unknown
116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
"""
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示例 5: 异步流水线处理（单传感器高吞吐模式）
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本示例演示传感器的异步三阶段流水线：
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- 图像采集、光流计算、HHD分解并行执行
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- 通过队列在线程间传递数据
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- 实现更高的帧率和吞吐量
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流水线架构：
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[采集线程] → 队列 → [光流线程] → 队列 → [HHD线程] → 队列 → [主线程显示]
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时间线示例（每格代表一帧的处理时间）：
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采集: |帧1|帧2|帧3|帧4|帧5|...
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光流: |帧1|帧2|帧3|帧4|...
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HHD: |帧1|帧2|帧3|...
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优势：三个阶段重叠执行，总吞吐量约为最慢阶段的速度
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适用场景：
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- 单传感器需要最大帧率
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- CPU/GPU 利用率需要最大化
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- 可以接受 2-3 帧的处理延迟
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对比同步模式：
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- 同步模式：采集→光流→HHD 串行执行，FPS 受限于总时间
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- 异步模式：三阶段并行，FPS 约为同步模式的 2-3 倍
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"""
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import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" # Windows 兼容性设置
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import cv2
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import orisys
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import numpy as np
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def main():
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# =========================================================================
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# 1. 初始化传感器
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# =========================================================================
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sensor = orisys.Sensor(0, cuda=True, verbose=True, config_name="prototype")
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# =========================================================================
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# 2. 启用异步流水线
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# =========================================================================
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# queue_size: 每个阶段间队列的大小
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# - 较小的值（1-2）：延迟低，但可能丢帧
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# - 较大的值（3-5）：吞吐稳定，但延迟增加
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sensor.enable_async(queue_size=2, compute_contact=True)
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print("\n异步模式已启用")
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print("按 'q' 退出，按 'r' 重置光流追踪器")
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# =========================================================================
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# 3. 主循环：非阻塞获取数据
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# =========================================================================
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try:
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while True:
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# 非阻塞获取最新结果
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# timeout: 等待超时时间（秒）
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# - 返回 None 表示暂无新数据
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# - 返回 dict 包含所有传感器数据
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data = sensor.get_async(timeout=0.05)
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if data is None:
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# 暂无新数据，继续等待
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continue
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# 打印力的数值
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print(f"FPS: {data['fps']:5.1f}, "
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f"法向力: {data['fnormal']:8.2f}, "
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f"切向力X: {data['fshearx']:8.2f}, "
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f"切向力Y: {data['fsheary']:8.2f}")
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# 显示形变箭头图
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if data['img'] is not None and data['flow'] is not None:
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arrows = orisys.util.draw_arrows(
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data['img'].copy(),
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data['flow'],
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threshold=5
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)
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cv2.imshow("异步模式 - 形变", arrows)
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if data['centroid'] is not None:
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# print(f"触碰点: {data['centroid']}")
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image_with_foe = orisys.util.draw_contact(data['img'].copy(), data['contour'], data['centroid'])
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cv2.imshow("Contact Centroid", image_with_foe)
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if data['depth_map'] is not None:
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# 归一化散度值到0-255范围（使用绝对值，然后归一化）
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div_abs = data['depth_map']
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if div_abs.max() > div_abs.min():
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div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
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else:
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div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
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cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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# 键盘控制
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key = cv2.waitKey(1) & 0xFF
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if key == ord('q'):
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break
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elif key == ord('r'):
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sensor.reset()
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print("光流追踪器已重置")
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finally:
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# =====================================================================
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# 4. 清理资源
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# =====================================================================
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# disconnect() 会自动调用 disable_async() 停止所有线程
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sensor.disconnect()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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