add debug option
This commit is contained in:
bluepulsar
@@ -14,14 +14,30 @@ os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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import cv2
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import orisys
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import numpy as np
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import argparse
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--video","-v", type=str, default="0", help="Camera ID / Video file path")
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parser.add_argument("--config","-c", type=str, default="./config/prototype_of3.json", help="Configuration file path")
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parser.add_argument("--cal","-cal", type=str, default="./config/dd01a_0.npy", help="Calibration file path")
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parser.add_argument("--verbose","-verbose", type=bool, default=True, help="Print detailed information")
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args = parser.parse_args()
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# 步骤1：创建传感器对象 (必要步骤)，
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# 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）
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# 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
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# 方式1: 从视频文件读取
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sensor = orisys.Sensor("s1.mp4", config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_0.npy", isstitch=True, cuda=True, verbose=True)
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try:
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input = int(args.video)
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is_camera = True
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sensor = orisys.Sensor(input, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
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except:
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is_camera = False
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sensor = orisys.Sensor(args.video, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
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# sensor = orisys.Sensor(args.input, config_name=args.config, cal_path= args.cal, cuda=True, verbose=args.verbose)
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# 方式2: 从摄像头读取（取消注释使用）
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# sensor = orisys.Sensor(0, config_name="./config/prototype_of3.json", cal_path= "./config/cal.npy", isstitch=True, cuda=True, verbose=True)
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@@ -1,308 +0,0 @@
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import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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import cv2
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import orisys
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import numpy as np
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import time
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import pyqtgraph as pg
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from PyQt5.QtGui import QImage
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def create_plot():
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p1 = win.addPlot(title="法相")
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p1.setLabel('bottom', '时间', units='s')
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win.nextRow()
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p2 = win.addPlot(title="切向 X")
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p2.setLabel('bottom', '时间', units='s')
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win.nextRow()
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p3 = win.addPlot(title="切向 Y")
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p3.setLabel('bottom', '时间', units='s')
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plot_af = p1.plot(pen='w')
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plot_tfx = p2.plot(pen='w')
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plot_tfy = p3.plot(pen='w')
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return p1, p2, p3, plot_af, plot_tfx, plot_tfy
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def update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3):
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# global plot_af, plot_tfx, plot_tfy, data, p1, p2, p3
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plot_af.setData(data[:,:,0])
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p1.setYRange(min(data[:,1,0]),max(data[:,1,0]))
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plot_tfx.setData(data[:,:,1])
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p2.setYRange(min(data[:,1,1]),max(data[:,1,1]))
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plot_tfy.setData(data[:,:,2])
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p3.setYRange(min(data[:,1,2]),max(data[:,1,2]))
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def plot_to_frame(plot_widget, width=None, height=None):
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"""
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Convert pyqtgraph GraphicsLayoutWidget to numpy array (BGR format for OpenCV).
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Args:
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plot_widget: pyqtgraph GraphicsLayoutWidget to capture
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width: Optional width for output frame (default: widget width)
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height: Optional height for output frame (default: widget height)
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Returns:
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np.ndarray: BGR image array (height, width, 3) ready for cv2.VideoWriter
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"""
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# Get the widget size
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if width is None:
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width = plot_widget.width()
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if height is None:
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height = plot_widget.height()
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# Grab the widget as a QPixmap
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pixmap = plot_widget.grab()
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# Resize if needed
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if pixmap.width() != width or pixmap.height() != height:
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pixmap = pixmap.scaled(width, height)
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# Convert QPixmap to QImage
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qimage = pixmap.toImage()
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# Convert QImage to RGB format
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qimage = qimage.convertToFormat(QImage.Format_RGB888)
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# Get image dimensions
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img_width = qimage.width()
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img_height = qimage.height()
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# Convert QImage to numpy array
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# Use constBits() to get raw bytes, then reshape
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buffer = qimage.constBits()
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if buffer is not None:
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arr = np.frombuffer(buffer, dtype=np.uint8).reshape((img_height, img_width, 3))
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else:
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# Fallback: create empty array
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arr = np.zeros((img_height, img_width, 3), dtype=np.uint8)
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# Convert RGB to BGR for OpenCV
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bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
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return bgr
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# 深度图
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def draw_magnitude_map(vfield, threshold=3.0, colormap=cv2.COLORMAP_JET) -> np.ndarray:
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"""
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Draw color map of pointwise magnitude of velocity field.
