update example and coomputation dependency error handling
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"""
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示例 1: 基础用法 - 单传感器同步处理
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本示例演示 OrisysSensor 的基本使用流程：
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1. 初始化传感器（视频文件或摄像头）
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2. 循环获取图像并计算形变
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3. 读取各类传感器数据
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4. 可视化显示
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适用场景：单传感器、简单应用、学习入门
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"""
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import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" # Windows 下禁用硬件加速，避免兼容性问题
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import cv2
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import orisys
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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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# 参数说明：
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# vid_src: 视频源，可以是文件路径（如 "./s1.mp4"）或摄像头索引（如 0）
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# isstitch: 是否启用复眼拼接模式（多单元传感器需要开启）
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# cuda: 是否使用 CUDA GPU 加速（需要 CuPy 和 CUDA 支持）
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# verbose: 是否打印详细初始化日志
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# 方式1: 从视频文件读取
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sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True,config_name="prototype")
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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, isstitch=True, cuda=True, verbose=True)
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print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
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# =========================================================================
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# 2. 主循环：获取图像 → 计算形变 → 读取数据 → 显示
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# =========================================================================
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while True:
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# 步骤 2.1: 获取并拼接图像
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sensor.get_img()
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# 步骤 2.2: 计算形变场
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sensor.compute_deformation()
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# 步骤 2.3: 读取传感器数据
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# read_info() 支持以下数据类型：
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# 向量场数据：
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# - VRAW: 原始光流场 (H, W, 2) 的 numpy 数组
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# - VNORMAL: 法向分量场
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# - VSHEAR: 切向分量场
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# 标量数据：
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# - FNORMAL: 法向力总和（正压力）
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# - FSHEARX: X方向切向力
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# - FSHEARY: Y方向切向力
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# 其他：
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# - FPS: 当前帧率
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# - TIMESTAMP: 时间戳字符串
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fps, fn, fx, fy, flow = sensor.read_info(
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sensor.info.FPS,
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sensor.info.FNORMAL,
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sensor.info.FSHEARX,
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sensor.info.FSHEARY,
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sensor.info.VRAW
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)
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# 打印力的数值
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print(f"FPS: {fps:5.1f}, 法向力: {fn:8.2f}, 切向力X: {fx:8.2f}, 切向力Y: {fy:8.2f}")
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# =====================================================================
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# 3. 可视化显示
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# =====================================================================
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# 绘制光流箭头图
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# 参数说明：
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# img: 背景图像
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# flow: 光流场
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# threshold: 显示阈值（小于此值的箭头不显示）
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# grid_spacing: 箭头网格间距（像素）
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# arrow_scale: 箭头缩放比例
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arrows = orisys.util.draw_arrows(
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sensor.img.copy(), # 使用 copy() 避免修改原图
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flow,
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threshold=5,
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grid_spacing=40,
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arrow_scale=1.0
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)
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cv2.imshow("形变场 Deformation", arrows)
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# =====================================================================
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# 4. 键盘控制
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# =====================================================================
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key = cv2.waitKey(1) & 0xFF
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if key == ord("q"):
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print("\n退出程序...")
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break
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if key == ord("r"):
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# 重置光流追踪器（当追踪漂移时使用）
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sensor.reset()
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print("光流追踪器已重置")
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# =========================================================================
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# 5. 清理资源
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# =========================================================================
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sensor.disconnect() # 释放视频源和相关资源
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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@@ -1,30 +1,42 @@
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"""
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示例 1: 基础用法 - 单传感器同步处理
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本示例演示 OrisysSensor 的基本使用流程：
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1. 初始化传感器（视频文件或摄像头）
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2. 循环获取图像并计算形变
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3. 读取各类传感器数据
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适用场景：单传感器、简单应用、学习入门
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"""
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import os
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0" # Windows 下禁用硬件加速，避免兼容性问题
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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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def main():
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# 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）
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# 方式1: 从视频文件读取
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# sensor = orisys.Sensor("./s1.mp4", isstitch=True, cuda=True, verbose=True,config_name="prototype")
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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, isstitch=True, cuda=True, verbose=True,config_name="prototype")
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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, cuda=True, verbose=True, config_name="prototype")
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print("\n按 'q' 退出程序，按 'r' 重置光流追踪器")
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while True:
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# 步骤 2.1: 获取并拼接图像
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sensor.get_img()
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# 步骤2：信息提取
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# 步骤 2.1: 获取并拼接图像 (必要步骤)
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sensor.get_img()
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# 步骤 2.2: 计算形变场
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# 步骤 2.2: 计算形变场 (必要步骤)
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sensor.compute_deformation()
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# 计算触碰点
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# sensor.compute_contact(min_magnitude=0.1,expansion_threshold=0.01,denoise_sigma=15)
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# 步骤 2.3: 计算触碰点
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sensor.compute_contact()
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#计算后读取信息
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#步骤 2.4: 读取信息
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fps, flow, vnormal, img, contour, centroid, depth_map = sensor.read_info(
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sensor.info.FPS,
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sensor.info.VRAW,
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@@ -34,20 +46,23 @@ def main():
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sensor.info.CENTROID,
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sensor.info.DEPTH
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)
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#步骤 3: 可视化显示
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# 步骤 3.1: 绘制形变场
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arrows = orisys.util.draw_arrows(
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img, # 使用 copy() 避免修改原图
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vnormal,
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img,
