([overlay]*3, axis=-1)
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cv2.circle(overlay, (ccol, crow), radius, (1, 0, 0), 2)
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plt.subplot(1, 3, 3)
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plt.imshow(overlay)
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plt.title(f'低频区域（红圈内）\n高频能量占比: {ratio:.3f}')
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plt.axis('off')
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plt.tight_layout()
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plt.savefig("fft_analysis_result.png", dpi=150, bbox_inches='tight')
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plt.close() # 释放内存
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# 输出结果
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print(f"✅ 图像尺寸: {cols}x{rows}")
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print(f"📊 高频能量占比: {ratio:.3f}")
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if ratio < 0.8:
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print("🟢 评价: 表面较光滑")
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elif ratio < 0.95:
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print("🟡 评价: 表面中等粗糙")
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else:
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print("🔴 评价: 表面非常粗糙")
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return ratio
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# 运行
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if __name__ == "__main__":
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analyze_surface_roughness("1.jpg")
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