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Args:
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vfield: Velocity field to visualize (2D vector field)
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threshold: Minimum magnitude to display (below threshold shows as zero/blue)
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colormap: OpenCV colormap to use (default: COLORMAP_JET)
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Returns:
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np.ndarray: 400x400 BGR image with colored magnitude map
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"""
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flow_field = vfield
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# Compute magnitude at each point
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magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2)
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# Smooth by downsampling and interpolating back
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downsample_factor = 10
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h, w = magnitude.shape
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magnitude_downsampled = magnitude[::downsample_factor, ::downsample_factor]
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magnitude_smoothed = cv2.resize(magnitude_downsampled, (w, h), interpolation=cv2.INTER_CUBIC)
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# Apply threshold - set below-threshold values to zero
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magnitude_thresholded = magnitude_smoothed.copy()
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magnitude_thresholded[magnitude_smoothed < threshold] = 0
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# Normalize to 0-255 range
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mag_min, mag_max = magnitude_thresholded.min(), magnitude_thresholded.max()
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if mag_max > mag_min:
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magnitude_norm = ((magnitude_thresholded - mag_min) / (mag_max - mag_min) * 255).astype(np.uint8)
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else:
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magnitude_norm = np.zeros_like(magnitude_thresholded, dtype=np.uint8)
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# Apply colormap
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magnitude_colored = cv2.applyColorMap(magnitude_norm, colormap)
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# Resize to output size
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out_img = cv2.resize(magnitude_colored, (400, 400), interpolation=cv2.INTER_CUBIC)
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return out_img
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# 实时图表
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npoints = 100 # 图表数据长度
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pg.setConfigOption('background', 'k')
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pg.setConfigOption('foreground', 'w')
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app = pg.mkQApp("Plotting Example")
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win = pg.GraphicsLayoutWidget(show=True, title="Basic plotting examples")
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win.setWindowTitle('pyqtgraph example: Plotting')
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p1, p2, p3, plot_af, plot_tfx, plot_tfy = create_plot()
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data = np.zeros([npoints,2,3])
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start_time = None # Will be set when sensor starts
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for i in range(3):
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data[:,0,i] = np.linspace(-npoints,0,npoints)
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def main():
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global start_time
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# 打开视频
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# sensor1 = Sensor("./s1.mp4",isstitch=True,cuda = True,verbose = True)
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# 方式1: 从视频文件读取
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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)
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start_time = time.time()
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is_save = True
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frame_count = 0
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writer = None
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save_path = None
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# Video writer for plot
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is_save_plot = True
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plot_writer = None
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plot_frame_count = 0
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plot_save_path = None
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if True:
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save_path = f"./data/pulse_test_arrows.mp4"
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# Ensure directory exists
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os.makedirs(os.path.dirname(save_path), exist_ok=True)
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# Get frame dimensions from the actual frame
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save_img = sensor1.frame
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# if save_img is not None:
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if True:
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# Convert grayscale (1 channel) to BGR (3 channels) if needed for dimension check
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# temp_img = save_img.copy()
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# if len(temp_img.shape) == 2 or (len(temp_img.shape) == 3 and temp_img.shape[2] == 1):
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# if len(temp_img.shape) == 3:
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# temp_img = temp_img.squeeze(2)
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# temp_img = cv2.cvtColor(temp_img, cv2.COLOR_GRAY2BGR)
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# frame_height, frame_width = temp_img.shape[:2]
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frame_height = 400
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frame_width = 400
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writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (frame_width, frame_height))
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if writer.isOpened():
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is_save = True
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print(f"Start writing to {save_path}")
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else:
