以下是基于Python和OpenCV编写的代码，能够检测图像中的矩形并返回它们的像素坐标。该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]^。 ### 矩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。 - **筛选矩形**：对于每条轮廓，使用`approxPolyDP`评估其是否接近四边形；同时验证面积大小以排除异常情况。 - **计算中心点**：针对符合条件的矩形，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 find_rectangles(image_path): img = cv2.imread(image_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Apply Gaussian blur to reduce noise before edge detection blurred = cv2.GaussianBlur(gray, (5, 5), 0) # Perform Canny Edge Detection edged = cv2.Canny(blurred, 50, 150) # Find all external contours on the binary image cnts, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2:] rects_info = [] for c in cnts: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.04 * peri, True) if len(approx) == 4: # Check if it's quadrilateral M = cv2.moments(c) try: cx = int(M["m10"] / M["m00"]) cy = int(M["m01"] / M["m00"]) # Store rectangle information including center coordinates and contour points rects_info.append((cx, cy)) # Draw detected rectangles with their centers marked cv2.drawContours(img, [c], -1, (0, 255, 0), 2) cv2.circle(img, (cx, cy), 7, (255, 255, 0), -1) cv2.putText(img, "center", (cx - 20, cy - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2) except ZeroDivisionError: continue return img, rects_info if __name__ == '__main__': output_image, rectangles_coordinates = find_rectangles('test_image.png') print(f"Detected {len(rectangles_coordinates)} rectangles.") for idx, coord in enumerate(rectangles_coordinates, start=1): print(f"Rectangle #{idx} Center Coordinates -> ({coord[0]}, {coord[1]})") cv2.imshow("Output Image With Detected Rectangles", output_image) cv2.waitKey(0) cv2.destroyAllWindows() ``` --- ###