add debug option

example1_basic_pipeline.py

@@ -14,14 +14,30 @@ 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/prototype_of3.json", help="Configuration file path")
+    parser.add_argument("--cal","-cal", type=str, default="./config/dd01a_0.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
-    sensor = orisys.Sensor("s1.mp4", config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_0.npy", isstitch=True, cuda=True, verbose=True)
+    try:
+        input = int(args.video)
+        is_camera = True
+        sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
+    except:
+        is_camera = False
+        sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
+
+    # sensor = orisys.Sensor(args.input, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
 
     # 方式2: 从摄像头读取（取消注释使用） 
     # sensor = orisys.Sensor(0, config_name="./config/prototype_of3.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)

pulse_process.py

@@ -1,308 +0,0 @@
-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()

src/orisys/HHD.py

@@ -51,7 +51,8 @@ class nHHD(object):
             backend = 'cpu'
         elif backend == 'opencl' and not OPENCL_AVAILABLE:
             backend = 'cpu'
-        
+
+        self.debug = kwargs.get('debug', False)
         self.poisson_backend = backend
         
         # Initialize CUDA streams for cupy backend
@@ -103,7 +104,7 @@ class nHHD(object):
         Dispatch Poisson solve using the selected backend.
         """
         if self.poisson_backend == 'opencl':
-            return self.psolver.solve_opencl(field, verbose, profile=True)
+            return self.psolver.solve_opencl(field, verbose, profile=self.debug)
 
         if self.poisson_backend == 'cupy':
             return self.psolver.solve_gpu(field, verbose)
@@ -136,7 +137,7 @@ class nHHD(object):
 
             # compute potentials
             if self.poisson_backend == 'opencl' and ('OPENCL_AVAILABLE' in globals() and OPENCL_AVAILABLE):
-                self.nD, self.nRu = self.psolver.solve_opencl_batch2(self.div, self.curlw, verbose > 0, profile=True)
+                self.nD, self.nRu = self.psolver.solve_opencl_batch2(self.div, self.curlw, verbose > 0, profile=self.debug)
             else:
                 self.nD = self._solve_poisson_backend(self.div, verbose > 0)
                 self.nRu = self._solve_poisson_backend(self.curlw, verbose > 0)
@@ -1655,7 +1656,7 @@ class PoissonSolver(object):
         return full_conv[slices]
 
 
-    def solve_opencl_batch2(self, f1, f2, verbose=False, profile=True):
+    def solve_opencl_batch2(self, f1, f2, verbose=False, profile=False):
         """
         Batched OpenCL Poisson solver for two 2D fields.
         Optimized: scatter H2D (~4x smaller transfer), fused multiply

src/orisys/sensor.py

@@ -59,7 +59,7 @@ class Sensor:
     # Initialization
     # =========================================================================
     
-    def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True, 
+    def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True, debug=False,
                  config_name="prototype", cal_path=None, override=False, box=None, mirror=True, poisson_backend='auto'):
         """
         Initialize Sensor with video source and configuration.
@@ -86,6 +86,7 @@ class Sensor:
         self.isstitch = isstitch
         self.cuda = cuda
         self.verbose = verbose
+        self.debug = debug
         self.config_name = config_name
         self.cal_path = cal_path
         self.mirror = mirror
@@ -130,7 +131,7 @@ class Sensor:
         
         # Initialize components
         self.stitcher = Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose)
-        self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, dis_preset=self._config_dis_preset, config_tracker=self.__config_tracker)
+        self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, config_tracker=self.__config_tracker)
         self.__contact3d = Contact3D(config_contact=self.__config_contact, verbose=self.verbose)
         self.__hhd = None
         self.poisson_backend = poisson_backend
@@ -252,7 +253,7 @@ class Sensor:
         
         # Initialize HHD solver
         if self.__hhd is None:
-            self.__hhd = nHHD(cuda=self.cuda, grid=flow.shape[:2], spacings=(18, 18), poisson_backend=self.poisson_backend)
+            self.__hhd = nHHD(cuda=self.cuda, grid=flow.shape[:2], spacings=(18, 18), poisson_backend=self.poisson_backend, debug=self.debug)
 
         # Force computation
         # When OpenCL backend is selected, use host force path to ensure

src/orisys/tracker.py

@@ -11,7 +11,7 @@ class Tracker:
                  lower_threshold=12.0,
                  upper_threshold=18.0,
                  loop_closure_interval=1,
-                 dis_preset='medium',
+                #  dis_preset='medium',
                  verbose=False):
         # Check if OpenCV is built with CUDA support
         # if cuda and not cv2.cuda.getCudaEnabledDeviceCount():