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print(f"Failed to open video writer for {save_path}")
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writer = None
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# Setup video writer for plot
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plot_save_path = f"./data/pulse_test_plot.mp4"
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os.makedirs(os.path.dirname(plot_save_path), exist_ok=True)
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# Get plot widget dimensions
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plot_width = win.width()
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plot_height = win.height()
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# Use fixed dimensions if widget not yet rendered
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if plot_width <= 0 or plot_height <= 0:
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plot_width = 800
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plot_height = 600
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plot_writer = cv2.VideoWriter(plot_save_path, cv2.VideoWriter_fourcc(*'mp4v'), 30, (plot_width, plot_height))
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if plot_writer.isOpened():
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is_save_plot = True
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print(f"Start writing plot to {plot_save_path}")
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else:
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print(f"Failed to open plot video writer for {plot_save_path}")
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plot_writer = None
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while True:
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sensor1.get_img() # 提取并拼接复眼图像
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sensor1.compute_deformation() # 计算形变和分解
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sensor1.compute_contact()
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# 提取信息：
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# 向量场: VRAW, VNORMAL, VSHEAR: 原始，法向，切向
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# 帧率，时间戳: FPS, TIMESTAMP
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# 变形量总和: FNORMAL , FSHEARX, FSHEARY : 法向，切向（X)，切向（Y)
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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,
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sensor1.info.CENTROID,
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sensor1.info.DEPTH)
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print(fps,fn,fx,fy)
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# 显示触碰点和区域 # 步骤 3.2: 在图像上触碰点和区域
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white_img = np.full_like(sensor1.img, 0)
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image_with_foe = orisys.util.draw_contact(white_img, contour, centroid,region_color=(255, 255, 255))
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cv2.imshow("Contact Centroid", image_with_foe)
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# 显示向量场
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arrows = orisys.util.draw_arrows(white_img, flow, threshold=5, grid_spacing=10, arrow_scale=1.0)
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cv2.imshow("Deformation", arrows)
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# 显示深度图
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mag = draw_magnitude_map(flow)
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cv2.imshow("Magnitude", mag)
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if is_save and writer is not None and sensor1.frame is not None:
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frame_count += 1
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save_img = arrows
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# Convert grayscale (1 channel) to BGR (3 channels) if needed
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if len(save_img.shape) == 2 or (len(save_img.shape) == 3 and save_img.shape[2] == 1):
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if len(save_img.shape) == 3:
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save_img = save_img.squeeze(2)
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save_img = cv2.cvtColor(save_img, cv2.COLOR_GRAY2BGR)
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writer.write(save_img)
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# 显示图表 - 更新时间轴
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current_time = time.time() - start_time
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for i in range(3):
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data[:-1, 0, i] = data[1:, 0, i] # shift time data left
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data[:-1, 1, i] = data[1:, 1, i] # shift force data left
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data[-1, 0, :] = current_time # set current time for all plots
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data[-1, 1, 0] = fn
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data[-1, 1, 1] = fx
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data[-1, 1, 2] = fy
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update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # Update plot
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# Capture and save plot frame
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if is_save_plot and plot_writer is not None:
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plot_frame_count += 1
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# Process Qt events to ensure plot is updated
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app.processEvents()
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# Capture plot as frame
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plot_frame = plot_to_frame(win)
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plot_writer.write(plot_frame)
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key = cv2.waitKey(1)
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if key & 0xFF == ord("q"):
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print("\n 停止程序")
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break
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if key == ord("e"):
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is_save = False
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if writer is not None:
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writer.release()
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writer = None
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if save_path is not None:
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print(f"Stop writing to {save_path}, total frames: {frame_count}")
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else:
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print(f"Stop writing, total frames: {frame_count}")
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frame_count = 0
