以下是基于Python和OpenCV编写的代码，能够检测图像中的正方形并返回它们的像素坐标。此实现结合了轮廓提取、近似多边形拟合以及几何属性计算的技术[^1]^。 ### 正方形检测与像素坐标的获取 #### 方法概述 - **图像预处理**：加载输入图像后，将其转换为灰度模式并通过高斯模糊降低噪声影响。 - **边缘检测**：采用Canny算法寻找显著的边界信息。 - **轮廓发现**：利用`findContours`方法捕获所有潜在目标物的外接轮廊。 - **筛选正方形**：对于每条轮廓，使用`approxPolyDP`评估其是否接近四边形；同时验证宽高比例是否接近于1来确认正方形条件。 - **计算中心点**：针对符合条件的正方形，借助Hu不变矩或其他统计量求解其中心位置，并记录对应的像素坐标。 ```python import cv2 import numpy as np def detect_squares(image_path): img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 应用高斯模糊减少噪音干扰 blurred = cv2.GaussianBlur(gray, (5, 5), 0) # 执行Canny边缘检测 edges = cv2.Canny(blurred, 50, 150) # 查找外部轮廓 contours, _ = cv2.findContours(edges.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:] squares_info = [] for contour in contours: perimeter = cv2.arcLength(contour, True) approx = cv2.approxPolyDP(contour, 0.04 * perimeter, True) if len(approx) == 4: # 判断是否为四边形 rect = cv2.minAreaRect(contour) box = cv2.boxPoints(rect) box = np.intp(box) width = abs(box[0][0] - box[1][0]) height = abs(box[0][1] - box[1][1]) aspect_ratio = float(width) / height if height != 0 else None if aspect_ratio is not None and 0.9 <= aspect_ratio <= 1.1: # 验证宽高比是否接近1 moments = cv2.moments(contour) try: cx = int(moments['m10'] / moments['m00']) cy = int(moments['m01'] / moments['m00']) squares_info.append((cx, cy)) cv2.drawContours(img, [box], 0, (0, 255, 0), 2) cv2.circle(img, (cx, cy), 7, (255, 255, 0), -1) cv2.putText(img, "Square", (cx - 20, cy - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) except ZeroDivisionError: pass return img, squares_info if __name__ == "__main__": result_img, square_centers = detect_squares('input_image.jpg') print(f"Detected {len(square_centers)} squares.") for i, center in enumerate(square_centers, start=1): print(f"Square #{i} Center Coordinates -> ({center[0]}, {center[1]})") cv2.imshow("Squares Detected", result_img) cv2.waitKey(0) cv2.destroyAllWindows() ``` --- ###