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organize exampels according to products

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ykcheungab
2026-08-03 15:52:15 +08:00
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examples/products/__init__.py
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"""Product-facing example entry points for ORY-BOX / ORY-MINI / ORY-FINGER."""
examples/products/ory_box/basic_pipeline.py
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"""
ORY-BOX：单传感器基础流程
本示例演示 Orisys SDK 的标准处理流程：
1. 初始化传感器（摄像头或视频文件）
2. 获取图像并计算形变
3. 按需计算接触区域
4. 读取并可视化结果
SDK version: 0.4.2
"""
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
import cv2
import orisys
import numpy as np
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--video","-v", type=str, default="0", help="视频源：摄像头编号或视频文件路径")
parser.add_argument("--config","-c", type=str, default="ddjx01", help="配置文件名称或路径")
parser.add_argument("--verbose","-verbose", type=bool, default=True, help="是否输出详细日志")
args = parser.parse_args()
# 步骤 1：创建传感器对象（必需）
# config_name 可以是内置配置名称，也可以是自定义配置文件路径
# cal_path 为标定文件路径；每台设备建议使用独立的标定文件
# 输入既可以是摄像头编号，也可以是视频文件路径
try:
input = int(args.video)
is_camera = True
sensor = orisys.Sensor(input, config_name=args.config, verbose=args.verbose)
except:
is_camera = False
sensor = orisys.Sensor(args.video, config_name=args.config, verbose=args.verbose)
print("\n按键说明：'q' 退出程序，'r' 重置追踪器。")
while True:
# 步骤 2：数据处理
# 步骤 2.1：获取并拼接图像（必需）
sensor.get_img()
# 步骤 2.2：计算形变场（必需）；返回值表示当前帧是否检测到接触/运动
is_contact_now = sensor.compute_deformation(check_motion=True, threshold=0)
# 步骤 2.3：仅在当前帧检测到接触/运动时计算接触区域
# if is_contact_now:
sensor.compute_contact()
# 步骤 2.4：读取结果
fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info(
sensor.info.FPS,
sensor.info.VRAW,
sensor.info.VNORMAL,
sensor.info.IMG,
sensor.info.CONTOUR,
sensor.info.CENTROID,
sensor.info.DEPTH,
sensor.info.FNORMAL,
sensor.info.FSHEARX,
sensor.info.FSHEARY
)
# 步骤 3：可视化显示
# 步骤 3.1：显示形变矢量场
arrows = orisys.util.draw_arrows(
img,
flow,
threshold=2,
grid_spacing=20,
arrow_scale=1.0
)
cv2.imshow("形变矢量场", arrows)
# 步骤 3.2：显示接触区域和质心
image_with_foe = orisys.util.draw_contact(img, contour, centroid)
cv2.imshow("接触区域与质心", image_with_foe)
# 步骤 3.3：显示深度图
# 将深度值归一化到 0-255 范围
div_abs = depth_map
if div_abs.max() > div_abs.min():
div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
else:
div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
# 应用伪彩色映射
cv2.imshow("深度图", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
# 步骤 3.4：打印关键结果
print(
f"FPS={fps:.2f}, 法向力={fnormal:.4f}, 切向力X={fshearx:.4f}, "
f"切向力Y={fsheary:.4f}, 深度图尺寸={depth_map.shape}, 原始分辨率={sensor.img_size_raw}"
)
# 步骤 3.5：键盘控制
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
print("\n正在退出示例程序...")
break
if key == ord("r"):
# 当光流追踪出现漂移时，可手动重置追踪器
sensor.reset()
print("追踪器已重置。")
# =========================================================================
# 4. 释放资源
# =========================================================================
sensor.disconnect() # 释放视频源及相关资源
if __name__ == '__main__':
main()
examples/products/ory_finger/finger_pipeline.py
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#!/usr/bin/env python3
"""
ORY-FINGER：Minimum UV-unroll finger tactile pipeline.
Offline: load Blender export -> build_or_load_domain -> unroll_map_uv.npz
Runtime: camera frame -> FrameUnwarper -> TactileProcessor -> forces/contact
Recommended entry:
python examples/products/ory_finger/finger_pipeline.py --camera_id 0 --config dd02
Current config alias: ``dd02`` resolves to ``dd02-ov`` for compatibility.