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flow,
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threshold=5,
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grid_spacing=10,
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arrow_scale=1.0
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)
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cv2.imshow("形变场 Deformation", arrows)
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# 在图像上触碰点和区域
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# 步骤 3.2: 在图像上触碰点和区域
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image_with_foe = orisys.util.draw_contact(img, contour, centroid)
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cv2.imshow("Contact Centroid", image_with_foe)
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# 步骤 3.3: 绘制深度图
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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 = depth_map
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if div_abs.max() > div_abs.min():
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@@ -58,12 +73,10 @@ def main():
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# 应用颜色映射（使用JET：蓝色=低散度，红色=高散度）
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cv2.imshow("Depth Map", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
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#步骤 3.4: 打印资料
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print(f"fps: {fps}")
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# =====================================================================
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# 4. 键盘控制
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# =====================================================================
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#步骤 3.5: 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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print("\n退出程序...")
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@@ -74,10 +87,9 @@ def main():
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print("光流追踪器已重置")
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# =========================================================================
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# 5. 清理资源
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# 4. 清理资源
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# =========================================================================
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sensor.disconnect() # 释放视频源和相关资源
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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@@ -1,45 +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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def main():
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global start_time
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box = []
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dx = 90
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dy = -95
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for i in range(15):
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box.append((410+dx, 900+dy, 620+dx, 1110+dy))
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# 打开视频，手动设置box 的坐标
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sensor1 = orisys.Sensor("./s1.mp4",isstitch=False,cuda = True,verbose = True, override=True, box=box)
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# sensor1 = orisys.Sensor("./uv_2.mp4",isstitch=True,cuda = True,verbose = True, override=True, box=box)
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# 打开摄像头
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# sensor1 = Sensor(0,isstitch=True,cuda = True,verbose = True)
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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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# 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=40, arrow_scale=1.0)
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cv2.imshow("Deformation", arrows)
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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 & 0xFF == ord("r"):
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sensor1.reset()
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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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@@ -27,9 +27,9 @@ class Stitcher:
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self.nominal_ov_pos=config_stitcher["nominal_ov_pos"]
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self.margin=config_stitcher["margin"]
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self.bound=self.nominal_ov_pos - self.margin
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# for sensor 1
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self.pos_default = config_stitcher["pos_default"]
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if self.verbose:
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print(f"Stitcher initialized with config: {self.config_stitcher}")
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def stitch_rt_optimized(self, change_sv=False, s_mean=28., v_mean=180.):
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tile_list = self.tile_list
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@@ -11,19 +11,24 @@
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"stitcher": {
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"min_area_frac": 0.018,
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"max_area_frac": 0.035,
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"max_aspect_ratio": 20,
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"max_aspect_ratio": 100,
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"index": [1,2,3,6,7,8,11,12,13],
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"pos_default": [[260, 56], [577, 62], [897, 69], [1214, 75], [1530, 81], [256, 391], [570, 397], [889, 403], [1208, 409], [1523, 416], [248, 724], [563, 730], [882, 738], [1201, 743], [1516, 751]],
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"margin": 10,
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"nominal_ov_pos": 100,
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"n": [3,3]
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},
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"tracker": {
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"adaptive": true,
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"lower_threshold": 12.0,
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"upper_threshold": 18.0,
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"loop_closure_interval": 1,
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"dis_preset": "medium"
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},
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"contact": {
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"min_contour_area": 3000,
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"flow_threshold":1,
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"div_threshold":0.02,
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"expansion_threshold": 0.01,
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"downsample_factor": 1,
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"kernel_size": [5,5],
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"denoise_sigma": 15
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}
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@@ -19,7 +19,7 @@
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"min_contour_area": 3000,
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"min_magnitude": 1.0,
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"expansion_threshold": 0.01,
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"downsample_factor": 1,
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"contact_ds_factor": 10,
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"kernel_size": [5,5],
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"denoise_sigma": 15
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}
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@@ -19,7 +19,7 @@ class Contact3D:
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self.config_contact = config_contact
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self.flow_threshold = config_contact["flow_threshold"]
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self.expansion_threshold = config_contact["expansion_threshold"]
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self.downsample_factor = config_contact["downsample_factor"]
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self.downsample_factor = 1
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self.kernel_size = config_contact["kernel_size"]
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self.denoise_sigma = config_contact["denoise_sigma"]
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self.min_contour_area = config_contact["min_contour_area"]
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@@ -75,7 +75,6 @@ class Contact3D:
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return result
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def compute_foe(self, flow_field, return_all_contours=False):
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# def compute_foe(self, flow_field, min_magnitude=1.0, expansion_threshold=1, downsample_factor=1, return_all_contours=False, kernel_size=(5,5), denoise_sigma=10):
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"""
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计算光流场的扩张焦点(Focus of Expansion)。
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+27
-13
@@ -53,16 +53,16 @@ class Sensor:
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# Initialization
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# =========================================================================
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def __init__(self, vid_src, isstitch=False, verbose=True, cuda=True,
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config_name="prototype", cal_path=None, override=False, box=None,mirror=True):
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def __init__(self, vid_src, cuda=True, isstitch=True, verbose=True,
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config_name="prototype", cal_path=None, override=False, box=None, mirror=True):
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"""
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Initialize Sensor with video source and configuration.
