71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
import cv2
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import numpy as np
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import os
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def detect_cracks_in_wood(image_path, output_dir="output"):
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"""
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检测木条图像中的裂缝缺陷（线状或不规则形状）
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Args:
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image_path (str): 输入图像路径
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output_dir (str): 结果保存目录
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"""
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os.makedirs(output_dir, exist_ok=True)
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# 1. 读取图像
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img = cv2.imread(image_path)
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if img is None:
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raise FileNotFoundError(f"无法读取图像: {image_path}")
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original = img.copy()
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 2. 使用高斯模糊去除噪声，并增强裂缝对比度
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# 3. 使用Canny边缘检测算法查找边缘
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edges = cv2.Canny(blurred, 50, 150)
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# 4. 形态学操作：使用闭运算填充裂缝间的空隙
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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closed_edges = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel, iterations=2)
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# 5. 查找轮廓
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contours, _ = cv2.findContours(closed_edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# 6. 筛选疑似裂缝的轮廓
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min_length = 200 # 最小长度（像素），根据实际情况调整
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max_gap = 10 # 连接断开点的最大距离（像素）
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crack_contours = []
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for cnt in contours:
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length = cv2.arcLength(cnt, False) # 计算曲线长度
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if length > min_length:
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crack_contours.append(cnt)
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# 7. 在原图上绘制检测到的裂缝
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result_img = original.copy()
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cv2.drawContours(result_img, crack_contours, -1, (0, 0, 255), 2) # 红色框
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# 8. 保存结果
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base_name = os.path.splitext(os.path.basename(image_path))[0]
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cv2.imwrite(os.path.join(output_dir, f"{base_name}_edges.png"), edges)
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cv2.imwrite(os.path.join(output_dir, f"{base_name}_closed_edges.png"), closed_edges)
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cv2.imwrite(os.path.join(output_dir, f"{base_name}_result.png"), result_img)
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print(f"✅ 检测完成！共找到 {len(crack_contours)} 个疑似裂缝")
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print(f"📁 结果已保存至: {output_dir}")
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# （可选）显示
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cv2.imshow("Original", cv2.resize(original, (640, 480)))
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cv2.imshow("Edges", cv2.resize(edges, (640, 480)))
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cv2.imshow("Closed Edges", cv2.resize(closed_edges, (640, 480)))
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cv2.imshow("Result", cv2.resize(result_img, (640, 480)))
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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if __name__ == "__main__":
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image_path = "1.jpg" # ← 替换为你的木条图像路径
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detect_cracks_in_wood(image_path) |