"""
import argparse
import os
import sys
os.environ.setdefault("OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS", "0")
import cv2
import orisys
from orisys.finger import FingerRuntime
def main():
parser = argparse.ArgumentParser(description="Minimum UV-unroll finger tactile pipeline")
parser.add_argument("--camera_id", type=int, default=0)
parser.add_argument("--video", help="Video file path (overrides camera)")
parser.add_argument("--config", "-c", default="dd02", help="Orisys SDK config name or path")
parser.add_argument("--show", action="store_true", default=True, help="Show unrolled image window")
parser.add_argument("--verbose", action="store_true", help="SDK verbose logs")
args = parser.parse_args()
try:
video = args.camera_id
if args.video:
video = args.video
runtime = FingerRuntime.open(
config=args.config,
video=video,
verbose=args.verbose,
)
except RuntimeError as exc:
print(str(exc), file=sys.stderr)
sys.exit(1)
if args.verbose:
geometry = runtime.geometry
print(
f"UV geometry: native={geometry.native_width}x{geometry.native_height}, "
f"source={geometry.source_width}x{geometry.source_height} "
f"output={geometry.out_width}x{geometry.out_height} "
)
runtime.warmup(color=False)
tactile = runtime.tactile
print("Press 'q' to quit, 'r' to reset tracker.")
try:
while True:
ok, unrolled = runtime.grab_and_process(color=False)
if not ok:
break
tactile.compute_deformation(check_motion=True, threshold=0)
tactile.compute_contact()
fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = tactile.read_info(
tactile.info.FPS,
tactile.info.VRAW,
tactile.info.VNORMAL,
tactile.info.IMG,
tactile.info.CONTOUR,
tactile.info.CENTROID,
tactile.info.DEPTH,
tactile.info.FNORMAL,
tactile.info.FSHEARX,
tactile.info.FSHEARY,
)
status = (
f"FPS={fps:.2f} normal={fnormal:.2f} shear=({fshearx:.2f}, {fsheary:.2f}) "
f"mask={tactile.mask_stats['coverage']:.2f}"
)
if args.show:
arrows = orisys.util.draw_arrows(
unrolled,
flow,
threshold=0.4,
grid_spacing=10,
arrow_scale=1.0,
)
cv2.imshow("UV Unrolled", arrows)
print(status, end="\r")
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
print("\nExiting.")
break
if key == ord("r"):
tactile.reset()
print("\nTracker reset.")
finally:
runtime.close()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
examples/products/ory_finger/finger_pipeline_3dview.py
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#!/usr/bin/env python3
"""
ORY-FINGER：UV-unroll finger tactile pipeline with unified Qt dashboard.
Where to customize the GUI:
Layout (sizes, proportions) -> examples/gui/viewer3d/layout.py
Colors / QSS -> examples/gui/viewer3d/styles.py
Widget rendering logic -> examples/gui/viewer3d/widgets/
Window behavior -> examples/gui/viewer3d/dashboard.py
Recommended entry:
python examples/products/ory_finger/finger_pipeline_3dview.py --video 0 --config dd02
"""
import argparse
import os
import sys
from pathlib import Path
os.environ.setdefault("OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS", "0")
# Make shared packages importable when launched as a file path.
_EXAMPLES_DIR = Path(__file__).resolve().parents[2]
if str(_EXAMPLES_DIR) not in sys.path:
sys.path.insert(0, str(_EXAMPLES_DIR))
from gui.app_paths import configure_frozen_runtime, default_config_path, ensure_examples_import_path
configure_frozen_runtime()
ensure_examples_import_path()
from gui import run_dashboard
from pipeline import PipelineConfig
from viz import DEFAULT_FINGER_VIZ_POSE
def _parse_args():
parser = argparse.ArgumentParser(
description="UV-unroll finger tactile pipeline with unified Qt dashboard"
)
parser.add_argument("--video", "-v", default="0", help="Camera index or video file path")
parser.add_argument("--config", "-c", default=default_config_path(), help="Orisys SDK config")
parser.add_argument("--cal", default="", help="Optional calibration file path")
