""" 示例 1: 基础用法 - 单传感器同步处理 本示例演示 orisys 的基本使用流程: 1. 初始化传感器(视频文件或摄像头) 2. 循环获取图像并计算形变和触碰点 3. 读取各类传感器数据 4. 可视化显示 """ import os import argparse import cv2 import orisys os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" def main(): parser = argparse.ArgumentParser() parser.add_argument("--video", "-v", type=str, default="0", help="Camera ID / Video file path") parser.add_argument("--config", "-c", type=str, default="./config/ddjx01.json", help="Configuration file path") parser.add_argument("--cal", "-cal", type=str, default="./config/ddjx01.npy", help="Calibration file path") parser.add_argument("--verbose", "-verbose", type=bool, default=True, help="Print detailed information") parser.add_argument( "--match-brightness", dest="match_brightness", action="store_true", help="Enable per-tile brightness matching", ) parser.set_defaults(match_brightness=False) args = parser.parse_args() try: vid_src = int(args.video) except ValueError: vid_src = args.video # 步骤1:创建传感器对象 (必要步骤), # config_name为配置文件名(内置配置文件名)或路径(自定义配置文件路径) # cal_path为校准文件路径, 每一个设备需要创建/使用一个校准文件 sensor = orisys.Sensor(vid_src, config_name=args.config, cal_path=args.cal, verbose=args.verbose) sensor.stitcher.configure_feature_stitch(match_brightness=args.match_brightness) print("\n按 'q' 退出程序,按 'r' 重置传感器追踪器") while True: # 步骤2:信息提取 # 步骤 2.1: 获取并拼接图像 (必要步骤) img = sensor.get_img() if img is None: print("Failed to get image (end of stream or capture error). Exiting.") break if img is not None: cv2.imshow("OpenCV Stitch (feature-based)", img) # 步骤 2.2: 计算形变场 (必要步骤) — sensor.compute_deformation(...) # 步骤 2.3: 计算触碰点 # 步骤 3: 键盘控制 key = cv2.waitKey(1) & 0xFF if key == ord("q"): print("\n退出程序...") break if key == ord("r"): # 重置光流追踪器(当追踪漂移时使用) sensor.reset() print("传感器已重置") # ========================================================================= # 4. 清理资源 # ========================================================================= sensor.disconnect() # 释放视频源和相关资源 if __name__ == "__main__": main()