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# Stop plot recording
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is_save_plot = False
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if plot_writer is not None:
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plot_writer.release()
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plot_writer = None
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if plot_save_path is not None:
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print(f"Stop writing plot to {plot_save_path}, total frames: {plot_frame_count}")
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else:
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print(f"Stop writing plot, total frames: {plot_frame_count}")
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plot_frame_count = 0
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sensor1.disconnect() # 断开传感器连接
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# Clean up video writers
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if writer is not None:
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writer.release()
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if plot_writer is not None:
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plot_writer.release()
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print(f"Plot video saved: {plot_save_path}, total frames: {plot_frame_count}")
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if __name__ == '__main__':
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main()
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+4
-3
@@ -52,6 +52,7 @@ class nHHD(object):
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elif backend == 'opencl' and not OPENCL_AVAILABLE:
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backend = 'cpu'
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self.debug = kwargs.get('debug', False)
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self.poisson_backend = backend
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# Initialize CUDA streams for cupy backend
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@@ -103,7 +104,7 @@ class nHHD(object):
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Dispatch Poisson solve using the selected backend.
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"""
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if self.poisson_backend == 'opencl':
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return self.psolver.solve_opencl(field, verbose, profile=True)
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return self.psolver.solve_opencl(field, verbose, profile=self.debug)
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if self.poisson_backend == 'cupy':
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return self.psolver.solve_gpu(field, verbose)
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@@ -136,7 +137,7 @@ class nHHD(object):
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# compute potentials
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if self.poisson_backend == 'opencl' and ('OPENCL_AVAILABLE' in globals() and OPENCL_AVAILABLE):
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self.nD, self.nRu = self.psolver.solve_opencl_batch2(self.div, self.curlw, verbose > 0, profile=True)
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self.nD, self.nRu = self.psolver.solve_opencl_batch2(self.div, self.curlw, verbose > 0, profile=self.debug)
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else:
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self.nD = self._solve_poisson_backend(self.div, verbose > 0)
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self.nRu = self._solve_poisson_backend(self.curlw, verbose > 0)
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@@ -1655,7 +1656,7 @@ class PoissonSolver(object):
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return full_conv[slices]
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def solve_opencl_batch2(self, f1, f2, verbose=False, profile=True):
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def solve_opencl_batch2(self, f1, f2, verbose=False, profile=False):
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"""
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Batched OpenCL Poisson solver for two 2D fields.
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Optimized: scatter H2D (~4x smaller transfer), fused multiply
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@@ -59,7 +59,7 @@ class Sensor:
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# Initialization
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# =========================================================================
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def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True,
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def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True, debug=False,
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config_name="prototype", cal_path=None, override=False, box=None, mirror=True, poisson_backend='auto'):
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"""
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Initialize Sensor with video source and configuration.
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@@ -86,6 +86,7 @@ class Sensor:
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self.isstitch = isstitch
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self.cuda = cuda
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self.verbose = verbose
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self.debug = debug
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self.config_name = config_name
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self.cal_path = cal_path
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self.mirror = mirror
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@@ -130,7 +131,7 @@ class Sensor:
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# Initialize components
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self.stitcher = Stitcher(isstitch=isstitch, config_stitcher=self.__config_stitcher, verbose=self.verbose)
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self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, dis_preset=self._config_dis_preset, config_tracker=self.__config_tracker)
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self.__tracker = Tracker(adaptive=True, cuda=cuda, verbose=self.verbose, config_tracker=self.__config_tracker)
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self.__contact3d = Contact3D(config_contact=self.__config_contact, verbose=self.verbose)
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self.__hhd = None
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self.poisson_backend = poisson_backend
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@@ -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
|
||||
|
||||
@@ -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():
|
||||
|
||||
Reference in New Issue
Block a user