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Args:
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vid_src: Video source (int for camera ID, str for file path)
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cuda: Enable CUDA acceleration (default: True)
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isstitch: Enable stitching mode for multi-tile sensor (default: False)
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verbose: Print initialization logs (default: True)
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cuda: Enable CUDA acceleration (default: True)
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config_name: Configuration name (default: "prototype")
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cal_path: Path to calibration file (default: None)
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override: Skip auto-detection, use provided box (default: False)
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@@ -89,9 +89,9 @@ class Sensor:
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# State variables
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self.stitch_valid = False
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self.imgupdate = False
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self.vupdate = False
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self.stitch_calibration = []
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self.compute_deformation_valid = False
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self.compute_contact_valid = False
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# Data Variables
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self.__img = None
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@@ -193,7 +193,6 @@ class Sensor:
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self.__img_filtered = alpha * img + (1 - alpha) * self.__img_filtered
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self.__img = self.__img_filtered.astype(np.uint8)
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self.__imgupdate = True
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if log_timing:
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timings['total'] = (time.perf_counter() - t_start) * 1000
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@@ -210,6 +209,8 @@ class Sensor:
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Returns:
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np.ndarray: Optical flow field
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"""
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if self.__img is None:
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raise Exception("Warning: Get image first")
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if log_timing:
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timings = {}
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t_start = time.perf_counter()
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@@ -245,9 +246,7 @@ class Sensor:
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# Update state
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self.__flow = flow
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self.imgupdate = False
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self.vupdate = True
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self.compute_deformation_valid = True
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# FPS calculation
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self._fps = 1.0 / (time.perf_counter() - self.last_log_time)
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self.last_log_time = time.perf_counter()
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@@ -258,8 +257,13 @@ class Sensor:
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return True
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def compute_contact(self, multiple_foe=False, min_contour_area=3000, min_magnitude=1.0, expansion_threshold=0.01, downsample_factor=1, kernel_size=(5,5), denoise_sigma=15):
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def compute_contact(self):
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multiple_foe=False
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if not self.compute_deformation_valid:
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||||
raise Exception("Warning: Compute deformation first")
|
||||
|
||||
if multiple_foe:
|
||||
result = self.__contact3d.compute_multiple_foe(self.__vnormal)
|
||||
else:
|
||||
@@ -274,6 +278,7 @@ class Sensor:
|
||||
self.__contour = result['contour']
|
||||
self.__depth_map = result['divergence_map']
|
||||
|
||||
self.compute_contact_valid = True
|
||||
return True
|
||||
|
||||
|
||||
@@ -307,9 +312,18 @@ class Sensor:
|
||||
Returns:
|
||||
Tuple: Requested data in order
|
||||
"""
|
||||
if not self.vupdate:
|
||||
print("Warning: Call compute_deformation() first.")
|
||||
|
||||
if any(arg in [self.info.IMG] for arg in args):
|
||||
if self.__img is None:
|
||||
raise Exception("Warning: Get image first")
|
||||
|
||||
if any(arg in [self.info.VRAW, self.info.VNORMAL, self.info.VSHEAR, self.info.FNORMAL, self.info.FSHEARX, self.info.FSHEARY] for arg in args):
|
||||
if not self.compute_deformation_valid:
|
||||
raise Exception("Warning: Compute deformation first")
|
||||
|
||||
if any(arg in [self.info.CENTROID, self.info.CONTOUR, self.info.DEPTH] for arg in args):
|
||||
if not self.compute_contact_valid:
|
||||
raise Exception("Warning: Compute contact first")
|
||||
|
||||
mapping = {
|
||||
# Return copies of arrays to prevent external modification of internal state
|
||||
self.info.VRAW: lambda: self.__flow.copy() if self.__flow is not None else np.array([]),
|
||||
|
||||
@@ -75,62 +75,6 @@ def draw_arrows(image, optical_flow,fps=True, threshold=5, grid_spacing=40, arro
|
||||
cv2.arrowedLine(image, (x_axis[i], y_axis[i]), (end_x[i], end_y[i]), color, thickness=thickness, tipLength=0.3)
|
||||
return image
|
||||
|
||||
def draw_magnitude_map(image, flow_field, threshold=3.0, colormap=cv2.COLORMAP_JET,downsample_factor=10):
|
||||
"""
|
||||
Draw color map of pointwise magnitude of velocity field.
