""" 示例: 密度图可视化 本示例演示如何： 1. 获取拼接图像 2. 计算光流 3. 基于光流生成 density map 4. 使用 colormap 实时显示 """ import os import argparse import cv2 import numpy as np import orisys from getDensity import getFilteredDensity os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" def density_to_colormap(density): """Convert density map to a displayable heatmap.""" if density is None or density.size == 0: return np.zeros((1, 1, 3), dtype=np.uint8) dmin = float(np.min(density)) dmax = float(np.max(density)) if dmax > dmin: density_u8 = ((density - dmin) * (255.0 / (dmax - dmin))).astype(np.uint8) else: density_u8 = np.zeros_like(density, dtype=np.uint8) return cv2.applyColorMap(density_u8, cv2.COLORMAP_TURBO) 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") args = parser.parse_args() # 步骤1：创建传感器对象 (必要步骤) try: vid_src = int(args.video) except ValueError: vid_src = args.video sensor = orisys.Sensor(vid_src, config_name=args.config, cal_path=args.cal, verbose=args.verbose) print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器") while True: # 步骤2：获取图像并计算形变 img = sensor.get_img() if img is None: print("Failed to get image (end of stream or capture error). Exiting.") break sensor.compute_deformation(check_motion=False) sensor.compute_contact() (fps, flow, contour, centroid) = sensor.read_info( sensor.info.FPS, sensor.info.VRAW, sensor.info.CONTOUR, sensor.info.CENTROID, ) # 步骤3：根据光流计算 density map 并显示 density = getFilteredDensity(flow, use_cuda=sensor.cuda) density_view = density_to_colormap(density) cv2.imshow("Density Map", density_view) image_with_foe = orisys.util.draw_contact(img, contour, centroid) cv2.imshow("Contact Centroid", image_with_foe) print(f"fps: {fps:.2f}, density_shape: {density.shape}, flow_shape: {flow.shape}") # 步骤4：键盘控制 key = cv2.waitKey(1) & 0xFF if key == ord("q"): print("\n退出程序...") break if key == ord("r"): sensor.reset() print("传感器已重置") sensor.disconnect() if __name__ == "__main__": main() """ 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 os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" import cv2 import orisys import numpy as np import argparse 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") args = parser.parse_args() # 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 # 方式1: 从视频文件读取 try: input = int(args.video) is_camera = True sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, verbose=args.verbose) except: is_camera = False sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, verbose=args.verbose) 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) sensor.get_img() # 步骤 2.2: 计算形变场 (必要步骤) is_touched = sensor.compute_deformation(check_motion=True, threshold=6) # 步骤 2.3: 计算触碰点 if is_touched: sensor.compute_contact() #步骤 2.4: 读取信息 fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info( sensor.info.FPS, sensor.info.VRAW, sensor.info.VNORMAL, sensor.info.IMG, sensor.info.CONTOUR, sensor.info.CENTROID, sensor.info.DEPTH, sensor.info.FNORMAL, sensor.info.FSHEARX, sensor.info.FSHEARY ) #步骤 3: 可视化显示 # 步骤 3.1: 绘制形变场 arrows = orisys.util.draw_arrows( img, flow, threshold=2, grid_spacing=20, arrow_scale=1.0 ) cv2.imshow("Deformation", arrows) # 步骤 3.2: 在图像上触碰点和区域 image_with_foe = orisys.util.draw_contact(img, contour, centroid) cv2.imshow("Contact Centroid", image_with_foe) # 步骤 3.3: 绘制深度图 # 归一化散度值到0-255范围（使用绝对值，然后归一化） div_abs = depth_map if div_abs.max() > div_abs.min(): div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8) else: div_normalized = np.zeros_like(div_abs, dtype=np.uint8) # 应用颜色映射（使用JET：蓝色=低散度，红色=高散度） cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET)) #步骤 3.4: 打印资料 print(f"fps: {fps}, fnormal: {fnormal}, fshearx: {fshearx}, fsheary: {fsheary}, depth_shape: {depth_map.shape}, sensor.resolution_raw: {sensor.img_size_raw}") #步骤 3.5: 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()