""" 示例 3：实时曲线与幅值图显示 本示例演示如何在读取传感器结果的同时： 1. 显示形变矢量场 2. 显示流场幅值热力图 3. 使用 PyQtGraph 实时绘制法向力与切向力曲线 4. 按键控制录像开始与结束 SDK version: 0.3.1 """ import os import sys os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" import cv2 import orisys import numpy as np import time import argparse from datetime import datetime try: import pyqtgraph as pg except: print("PyQtGraph is not installed. Please install it using 'pip install pyqt5 pyqtgraph'.") exit() def create_plot(): p1 = win.addPlot(title="法向力") p1.setLabel('bottom', '时间', units='s') win.nextRow() p2 = win.addPlot(title="切向力 X") p2.setLabel('bottom', '时间', units='s') win.nextRow() p3 = win.addPlot(title="切向力 Y") p3.setLabel('bottom', '时间', units='s') plot_af = p1.plot(pen='k') plot_tfx = p2.plot(pen='k') plot_tfy = p3.plot(pen='k') return p1, p2, p3, plot_af, plot_tfx, plot_tfy def update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3): plot_af.setData(data[:,:,0]) p1.setYRange(min(data[:,1,0]),max(data[:,1,0])) plot_tfx.setData(data[:,:,1]) p2.setYRange(min(data[:,1,1]),max(data[:,1,1])) plot_tfy.setData(data[:,:,2]) p3.setYRange(min(data[:,1,2]),max(data[:,1,2])) # 深度图 def draw_magnitude_map(vfield, threshold=3, colormap=cv2.COLORMAP_JET) -> np.ndarray: """ 绘制流场幅值伪彩色图。 Args: vfield: 需要可视化的二维矢量场 threshold: 最小显示阈值；低于阈值的区域显示为低值颜色 colormap: OpenCV 颜色映射类型（默认 COLORMAP_JET） Returns: np.ndarray: 400x400 的 BGR 伪彩图像 """ flow_field = vfield # 计算每个像素点的幅值 magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2) # 先降采样再插值回原尺寸，使显示更平滑 downsample_factor = 10 h, w = magnitude.shape magnitude_downsampled = magnitude[::downsample_factor, ::downsample_factor] magnitude_smoothed = cv2.resize(magnitude_downsampled, (w, h), interpolation=cv2.INTER_CUBIC) # 应用阈值，将低于阈值的区域置零 magnitude_thresholded = magnitude_smoothed.copy() magnitude_thresholded[magnitude_smoothed < threshold] = 0 # 归一化到 0-255 mag_min, mag_max = magnitude_thresholded.min(), magnitude_thresholded.max() if mag_max > mag_min: magnitude_norm = ((magnitude_thresholded - mag_min) / (mag_max - mag_min) * 255).astype(np.uint8) else: magnitude_norm = np.zeros_like(magnitude_thresholded, dtype=np.uint8) # 应用伪彩色映射 magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap) # 缩放到固定输出尺寸 out_img = cv2.resize(magnitude_colored, (400, 400), interpolation=cv2.INTER_CUBIC) # 添加边距，使图像在窗口中居中显示 # margin = 30 # out_img = cv2.copyMakeBorder(out_img, margin, margin, margin, margin, # cv2.BORDER_CONSTANT, value=(22, 16, 11)) return out_img # 实时图表 npoints = 100 # 曲线缓冲区长度 pg.setConfigOption('background', 'w') pg.setConfigOption('foreground', 'k') app = pg.mkQApp("Orisys Plot Example") win = pg.GraphicsLayoutWidget(show=True, title="Orisys 实时力曲线") win.setWindowTitle('Orisys 实时力曲线') win.resize(640, 400) p1, p2, p3, plot_af, plot_tfx, plot_tfy = create_plot() data = np.zeros([npoints,2,3]) start_time = None # Will be set when sensor starts for i in range(3): data[:,0,i] = np.linspace(-npoints,0,npoints) def main(): global start_time # 将所有输出重定向到日志文件 script_dir = os.path.dirname(os.path.abspath(__file__)) log_dir = os.path.join(script_dir, "logs") os.makedirs(log_dir, exist_ok=True) log_path = os.path.join(log_dir, f"plot_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log") sys.stdout = open(log_path, 'w', encoding='utf-8') sys.stderr = sys.stdout parser = argparse.ArgumentParser() parser.add_argument("--video","-v", type=str, default="0", help="视频源：摄像头编号或视频文件路径") parser.add_argument("--config","-c", type=str, default="./config/ddjx01.json", help="配置文件名称或路径") parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.npy", help="标定文件路径") parser.add_argument("--verbose","-verbose", type=bool, default=True, help="是否输出详细日志") args = parser.parse_args() # 步骤 1：创建传感器对象（必需） # config_name 可以是内置配置名称，也可以是自定义配置文件路径 # cal_path 为标定文件路径；每台设备建议使用独立的标定文件 # 输入既可以是摄像头编号，也可以是视频文件路径 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) # 下面保留了历史示例写法，便于参考 # sensor1 = orisys.Sensor("./s1.mp4", config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True) start_time = time.time() is_save = False frame_count = 0 writer = None save_path = None print("\n按键说明：'q' 退出程序，'s' 开始录像，'e' 结束录像。") while True: sensor.get_img() # 获取并拼接复眼图像 if is_save and writer is not None and sensor.frame is not None: frame_count += 1 save_img = sensor.frame # 若为灰度图，则先转换为 BGR 再写入视频 if len(save_img.shape) == 2 or (len(save_img.shape) == 3 and save_img.shape[2] == 1): if len(save_img.shape) == 3: save_img = save_img.squeeze(2) save_img = cv2.cvtColor(save_img, cv2.COLOR_GRAY2BGR) writer.write(save_img) sensor.compute_deformation(check_motion=True, threshold=0) # 计算形变与分解结果 # 读取信息： # 向量场：VRAW、VNORMAL、VSHEAR -> 原始 / 法向 / 切向 # 标量：FNORMAL、FSHEARX、FSHEARY -> 法向力 / X 向切向力 / Y 向切向力 fps, fn, fx, fy, flow, vnormal, vshear = sensor.read_info(sensor.info.FPS, sensor.info.FNORMAL, sensor.info.FSHEARX, sensor.info.FSHEARY, sensor.info.VRAW, sensor.info.VNORMAL,sensor.info.VSHEAR) print(f"FPS={fps:.2f}, 法向力={fn:.4f}, 切向力X={fx:.4f}, 切向力Y={fy:.4f}") # 显示形变矢量场 # arrows = orisys.util.draw_arrows(sensor.img, flow, threshold=5, grid_spacing=10, arrow_scale=1.0) _bg_bgr = (22, 16, 11) _img = sensor.img _h, _w = _img.shape[:2] black_img = np.empty((_h, _w, 3), dtype=_img.dtype) black_img[:, :, 0] = _bg_bgr[0] black_img[:, :, 1] = _bg_bgr[1] black_img[:, :, 2] = _bg_bgr[2] arrows = orisys.util.draw_arrows( black_img, flow, threshold=2, grid_spacing=20, arrow_scale=0.5, below_threshold_color=(160, 160, 160), # target_size=int(side), ) arrows = cv2.resize(arrows, (400, 400), interpolation=cv2.INTER_CUBIC) arrows = cv2.rotate(arrows, cv2.ROTATE_90_COUNTERCLOCKWISE) cv2.imshow("形变矢量场".encode("gbk"), arrows) # 显示流场幅值图 mag = draw_magnitude_map(flow) mag = cv2.rotate(mag, cv2.ROTATE_90_COUNTERCLOCKWISE) cv2.imshow("流场幅值图".encode("gbk"), mag) # 更新实时曲线 current_time = time.time() - start_time for i in range(3): data[:-1, 0, i] = data[1:, 0, i] # 时间轴左移 data[:-1, 1, i] = data[1:, 1, i] # 力数据左移 data[-1, 0, :] = current_time # 写入当前时间 data[-1, 1, 0] = fn data[-1, 1, 1] = fx data[-1, 1, 2] = fy update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # 更新曲线 key = cv2.waitKey(1) if key & 0xFF == ord("q"): print("\n正在退出示例程序...") break if key == ord("s"): save_path = f"./data/pulse_test.mp4" # 确保输出目录存在 os.makedirs(os.path.dirname(save_path), exist_ok=True) # 从当前帧获取视频尺寸 save_img = sensor.frame if save_img is not None: # 如为灰度图，先转换为 BGR 以获取视频尺寸 temp_img = save_img.copy() if len(temp_img.shape) == 2 or (len(temp_img.shape) == 3 and temp_img.shape[2] == 1): if len(temp_img.shape) == 3: temp_img = temp_img.squeeze(2) temp_img = cv2.cvtColor(temp_img, cv2.COLOR_GRAY2BGR) frame_height, frame_width = temp_img.shape[:2] writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height)) if writer.isOpened(): is_save = True print(f"开始录像：{save_path}") else: print(f"无法创建录像文件：{save_path}") writer = None else: print("当前没有可用帧，无法确定录像尺寸。") if key == ord("e"): is_save = False if writer is not None: writer.release() writer = None if save_path is not None: print(f"录像结束：{save_path}，总帧数：{frame_count}") else: print(f"录像结束，总帧数：{frame_count}") frame_count = 0 sensor.disconnect() # 断开传感器连接并释放资源 if __name__ == '__main__': main()