利用 OpenCV 进行棋盘检测与透视变换

1. 引言

在计算机视觉领域，棋盘检测与透视变换是一个常见的任务，广泛应用于 摄像机标定、文档扫描、增强现实（AR） 等场景。本篇文章将详细介绍如何使用 OpenCV 进行 棋盘检测，并通过 透视变换 将棋盘区域转换为一个标准的矩形图像。

我们将基于一段 Python 代码 进行分析，代码的主要任务包括：

读取图像并进行预处理（灰度转换、自适应直方图均衡化、去噪）
检测边缘并提取棋盘区域
计算透视变换矩阵并进行变换
展示和保存结果

2. 代码解析

完整代码如下：

import cv2
import numpy as npdef detect_and_transform_chessboard(image_path):# 读取图像img = cv2.imread(image_path)if img is None:print("无法读取图像文件")return# 保存原始图像尺寸original_img = img.copy()# 图像预处理scale_percent = 50width = int(img.shape[1] * scale_percent / 100)height = int(img.shape[0] * scale_percent / 100)img = cv2.resize(img, (width, height))gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)# 自适应直方图均衡化clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))gray = clahe.apply(gray)# 使用双边滤波减少噪声gray = cv2.bilateralFilter(gray, 11, 17, 17)found = Falseedges = cv2.Canny(gray, 50, 150)contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)if len(contours) > 0:contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5]for contour in contours:epsilon = 0.02 * cv2.arcLength(contour, True)approx = cv2.approxPolyDP(contour, epsilon, True)if len(approx) == 4 and cv2.contourArea(approx) > 1000:cv2.drawContours(img, [approx], -1, (0, 0, 255), 2)corners_pts = approx.reshape(4, 2).astype(np.float32)corners_pts = order_points(corners_pts)found = Truebreakif found and corners_pts is not None:target_size = (400, 400)target_pts = np.array([[0, 0],[target_size[0], 0],[target_size[0], target_size[1]],[0, target_size[1]]], dtype=np.float32)matrix = cv2.getPerspectiveTransform(corners_pts, target_pts)warped = cv2.warpPerspective(img, matrix, target_size)cv2.namedWindow('yuantu', cv2.WINDOW_NORMAL)cv2.imshow('yuantu', img)cv2.namedWindow('zhentu', cv2.WINDOW_NORMAL)cv2.imshow('zhentu', warped)cv2.imwrite('detected_chessboard.png', img)cv2.imwrite('transformed_chessboard.png', warped)else:print("无法进行透视变换：未检测到有效的棋盘角点")cv2.waitKey(0)cv2.destroyAllWindows()def order_points(pts):rect = np.zeros((4, 2), dtype=np.float32)s = pts.sum(axis=1)rect[0] = pts[np.argmin(s)]  # 左上rect[2] = pts[np.argmax(s)]  # 右下diff = np.diff(pts, axis=1)rect[1] = pts[np.argmin(diff)]  # 右上rect[3] = pts[np.argmax(diff)]  # 左下return rectif __name__ == "__main__":image_path = "1.jpg"detect_and_transform_chessboard(image_path)