### 结合双目摄像头、YOLO模型的目标物体三维坐标计算与直径测量 以下是结合双目摄像头生成视差图，利用YOLO模型检测目标物体并计算其三维坐标和直径的完整Python代码示例： ```python import cv2 import numpy as np from yolov5 import YOLOv5 # 假设已安装yolov5库 # 初始化YOLOv5模型 model = YOLOv5('yolov5s.pt', device='cpu') # 使用预训练权重文件 # 加载双目摄像头参数 stereo_calib_file = 'stereo_calibration.npz' # 替换为实际校准文件路径 with np.load(stereo_calib_file) as data: Q = data['Q'] # 还原投影矩阵 def compute_disparity_map(img_left, img_right): """ 计算视差图 """ window_size = 3 min_disp = 0 num_disp = 16 * 8 stereo = cv2.StereoSGBM_create( minDisparity=min_disp, numDisparities=num_disp, blockSize=window_size, uniquenessRatio=10, speckleWindowSize=100, speckleRange=32, disp12MaxDiff=1 ) disparity = stereo.compute(img_left, img_right).astype(np.float32) / 16.0 return disparity def calculate_3d_coordinates(disparity, bbox_center_x, bbox_center_y): """ 根据视差和像素位置计算三维坐标 """ points_3d = cv2.reprojectImageTo3D(disparity, Q) z_coord = points_3d[bbox_center_y, bbox_center_x, 2] x_coord = points_3d[bbox_center_y, bbox_center_x, 0] y_coord = points_3d[bbox_center_y, bbox_center_x, 1] return (x_coord, y_coord, z_coord) def estimate_diameter(bbox_width, focal_length, real_object_width, distance_z): """ 根据bounding box宽度估计物体直径 """ diameter = (real_object_width * distance_z) / focal_length return diameter def main(): cap_left = cv2.VideoCapture(0) # 左摄像头索引 cap_right = cv2.VideoCapture(1) # 右摄像头索引 while True: ret_left, frame_left = cap_left.read() ret_right, frame_right = cap_right.read() if not ret_left or not ret_right: break gray_left = cv2.cvtColor(frame_left, cv2.COLOR_BGR2GRAY) gray_right = cv2.cvtColor(frame_right, cv2.COLOR_BGR2GRAY) # 生成视差图 disparity = compute_disparity_map(gray_left, gray_right) # 使用YOLOv5进行目标检测 results = model.predict(frame_left) detections = results.xyxy[0] for detection in detections: x_min, y_min, x_max, y_max, conf, cls_id = detection.numpy() center_x = int((x_min + x_max) / 2) center_y = int((y_min + y_max) / 2) # 获取三维坐标 coords_3d = calculate_3d_coordinates(disparity, center_x, center_y) # 预定义焦距和真实物体宽度（单位：毫米） focal_length = 700 # 替换为实际焦距 real_object_width = 100 # 替换为目标物的实际宽度 estimated_diameter = estimate_diameter(x_max - x_min, focal_length, real_object_width, abs(coords_3d[2])) print(f"Object Detected: Class {cls_id}, Confidence {conf:.2f}") print(f"3D Coordinates: X={coords_3d[0]:.2f}mm, Y={coords_3d[1]:.2f}mm, Z={coords_3d[2]:.2f}mm") print(f"Estimated Diameter: {estimated_diameter:.2f}mm") # 绘制边界框和标签 label = f"{int(cls_id)} ({abs(coords_3d[2]):.0f}mm)" cv2.rectangle(frame_left, (int(x_min), int(y_min)), (int(x_max), int(y_max)), (0, 255, 0), 2) cv2.putText(frame_left, label, (int(x_min), int(y_min)-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2) cv2.imshow("Left Camera", frame_left) key = cv2.waitKey(1) if key == ord('q'): break cap_left.release() cap_right.release() cv2.destroyAllWindows() if __name__ == "__main__": main() ``` #### 关键说明 - **视差图生成**：通过`cv2.StereoSGBM_create()`构建半全局匹配算法来生成视差图[^3]。 - **三维坐标计算**：调用`cv2.reprojectImageTo3D()`函数将二维像素映射至三维空间。 - **YOLOv5目标检测**：加载预训练模型并对左摄像头图像执行推理操作[^4]。 - **直径估算**：基于几何光学原理，假设物体的真实尺寸已知，则可通过三角比例法推导出目标直径。