update after ojzm
This commit is contained in:
TPP-G6
@@ -0,0 +1,192 @@
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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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def create_plot():
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p1 = win.addPlot(title="法相力")
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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="切向力 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="切向力 Y")
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p3.setLabel('bottom', '时间', units='s')
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plot_af = p1.plot(pen='k')
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plot_tfx = p2.plot(pen='k')
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plot_tfy = p3.plot(pen='k')
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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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# 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', 'w')
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pg.setConfigOption('foreground', 'k')
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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(8, config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True)
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start_time = time.time()
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is_save = False
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frame_count = 0
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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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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 = sensor1.frame
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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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sensor1.compute_deformation(check_motion=False) # 计算形变和分解
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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 = sensor1.read_info(sensor1.info.FPS, sensor1.info.FNORMAL, sensor1.info.FSHEARX, sensor1.info.FSHEARY, sensor1.info.VRAW)
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# print(fps,fn,fx,fy)
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# 显示向量场
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arrows = orisys.util.draw_arrows(sensor1.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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# 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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fn_mean = np.mean(data[:,1,0])
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fx_mean = np.mean(data[:,1,1])
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fy_mean = np.mean(data[:,1,2])
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print(f"fps: {fps}, fn_mean: {fn_mean:.2f}, fx_mean: {fx_mean:.2f}, fy_mean: {fy_mean:.2f}")
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update(plot_af, plot_tfx, plot_tfy, data, p1, p2, p3) # Update plot
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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("s"):
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save_path = f"./data/pulse_test.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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# 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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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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else:
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print("No frame available to determine video dimensions")
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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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sensor1.disconnect() # 断开传感器连接
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if __name__ == '__main__':
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main()
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+1
-1
@@ -12,7 +12,7 @@
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"stitcher": {
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"min_area_frac": 0.01,
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"max_area_frac": 0.04,
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"max_aspect_ratio": 100,
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"max_aspect_ratio": 50,
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"index": [1,2,3,6,7,8,11,12,13],
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"margin": 10,
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"nominal_ov_pos": 100,
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@@ -124,21 +124,21 @@ def main():
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# 1. 配置多个传感器
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# =========================================================================
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# 目标帧率：设为正数时，两路传感器会按此帧率限速，使 FPS 接近一致；设为 None 则不限速
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target_fps = 15
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target_fps = 30
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num_sensors = 2
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sensor_configs = [
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{
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'sensor_id': 0,
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'vid_src': './s1_720p.mp4',
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'config_name': './config/prototype_of3_720p.json',
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'cal_path': './config/dd01a_0_720p.npy'
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},
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'vid_src': 4,
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'config_name': './config/prototype_of3_480p.json',
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'cal_path': './config/dd01a_0_480p.npy'
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},
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{
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'sensor_id': 1,
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'vid_src': './s1_720p.mp4', # 可以改为不同的视频源或摄像头索引
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'config_name': './config/prototype_of3_720p.json',
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'cal_path': './config/dd01a_0_720p.npy'
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'vid_src': 6, # 可以改为不同的视频源或摄像头索引
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'config_name': './config/prototype_of3_480p.json',
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'cal_path': './config/dd01a_0_480p.npy'
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},
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]
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@@ -94,7 +94,7 @@ def main():
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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(0, config_name="./config/prototype_of3.json", cal_path= "./config/dd01a_0.npy", isstitch=True, cuda=False, verbose=True)
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sensor1 = orisys.Sensor(8, config_name="./config/ddjx01.json", cal_path= "./config/ddjx01.npy", cuda=False, verbose=True)
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start_time = time.time()
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is_save = False
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@@ -115,7 +115,7 @@ def main():
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writer.write(save_img)
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sensor1.compute_deformation() # 计算形变和分解
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sensor1.compute_deformation(check_motion=False) # 计算形变和分解
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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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@@ -115,8 +115,10 @@ def generate_color_pattern(h, w, d, r):
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def main():
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# Parameters for the pattern.
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# For example, using 100x100 pixels image with patches of 3x3 pixels.
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height = 2750
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width = 1950
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# height = 2750
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# width = 1950
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height = 1200
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width = 1000
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patch_size = 12
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# Set r to a value that gives sufficient difference.
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# Note: r is the threshold for squared difference per channel, and the condition is
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@@ -449,7 +449,7 @@ class Stitcher:
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n_boxes = 0
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bk_thresh = 0 # initial black threshold
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bk_thresh_step = 5
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bk_thresh_max = 100
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bk_thresh_max = 150
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# Also include a low global V threshold to capture colored but dark tiles
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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@@ -458,7 +458,12 @@ class Stitcher:
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while n_boxes != n_boxes_target and bk_thresh <= bk_thresh_max:
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_, v_mask = cv2.threshold(v, bk_thresh, 255, cv2.THRESH_BINARY) # tune 50 if needed
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mask = v_mask
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mask = v_mask.copy()
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# Exclude left and right boundary (20 pixels each) from detection
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edge = 20
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if w > edge * 2:
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mask[:, :edge] = 0
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mask[:, -edge:] = 0
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# Morphology: remove small specks, fill small holes
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k = max(1, morph_k)
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@@ -0,0 +1,33 @@
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import pyopencl as cl
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# Get all platforms
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platforms = cl.get_platforms()
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if not platforms:
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raise RuntimeError("No OpenCL platforms found")
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# Use the first platform (rusticl in your case)
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platform = platforms[0]
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print("Using platform:", platform.name)
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# Get devices for that platform
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devices = platform.get_devices()
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if not devices:
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raise RuntimeError("No devices found on platform")
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for platform in cl.get_platforms():
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for device in platform.get_devices():
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print("Device:", device.name)
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# if device.type & cl.device_type.GPU == 0:
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# print("Skipping non-GPU device:", device.name)
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# Use the first device (your Adreno GPU)
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device = devices[0]
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print("Using device:", device.name)
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# Create a context with that device
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ctx = cl.Context([device])
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# Create a command queue
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queue = cl.CommandQueue(ctx)
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print("Context and command queue created successfully!")
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