import os os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" import cv2 import orisys import numpy as np import time import pyqtgraph as pg from PyQt5.QtGui import QImage def create_plot(): p1 = win.addPlot(title="法相") p1.setLabel('bottom', '时间', units='s') win.nextRow() p2 = win.addPlot(title="切向 X") p2.setLabel('bottom', '时间', units='s') win.nextRow() p3 = win.addPlot(title="切向 Y") p3.setLabel('bottom', '时间', units='s') plot_af = p1.plot(pen='w') plot_tfx = p2.plot(pen='w') plot_tfy = p3.plot(pen='w') return p1, p2, p3, plot_af, plot_tfx, plot_tfy def update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3): # global 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])) def plot_to_frame(plot_widget, width=None, height=None): """ Convert pyqtgraph GraphicsLayoutWidget to numpy array (BGR format for OpenCV). Args: plot_widget: pyqtgraph GraphicsLayoutWidget to capture width: Optional width for output frame (default: widget width) height: Optional height for output frame (default: widget height) Returns: np.ndarray: BGR image array (height, width, 3) ready for cv2.VideoWriter """ # Get the widget size if width is None: width = plot_widget.width() if height is None: height = plot_widget.height() # Grab the widget as a QPixmap pixmap = plot_widget.grab() # Resize if needed if pixmap.width() != width or pixmap.height() != height: pixmap = pixmap.scaled(width, height) # Convert QPixmap to QImage qimage = pixmap.toImage() # Convert QImage to RGB format qimage = qimage.convertToFormat(QImage.Format_RGB888) # Get image dimensions img_width = qimage.width() img_height = qimage.height() # Convert QImage to numpy array # Use constBits() to get raw bytes, then reshape buffer = qimage.constBits() if buffer is not None: arr = np.frombuffer(buffer, dtype=np.uint8).reshape((img_height, img_width, 3)) else: # Fallback: create empty array arr = np.zeros((img_height, img_width, 3), dtype=np.uint8) # Convert RGB to BGR for OpenCV bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR) return bgr # 深度图 def draw_magnitude_map(vfield, threshold=3.0, colormap=cv2.COLORMAP_JET) -> np.ndarray: """ Draw color map of pointwise magnitude of velocity field. Args: vfield: Velocity field to visualize (2D vector field) threshold: Minimum magnitude to display (below threshold shows as zero/blue) colormap: OpenCV colormap to use (default: COLORMAP_JET) Returns: np.ndarray: 400x400 BGR image with colored magnitude map """ flow_field = vfield # Compute magnitude at each point magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2) # Smooth by downsampling and interpolating back 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) # Apply threshold - set below-threshold values to zero magnitude_thresholded = magnitude_smoothed.copy() magnitude_thresholded[magnitude_smoothed < threshold] = 0 # Normalize to 0-255 range 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) # Apply colormap magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap) # Resize to output size out_img = cv2.resize(magnitude_colored, (400, 400), interpolation=cv2.INTER_CUBIC) return out_img # 实时图表 npoints = 100 # 图表数据长度 pg.setConfigOption('background', 'k') pg.setConfigOption('foreground', 'w') app = pg.mkQApp("Plotting Example") win = pg.GraphicsLayoutWidget(show=True, title="Basic plotting examples") win.setWindowTitle('pyqtgraph example: Plotting') 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 # 打开视频 # sensor1 = Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True) # 方式1: 从视频文件读取 sensor1 = orisys.Sensor("./data/pulse_test_1.mp4", config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_98.npy", isstitch=True, cuda=False, verbose=True) start_time = time.time() is_save = True frame_count = 0 writer = None save_path = None # Video writer for plot is_save_plot = True plot_writer = None plot_frame_count = 0 plot_save_path = None if True: save_path = f"./data/pulse_test_arrows.mp4" # Ensure directory exists os.makedirs(os.path.dirname(save_path), exist_ok=True) # Get frame dimensions from the actual frame save_img = sensor1.frame # if save_img is not None: if True: # Convert grayscale (1 channel) to BGR (3 channels) if needed for dimension check # 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] frame_height = 400 frame_width = 400 writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height)) if writer.isOpened(): is_save = True print(f"Start writing to {save_path}") else: print(f"Failed to open video writer for {save_path}") writer = None # Setup video writer for plot plot_save_path = f"./data/pulse_test_plot.mp4" os.makedirs(os.path.dirname(plot_save_path), exist_ok=True) # Get plot widget dimensions plot_width = win.width() plot_height = win.height() # Use fixed dimensions if widget not yet rendered if plot_width <= 0 or plot_height <= 0: plot_width = 800 plot_height = 600 plot_writer = cv2.VideoWriter(plot_save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (plot_width, plot_height)) if plot_writer.isOpened(): is_save_plot = True print(f"Start writing plot to {plot_save_path}") else: print(f"Failed to open plot video writer for {plot_save_path}") plot_writer = None while True: sensor1.get_img() # 提取并拼接复眼图像 sensor1.compute_deformation() # 计算形变和分解 sensor1.compute_contact() # 提取信息： # 向量场: VRAW, VNORMAL, VSHEAR: 原始，法向，切向 # 帧率，时间戳: FPS, TIMESTAMP # 变形量总和: FNORMAL , FSHEARX, FSHEARY : 法向，切向（X)，切向（Y) fps, fn, fx, fy, flow, contour, centroid, depth_map = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW,sensor1.info.CONTOUR, sensor1.info.CENTROID, sensor1.info.DEPTH) print(fps,fn,fx,fy) # 显示触碰点和区域 # 步骤 3.2: 在图像上触碰点和区域 white_img = np.full_like(sensor1.img, 0) image_with_foe = orisys.util.draw_contact(white_img, contour, centroid,region_color=(255, 255, 255)) cv2.imshow("Contact Centroid", image_with_foe) # 显示向量场 arrows = orisys.util.draw_arrows(white_img, flow, threshold=5, grid_spacing=10, arrow_scale=1.0) cv2.imshow("Deformation", arrows) # 显示深度图 mag = draw_magnitude_map(flow) cv2.imshow("Magnitude", mag) if is_save and writer is not None and sensor1.frame is not None: frame_count += 1 save_img = arrows # Convert grayscale (1 channel) to BGR (3 channels) if needed 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) # 显示图表 - 更新时间轴 current_time = time.time() - start_time for i in range(3): data[:-1, 0, i] = data[1:, 0, i] # shift time data left data[:-1, 1, i] = data[1:, 1, i] # shift force data left data[-1, 0, :] = current_time # set current time for all plots 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) # Update plot # Capture and save plot frame if is_save_plot and plot_writer is not None: plot_frame_count += 1 # Process Qt events to ensure plot is updated app.processEvents() # Capture plot as frame plot_frame = plot_to_frame(win) plot_writer.write(plot_frame) key = cv2.waitKey(1) if key & 0xFF == ord("q"): print("\n 停止程序") break if key == ord("e"): is_save = False if writer is not None: writer.release() writer = None if save_path is not None: print(f"Stop writing to {save_path}, total frames: {frame_count}") else: print(f"Stop writing, total frames: {frame_count}") frame_count = 0 # Stop plot recording is_save_plot = False if plot_writer is not None: plot_writer.release() plot_writer = None if plot_save_path is not None: print(f"Stop writing plot to {plot_save_path}, total frames: {plot_frame_count}") else: print(f"Stop writing plot, total frames: {plot_frame_count}") plot_frame_count = 0 sensor1.disconnect() # 断开传感器连接 # Clean up video writers if writer is not None: writer.release() if plot_writer is not None: plot_writer.release() print(f"Plot video saved: {plot_save_path}, total frames: {plot_frame_count}") if __name__ == '__main__': main()