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bluepulsar
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"""
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示例: 密度图可视化
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本示例演示如何：
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1. 获取拼接图像
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2. 计算光流
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3. 基于光流生成 density map
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4. 使用 colormap 实时显示
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"""
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import os
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import argparse
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import cv2
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import numpy as np
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import orisys
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from getDensity import getFilteredDensity
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os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
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def density_to_colormap(density):
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"""Convert density map to a displayable heatmap."""
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if density is None or density.size == 0:
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return np.zeros((1, 1, 3), dtype=np.uint8)
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dmin = float(np.min(density))
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dmax = float(np.max(density))
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if dmax > dmin:
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density_u8 = ((density - dmin) * (255.0 / (dmax - dmin))).astype(np.uint8)
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else:
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density_u8 = np.zeros_like(density, dtype=np.uint8)
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return cv2.applyColorMap(density_u8, cv2.COLORMAP_TURBO)
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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/ddjx01.json", help="Configuration file path")
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parser.add_argument("--cal", "-cal", type=str, default="./config/ddjx01.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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try:
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vid_src = int(args.video)
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except ValueError:
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vid_src = args.video
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sensor = orisys.Sensor(vid_src, config_name=args.config, cal_path=args.cal, verbose=args.verbose)
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print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器")
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while True:
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# 步骤2：获取图像并计算形变
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img = sensor.get_img()
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if img is None:
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print("Failed to get image (end of stream or capture error). Exiting.")
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break
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sensor.compute_deformation(check_motion=False)
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sensor.compute_contact()
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(fps, flow, contour, centroid) = sensor.read_info(
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sensor.info.FPS,
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sensor.info.VRAW,
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sensor.info.CONTOUR,
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sensor.info.CENTROID,
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)
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# 步骤3：根据光流计算 density map 并显示
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density = getFilteredDensity(flow, use_cuda=sensor.cuda)
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density_view = density_to_colormap(density)
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cv2.imshow("Density Map", density_view)
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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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print(f"fps: {fps:.2f}, density_shape: {density.shape}, flow_shape: {flow.shape}")
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# 步骤4：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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break
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if key == ord("r"):
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sensor.reset()
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print("传感器已重置")
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sensor.disconnect()
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if __name__ == "__main__":
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main()
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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 orisys 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和触碰点
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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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"""
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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 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/ddjx01.json", help="Configuration file path")
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parser.add_argument("--cal","-cal", type=str, default="./config/ddjx01.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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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, 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, verbose=args.verbose)
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print("\n按 'q' 退出程序，按 'r' 重置传感器追踪器")
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while True:
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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 (必要步骤)
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is_touched = sensor.compute_deformation(check_motion=True, threshold=6)
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# 步骤 2.3: 计算触碰点
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if is_touched:
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sensor.compute_contact()
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#步骤 2.4: 读取信息
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fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info(
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sensor.info.FPS,
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sensor.info.VRAW,
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sensor.info.VNORMAL,
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sensor.info.IMG,
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sensor.info.CONTOUR,
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sensor.info.CENTROID,
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sensor.info.DEPTH,
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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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)
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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,
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flow,
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threshold=2,
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grid_spacing=20,
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arrow_scale=1.0
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)
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cv2.imshow("Deformation", arrows)
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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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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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# 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}, fnormal: {fnormal}, fshearx: {fshearx}, fsheary: {fsheary}, depth_shape: {depth_map.shape}, sensor.resolution_raw: {sensor.img_size_raw}")
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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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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("传感器已重置")
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# =========================================================================
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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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if __name__ == '__main__':
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main()
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