parser.add_argument("--verbose", action="store_true", help="SDK verbose logs")
parser.add_argument(
"--mesh-map",
choices=["flow", "depth"],
default="depth",
help="Color the 3D mesh from flow magnitude or contact depth.",
)
parser.add_argument("--threshold", type=float, default=None, help="Mesh/arrow threshold")
parser.add_argument("--no-3d", action="store_true", help="Disable 3D mesh view")
parser.add_argument(
"--viz-pose",
default=DEFAULT_FINGER_VIZ_POSE,
help="world_to_cam.npy for the default view; pass 'none' to skip",
)
parser.add_argument(
"--cutoff",
type=float,
default=5.0,
help="Low-pass cutoff for force curves (Hz)",
)
parser.add_argument(
"--profile",
action="store_true",
help="Print per-stage timing to the log panel / console",
)
parser.add_argument(
"--profile-every",
type=int,
default=30,
help="When --profile is set, print rolling averages every N frames",
)
return parser.parse_args()
def main():
args = _parse_args()
config = PipelineConfig.from_args(args)
sys.exit(run_dashboard(config))
if __name__ == "__main__":
main()
examples/products/ory_mini/basic_pipeline.py
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"""
ORY-MINI：单传感器基础流程
本示例演示 Orisys SDK 的标准处理流程：
1. 初始化传感器（摄像头或视频文件）
2. 获取图像并计算形变
3. 按需计算接触区域
4. 读取并可视化结果
SDK version: 0.4.2
"""
import os
os.environ["OPENCV_VIDEOIO_MSMF_ENABLE_HW_TRANSFORMS"] = "0"
import cv2
import orisys
import numpy as np
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--video","-v", type=str, default="0", help="视频源：摄像头编号或视频文件路径")
parser.add_argument("--config","-c", type=str, default="mini", help="配置文件名称或路径")
parser.add_argument("--verbose","-verbose", type=bool, default=True, help="是否输出详细日志")
args = parser.parse_args()
# 步骤 1：创建传感器对象（必需）
# config_name 可以是内置配置名称，也可以是自定义配置文件路径
# cal_path 为标定文件路径；每台设备建议使用独立的标定文件
# 输入既可以是摄像头编号，也可以是视频文件路径
try:
input = int(args.video)
is_camera = True
sensor = orisys.Sensor(input, config_name=args.config, verbose=args.verbose)
except:
is_camera = False
sensor = orisys.Sensor(args.video, config_name=args.config, verbose=args.verbose)
print("\n按键说明：'q' 退出程序，'r' 重置追踪器。")
while True:
# 步骤 2：数据处理
# 步骤 2.1：获取并拼接图像（必需）
sensor.get_img()
# 步骤 2.2：计算形变场（必需）；返回值表示当前帧是否检测到接触/运动
is_contact_now = sensor.compute_deformation(check_motion=True, threshold=0)
# 步骤 2.3：仅在当前帧检测到接触/运动时计算接触区域
# if is_contact_now:
sensor.compute_contact()
# 步骤 2.4：读取结果
fps, flow, vnormal, img, contour, centroid, depth_map, fnormal, fshearx, fsheary = sensor.read_info(
sensor.info.FPS,
sensor.info.VRAW,
sensor.info.VNORMAL,
sensor.info.IMG,
sensor.info.CONTOUR,
sensor.info.CENTROID,
sensor.info.DEPTH,
sensor.info.FNORMAL,
sensor.info.FSHEARX,
sensor.info.FSHEARY
)
# 步骤 3：可视化显示
# 步骤 3.1：显示形变矢量场
arrows = orisys.util.draw_arrows(
img,
flow,
threshold=2,
grid_spacing=20,
arrow_scale=1.0
)
cv2.imshow("形变矢量场", arrows)
# 步骤 3.2：显示接触区域和质心
image_with_foe = orisys.util.draw_contact(img, contour, centroid)
cv2.imshow("接触区域与质心", image_with_foe)
# 步骤 3.3：显示深度图
# 将深度值归一化到 0-255 范围
div_abs = depth_map
if div_abs.max() > div_abs.min():
div_normalized = ((div_abs - div_abs.min()) / (div_abs.max() - div_abs.min()) * 255).astype(np.uint8)
else:
div_normalized = np.zeros_like(div_abs, dtype=np.uint8)
# 应用伪彩色映射
cv2.imshow("深度图", cv2.applyColorMap(div_normalized, cv2.COLORMAP_JET))
# 步骤 3.4：打印关键结果
print(
f"FPS={fps:.2f}, 法向力={fnormal:.4f}, 切向力X={fshearx:.4f}, "
f"切向力Y={fsheary:.4f}, 深度图尺寸={depth_map.shape}, 原始分辨率={sensor.img_size_raw}"
)
# 步骤 3.5：键盘控制
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
print("\n正在退出示例程序...")
break
if key == ord("r"):
# 当光流追踪出现漂移时，可手动重置追踪器
sensor.reset()
print("追踪器已重置。")
# =========================================================================
# 4. 释放资源
# =========================================================================
sensor.disconnect() # 释放视频源及相关资源
if __name__ == '__main__':
main()