Class CannyOp

java.lang.Object
cloud.opencode.base.image.edge.CannyOp
public final class CannyOp extends Object
Canny Edge Detection Operator Canny 边缘检测算子

Implements the full Canny edge detection pipeline: Gaussian blur, Sobel gradient computation, non-maximum suppression, double thresholding, and hysteresis edge tracking. Produces a binary edge map (0 or 255).

实现完整的 Canny 边缘检测流程：高斯模糊、Sobel 梯度计算、 非极大值抑制、双阈值处理和滞后边缘跟踪。输出二值边缘图（0 或 255）。

Features | 主要功能:

  • Full Canny pipeline with configurable thresholds - 完整 Canny 流程支持可配置阈值
  • Gaussian smoothing via separable convolution (sigma=1.4) - 通过可分离卷积实现高斯平滑 (sigma=1.4)
  • Non-maximum suppression for thin edges - 非极大值抑制实现细化边缘
  • Hysteresis tracking for edge continuity - 滞后跟踪确保边缘连续性
  • Binary output: only 0 or 255 pixel values - 二值输出：仅 0 或 255 像素值

Usage Examples | 使用示例:

// Canny with custom thresholds
BufferedImage edges = CannyOp.apply(image, 50, 150);

// Canny with default thresholds (50, 150) and sigma=1.4
BufferedImage edges = CannyOp.apply(image);

Performance | 性能特性:

  • Time complexity: O(w * h) for each pipeline stage - 时间复杂度: 每个流水线阶段 O(w * h)
  • Space complexity: O(w * h) for intermediate buffers - 空间复杂度: 中间缓冲区 O(w * h)

Security | 安全性:

  • Thread-safe: Yes (stateless) - 线程安全: 是（无状态）
  • Null-safe: No - 空值安全: 否
Since:
JDK 25, opencode-base-image V2.0.0
Author:
Leon Soo www.LeonSoo.com
See Also:

  • Method Details

    • apply

      public static BufferedImage apply(BufferedImage image)
      Apply Canny edge detection with default thresholds (50, 150) and sigma=1.4. 使用默认阈值 (50, 150) 和 sigma=1.4 应用 Canny 边缘检测。
      Parameters:
      image - the source image | 源图像
      Returns:
      the binary edge image (0 or 255) | 二值边缘图像（0 或 255）
      Throws:
      NullPointerException - if image is null | 当图像为 null 时抛出

    • apply

      public static BufferedImage apply(BufferedImage image, double lowThreshold, double highThreshold)
      Apply Canny edge detection with specified thresholds and sigma=1.4. 使用指定阈值和 sigma=1.4 应用 Canny 边缘检测。
      Parameters:
      image - the source image | 源图像
      lowThreshold - the low threshold for hysteresis (must be >= 0 and invalid input: '<'= highThreshold) | 滞后低阈值
      highThreshold - the high threshold for hysteresis (must be >= lowThreshold) | 滞后高阈值
      Returns:
      the binary edge image (0 or 255) | 二值边缘图像（0 或 255）
      Throws:
      NullPointerException - if image is null | 当图像为 null 时抛出
      ImageOperationException - if lowThreshold > highThreshold or thresholds are negative | 当低阈值大于高阈值或阈值为负时抛出