commit f91b09da9de51c5ffb55817233e14b13781a1f16 Author: koko <1429659362@qq.com> Date: Wed Oct 1 10:43:55 2025 +0800 origin diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..35410ca --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml +# 基于编辑器的 HTTP 客户端请求 +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/innovate_project.iml b/.idea/innovate_project.iml new file mode 100644 index 0000000..909438d --- /dev/null +++ b/.idea/innovate_project.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..bebfaed --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,23 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml 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print(f"错误: 无法加载图像 '{image_path}'。") + return + visualization_image = image.copy() + height, width, _ = image.shape + image_center_x = width / 2 + + # --- 步骤 2: 图像预处理 (超强版) --- + gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # 2.1 高斯模糊以去除噪声 + blurred = cv2.GaussianBlur(gray, (5, 5), 0) + + # 2.2 自适应阈值二值化 (关键步骤!) + # 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 + # blocksize: 邻域大小，必须是奇数 + # C: 从均值或加权均值中减去的常数，用于微调 + binary_image = cv2.adaptiveThreshold( + blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, + cv2.THRESH_BINARY_INV, 21, 5 + ) + # 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 + + # 2.3 形态学操作来清理和连接线条 + # 创建一个垂直的结构元素，因为我们的目标是垂直线 + kernel = np.ones((15, 1), np.uint8) # 强调垂直方向 + # 使用“闭运算”来填充断开的线段中的小洞 + closed = cv2.morphologyEx(binary_image, cv2.MORPH_CLOSE, kernel) + + # --- 步骤 3: 霍夫直线变换 (直接在处理后的二值图上进行) --- + # Canny不再是必需的，因为二值化已经完成了边缘提取 + lines = cv2.HoughLinesP( + closed, # 在闭运算结果上检测 + rho=1, + theta=np.pi / 180, + threshold=100, # 需要较高的阈值，因为线现在更连续了 + minLineLength=int(height * 0.5), # 线至少要有一半图像高度那么长 + maxLineGap=50 # 允许较大的间隙 + ) + + # --- 步骤 4: 筛选最佳直线 (逻辑与之前类似) --- + best_line = None + max_length = 0 + + if lines is not None: + for line in lines: + x1, y1, x2, y2 = line[0] + # 角度过滤，寻找近乎垂直的线 + angle = abs(math.degrees(math.atan2(y2 - y1, x2 - x1))) + if abs(angle - 90) < 5: # 检查角度是否在90度左右（垂直） + length = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + if length > max_length: + max_length = length + best_line = line[0] + + # --- 步骤 5: 处理和显示结果 --- + if best_line is not None: + bx1, by1, bx2, by2 = best_line + print(f"成功找到直线！起点:({bx1}, {by1}), 终点:({bx2}, {by2})") + + cv2.line(visualization_image, (bx1, by1), (bx2, by2), (0, 0, 255), 3) + + # 显示中间处理结果以供调试 + cv2.imshow('1. Original Image', image) + cv2.imshow('2. Adaptive Threshold', binary_image) + cv2.imshow('3. Morphological Closing', closed) # 检查闭运算效果 + cv2.imshow('4. Final Detection', visualization_image) + + else: + print("未能在图像中找到符合条件的直线。") + cv2.imshow('1. Original Image', image) + cv2.imshow('2. Adaptive Threshold', binary_image) + cv2.imshow('3. 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