|
||||
|
||||
Args:
|
||||
info_type: Type of flow field to visualize (VRAW, VNORMAL, VSHEAR, or None for total)
|
||||
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
|
||||
"""
|
||||
|
||||
# Compute magnitude at each point
|
||||
magnitude = np.sqrt(flow_field[:, :, 0]**2 + flow_field[:, :, 1]**2)
|
||||
|
||||
# Smooth by downsampling and interpolating back
|
||||
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)
|
||||
|
||||
return magnitude_colored
|
||||
|
||||
def draw_fps(image, fps):
|
||||
"""
|
||||
Draw FPS counter on the top left corner of the image.
|
||||
"""
|
||||
font_scale=0.5
|
||||
thickness=2
|
||||
color=(0, 255, 0)
|
||||
image = image.copy()
|
||||
|
||||
# Draw FPS text
|
||||
fps_text = f"FPS: {fps:.1f}"
|
||||
position = (10, 30) # Top left corner with margin
|
||||
font = cv2.FONT_HERSHEY_SIMPLEX
|
||||
|
||||
# Draw FPS text
|
||||
cv2.putText(image, fps_text, position, font, font_scale, color, thickness, cv2.LINE_AA)
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def draw_contact(image, contour, centroid, point_color=(0, 255, 0), region_color=(0, 255, 255)):
|
||||
"""
|
||||
@@ -159,60 +103,3 @@ def draw_contact(image, contour, centroid, point_color=(0, 255, 0), region_color
|
||||
cv2.circle(image, (cx, cy), 10, point_color, 2)
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def draw_multiple_foe(image, foe_result, point_color=(0, 255, 0),
|
||||
region_color=(0, 255, 255), label_colors=None):
|
||||
"""
|
||||
在图像上绘制多个扩张焦点和扩张区域。
|
||||
|
||||
Args:
|
||||
image: 背景图像
|
||||
foe_result: compute_multiple_foe 函数的返回结果
|
||||
point_color: 默认焦点点的颜色 (BGR)
|
||||
region_color: 扩张区域轮廓的颜色 (BGR)
|
||||
label_colors: 可选的颜色列表，用于不同焦点的标签颜色
|
||||
|
||||
Returns:
|
||||
np.ndarray: 绘制了多个焦点和区域的图像
|
||||
"""
|
||||
image = image.copy()
|
||||
|
||||
# 默认标签颜色（彩虹色系）
|
||||
if label_colors is None:
|
||||
label_colors = [
|
||||
(255, 0, 0), # 蓝色
|
||||
(0, 255, 0), # 绿色
|
||||
(0, 0, 255), # 红色
|
||||
(255, 255, 0), # 青色
|
||||
(255, 0, 255), # 洋红色
|
||||
(0, 255, 255), # 黄色
|
||||
(255, 128, 0), # 橙色
|
||||
(128, 0, 255) # 紫色
|
||||
]
|
||||
|
||||
# 绘制扩张区域轮廓
|
||||
if 'expansion_regions' in foe_result and foe_result['expansion_regions']:
|
||||
cv2.drawContours(image, foe_result['expansion_regions'], -1, region_color, 2)
|
||||
|
||||
# 绘制焦点点并添加标签
|
||||
if 'foe_points' in foe_result:
|
||||
for i, (cx, cy) in enumerate(foe_result['foe_points']):
|
||||
# 选择颜色（循环使用颜色列表）
|
||||
color = label_colors[i % len(label_colors)]
|
||||
|
||||
# 绘制焦点点
|
||||
cv2.circle(image, (cx, cy), 8, color, -1)
|
||||
cv2.circle(image, (cx, cy), 10, color, 2)
|
||||
|
||||
# 添加标签
|
||||
label = f"FOE{i+1}"
|
||||
cv2.putText(image, label, (cx + 15, cy),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
|
||||
|
||||
# 显示检测到的焦点数量
|
||||
foe_count = len(foe_result.get('foe_points', []))
|
||||
cv2.putText(image, f"FOE Count: {foe_count}", (10, 60),
|
||||
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
|
||||
|
||||
return image
|
||||
Reference in New Issue
Block a user