112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
import cv2
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import numpy as np
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import math
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import os
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def find_and_mask_weld_seam_v3(image_path):
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"""
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加载图像，使用更激进的预处理方法检测低对比度直线。
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:param image_path: 输入图像的完整路径。
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:return: None
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"""
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# --- 步骤 1: 加载图像 ---
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image = cv2.imread(image_path)
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if image is None:
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print(f"错误: 无法加载图像 '{image_path}'。")
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return
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visualization_image = image.copy()
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height, width, _ = image.shape
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image_center_x = width / 2
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# --- 步骤 2: 图像预处理 (超强版) ---
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 2.1 高斯模糊以去除噪声
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# 2.2 自适应阈值二值化 (关键步骤!)
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# 这个操作对于光照不均、对比度低的区域效果非常好，能强制分离出边缘
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# blocksize: 邻域大小，必须是奇数
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# C: 从均值或加权均值中减去的常数，用于微调
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binary_image = cv2.adaptiveThreshold(
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blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, 21, 5
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)
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# 使用THRESH_BINARY_INV是因为我们感兴趣的边缘是暗色背景下的亮色线条
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# 2.3 形态学操作来清理和连接线条
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# 创建一个垂直的结构元素，因为我们的目标是垂直线
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kernel = np.ones((15, 1), np.uint8) # 强调垂直方向
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# 使用“闭运算”来填充断开的线段中的小洞
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closed = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel)
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# --- 步骤 3: 霍夫直线变换 (直接在处理后的二值图上进行) ---
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# Canny不再是必需的，因为二值化已经完成了边缘提取
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lines = cv2.HoughLinesP(
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closed, # 在闭运算结果上检测
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rho=1,
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theta=np.pi / 180,
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threshold=100, # 需要较高的阈值，因为线现在更连续了
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minLineLength=int(height * 0.5), # 线至少要有一半图像高度那么长
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maxLineGap=50 # 允许较大的间隙
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)
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# --- 步骤 4: 筛选最佳直线 (逻辑与之前类似) ---
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best_line = None
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max_length = 0
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if lines is not None:
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for line in lines:
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x1, y1, x2, y2 = line[0]
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# 角度过滤，寻找近乎垂直的线
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angle = abs(math.degrees(math.atan2(y2 - y1, x2 - x1)))
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if abs(angle - 90) < 5: # 检查角度是否在90度左右（垂直）
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length = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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if length > max_length:
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max_length = length
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best_line = line[0]
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# --- 步骤 5: 处理和显示结果 ---
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if best_line is not None:
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bx1, by1, bx2, by2 = best_line
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print(f"成功找到直线！起点:({bx1}, {by1}), 终点:({bx2}, {by2})")
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cv2.line(visualization_image, (bx1, by1), (bx2, by2), (0, 0, 255), 3)
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# 显示中间处理结果以供调试
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cv2.imshow('1. Original Image', image)
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cv2.imshow('2. Adaptive Threshold', binary_image)
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cv2.imshow('3. Morphological Closing', closed) # 检查闭运算效果
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cv2.imshow('4. Final Detection', visualization_image)
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else:
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print("未能在图像中找到符合条件的直线。")
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cv2.imshow('1. Original Image', image)
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cv2.imshow('2. Adaptive Threshold', binary_image)
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cv2.imshow('3. Morphological Closing', closed)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# --- 主程序入口 ---
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if __name__ == '__main__':
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try:
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script_dir = os.path.dirname(os.path.abspath(__file__))
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except NameError:
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script_dir = os.getcwd()
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# 请确保你的图片路径正确
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image_filename = 'data/test3/l2.jpeg'
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image_path = os.path.join(script_dir, image_filename)
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if not os.path.exists(image_path):
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# 如果找不到，尝试直接使用文件名（假设图片和脚本在同一目录）
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image_path = '13-l.bmp'
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if not os.path.exists(image_path):
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print(f"错误: 输入文件 '{image_path}' 不存在。")
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else:
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find_and_mask_weld_seam_v3(image_path)
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else:
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find_and_mask_weld_seam_v3(image_path) |